Tracing the cosmic web with Vweb & DisPerSE

Transcription

1 Tracing the cosmic web with Vweb & DisPerSE Tracing the Cosmic Web work shop Leiden, Netherlands 18/Feb./2014 Weiguang Cui* Vweb Authors: Yehuda Hoffman, Noam I. Libeskind, Ofer Metuki, Gustavo Yepes, Alexander Knebe, et al. DisPerSE Author: Thierry Sousbie

2 Outline Brief introduction A comparison seeing from two methods Vweb DisPerSE: The code & post processing The Vweb VS. DisPerSE: seeing from filaments Future works 2

3 Outline Brief introduction A comparison seeing from two methods Vweb DisPerSE: The code & post processing The Vweb VS. DisPerSE: seeing from filaments Future works preliminary 2

4 Brief introduction Separating the structures into clusters, filaments, sheets, and voids normally uses the geometric and the dynamic approaches: The geometric methods focus on the point process exhibited by the distribution of galaxies or DM halos in simulations, say, and describes it mathematically. This has been often applied independently of any dynamical context: Lemson & Kauffmann, 1999; Novikov, et al. 2006; Aragon-Calvo, et al. 2007; Sousbie, et al & Sousbie, 2011(Topology method), etc. The dynamic methods root in the Zeldovich approximation, the first analytical tool that enables the tracking of the formation of aspherical objects, thereby accounting for the formation of voids, sheets, filament and knots. Zeldovich, 1970; Arnold et al. 1982; Klypin & Shandarin 1983; Lee & Lee, 2008, etc. The Hessian matrix approche: gravitational potential, Hahn, et al. 2007; density field, Zhang, et al. 2009; velocity filed, Hoffman, et al. 2012, Libeskind, et al. 2012, etc. 3

5 A comparison seeing from two methods Density field Hessian matrix VS. Segment extraction method Zhang et al The local Hessian matrix H of the smoothed mass density field, defined as Hαβ= 2 ρs(x) / xα xβ Method I where ρs(x) is the smoothed density field. α and β denote the Hessian matrix indices with values of 1, 2, or 3. The number of positive eigenvalues of Hαβ can be used to classify the possible environments in which a halo can reside into four regions, according to: 1. cluster: a region with no positive eigenvalue; 2. filament: a region with one positive and two negative eigenvalues; 3. sheet: a region with two positive and one negative eigenvalues; Method II This halo-based method for filament finding is based on a slightly modified version of the Candy model proposed by Stoica et al. (2005). The Candy model reconstructs filaments by connecting individual segments that are found in a basic point distribution (galaxies, halos, etc.). Dark matter halos are the building blocks for the filamentary structure of the cosmic web. Therefore, a candidate segment is assumed to be a cylinder, with a length in the range of [Lmin, Lmax ] and a radius in the range [Rmin, Rmax ]. The mean mass density within the segment should be at least Nρ times that of the average mass density of all halos, ρh = Mh,tot/V, where Mh,tot is the total mass of the halos with more than 500 particles and V is the volume of the simulation. Finally, a segment should have at least Nmin member halos. 4. void: a region with three positive eigenvalues. Presentation Title (alter in master slide) 4

6 A comparison seeing from two methods Zhang, et al Mass function of the FOF halos residing in filaments in Method I (red dashed) and Method II (green dotted). Probability distribution of the cosine of the angle between the directions of the filaments from Method I and the filament vectors from Method II for all the filament halos (black solid) and different mass range of halos. gure 16. Probability distribution of the cosine of the angle between t Presentation Title (alter in master slide) 5

7 A comparison seeing from two methods Probability distribution of the cosine of the angle between the halo angular momentum vector and the direction of the filament in Methods I (left panel) and II (right panel). Probability distribution of the cosine of the angles between the halo major axis vectors and the directions of the filaments in Methods I (left panel) and II (right panel). Zhang, et al Presentation Title (alter in master slide) 6

8 Simulation VS. Observation xy-sheet pairs in each bin of (θ GX ) is the average numm the 100 random samples. also compute the standard θ GX )/ N R (θ GX ), which is e of any detected alignment of any alignment is quantiypothesis, throughout this on top of the P R (θ GX )=1, we also calculate the mean is the the standard deviaandom samples. The angle ge 0 θ GX < 90.Inthe θ GX )=1and θ GX =45. te that the galaxy orientahe filaments or the normal θ GX > 45 indicates per- N-body Simulations erpretation of our analysis, on carried out at the Shangsing the massively parallel Springel 2005). The simutter particles in a periodic Glass-like cosmological iniwere generated at redshift approximation. The pargth are h 1 M. The adopted cosmologi- galaxies in the filaments 5 6 Zhang et al. Fig. 3. Same as Fig. 2, but for the alignment angle θ HF between the projected orientation of a dark matter halo and that of the filament in which it resides. These results have been obtained using the numerical N-body simulation described in 2.4. Fig. 4. Same as Fig. 2, but for different subsamples ( red and blue ) of central and satellite galaxies, as indicated. Fig. Fig shows Normalized the equivalent probability plot distribution for θ HF, of the the angle θ GF between alignment the projected angle between orientation the projected of the major orientation axis of of SDSS ment galaxies angle that is consistent with no alignment at the and filament that of dark the matter filament halos in which and that its group of the resides. filament The 2.3σ-level. horizontal Satellite galaxies, overall, are less strongly in which they reside. As in the SDSS data, their is a dotted line corresponds to an isotropic distribution of aligned alignment with the filaments in which their host-groups re- fromthan 100 centrals. In particular, the orientations of blue clear and significant alignment between halos and filaments, in the sense that the major axis of dark mat- angles, while the error bars indicate the scatter obtainedside realizations in which the orientations of the galaxies havesatellites been randomized. The average value of θ GF and its error (obtained random from (projected) orientation with respect to their fil- are consistent at the 1.2σ-level with having a ter halos preferentially aligns parallel to the filament in which it resides. The average alignment angle of the θ 100 random realizations) are indicated. HF =43.30 ±0.12 ament. In the case of red satellites, however, the alignment signal is θ is smaller than for the SDSS galaxies. In addition, from N-body simulation we select a sample ofsimulationzhang, dark matter halos, and methodology et al. GF =44.14 ±0.19, which is significant this with mass distribution were2013 equivalent to the host between halos of central the spins galaxies and in SDSS shapes galaxy of darkhas matter been found for the alignment between the orien- used to study at the 4.5σ-level. the A quantitatively similar dependence on galaxy color alignment catalog. We find the alignment signals for the dark matter halos halos and aretheir stonger large than scale those of environment. central galaxiesthey in find tationthat of central galaxies and the angular distribution both SDSS galaxy the spin catalog, and the major axes of filament halos of their with satellites (Yang et al. 2006; Azzaro et al. 2007; masses h 1 and the average alignment angle is θ HF =42.19 ±0.38 Wang et al. 2008; Agustsson & Brainerd 2010), and suggests withthat thered centrals are more accurately aligned with. MThis suggests are preferentially a net misalignment aligned direction between theof major theaxes filaments. of galaxies and The their spins dark matter halos, in excellent agreement with a number of previ- Title than(alter blue centrals. in master We slide) caution, though, 7 and major Presentation their host axeshalo that, as demonstrated by Kang et al. (2007), the inter-

9 Vweb See the paper: Hoffman, et al & Libeskind, et al. 2012, etc. Also see the talk by Yehuda Hoffman & Noam Libeskind A description of the simulation can be provided by the potential and velocity fields evaluated on a finite grid, φ(r) and v(r). The potential tidal tensor is defined as the Hessian of φ, namely Tαβ = 2 φ/ rα rβ the shear tensor is defined as the hessian of V: Σαβ = 1/2( vα/ rβ+ vβ / rα)/h0 A web classification scheme based on how many eigenvalues are above an arbitrary threshold is carried out. If none, one, two or three eigenvalues are above this threshold, the grid cell is classified as belonging to a void, sheet, filament or knot. The choice of this arbitrary threshold has been found as

10 DisPerSE: The code Discrete Persistent Structures Extractor (DisPerSE) : See the papers: Sousbie, T & Sousbie, T., et al Also the talk by Thierry Sousbie and his website. Ascending 0,1,2 and 3-manifolds in 3D trace the voids (3D), walls (2D), filaments (1D) and maxima/sources (0D). How can we identify the particles belong to each structures in simulation? 9

11 DisPerSE: Post processing To assign the particles to the structures identified by DisPerSE: 1. SO halos are identified first with the maxima as the centre of the halo. The over density, c = The simulation particles are attached to its nearest filament identifier. Excluding halo particles or not. Free parameter: the nearest distance Rc, e.g Kpc/h 3. The left over particles are assigned to the wall structures as previous step by the nearest choice with the same parameter. 4. The remain particles are separated to voids by its position. 10

12 DisPerSE: Post processing 11

13 DisPerSE: Post processing 12

14 DisPerSE: Post processing 13

15 DisPerSE: Post processing 13

16 The Vweb VS. DisPerSE: seeing from filaments 14

17 The Vweb VS. DisPerSE: seeing from filaments 14

18 The Vweb VS. DisPerSE: seeing from filaments 14

19 The Vweb VS. DisPerSE: seeing from filaments 14

20 Future works (No conclusions) Coordinate Vweb and DisPerSE to get better constrains on the free parameters, Lth & Nsig A detailed filament comparison between the two codes to understand the persistent cosmic web. Filaments under different cosmology models, LCDM, MG, DE, etc. 15

21 Presentation Title (alter in master slide) 16