AS3 MaNGA: Mapping Nearby Galaxies at APO

Transcription

1 AS3 MaNGA: Mapping Nearby Galaxies at APO ABSTRACT We propose a new Sloan legacy survey called MaNGA (Mapping Nearby Galaxies at APO) that will enable detailed studies of the internal 2D structure, kinematic properties and stellar populations of an unprecedented sample of 10,000 local galaxies. SDSS- I/II characterized the clustering of galaxies and the relations between their central physical properties in great detail. MaNGA aims to channel the SDSS-III spectrograph s capabilities in a fundamentally new direction by bundling the current BOSS fibers into sets of deployable IFUs. Spatially resolved spectroscopy on scales of 2 kpc will be obtained out to 2.5R e for galaxies with stellar masses in the range M and redshifts less than The large sample size provided by MaNGA will allow the internal kinematics and spatially-resolved properties of stellar populations and gas inside galaxies to be studied as a function of local environment and halo mass for the very first time. MaNGA s core science program is aimed at answering three key questions: 1) What is the nature of galaxy growth at the present day? 2) What are the processes that shut down star formation at late times? 3) How did bulges and disks form and how did this influence the structure of their dark matter halos? MaNGA will provide a legacy dataset that will be a defining benchmark by which to judge the success of future galaxy formation models. It will also satisfy an urgent need for local comparison samples with which to interpret next-generation IFU observations from facilities like the Thirty Meter Telescope and JWST. Finally, MaNGA opens up a new parameter space, that of internal galaxy properties, which has tremendous potential for exciting discoveries and insights. Contents 1 Executive Summary 2 2 Science Goals: Introduction The Lives of Galaxies: gas accretion, mergers, and disk instabilities Gas accretion and the growth of disks The present-day growth of spheroids and galactic bulges The Death of Galaxies: What causes star formation quenching? The relationship between quenching, AGN, and star formation Quenching by externally-driven processes in massive groups and clusters The Birth and Formation of Galaxies from the Fossil Record: How do different mass components form and interact? Measuring the distribution and transfer of angular momentum between galaxy subcomponents via dynamical scaling relations

2 Weighing galaxy subcomponents: how baryons and stars trace and influence their dark matter halos Interpreting z =2 3 IFU observations via comparisons to present-day modes of galaxy growth The Survey Survey Design and Sample Selection Scientific Gain from Overlap with Future Ancillary Data Sets The Reason for MaNGA s 10k Sample Size Complementarity with existing, on-going, and planned IFU surveys MaNGA Observables and Data Products Analysis of the Expected Data Quality Absorption line diagnostics Emission line diagnostics Advanced Analyses and Data Products Observing Strategy and Data Management Dithering Sky subtraction Survey Operations and Duration Data Management and Archiving Instrument Design 30 7 Budget and Funding 33 8 Management Strategy 34 9 Non-technical Summary MaNGA Team Members Selected References Executive Summary The study of galaxy formation is ultimately about discovering the processes that formed a heterogeneous Universe from a homogeneous beginning. This encompasses an enormously rich

3 3 range of different physical phenomena, from the growth of primordial fluctuations in the early Universe, through the cooling and condensation of gas into molecular clouds and stars, to the formation of supermassive black holes that emit copious radiation as they grow within galactic bulges. Galaxy formation has remained one of the most active research areas in astrophysics for the past 50 years. Major advances in our understanding of galaxy formation owe a great deal to the highly successful panoramic galaxy surveys conducted with the Sloan Telescope. SDSS-I/II not only mapped the cosmic distribution of luminous galaxies in the local universe, but also provided high quality imaging and spectroscopy that revealed how the star formation rates, metallicities, morphologies, and black hole growth rates of galaxies in the local Universe vary with mass and with environment. SDSS-III is now characterizing the cosmic distribution of galaxies out to z 0.7 as part of the Baryon Oscillation Spectroscopic Survey (BOSS). BOSS only surveys the very brightest galaxies at these redshifts and because exposure times are short, the available physical information is limited. With MaNGA, we propose channeling the SDSS-III spectrograph s impressive capabilities in a fundamentally new direction by marshaling the unique power of 2D spectroscopy. MaNGA will deploy 15 pluggable Integral Field Units (IFUs) made by grouping fibers into hexagonal bundles ranging from 19 to 125 fibers each. The spectra obtained by MaNGA will cover the wavelength range ,000 Å and will characterize the internal composition and the dynamical state of a sample of 10,000 galaxies with stellar masses greater than 10 9 M over the redshift range from to With individual fibers spanning physical diameters of kpc, more than 80% of the targeted galaxies will be resolved by at least 19 spatial elements out to twice their half-light radius. An exposure time of 3 hours on-sky ensures a per-fiber r-band continuum S/N (per pixel) greater than 5 at the galaxy s outskirts, with much higher S/N towards the center. IFU observations enable a leap forward, because they provide an added dimension to the information available for each galaxy. From a single fiber spectrum for each object the observing mode of past SDSS programs information about star formation, metallicity and one-dimensional kinematics can be recovered. However, this information is limited to a single spatial position at the galaxy s center. With an IFU, one can recover the full two-dimensional map of this information. Clues to the nature of the physical processes that shape the formation histories of galaxies are encoded in the map. MaNGA will provide two-dimensional maps of stellar velocity and velocity dispersion, mean stellar age and star formation history, stellar metallicity, element abundance ratio, stellar mass surface density, ionized gas velocity, ionized gas metallicity, star formation rate and dust extinction for a statistically powerful sample. The galaxies are selected to span a stellar mass interval of nearly 3 orders of magnitude. No cuts are made on color, morphology or environment, so the sample is fully representative of the local galaxy population. This legacy dataset will address urgent questions in our understanding of galaxy formation, including 1) The nature of present-day galaxy growth via merging and gas accretion, 2) The processes responsible for terminating star formation in galaxies, and 3) The formation history of galaxy subcomponents, including the disk, bulge, and dark matter halo. In this proposal, we explain how MaNGA s design and capabilities will enable significant advances in these topics. Two-dimensional spatial and kinematic analyses of stellar absorption and

4 4 nebular emission lines will probe how galaxies grow through merging as well as stellar and gas accretion. By studying gradients in the motions of gas using high-ionization emission lines as well as interstellar absorption lines, MaNGA will determine the role of AGN and galactic winds in quenching star formation. Finally, stellar and dynamical mass profiles will constrain the influence of baryons on the host dark matter halo as well as the distribution and transfer of angular momentum that accompanies early stages of galaxy formation. The power of IFUs has motivated a number of efforts with IFU facilities such as SAURON, ATLAS 3D, and CALIFA, but observations with standard integral-field spectrographs are expensive, so data sets including more than a few hundred galaxies do not yet exist. The true power of MaNGA lies in the fact that it is more than an order of magnitude larger than these surveys, which will enable us to study galaxy assembly and evolutionary processes as a function of local environment and dark matter halo mass for the very first time. This is absolutely critical for constraining galaxy formation theory, which predicts that all such processes depend strongly on halo mass. The large sample size will also allow the characterization of important, short-lived evolutionary phases, including statistically significant samples of galaxies that are merging, actively growing their central supermassive black holes, undergoing starbursts with strong outflows, or in a post-starburst phase. In addition to addressing key questions in the formation and evolution of galaxies, the MaNGA survey is designed to follow in the footsteps of SDSS-I/II/III by providing a legacy dataset to the community with enormous discovery potential. Finally, MaNGA will also play a vital role in the coming era of advanced IFU instrumentation, serving as the low-z anchor for interpreting IFU observations of galaxies at z 2 4. To reach our target sample size of 10,000 galaxies we require 688 plates covering 4800 deg 2. Assuming full use of dark time and an exposure time of 3 hours per plate, we estimate that it will take 3.8 years to complete the survey. MaNGA s flexible design allows it to be integrated with other programs by sharing both time and plates. 2. Science Goals: Introduction Our understanding of galaxy formation has significantly advanced in the last decade thanks to SDSS surveys at low redshift, a variety of higher redshift surveys and observations, and improvements in simulating non-linear structure formation. While at the present day the largest dark matter halos continue to grow from the bottom-up by merging with smaller halos, the galaxies living in these halos experienced peak star formation rates at z 1 3. From this time on, activity steadily declines while galaxies continue to grow via mergers and star formation fueled by the accretion of fresh gas. Eventually, star formation is somehow shut down. This quenching process, a kind of galaxy death, increasingly affects lower mass systems with time and may soon be the fate of our own Milky Way. Present-day galaxies less massive than the Milky Way formed their stars continuously over all cosmic epochs. In fact, the majority of the stars in galactic disks formed in the last 7-9 billion years. In spite of significant progress in delineating this basic picture, important pieces of the puzzle remain to be understood. Very importantly, we still seek the physical origin of the patterns of evolution described above. Gravitational collapse is only one of many processes at work in the

5 5 formation of a galaxy. Gas dissipation and infall, star formation, heating and ejection of gas by supernova explosions, galaxy merging and associated growth of central black holes are all key processes that remain poorly understood. In the following sections we show how MaNGA will help reveal the physics driving the life cycle of galaxies. Late-time growth can be studied directly from maps of mean stellar age, gasphase metallicity, and 2D kinematics which provides clues about mergers and accretion events. By mapping outflow rates as a function of local star formation rate or accretion rate onto central supermassive black holes, MaNGA will seek the processes that quench star formation in galaxies. Finally, clues to the early formation history of galaxy subcomponents (bulge, disk, halo) are encoded in their present-day two-dimensional structure and composition. MaNGA s Key Science Questions: 1. How are galactic disks growing at the present day through accretion of gas, and how does this depend on the mass of the dark matter halo in which the galaxy is located? (Section 2.1.1) 2. What are the relative roles of stellar accretion, major mergers, and disk instabilities in contributing to the present-day growth of galactic bulges? (Section 2.1.2) 3. Is star formation in present-day galaxies quenched by energetic input from accreting black holes, from star formation, or from some combination of the two? (Section 2.2.1) 4. How is the shutdown of star formation regulated by externally-driven processes in massive groups and clusters? (Section 2.2.2) 5. How was angular momentum distributed among baryonic and non-baryonic subcomponents as the galaxy formed? (Section 2.3.1) 6. How do baryons and stars trace and influence the shape of the dark matter halos in which they reside? (Section 2.3.2) 7. Does galaxy growth at low and high redshifts proceed in the same way? (Section 2.3.3) MaNGA s ability to address this broad array of topics stems from the advantages of resolved spectroscopy and the statistically powerful sample size of 10,000 galaxies. No existing or planned spectroscopic survey is competitive, either because 2D spectroscopic data is lacking, or because samples are small, incomplete, and not representative of all galaxy types and environments. The MaNGA sample will be selected from the well-characterized SDSS-DR7 main sample using only redshift and stellar mass cuts. The redshift range is and the stellar mass ranges from 10 9 to M. All environments from isolated to extremely dense will be sampled, enabling strong synergies with future wide-field deep imaging, HI, and X-ray surveys. For all of these reasons, MaNGA represents a significant advance and will shape our future understanding of the evolution of baryons in the Universe.

6 The Lives of Galaxies: gas accretion, mergers, and disk instabilities Gas accretion and the growth of disks More than half the stars in the Universe were formed at redshifts less than 1, primarily in galactic disks. At low redshifts, massive spiral galaxies must accrete gas at a rate of a few solar masses per year to maintain their observed star formation rates. Where this gas comes from is, however, poorly understood. In the most massive galaxies, the accreted gas is hypothesized to originate from a surrounding hot corona. In lower mass galaxies, gas may be supplied through major or minor mergers with other galaxies, or from a surrounding filamentary medium of ionized gas. In addition to fueling star formation, gas accretion will have strong impact on the distribution of heavy elements in galaxies. MaNGA will determine how disk fueling occurs in practice by measuring the distribution of young stars and heavy elements as a function of position within disk galaxies. Spatially-dependent star formation rates and gas-phase metallicities measured using nebular emission lines will constrain recent gas accretion events, even at large radii. By stacking fibers in possibly elliptical annular bins, for example, this information can be recovered at 2.5R e (where R e is the effective or halflight radius) for 70% of the star-forming population (see Figure 8, Section 4). At smaller radii, if cold gas is accreting along filaments, one might expect to find localized regions in the disk with close-to-primordial metallicities. If the accretion is from a surrounding hot corona, then recent star formation might be expected to be distributed more evenly throughout the disk or concentrated more towards the center. If the disk is growing through re-accretion of gas that has been ejected out of the galaxy by supernovae or AGN, then the star-forming regions would be expected to have relatively high metallicities. Finally, the strength of metallicity gradients in disk galaxies will provide important constraints on recent gas accretion processes (Moran et al. 2011, in preparation, see Figure 1). Stellar absorption lines will constrain the timescales over which disks have formed their stars, as well as the relative roles of asymptotic giant branch populations and different types of supernovae (for more on the local environment of supernovae, see Section 3.2) in enriching the disk. As shown in Figure 7, this again will be possible out to a radius of R e for most blue galaxies after fiber stacking. The results will provide additional measures of the gas accretion history and constrain radial stellar transport mechanisms that shape the outer disk (e.g., Schönrich & Binney 2008). By stacking samples of several hundred edge-on galaxies, a chemical decomposition of thick and thin disks may be possible for the first time outside the Local Group. Finally, late-time accretion can be characterized by the incidence of counter-rotating components in the velocity profiles of the stars and gas The present-day growth of spheroids and galactic bulges In addition to gas accretion followed by star formation, present-day galaxy growth is also driven by mergers which preferentially add mass to the bulge. The degree and nature of merging activity remains highly uncertain, however, despite its importance in assembling stellar mass and driving evolution in galaxy properties (e.g., Bundy et al. 2009). The surprising degree of size evolution

7 7 Absorption line diagnostic gradients. Early-type galaxies Gas phase metallicity gradients. Star-forming galaxies Fig. 1. Measurements of diagnostic radial gradients. Left: Gas-phase metallicity gradients derived from emission lines for a sample of 150 galaxies with long-slit spectra. Gradients are stronger in lower mass galaxies, suggestive of different fueling histories (Moran et al. 2011). Right: A comparison of two absorption line diagnostics from the SAURON early-type sample that breaks the age-metallicity degeneracy (Kuntschner et al. 2010). The center of each galaxy is indicated by a filled circle, while the associated line traces the gradient of both diagnostics towards the largest measured radius. A grid of predicted age-metallicity tracks is overlaid. Metallicity gradients are surprisingly mild for the oldest and most massive early-types (points with short tails, bottom of the figure). MaNGA will measure many more diagnostic features over a larger range in radius (see Section 4) recently detected for passive ellipticals from z 2 to today is one example (e.g., van Dokkum et al. 2009). A popular explanation is that the expansion in size results from the accretion of stripped material during minor mergers which increasingly build extended wings around the in situ dense core (e.g., Naab et al. 2009). But whether there is enough accretion to explain the evolution remains a key question. Accreted stars should have younger ages and metallicities because they were formed in lower mass halos. As shown in Figure 1, spatially resolved spectroscopy of a small sample of spheroidals shows well-defined gradients in metallicity (higher metal content in the centers), but, quite surprisingly, no gradient in ages or element abundance ratio for most massive early-types (Mehlert et al. 2003, Sanchez-Blazquez et al. 2006, Kuntschner et al. 2010). MaNGA will obtain the samples required to test this result and compare patterns in observed gradients 1 as a function of mass, structure, and environment. This will determine the ratio of 1 MaNGA will measure radially-averaged ages, metallicities, and element abundance ratios from stellar absorption lines to an accuracy of better than 0.1 dex to 1.5 2R e. For more details, see Section 4, Figure 7.

8 8 accreted material compared to the in situ component. This ratio is predicted to be a strong function of halo mass, with ellipticals in high mass halos undergoing more merging at late times than those in lower mass halos. MaNGA will test and quantify this mass dependence. Finally, whereas the single-fiber SDSS-I/II spectra sampled only the inner 3 5 kpc, measuring properties pertaining mainly to the in situ component, MaNGA will be the first survey capable of studying how dichotomies in stellar population age, metallicity, and element abundance ratio vary systematically with halo mass and environment. In addition to measuring the amount of material accreted by spheroidal galaxies in the past, MaNGA will also provide the first kinematic census of merging galaxies in a mass-limited sample. The observations will be compared to suites of hydrodynamical merger simulations (Lotz et al. 2010a,b) to test new statistical metrics of 2D merger kinematics that are aimed at diagnosing the merger phase (i.e., first approach, 2nd passage, final coalescence) and the mass ratio of the interacting galaxies. Because major mergers are relatively rare events (around 4% of galaxies in the local Universe), we require large samples for these kinds of analyses. Given the ubiquitous presence of central black holes, galaxy mergers should eventually cause black hole mergers. In recent work, Comerford et al. (2009) examined slit spectra of 91 AGN from the DEEP2 Galaxy Redshift Survey. In a third of these objects, the AGN s [OIII] line was clearly offset in velocity from the stars, suggesting an inspiralling supermassive black hole binary. This claim has turned out to be controversial, however, because velocity shifts and double-peaked lines can also be produced by mechanisms like ionized gas outflows in the nucleus of the galaxy, or by rotating central gas disks. IFU data is critical to confirming binary black holes (see for example McGurk et al, arxiv: ). With MaNGA, the true incidence of large-separation dual AGN can be inferred. This will serve as important input for interpreting observations from the LISA satellite, which is designed to detect gravitational radiation from merging black holes just before coalescence. Finally, many galaxy formation models invoke disk instabilities and bar formation, in addition to mergers, to build galaxy bulges. The efficiency of such mechanisms remains very controversial, however (e.g., Fisher & Drory 2011). MaNGA will test whether bars are indeed associated with V unstable disks satisfying the standard Toomre Q-parameter criterion, max < ɛ It (GM disk /R d ) 1/2 will also be possible to quantify the dynamical effect of the bar on the velocity dispersion profile of the galaxy and perform studies of stellar populations in bars and bulges, thus providing additional insight on their formation histories. By correlating non-circular motions and gas flows in galactic disks with the presence or lack of bars and spiral structure observed in SDSS and deep imaging data, MaNGA will further constrain secular evolution processes and their driving mechanisms The Death of Galaxies: What causes star formation quenching? The physical mechanism responsible for quenching star formation in massive galaxies is one of the most hotly debated questions in galaxy formation. Standard explanations invoke galaxy mergers which can drive inflows of cold gas that fuel central starbursts and active galactic nuclei (AGN). With a large fraction of the gas consumed by the starburst, outflows powered either by winds generated by the accreting black hole or by the starburst itself can heat the remaining gas

9 9 and perhaps even expel it from the galaxy altogether. Alternatively, the heating may occur outside the galaxy. This could take the form of tidal harassment from a nearby companion or, or if the galaxy is a satellite in a group or cluster, could be related to the hot gaseous medium surrounding it. In these environments, internal gas supplies may be stripped away (e.g., ram-pressure stripping) or prevented from replenishing (e.g., starvation ). So far, observers have struggled to discriminate between these two pictures. MaNGA will help in two important ways. First, it will enable studies of outflows and winds and will test what drives them. Second, by correlating maps of stellar population ages and star formation histories with information about the state of the surrounding gas from HI and X-ray surveys, we will study the role of external quenching processes and how they depend on group and cluster environment The relationship between quenching, AGN, and star formation The origin of galactic outflows, their relationship with AGN and star formation, and the potential role they play in quenching remain highly uncertain despite the popularity of AGN feedback in galaxy formation models. Evidence for outflows from strongly blue-shifted interstellar absorption has been detected in ultra-luminous infrared galaxies (Martin 2005), in post-starburst galaxies (Tremonti et al. 2007), and in AGN hosts (Crenshaw et al. 2010). However, the available samples are too small to determine whether outflows cause quenching. With larger samples, additional insight on the geometry, velocity, and mass loading of outflows can be obtained. Progress in this direction has come from stacking NaD absorption lines in single-fiber SDSS samples, which has confirmed outflows in normal star-forming galaxies and demonstrated the power of stacks based on galaxy inclination (e.g., Chen et al. 2010). This work was carried out using only central fiber spectra, however, so it was not possible to draw conclusions about the spatial extent of the outflow. MaNGA will advance this subject to a new level by enabling studies of the kinematics of outflowing gas at different spatial positions. Outflows powered by accreting black holes should be centrally concentrated in the galactic bulge with outflow velocities that scale with some measure of the accretion rate onto the black hole (for example the [OIII] line luminosity). Outflows powered by star formation can be generated throughout the disk, and the outflow velocities might be expected to scale with the local star formation rate. We note that recent work has indicated that outflows traced by NaD track outflows observed in the molecular component of galaxies remarkably well Quenching by externally-driven processes in massive groups and clusters The quenching of star formation in galaxies is even more prevalent in high-density regions like groups and clusters. The physical explanation for why this is the case remains elusive, however. A variety of studies from SDSS-I/II have concluded that quenching in high-density environments appears to be a gradual process with e-folding times of 2 3 Gyr (e.g. Weinmann et al. 2009). If they were much shorter, a significant population of galaxies should have been observed with spectra showing strong Balmer absorption lines, but no ongoing star formation, indicative of recent rapid truncation of star formation (Kauffmann et al. 2004). These results suggest that cluster galaxies are quenched primarily by gradual starvation, i.e., the hot cluster medium prevents replenishment

10 10 of gas supplies as the cold gas reservoir is slowly exhausted. Further insight on this mechanism will come from MaNGA s ability to map the radial dependence of the star formation history of recently quenched galaxies, thereby determining to what extent and under what conditions quenching occurs from the outside-in. In addition to starvation, however, the removal of HI gas by the more violent process known as ram-pressure stripping has been directly observed in spirals residing in nearby clusters such as Virgo. In some of these objects, IFU spectra have been obtained that show strong Balmer absorption lines in the outer regions of the galaxy (Figure 2). Again, available samples are extremely small. As described in Section 4 (see Figure 10), MaNGA will survey many hundreds of galaxies in rich groups and clusters. These samples will be large enough to ascertain 1) whether stripping-induced truncation events are ubiquitous in cluster galaxies, 2) the typical radii at which the truncation is occurring, 3) how the truncation radius depends on galaxy properties, halo mass, and cluster-centric radius. Ancillary HI and X-ray data from surveys undertaken with Apertif and erosita (Section 3.2) will provide maps of the cold gas disks and probes of the density and temperature structure of the surrounding hot gas for galaxies in groups and clusters in our survey. By relating such information to the spatially-resolved maps of star formation provided by MaNGA, we will obtain a definitive answer to how star formation is quenched in high density environments. NGC 4522 SP and HI on R NGC 4522 SP and HI on Hα 1.75E E 16 NGC Flux (erg/s/cm ) 1.25E E E 17 Ηδ Ηγ Mgb Ηβ NaD Ηα 5.00E E Wavelength (Angstroms) Fig. 2. An example of a galaxy in the Virgo cluster that is undergoing ram-pressure stripping (Kenney 1994). The HI map is shown as a contour plot on top of the galaxy image. The location where the spectrum on the right was obtained is given by coadding the solid circles plotted on the left. The spectrum shows strong Balmer absorption lines indicative of recent truncation of the star formation in this system The Birth and Formation of Galaxies from the Fossil Record: How do different mass components form and interact? Measuring the distribution and transfer of angular momentum between galaxy subcomponents via dynamical scaling relations The distribution and transfer of angular momentum between the forming disk, bulge, and halo components of galaxies ultimately define their present-day sizes and dynamical states. Different formation channels from dissipative collapse to fueling by cold streams, as well as tidal forces from mergers or instabilities, have varying effects on the angular momentum distribution as observed

11 11 today. This fossil record remains poorly understood, however, in large part because robust measures of rotation have not been possible for large samples. MaNGA s 2D velocity maps will address this problem by providing detailed insight on dynamical scaling relations that describe how the sizes (R), velocities (Vmax for rotation, σ for dispersion), and stellar masses of galaxy components relate to one another. The shape of these relations encodes information about how subcomponents have formed, interacted, and distributed their angular momentum Log Mstar Log Mstar MaNGA 10σ Detection of fcontraction τbulge > 9 Gyr Log Vmax (km/s) Log Vmax (km/s) 1.2 MaNGA 10σ Detection of fang. mom. 1.0 Log Rd (kpc) Log Rd (kpc) Ngal = 600 Sample 2 3σ evidence of fcontraction λbulge > Log Vmax (km/s) 0.8 Ngal = 600 Sample 2 3σ evidence of fang. mom Log Vmax (km/s) Fig. 3. Demonstration using mock datasets of MaNGA s ability to distinguish between different scenarios for baryonic contraction (top panels) and angular momentum transfer between subcomponents (bottom panel). Two toy models are considered with observational scatter applied to each model galaxy consistent with expectations for MaNGA observations. In the top panels, early dissipative collapse causes a 25% increase in halo contraction for a subset of galaxies identified with old stellar bulges, leading to a change in slope of M versus Vmax. In the bottom panels, two scenarios for the amount of angular momentum transferred to the halo during bulge formation are considered. Galaxies with significant bulge rotation compared to dispersion (λbulge > 0.5) trace a different relation between disk scale length, Rd, and Vmax. In both cases, MaNGA s 10k sample (left-hand panels) is required to firmly detect the expected offsets, as compared to a sample with 600 galaxies (right-hand panels). The top panels of Figure 3 illustrate one example using the M -Vmax, or Tully-Fisher, relation to constrain the amount of contraction experienced by the dark halo due to the dissipative formation of the baryonic components ( baryonic contraction is also discussed further below). We imagine a subset of galaxies in our sample that form early from dissipative collapse before regrowing a disk, and we show model predictions for how the degree of baryonic contraction during the collapse

12 12 is reflected in an increase in the peak circular velocity of the disk, V max. Colored points assume f contraction = 25%. Grey points assume f contraction = 0. The difference reflects the range spanned by current theoretical contraction models. We see that with 10,000 galaxies, MaNGA is able to constrain the difference between the two predictions at the 10σ level. In contrast, a sample of 600 galaxies, twice the size of ATLAS 3D and similar in size to planned IFU surveys (like CALIFA and SAMI; for details on other IFU surveys, see Section 3.4), does not have the statistical power to see the difference. The bottom panels of Figure 3 illustrate how MaNGA can probe the amount of angular momentum transferred between the bulge, disk, and dark matter halo. Motivated by recent work from ATLAS 3D IFU observations that revealed high rotational velocities for spheroid-dominated galaxies (Emsellem et al. 2011), we show the difference between a model in which bulges form from mergers (significant loss of angular momentum) and a model in which bulges form from the disk (angular momentum retained). In the latter model, the bulges and disks rotate faster, causing an increase in V max at fixed R d. Once again, a sample size of roughly 10,000 galaxies is required to firmly detect and characterize the predicted differences Weighing galaxy subcomponents: how baryons and stars trace and influence their dark matter halos MaNGA will also be able to weigh the masses of galaxy subcomponents. Dynamical masses, M dyn, within 1.5R e will be estimated using the Jeans Axisymmetric Modeling (JAM) code developed by members of our team (Cappellari 2008) as applied to absorption line tracers. The full 3D velocity field (x y velocity) is required to uniquely constrain the mass (Cappellari et al. 2006; see Section 4.2). In disks, we will be able to determine M dyn from the rotation of the gas, as traced by the Hα emission line, which can generally be observed out to 2R e in star-forming galaxies. In addition, next-generation HI surveys such as WNSHS at Westerbork will provide HI rotation curves out to significantly larger radii, but at significantly lower spatial resolution. Finally, thanks to planned overlap with deep panoramic imaging surveys (see Section 3.2) the mass of the dark matter halo M halo can be measured via weak lensing, which provides factor of 2 uncertainties on M halo for stacks of roughly 1000 galaxies. Comparisons between a variety of mass probes provide key insights. One example described above is baryonic contraction, which influences predictions for direct detections of dark matter annihilation and the ability of cosmic shear surveys to obtain cosmological constraints (e.g., Zentner et al. 2008). In principle, comparisons between M dyn, M halo, and baryonic mass 2 probes like M could measure dark matter fractions and constrain halo contraction. Unfortunately, as a result of the unknown shape of the stellar initial mass function (IMF), M estimates are systematically uncertain at the same level as the expected signal from baryonic contraction. MaNGA can help overcome this dark matter IMF degeneracy and shed light on both problems by enabling detailed models of the full suite of mass probes available and by discriminating trends in how these models 2 The gas mass, M gas, must also be accounted for although in most cases M dominates. M gas estimates will come from HI surveys and assumptions based on observed SFRs.

13 13 depend on total mass and other properties. Additional, independent constraints on the IMF will come from spectral signatures of low-mass stars in the key regions of our spectra (including the NaI doublet at 8200 Å and the CaT at 8600 Å, see van Dokkum & Conroy 2010). Finally, there is a possibility that mass-modeling may enable us to explore modifications to General Relativity designed to explain cosmic acceleration. These theories are very different from MOND (Modified Newtonian Gravity), often breaking the symmetry between gravitating and inertial mass on large scales. This leads to differences in the lensing mass, M lens, compared to M dyn for the same object. Deviations would be strongest for isolated, low-mass galaxies (M = M ) where M dyn may be as much as 30% larger than M lens. In practice, detailed modeling will be required to account for the different spatial scales probed by our mass tracers and for possible degeneracies introduced by baryonic contraction. We note that stacked samples of 1000 are required to obtain a weak-lensing based mass estimate, so this is an application where MaNGA s large sample size is crucial Interpreting z =2 3 IFU observations via comparisons to present-day modes of galaxy growth In addition to present-day measurements of the fossil record of galaxy formation, MaNGA will provide a low-z baseline for interpreting high-z IFU observations of galaxies in the early stages of formation. This field is developing quickly with instruments like SINFONI and OSIRIS currently collecting samples of a few hundred galaxies at z = 2 3 (with several tens resolved down to 1 kpc with adaptive optics). Sample sizes will expand significantly with the commissioning of VLT s KMOS instrument and the eventual deployment of IFU instruments on thirty meter telescopes and JWST. A crucial limitation, however, is the lack of adequate comparison samples in the local Universe. High-z observations so far have revealed a surprising diversity of kinematic structure, including large rotating disks, compact dispersion-dominated objects, and major mergers (Law et al. 2007; Law et al. 2009; Förster Schreiber et al. 2009). High velocity dispersions dominate, even for the largest galaxies with the most disk-like velocity fields. Many of the galaxies host giant kpc-sized clumps of young stars (Förster Schreiber et al. 2011), which may be responsible for driving gaseous outflows into the surrounding gaseous medium. Does this activity represent fundamentally different modes of star formation and growth compared to those in present-day galaxies? The large sample size of MaNGA, as well as its similar spatial resolution and restframe wavelength coverage compared to high-z samples, will make it possible to select low-redshift comparison samples that are tailored to test this. For example, targeted observations of potential local analogs of high-z galaxies selected to be compact with very high present-to-past averaged star formation rates showed these systems to be merger-driven (Overzier et al. 2008) whereas their high-z counterparts are hypothesized to be fueled by smooth gas accretion. But comparisons selected based on kinematics and spanning the full range of dynamical behavior in present-day galaxies are sorely needed. MaNGA will study localized nebular line diagnostics in galaxies sampling a vast array of physical conditions, shedding light on whether star formation fundamentally differs at high redshift. MaNGA will also reveal patterns in kinematic

14 14 behavior that may be common between low and high redshift samples, constraining, for example, the physical origin of random motions that increasingly dominated at early times. Local analogs of high redshift galaxies are rare, and smaller surveys such as CALIFA do not contain enough galaxies to find them. 3. The Survey 3.1. Survey Design and Sample Selection The MaNGA sample is selected in the redshift range < z < 0.15 from the SDSS DR7 MAIN galaxy sample. For galaxies with z < 0.05 we use the latest NYU LOWZ atlas, which features improved background subtraction and more accurate size and luminosity measurements for large galaxies, as well as a 30% improvement in completeness over the standard SDSS spectroscopic catalogue for the very brightest sources. At redshifts larger than z = 0.05, we use the standard NYU VAGC. All stellar masses are from the MPA/JHU VAGC and have been estimated from SDSS 5-band photometry using standard SED-fitting methodology (e.g. Drory et al. 2004). All effective radii quoted are convolved to the median seeing of 1.3 and are calculated along the major axis of the galaxy. Our primary sample is designed to achieve three goals: 1) The apparent radius (defined as 1.5R e ) of target galaxies must be subtended by at least 2.5 fibers ( 5 ), corresponding to a minimum IFU size of 19 hexagonally packed fibers; 2) The r-band continuum S/N of the fiber spectra at r = 1.5R e must be greater than five (per pixel); 3) We require that the final distribution of stellar mass should be flat over the range 10 9 M < M < M so that the sample is not dominated by much more numerous lower-mass galaxies. During the first half-year of operations, we will also target a secondary sample of galaxies in which the radius subtended by at least 2.5 fibers corresponds to 2.5R e, rather than 1.5 R e. The purpose of this sample is to study the physical properties of disks out to large radii with a view to understanding gas accretion mechanisms (see Section 2.1.1). The primary and secondary samples are assigned roughly equal numbers of galaxies. After the trial period, we will evaluate the amount of time to be spent on each of the two samples. Since it is crucial that our samples be complete in stellar mass we apply no size cuts 3, but instead apply the redshift-dependent stellar mass cuts that maximize the number of galaxies in our sample that achieve our size and S/N requirements. Note that while stellar masses remain systematically uncertain at the dex level (Section 2.3.2), they are well defined and reproducible from the DR7 parent sample. The effect on target selection of improvements or changes in stellar mass estimates (based on MaNGA or other advances) can therefore be easily accounted for in post-survey analyses. The left panel of Figure 4 shows our stellar mass cuts for the primary and secondary samples. 3 We also avoid cuts on inclination to keep the sample as broad as possible in its science return. For example, circular bundles placed on highly inclined sources will recover information about the galaxy outskirts far off the major axis.

15 15 Fig. 4. Left: The stellar mass limits as a function of redshift for the primary and secondary samples. Right: The stellar mass distributions of the primary (dashed lines), secondary (dotted lines) and combined samples (solid lines). The black histograms show the full parent samples, whereas the red histograms show the samples after they have been random sampled to be approximately flat. For the primary sample, we allow the lower redshift boundary to drop below so as to include galaxies down to a stellar mass limit of 10 9 M. The secondary sample typically selects galaxies of a given mass at higher redshift, thus covering larger radii for the same angular size, at the expense of spatial resolution. It also has a high stellar mass cut to ensure that we do not include galaxies that are too large to be covered by a reasonable size IFU. The right panel of Figure 4 shows in black the stellar mass distributions of the galaxies that these cuts select. The interplay between these cuts, the volume, the apparent magnitude limit of the SDSS MAIN galaxies and the shape of the mass function causes the distributions to be highly peaked. We therefore propose to randomly choose galaxies so that the stellar mass distribution is flat with a target density of 1 per log M deg 2 for the primary sample and 0.5 per log M deg 2 for the secondary sample. The flattened stellar mass distributions are shown as the red lines in the figure. Figure 5 shows the success of these cuts in selecting a sample of galaxies that fulfill the selection criteria described above. We have assumed an exposure time of 3 hr and S/N estimates based on the BOSS data. For the primary sample, we reach our goals for >80% of the galaxies with M > M, with a slightly smaller success rate for the least massive galaxies (>60%). For the secondary sample the fraction with S/N> 5 in the continuum is lower. However, for these galaxies we wish to target emission lines and as shown in Section 4.1.2, the emission line S/N at 2.5R e will be sufficient to enable star formation and gas kinematic studies. Most of our galaxies (70%) are covered to their target radius with 6.5 or fewer radial fibers, and so we limit the maximum bundle size to this radius, corresponding to bundles with a total of 125 fibers. Galaxies with the largest angular diameters will lack some coverage in their outskirts. To investigate the optimal distribution of bundles sizes, we have tiled part of the north galactic cap with 495 plates. Each plate has on average 22 targets per plate (3.2 deg 2 ) with an r.m.s. of 22. Some plate positions would contain no targets and will obviously be avoided. Based on the apparent i-band radii of galaxies in our selection catalog, we find that the optimal

16 16 Fig. 5. Both plots show the fraction of galaxies in the primary sample that pass our sample selection criteria at different S/N levels as a function of stellar mass. Left: The fraction of galaxies with 3 radial fibers all of which have a S/N greater than 2 (red), 5 (black) or 10 (blue). Right: The fraction of galaxies with an expected S/N larger than 2 (red), 5 (black) or 10 (blue) at 1.5 R e. strategy is a distribution of bundle sizes: 2 with 19 fibers, 4 with 37, 3 with 61, 3 with 91, and 3 with 125 fibers. This leaves 283 fibers for sky subtraction and standards (assuming a total of 1300 fibers, see Section 6). This would enable 67% of galaxies to be allocated an IFU bundle that reaches the desired radius. The most efficient tiling strategy with no overlap would allow for 61% of the galaxies in our primary and secondary samples to be allocated an IFU bundle within the plate area. In this scheme, bundles would be without targets only 11% of the time. Almost all of these unallocated IFU bundles can be assigned to galaxies that pass the primary or secondary selection cuts but were not included when the stellar mass distribution was flattened. This yields an average of 14.5 galaxies with an IFU per plate. Once every three plates, one bundle would be without a target. In these cases, the bundle could be placed on the sky to assess sky-subtraction systematics or assigned to additional targets Scientific Gain from Overlap with Future Ancillary Data Sets We highlight several important ancillary datasets that significantly complement and expand MaNGA s scientific reach. Many are currently being planned, with observational timescales similar to MaNGA. Exact field choices for MaNGA plates that maximize overlap will be determined at a later stage. Radio HI Surveys: Recent results from the GALEX Arecibo SDSS Survey (Catinella et al. 2010) have shown that optical spectroscopy and imaging can be combined with HI measurements to derive a more complete picture of gas accretion and star formation processes in galaxies. There are a number of deep, wide-field HI surveys that will be producing data by Surveys in the northern hemisphere with the Apertif receiver system on the Westerbork Synthesis Radio Telescope (run by ASTRON in the Netherlands) are currently being planned. Most relevant for

17 17 MaNGA is the Westerbork Northern Sky HI Survey 4 (WNSHS, the PI, G. Jozsa, is a member of the MaNGA team), which will detect HI reservoirs in MaNGA galaxies down to a limit of 10 9 M (at z = 0.15). The HI maps will have the best resolution at declinations above 27. HI coverage at lower declinations would come from the WALLABY survey (part of the Australian SKA Pathfinder or ASKAP program), which has lower sensitivity and spatial resolution than WNSHS, but will cover a larger area of the sky. Additionally in the North, Apertif programs may include deeper data over smaller regions that will be top priority for MaNGA observations. Deep Optical Imaging: While SDSS imaging is more than sufficient for MaNGA target selection, future wide-field imaging surveys will be 4 5 magnitudes deeper. Because they are motivated by cosmological weak lensing studies, they will also provide improvements of a factor of 2 3 in the imaging PSF. The improved resolution will be important for correlating MaNGA s spectral maps with features such as bars, spiral structures, and clumpy star formation. The improved depth will make it possible to search for low surface brightness features such as stellar streams and tidal arms around galaxies. These imaging datasets also enable weak lensing analyses of MaNGA targets (see Section 4). Two programs will begin to provide deep imaging next year. These are the Hyper SuprimeCam Wide Survey (HSC-Wide, operated out of IPMU/U. of Tokyo by PI, M. Takada, also a MaNGA team member) on the Subaru 8m telescope and the Dark Energy Survey (DES) on the Blanco 4m telescope at Cerro Tololo. HSC-Wide will cover 1400 deg 2 in grizy filters to 25 AB. DES will cover 5000 deg 2 to a significantly brighter limiting magnitude. Field choices are currently being made, but it is clear that MaNGA can overlap with all of HSC-Wide (mostly equatorial fields), while much of DES will be too far south. A proposal to shift some DES fields to just below the equator (near Stripe 82) would increase the potential overlap between MaNGA and deep imaging to a total of 2800 deg 2, more than 60% of the MaNGA sample. Both data sets will eventually become publicly available. Additional coverage with Pan-STARRS will also be valuable, although the imaging quality from HSC-Wide and DES is forecast to be significantly better. Deep X-ray Imaging: erosita will be the primary instrument on-board the Russian Spectrum- Roentgen-Gamma (SRG) satellite which will be launched in 2012 and will perform an all-sky imaging survey in the medium energy X-ray range up to 10 kev with an unprecedented spectral and angular resolution. One major goal of erosita is to detect the hot intergalactic medium of galaxy clusters and groups, as well as hot gas in filaments between clusters. The complementarity with the MaNGA science is obvious the hot gas is an important reservoir of baryons in the most massive dark matter halos, and can only be studied using X-ray data. The influence of this reservoir of baryons on galaxy growth via star formation and on quenching processes such as ram-pressure stripping cannot, however, be deduced directly from X-ray observations, but requires optical spectroscopic surveys such as MaNGA. Surveys of Supernovae in Nearby Galaxies: Type Ia supernovae (SN Ia) are a powerful tracer of cosmic acceleration, but their intrinsic luminosity must be standardized by a correction factor, often denoted as stretch, that depends on the decline rate of the light curve. Dependencies of the stretch factor on host galaxy properties have been reported that may weaken or even bias the 4 jozsa/wnshs/

18 18 SNe redshift-distance relation. Current evidence suggests that the average stretch factor is larger for SN Ia in star-forming galaxies and anti-correlates with the age of the stellar population. There are also trends with metallicity and element abundance ratio (Johansson et al. 2011, in prep). A key obstacle in quantifying the dependencies in an accurate way is the fact that the SDSS spectra do not sample the local environment near the supernova explosion site. MaNGA will allow studies of the exact ages and element abundances of the progenitor population of the supernovae for the first time. The published nearby supernova sample currently consists of 300 well-observed SN Ia at z < 0.1, and is growing rapidly with the continued operation of the Lick Observatory Supernova Search, the Nearby SN Factory, the Palomar Transient Factory (PTF), SkyMapper, and Pan-STARRS1. PTF has already found 800 nearby SN Ia in its first two years of operation. By the end of its 5-year survey (2014), PTF alone will have discovered and studied approximately 2000 nearby SN Ia. There will thus be thousands of supernova host galaxy targets for MaNGA to observe The Reason for MaNGA s 10k Sample Size One of the most significant strengths of MaNGA is its large sample size which will enable unique science that no other project currently planned could achieve. The justification for the 10k sample comes from the required statistical precision in achieving our science goals and the rarity of important sub-populations. These arguments are summarized below: Statistically robust analyses of gradients (e.g., in age, element abundances) in galaxies as function of mass, galaxy type, and environment require a sample of at least 10,000 galaxies. As an example, we note that stellar metallicity can be measured from the MaNGA spectra with an accuracy of 0.1 dex to around 2R e. From Figure 1, we infer that the typical gradient in a low-mass, star-forming galaxy is 0.15 dex per log R e in metallicity; massive star-forming galaxies show almost no gradient. Characterizing trends in such gradients in detail will require 5 M bins, 5 bins to probe the kinematic state of the galaxy, and 5 bins in environmental density or halo mass. To robustly detect differences between each bin at the level of 0.05 dex, we require around 50 galaxies per bin, so a sample of more than 6000 galaxies would be needed. We note, however, that the number of galaxies as a function of halo mass does not have a flat distribution (see Figure 10), so large samples are required to adequately study galaxies in dense environments, rich groups, and clusters. Binning on rare subsamples (mergers, AGN, post-starbursts, galaxies with strong outflows) that account for < 10% of the population requires a parent sample of 10,000 galaxies. These sub-samples represent important, but short-lived phases of galaxy formation and we emphasize that they cannot all be defined beforehand because in many cases MaNGA observations are required to identify them. Detecting differences in baryonic contraction and angular momentum transport during galaxy formation imprinted as offsets in dynamical scaling relations. Figure 3 demonstrates that detecting and characterizing differences at the level of current uncertainties requires parent

19 19 samples of the order of 10,000 sources. Likewise, accurately characterizing properties in 3 bins for two observables (e.g., M dyn and M ) requires a parent sample of similar size. Exploiting the power of stacked weak lensing to measure M halo, both for goals that compare galaxy properties to the dark matter halo (e.g., baryonic contraction) as well as for stacks based on MaNGA observables, such as dynamical mass. Our lensing simulations show that binned samples of >1000 galaxies are required for robust lensing measurements Complementarity with existing, on-going, and planned IFU surveys No current or planned IFU survey can compete with MaNGA s unique combination of wavelength coverage and sample size. MaNGA is highly complementary to ongoing efforts which are targeting smaller samples of very nearby galaxies at corresponding higher spatial resolution. Examples include the SAURON (de Zeeuw et al. 2002: 72 E/S0 galaxies) and ATLAS 3D surveys (Cappellari et al. 2011; 260 E/S0 galaxies, volume-limited within 42 Mpc or z < 0.01), which were the first to employ the IFU technique on a reasonably large set of galaxies. While immensely successful in their science return, their focus on early-type galaxies, their small field-of-view (1R e ), and their shorter wavelength coverage mean that they address a much narrower range of science topics compared to MaNGA. The currently-ongoing CALIFA survey aims for a sample of 600 galaxies observed to an isophotal radius of 25mag/arcsec 2 with a wavelength coverage from 3750 to 7000 Å. CALIFA is in many ways a forerunner of MaNGA. The CALIFA Principal Investigator (Sanchez) and the Project Scientist (Walcher) are both active participants in the MaNGA collaboration. The main strength of MaNGA in comparison to CALIFA is the fact that the sample size will be 20 times larger and the galaxy sample will have uniform selection by stellar mass. The CALIFA sample has been selected based on galaxy size, because the observing mode (1 object at a time) makes it important to optimize the use of the fibers. In large samples, the effects of diameter selection can be corrected for, but the limited sample size of the CALIFA survey will restrict its ability to draw statistically firm conclusions. MaNGA spectra will also extend further into the red, covering the Calcium Triplet and other useful diagnostics of stellar populations. The SAMI instrument at the AAT, commissioned in summer 2011, is more similar to MaNGA and provides a valuable testing ground for the fiber bundle technologies that MaNGA may also employ. SAMI utilizes 13 hexabundle IFUs, each with 61 fibers deployed using a plugplate over a 1 deg diameter field of view. However, the wavelength coverage ( and Å) is not as wide as MaNGA. More importantly, as SAMI is primarily a proof-of-concept instrument, it is not yet clear how much time will be allocated for the SAMI survey. The most likely scenario is that SAMIs sample size would reach 1000 galaxies. The sample design has yet to be determined, but as with CALIFA and ATLAS 3D, MaNGA benefits strongly from the participation and experience of SAMI team members (Croom, Bland-Hawthorn, Colless, Hopkins).

20 20 4. MaNGA Observables and Data Products MaNGA will make use of fiber bundles consisting of individual fibers to provide 2- dimensional maps sampling radial distances that reach between 1.5 and 2.5R e in each galaxy. The spatial resolution, defined as the physical scale 5 of the 2 diameter fiber, ranges from 1 kpc at z = to 5.2 kpc at z = The median resolution is 2 kpc (z 0.05). Each individual spectrum carries a wealth of physical information about stellar populations and the properties of the nebular gas at a given location in the target galaxy. Our science case depends on the information that can be extracted from the spectra, so it is important to demonstrate that it will be feasible to estimate key quantities such as stellar age, metallicity, and star formation rate out to radii 2R e for the bulk of the galaxies in our sample. We begin with Table 1, which summarizes the observables (mainly absorption and emission lines) that allow us to derive a range of different physical parameters and products that will enable scientific exploitation of the data, as well as the S/N required. Table 1: MaNGA observables, S/N requirements, and physical parameters/science products Observable S/N Radius spectral science products per pixel R e component Absorption line indices, e.g. Ca H+K, Hβ, Mg b, NaD, Ca triplet Absorption line indices, e.g. D4000, CN 1,2, Ca4227, Hβ, Mg b, Fe5270, Fe5335, NaD, Ca triplet absorption stellar mass, recent SFH, stellar velocity dispersion, (10% errors in σ per fiber, for σ > 60 km s 1 ) absorption accurate gradients in age, metallicity, and element abundance ratios, wind kinematics Hα emission line fluxes emission disk kinematics, rotation curves, SFR Hβ,[OII],[OIII],Hα and emission ionization state of interstellar [NII] emission line fluxes gas, BPT classification, star formation rates, precise gas metallicities, Balmer decrement Hβ,[OIII],Hα and [NII] emission line fluxes (no or poor [OII]) emission ionization of interstellar gas, BPT classification, SFR, gas metallicities, Balmer decrement 5 We assume H 0 = 70 km s 1, Ω m = 0.3, and Ω λ = 0.7.

21 Analysis of the Expected Data Quality In this section, we apply the selection criteria of the MaNGA survey (see Section 3) and make use of available surface brightness profile fits for SDSS/DR7 galaxies to predict the expected spectral S/N ratio as a function of radius for galaxies in the MaNGA sample. Our estimates are bootstrapped from real BOSS data. Fig. 6. Expected r-band continuum S/N per pixel for single fibers (left panel) and stacked fibers (right panel) as a function of radius. The grey contours show the density distribution of S/N ratios (limited to S/N< 20) as a function of galaxy radius (scale on right y-axis). The lines indicate the fraction of galaxies in the MaNGA sample (scale on the left y-axis) that will be observed with a S/N above 10 per pixel in single fibers (left panel), or 40 per pixel in stacked fibers (right panel). Solid lines are massive galaxies with log M /M > 10.5, dashed lines are low-mass galaxies with log M /M < 10. Orange and blue lines indicate red and blue galaxies separated by g r (rest-frame at z = 0.1). Galaxy masses are adopted from the MPA/JHU catalog, k-corrections are taken from k-correct code, and the real inclination distribution has been used to estimate the stacked S/N. Figure 6 shows resulting S/N distribution for the r-band continuum (in units of S/N per pixel) for both single fibers (left) and stacked fibers in a given annulus (right). As expected, the fraction of the sample satisfying S/N > 10 at 1.5R e is lowest for low-mass blue galaxies (around 50%), but is as high as 80% for massive red galaxies. In almost half of the massive red galaxies, individual fiber spectra have acceptable S/N out to 2R e. Some science applications based on absorption lines will need significantly higher S/N (see Table 1) which can be achieved by stacking fibers in annuli at different radii (Figure 6, right panel) or via routine adaptive binning methods (e.g., Emsellem et al. 2004). It is interesting to note that stacking results in a maximum in the S/N distribution at 1R e. MaNGA ensures that at least half of the sample will have a S/N of at least 40 per pixel in stacked fibers at 1.5R e, independent of galaxy mass or color. Again, this fraction is lowest for low-mass blue galaxies (around 50%), but is as high as 80% for massive red galaxies. Note that MaNGA will recover excellent S/N in stacked fibers for almost half of the massive red galaxies out to even 2R e, enabling analyses of stellar populations and weak emission lines out to very large radii.

22 Absorption line diagnostics The stellar populations in a galaxy represent a record of the galaxy s star formation history. The age distribution of the stars in a galaxy trace the main epochs of star formation activity, while stellar metallicities carry information about the efficiency of star formation as well as the history of gas accretion and outflows of gas and heavy elements. Finally, the element abundance ratios in stars can be used to constrain the chemical enrichment history of the galaxy, as well as the timescale over which stars were formed (Thomas et al. 2010). The best way to tap into this wealth of information is through the analysis of absorption line features and the spectral slope 6. These can be exploited to derive properties such as ages, star formation histories and element abundances by comparing with stellar population models (Thomas et al. 2011). A unique feature of MaNGA compared to current IFU surveys is its large spectral range ( ,000 Å) which captures a large range of absorption features, from blue indices such as D4000, Ca H+K, and higher-order Balmer lines, through classical optical absorption features such as Hβ, Mgb, and Fe5270/Fe5335, to red, gravity-sensitive features such as the Ca triplet at 8600 Å. These features encode a great deal of information about star formation histories and the IMF, and can be used to measure element abundance ratios like [Mg/Fe], [N/Fe], [C/Fe] and [Ca/Fe]. Figure 7 shows the radial distributions of expected errors in quantities based on these diagnostics, including age, total metallicity, and element abundance ratio. MaNGA reaches target accuracies of 20% at 1.5R e for over half the sample, independent of mass and color, and will deliver accurate measures for the majority of massive red galaxies even out to 2.5R e. Fig. 7. Expected errors in age, total metallicity, and element ratio [α/fe] for stacked fibers as a function of radius. The grey contours show the density distribution of measurement errors as a function of galaxy radius (scale on right y-axis). The lines indicate the fraction of galaxies in the MaNGA sample (scale on the left y-axis) that will be observed with a measurement accuracy better than 0.15 dex for age and metallicity, or 0.10 dex for [α/fe] (right panel). Note that these estimates include contamination from emission lines. Solid lines are massive galaxies with log M /M > 10.5, dashed lines are low-mass galaxies with log M /M < 10. Orange and blue lines indicate red and blue galaxies separated by g r (rest-frame at z = 0.1). Errors have been estimated by scaling results from SDSS DR7 spectra (Johansson et al. 2011, in prep). Galaxy masses are adopted from the MPA/JHU catalog, k-corrections are taken from k-correct code. 6 MaNGA will enable fits to all 25 Lick indices and the S/N results shown here are based on this approach (Johansson et al. 2011a). We will also employ full spectral fitting which can improve age estimates but comes with additional uncertainties.

23 Emission line diagnostics The MaNGA survey will include a large number of late-type, star-forming galaxies with high S/N ratio emission lines. Measurements of [OII], [OIII], Hα, Hβ and [NII] will determine nebular gas metallicities and ionization parameters with high confidence (Brinchmann et al. 2004; Tremonti et al. 2004). Measurements of Hα and Hβ yield estimates of the star formation rate over timescales of 10 7 years and dust extinction in HII regions. The combination of [OIII], Hβ, [NII] and Hα allows us to place galaxies on the so-called Baldwin, Phillips & Terlevich (BPT) diagram that diagnoses whether the emission lines are excited by radiation from young stars or from an active nucleus (or from some combination thereof). Finally, if one also can measure [OI] and [SII] along with the BPT lines as a function of position within the galaxy, it may be possible to differentiate between shocks in the interstellar medium, ionization by post-agb stars, or the presence of a low-ionization active galactic nucleus (e.g., Sarzi et al. 2010). Informed by a recent analysis in which we found that emission line equivalent widths and flux ratios correlate strongly with local color and surface brightness, we have again used SDSS spectra with matching fiber colors and surface brightness to predict the radial run of emission line S/N for galaxies in the MaNGA survey (Figure 8). Each column in the figure corresponds to a different set of criteria for the required emission lines: Column 1) S/N> 3 in [OII], [OIII], [NII], Hα, and Hβ. This regime delivers high confidence estimates of SFR, dust, metallicity, and ionization parameter; Column 2) S/N> 3 in [OIII], [NII], Hα, and Hβ. Here metallicity estimates will be slightly less accurate; Column 3) S/N> 5 in Hα, a regime in which star formation can be estimated and Hα used as a kinematic tracer. The dependence of S/N on radius for emission lines is different to that of absorption lines. The S/N does not drop as steeply with radius. Low-mass, star-forming galaxies have the strongest emission. The strongest line (Hα) can be detected at S/N> 5 in individual fibers out to 2.5 R e for over 80% of all blue galaxies. This underlines the feasibility of kinematic studies of outer disks, as well as the 2-dimensional mapping of star formation out to large radii. The S/N in lines such as [OII] or Hβ drops strongly with radius in individual fiber spectra. Nevertheless, 2-dimensional BPT and gas-phase metallicity maps will be possible out to 1.5 R e for at least half the sample. As shown in the bottom panels in Figure 8, 1-dimensional metallicity, star formation and dust extinction gradients will be obtained for over 70% of blue galaxies out to the largest radii sampled Advanced Analyses and Data Products MaNGA s advanced data products derived from MaNGA observations alone or in combination with ancillary surveys are crucial to the science goals laid out in Section 2, so we summarize them here. Simulator: We have developed a 3D data simulator to test the ability of MaNGA observations to achieve our science goals. This simulator combines input flux maps with realistic galaxy spectra, an assumed velocity and velocity dispersion model, and produces input data cubes mimicking the target flux as a function of wavelength. These input data cubes are convolved with a model of the atmospheric seeing and pixelated to regions sampled by spe-

24 24 Fig. 8. Expected S/N ratios in emission lines for single fibers (top panels) and stacked fibers (bottom panels) as a function of radius. The grey contours show the density distribution of S/N ratios as a function of galaxy radius (scale on right y-axis). The lines indicate the fraction of galaxies in the MaNGA sample (scale on the left y-axis) that will be observed with a S/N above 3 per pixel in single fibers (top panel), or in stacked fibers (bottom panel). Each column refers to a particular combination of emission lines corresponding to the three regimes described in the text. Solid lines are massive galaxies with log M /M > 10.5, dashed lines are low-mass galaxies with log M /M < 10. Orange and blue lines indicate red and blue galaxies separated by g r (rest-frame at z = 0.1). Galaxy masses are adopted from the MPA/JHU catalog, k-corrections are taken from k-correct code. cific fibers in the IFU bundle. The input spectrum to each of these fibers is in turn passed through a model of the BOSS spectrograph to produce a flux-calibrated reduced (i.e., noisy and sky-subtracted) spectrum. These reduced spectra are then bundled back together using interpolation from the finite spatial sampling of the fiber bundle to create a spatially contiguous reduced data cube. Among other effects, these simulations take into account field rotation, dithering, crosstalk, the latest throughput, sky background, and instrumental noise characteristics of the BOSS spectrograph. Figure 9 shows an example of a simulation of an emission-line velocity field from a late-type galaxy at z = The bottom panels show that the recovered spectrum is of significantly higher quality in the central regions of the galaxy where the total flux is greater, but adequate (if noisy) spectra are obtained across the entire galaxy. By fitting the [OIII] λ5007 Å emission line at each location across the galaxy in the reconstructed data cube it is possible to obtain a velocity field map similar to that which would be obtained from real observational data (Figure 9, top right panel). In this example, the input velocity field was recovered to a mean accuracy of σ v = 17 km s 1, sufficient for advanced kinemetric analysis. Mass Models: Dynamical masses will be derived using the JAM software developed by

25 Relative Velocity (km/s) 0 Hα image of NGC 4450 MaNGA fiber bundle (with 3 dither positions) -200 Recovered velocity map Simulated spectrum (central fiber) Simulated spectrum (edge fiber) Fig. 9. Simulated MaNGA observations of a late-type galaxy. Simulations assume a galaxy with the physical morphology of NGC 4450 (as traced by the SINGS Hα image) and an SDSS-derived late type galaxy spectrum located at z = Green, blue, and red circles in the top middle panel illustrate the optimal dithering overlap for a 3-position realization of our intermediate-size 37-fiber IFU bundle. Simulated spectra for the brightest (central fiber) and faintest (edge fiber) regions within the galaxy are shown in the bottom panels. The top right-hand panel illustrates the recovered velocity map, obtained with an accuracy of σ v = 17 km s 1, sufficient for advanced kinemetric analysis. (Cappellari 2008). The method is based on a solution of the Jeans equations, which allows for orbital anisotropy, and is powerful because it provides an accurate description of the stellar kinematics of real galaxies with a small number of model parameters. This makes it highly efficient for large samples because it can be incorporated into an analysis pipeline. Typical fractional uncertainties in the recovered M/L are (M/L) (M/L) 2 V rms V rms Nbin, where V rms V 2 + σ 2, with V the mean stellar velocity and σ the velocity dispersion, and N bin the number of spatial bins. Assuming typical errors of 10% in the kinematics and N bin = 19, we obtain an estimate of the relative error in M/L of 6%. This error is smaller than the effect of a variation of the IMF or dark matter fractions within 1R e, which are typically on the order of a few 10%. Kinemetry: Complementary to JAM, kinemetry allows for a non-parametric characterization of galaxy kinematics that can be used to diagnose disturbances, irregularities, and kinematic characteristics of merging galaxies. It is similar to elliptical aperture surface-brightness fitting but is applied to higher order velocity field moments which are decomposed into a Fourier series (Krajnovic et al. 2006). In this way, degrees of regular or disturbed behavior

26 26 can be distinguished and quantified. The detailed kinemetry method used by the SINS team for determining the dynamical state of the Hα-emitting ionized gas in galaxies from SINFONI data (Shapiro et al. 2008) provides observables that can be compared directly with data from MaNGA because the two surveys have very similar spatial and spectral resolutions. For the nearby galaxies probed by MaNGA, however, we will also need to develop new kinemetric methods to compare the dynamical state of the ionized gas with that of the stars. Weak Lensing: Planned deep imaging surveys (see Section 3.2) will reach i and source densities of 30 arcmin 2, overlapping up to 60% of the MaNGA sample. We have performed lensing simulations which show that, with such high quality imaging data, lensing signals out to 1 Mpc can be obtained for stacks of MaNGA galaxies even at z = The number required to obtain estimates of M halo with uncertainties less than a factor of 2 depends on the mass range probed, but for halos hosting galaxies with M M, bins of 1000 galaxies are needed. Environmental Density: Because MaNGA targets are selected from the SDSS DR7 sample, environmental density statistics can be derived using nearest neighbor methods and group finders applied to DR7. These diagnostics can be compared to mock catalogs to provide another means of estimating the halo masses of MaNGA galaxies (Mandelbaum et al. 2009). Figure 10 shows mock catalogs of the MaNGA distribution in stellar mass versus M halo for red and blue galaxies separately. For the red population, the highest sampling density occurs at M M in halos with M halo M. For the blue population, halos with M halo = M are very well sampled up to M M. Despite the density peaks in Figure 10, MaNGA will have relatively even sampling of groups and clusters with M halo = M regardless of M or color. 5. Observing Strategy and Data Management 5.1. Dithering Although the observational seeing is expected to be 1.3 on average, it will not generally be possible to achieve this spatial resolution because individual fibers do not preserve information about flux gradients across each 2 arcsec diameter fiber. Using the 3D data simulator described in Section 4.2 we have modeled the recovered effective PSF, finding that it has a FWHM of 2.5 ± 0.5, varying across the fiber bundle due to information loss when a point-source falls between fibers (despite the relatively high fill factor of greater than 70%). It is possible to significantly improve the PSF recovery by dithering. Adopting a 3-point dither pattern that samples the field most uniformly (see top middle panel of Figure 9), our simulation suggests that it will be possible to obtain an effective PSF with FWHM 2.3±0.03 (this corresponds to physical scales of 2 kpc). While this is 10% better than the undithered case, the primary benefit would be achieving uniform spatial sampling that varies by only 1% across an individual object. This is important for reliable flux calibration and recovering small spatial features. Based on our experience with CALIFA, additional dithering strategies can improve angular resolution in

27 27 Fig. 10. Mock catalogs of the MaNGA sample demonstrating MaNGA s ability to characterize wide dynamic ranges in both M (x-axis) and M halo (y-axis) for both red galaxies (left) and blue galaxies (right). The galaxy formation model implemented in these simulations is that of Guo et al. (2010). The method used to generate the mocks is described in Li et al. (2006). some cases by up to 30%, although this depends on image reconstruction methods and comes with other drawbacks. We intend to pursue extensive testing of optimum strategies before MaNGA observations begin Sky subtraction As we will be mapping some targets to 2.5R e, the spatial elements, or spaxels, covering these outer regions will be sky-dominated, with the galaxy surface brightness at least 1 magnitude fainter than the sky background. Sky subtraction is therefore a crucial component of both the observational design and data reduction. Based on extensive tests with sky observations from actual BOSS data, an optimum strategy is to deploy dedicated sky fibers in close proximity to MaNGA targets simultaneously with the science observations. Each IFU science bundle will be accompanied by at least 20 separate sky fibers. This number ensures that the sky is measured with sufficient signalto-noise, such that the noise added to our science frames by sky subtraction is at most 2.5% for our faintest galaxy spectra, and only 1% for the majority of our spectra. The sky fibers will be placed on a rough annulus around each IFU bundle, at least 2 arcmin away from the bundle center, ensuring that contamination of the sky measurement from even the largest galaxies in our sample will be negligible. The SDSS imaging also enables nearby objects to be avoided when choosing the position of sky fibers. To mitigate against possible differential focal

28 28 ratio degradation (FRD) in bundled fibers compared to single fibers, occasional plate designs will place a fiber bundle on empty sky. We are currently exploring the efficacy of adding additional sky mini-bundles of 4 to 7 fibers to provide a second calibration of these effects. The SAMI instrument (currently being commissioned) will provide further guidance. Sky fibers will be woven in with science fibers at the slit head so that similar portions of the optical path and CCDs are sampled by both. Significantly fewer sky fibers (enabling more bundles and an increase in survey efficiency) would be needed if the sky variability over the plate is small or varies gradually with position. In that case we could adopt a different sky sampling strategy, placing sky fibers randomly on empty sky regions across the plate. We have rejected this strategy for now, as we found that the variations between sky spectra over one BOSS plate are too large (>20%). However, there are strong indications that this variation is caused by a changing PSF (D. Schlegel, private communication). The BOSS team is currently investigating this issue and implementing an improved data reduction algorithm ( Spectro-Perfectionism, see Bolton & Schlegel 2010), which can handle arbitrarily shaped PSFs. If the PSF variability is significant and can be accounted for, a principle component analysis (PCA) can further improve the sky subtraction, especially at red wavelengths (λ > 6700 Å). We will explore these options in more detail during MaNGA s instrument development phase by building more detailed simulations based on the nearly-complete analysis by the BOSS team Survey Operations and Duration MaNGA s operational strategy follows the SDSS-III/BOSS model closely, fully utilizing the efforts SDSS has invested over the years in optimizing operational efficiency. Plate drilling can continue at the University of Washington or be shared with new MaNGA institutions. Only minor plate modifications are required. For example, hole sizes for bundles will not be significantly larger than for current BOSS fibers, although the total number of holes required will of course decrease. Plate delivery and plugging can continue as with BOSS but is made easier by the fewer number of plates required per night (typical exposure times are 3 hours, see below) and the fewer holes that must be plugged. The cartridge, and cartridge delivery systems, as well as the current methods for cartridge exchange at APO will remain unchanged. To reach our target sample size of 10,000 galaxies we would require 688 plates covering 4800 deg 2. Over the last year BOSS has observed 617 complete plates with a nominal exposure time of 1 hr. This has been a particular good year in terms of weather and so we decrease this number by 20%, i.e., we assume hr plates per year as our baseline. We intend to have a target integration time of 3 hours (broken into sub-exposures) and so estimate that we will take 2.75 times as long as BOSS to observe a single plate. This includes an efficiency saving on overhead as each plate will be observed for longer and so fewer calibration frames and cartridge changes will be required per night. Assuming full use of dark time, this yields 179 plates per year or 3.8 years to complete the survey. MaNGA observations would be complementary to other SDSS/AS3 dark-time programs. Our preferred strategy would be to share time instead of sharing plates because this would simplify our dithering techniques, but we stress that plate sharing can be considered. With single-fiber programs

29 29 that require 1 hour observations, we could use a single plate with holes drilled for MaNGA bundles as well as holes corresponding to three sets of target positions for the single-fiber program. Each set could be observed during each of three dither positions, thereby obtaining the required S/N for MaNGA. We note that in the case of technical problems that limit performance (e.g., broken fiber bundles) our priority is foremost to maintain MaNGA s 10,000 object sample size. This could be accomplished, for example, by splitting some of the largest 125-fiber bundles into multiple, smaller bundles, thereby increasing the number of galaxies targeted per plate. While we would lose valuable measurements of the outskirts of large galaxies, maintaining the large sample size is what makes MaNGA so powerful in comparison to other IFU efforts Data Management and Archiving The MaNGA survey will yield an unprecedented wealth of 2D spectral information, consisting of nearly one million individual spectra, similar to the size of SDSS-I. As such, our data reduction and archiving plan is based on the Sloan model. IFU observations, however, are inherently more challenging to reduce, analyze, and interpret than 1D spectra. The essential steps in this process include the following: 1) Extraction of spectra for individual fibers: this will largely follow techniques developed for BOSS; 2) Sky subtraction: see Section 5.2; 3) Flux calibration; 4) Spatial reconstruction of how the fiber layout in a bundle samples each target galaxy; 5) Spatial interpolation and drizzling of the 2D data taking into account dependencies on wavelength due to atmospheric dispersion: methods for such post-processing will be based on CALIFA software, elements of which are already included in the simulator; 6) Extraction of maps of observables (kinematics, metallicities, SFRs, etc.). To address the challenges in each of these steps our team brings extensive IFU expertise that includes analyses of IFU data from SAURON (Weijmans, Emsellem, Cappellari, McDermid), PPAK (CALIFA: Walcher, Sanchez, Roth), OSIRIS (Law, Wright), SINFONI (Förster Schreiber), VIRUS (Drory), KMOS (Sharples), and the newly commissioned SAMI (Croom, Bland-Hawthorn, Colless). MaNGA data will also serve as a grounds for developing new IFU software tools under efforts led by the JWST IFU working group at Space Telescope Science Institute (including J. Lotz). We discuss two important data management issues below. Data pipeline: For quality control and a swift and homogeneous data reduction, data reduction will take place nightly with a dedicated MaNGA data reduction pipeline based largely on the existing BOSS data reduction software (which continues to be improved). Adaptations will include tracing of individual fibers within IFU bundles, improved sky subtraction and reconstruction of the data cube and point-spread function (PSF) based on our dithering scheme. The data will be photometrically calibrated with standards observed simultaneously with our science frames as well as with existing SDSS data. Automated routines will recover stellar and ionized gas kinematics, emission line fluxes, and absorption line strengths. Archiving and Public Access: All MaNGA observations including raw data and data products will become public following the model developed by SDSS-I/II, with regular data releases expected roughly 1 year after observations. Public servers will be hosted at the University of Portsmouth which has experience in archiving SDSS datasets. The MaNGA budget includes allocations to

30 30 support this effort. MaNGA data cubes will be made available in several formats to serve different users and their requirements. For non-experts, data cubes with processed, spatially interpolated spectra will be most useful while the more experienced user can obtain data cubes of raw spectra, providing more flexibility. Released data products will include the observables mentioned in Section 4 including maps of the stellar and gas kinematics, absorption line strengths and emission lines. In addition, we will provide visualization and analysis software so that also new users can quickly become familiar with the MaNGA dataset. Finally, we are enthusiastic about linking MaNGA with the highly successful Galaxy Zoo project, which includes a mix of public outreach and exciting science. The goal would be to make velocity and other MaNGA maps available for visual inspection enabling crowd-sourced object classification and pattern identification. This effort will be led by the University of Portsmouth. 6. Instrument Design MaNGA will use the existing SDSS/BOSS spectrographs, cameras, and detectors. One of MaNGA s strengths is that its science objectives can be realized with only modest modifications to the existing SDSS/BOSS hardware infrastructure. We plan to increase the total number of fibers from 1000 to around by packing them more closely along the slit. Our current design conservatively assumes 1300 fibers for a baseline survey design. We will bundle fibers together in hexagonal dense packing in the focal plane to form IFUs of various sizes. The choice of IFU sizes is adapted to the size distribution of our target galaxies, as described in Section 3. Finally, we will retain the plug-plate system albeit with a slightly modified drilling procedure to accommodate the IFUs. Spectrographs: The requirements for spectral coverage and resolution are driven by the range of observables presented in Section 4 and their S/N limits. The ideal spectral range spans [OII] to the Ca Triplet, or 3727 Å to 8500 Å (restframe). Although higher spectral resolution at the expense of wavelength coverage would improve kinematic measurements of lower-mass galaxies, the long wavelength range and especially access to red spectral features is more important to our science goals. As described in the previous sections, the configuration of the current SDSS/BOSS spectrographs enables an extremely broad range of science. Adding in the benefit of previous expertise about the spectrographs and existing reduction software, we have concluded that leaving the current spectrographs unmodified is the best way to proceed. Integral Field Units: The current fiber size of 120 µm core diameter (2 on sky) allows for a good compromise between spatial resolution (given the typical seeing), target density on the sky, apparent size of the target, and exposure time per plate for galaxies with z < 0.15 (see Section 3). The size distribution of the targets motivates a variety of IFU sizes, ranging from 19 to 125 fibers (3 7 annuli of hexagonally packed fibers; see Section 3). Within each IFU, we wish to pack fibers as densely as possible; the thinnest cladding that can be tolerated before significant transmission loss and degradation is 5 µm thick, so that the minimal fiber separation in the focal plane becomes 130 µm. This allows for a maximum fill factor of 77% within each IFU if perfect hexagonal dense packing is achieved. The length of the fiber bundles will be increased to be 2.5 m, sufficient to position each bundle anywhere in the focal plane. The

31 31 required minimum radius of curvature of the bundles is 100 mm, and the minimum radius of curvature of single fibers (occurring near the v-groove blocks) is 50 mm. We are evaluating two possible solutions for the bundling of fibers into IFUs: (1) a solution by C Technologies which has been used for the APOGEE calibration bundles, and, (2) the Hexabundles developed at the University of Sydney, which are in the process of on-sky testing. The C Technologies solution involves arranging the stripped ends of the fibers dry in the desired hexagonal packing, passing them through a hexagonally shaped stencil to remove excess fibers, and securing them in place using low-viscosity epoxy. The bundle is then cut and polished. The company has manufactured bundles of up to 37 fibers using this method. Larger bundles will require further study and testing, but the method is scalable. In conversations with engineers from C Technologies, fiber breakage during assembly of the bundles and mapping of the fibers to the v-groove blocks were identified as the main risks for larger bundles. In particular, a fully specified and repeatable mapping of fibers from the IFU head to the v-groove block requires a lot of manual handling. This risk can be partially mitigated by having smaller v-groove blocks, by relaxing the mapping specifications, and by relaxing the requirement of perfect dense hexagonal packing at the IFU head. Each fiber bundle will have its own v-groove block piece; these will later be placed adjacent to each other to form the slit assembly. The mapping from the IFU to the v-groove block need not be the same for all bundles and sizes. A sufficient requirement is that neighboring fibers on the slit also be adjacent on the sky to avoid additional non-local crosstalk. This leaves enough flexibility in the mapping to mitigate some of the risk due to excessive manual handling and testing. Lastly, a packing density of 70% can still be achieved with slightly irregular packing (see example in Fig. 11), which reduces the stresses suffered by the fibers during assembly of the IFU heads, particularly for the larger bundles. These measures allow us to keep the number of broken fibers at the percent level and achieve a high efficiency in bundle manufacturing process (for 4 cartridges we would need at least 60 bundles). From experience with the SAMI instrument and through our own testing, we will assess the additional focal ratio degradation (FRD) to expect from bundling the fibers into IFUs and how to minimize it. Stresses in the IFU head and stress due to the transition between buffered fiber and stripped fiber within the bundle could cause additional FRD compared to single fibers. Differences in FRD between fibers at the center and at the edges of the bundles, as well as between in-bundle fibers and single sky fibers should also be considered. The latter may require small bundles for proper sky sampling instead of single fibers. The second bundling solution is the Hexabundle concept described in Bland-Hawthorn et al. (2010) and Bryant et al. (2011). Hexabundles consist of fibers which are lightly fused over 2-3 cm length. These are arranged in a closely (but not strictly hexagonally) packed slightly irregular grid (up to 61 cores so far). To increase the packing density, the cladding can be etched down to 2 µm over the few cm of fused length without increasing crosstalk beyond 1%. This way, a fill factor of > 80% and thus similar to the hexagonal dense packing can be reached. FRD measurements in test bundles show that FRD is no worse than for single fibers. This technique is very promising, and 13 bundles of 61 fibers each are currently (July 2011) undergoing commissioning in the SAMI instrument feeding the AAOmega spectrograph (see Fig. 11). Finally, we note that we will leave 238 fibers in their current single-fiber configuration (see 3)

32 32 to be placed around our targets to sample the night sky. Slit Heads: The science objectives of MaNGA can be reached leaving the fiber diameter, spectral coverage, and resolution of the SDSS/BOSS spectrographs unchanged. However, our wish for a large sample size and large radial coverage of our target galaxies, pushes us to maximize the total number of fibers we can use for making IFUs. Currently, the BOSS fibers are spaced along the slit to produce images with 1% crosstalk between adjacent fibers. The fibers are 120 µm core diameter and are arranged with 266 µm pitch along the slit; these are imaged to 40 µm spots separated by 101 µm on the CCD. In dense IFU applications where the dominant crosstalk between neighboring fibers on the sky results from the seeing, we can tolerate a denser packing of fibers along the slit. Our simulations show that for typical atmospheric conditions and hexagonal dense packing of fibers, we expect 6% seeinginduced crosstalk between fibers. Since each fiber interior to an IFU has 6 neighbors on-sky, but only 2 along the slit, we can afford to pack the fibers significantly closer before the slit-induced crosstalk becomes significant. Optical modeling, verification by inspection of raw frames from BOSS, as well as extraction tests, show that we can expect to pack the fibers as tightly as 200 µm to accommodate 650 fibers per slit head, or 1300 fibers in total (an increase by a factor of 1.3). With this configuration, the slit-induced crosstalk remains below 5%. Denser packing up to 1500 fibers total may be possible but requires further study. Sky subtraction is crucial for our applications, and therefore the sky fibers should be distributed along the slit to sample the same optical paths through the instrument as the light from the targets. Sky fibers should be placed along the slit adjacent to their parent IFUs. Fig. 11. Left: Image of a 61-core hexabundle designed and tested at the University of Sydney. Right: Sketch of a preliminary design for the plug-plate interface (not to scale). The plug snaps into place using the spring fingers against the top end of the bore. The focus distance is determined by the tapered foot of the ferrule which rests against the lower plug-plate surface. Plug Plates: We will maintain the current plug-plate system with a newly designed but simple plug interface to hold the IFUs in place. A possible IFU head design involves machined fingers in the ferrule using the intrinsic spring constant of the steel fingers to lock the IFU head in place against the hole-plate interface, pushing against a slightly beveled bore surface at the top of the