THE POPULATIONS OF STAR FORMING AND QUENCHED GALAXIES

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1 THE POPULATIONS OF STAR FORMING AND QUENCHED GALAXIES TJITSKE STARKENBURG FLATIRON RESEARCH FELLOW CENTER FOR COMPUTATIONAL ASTROPHYSICS, FLATIRON INSTITUTE, NEW YORK CHANG HOON HAHN (LBL), CLAIRE DICKEY (YALE), AND THE IQ (ISOLATED & QUENCHED GALAXIES) COLLABORATORY

2 The goal: Compare isolated and quenched galaxies between a large set of simulated and observational samples to learn about the quenching processes The problem: How do we compare simulated and observed galaxies on equal footing? WHAT IS QUENCHING? Quenched, quiescent, passive, gas-poor, gas-free, red and dead, red sequence, early type, evolved, Define quenched/quenching: color-mass, color-color, SSFR cut, distance from SFMS (Fitted how?), Dn, emission lines Measure star formation rate: Dn, emission lines, UV, IR, full SED fit

3 THE IQ (ISOLATED AND QUENCHED GALAXIES) COLLABORATORY Daniel Angles-Alcazar (CCA) Marla Geha (Yale) John Moustakas (Siena) Michael Blanton (NYU) Shy Genel (CCA) Viraj Pandya (UCSC) Alyson Brooks (Rutgers) Johnny Grecco (Princeton) Mary Putman (Columbia) Greg Bryan (Columbia/CCA) Jenny Green (Princeton) Mika Rafieferantsoa (UWC) Jason Cao (NYU) Aaron Yung (Rutgers) David Schiminovich (Columbia) Ena Choi (Columbia) Melanie Habouzit (CCA) Ramon Sharma (Rutgers) Romeel Dave (ROE/UWC) Chang Hoon Hahn (LBL) Rachel Somerville (Rutgers/CCA) Claire Dickey (Yale) Chris Hayward (CCA) Tjitske Starkenburg (CCA) Nitya Mandyam-Doddamane (NYU) Ari Maller (CUNY) Jeremy Tinker (NYU) Andrew Emerick (Columbia) Nir Mandelker (Yale) Stephanie Tonnesen (CCA) Anna Wright (Rutgers)

4 THE IQ (ISOLATED AND QUENCHED GALAXIES) COLLABORATORY Compare isolated and quenched galaxies between simulated and observational samples to learn about the quenching processes Observations: SDSS volume limited sample (Tinker+), SDSS isolated dwarf galaxy sample (Dickey+ in prep.) Simulations: Illustris (Vogelsberger+; Genel+), EAGLE (Schaye+5; Crain+5), MUFASA (Dave+6), Santa Cruz semianalytical model (Somerville+5), High mass zoom simulations (Choi+7), Low mass zoom simulations (Munshi+7, Brooks+)

5 WHAT IS QUENCHING? Find a way to fit the star formation sequence in a uniform way, without needing any cuts or additional preparation of any dataset build mock galaxy spectra for all simulated galaxies, also in a uniform way, and measure spectral lines, indices, bands, and derived parameters compare quenching (and therefore star formation) indicators

6 FITTING THE STAR FORMATION MAIN SEQUENCE SFR [instant.] log ( SFR [M yr ]) 8 SFR [ Myr] SDSS Centrals 8 Illustris 8 EAGLE 8 MUFASA 8 log M [M ] Santa Cruz SAM 8 There is a well defined SFMS over the full mass range for all simulations Hahn, Starkenburg, Dickey, and IQ-collaboratory in prep.

7 FITTING THE STAR FORMATION MAIN SEQUENCE p ( log SSFR [yr ]) SFR [instant.]. < log M <.6 Illustris EAGLE MUFASA SC SAM SFR [ Myr] log( SSFR [yr ]) There is a well defined SFMS over the full mass range for all simulations but within stellar mass bins, the P(log SSFR) distribution can differ Hahn, Starkenburg, Dickey, and IQ-collaboratory in prep.

8 FITTING THE STAR FORMATION MAIN SEQUENCE log( SFR [M /yr] ) Illustris p ( log SSFR ) < log M < < log M <. 9 log( M [M ]). 9 log( SSFR [yr ]). 9 log( SSFR [yr ]) Gaussian mixture model fit in bins of stellar mass (each bin is independent) -> flexible and data-driven - components per bin are the optimal solution almost everywhere other fitting methods: Bluck+6, Lee+5, Feldmann 7, Bisigello+8 Hahn, Starkenburg, Dickey, and IQ-collaboratory in prep.

9 FITTING THE STAR FORMATION MAIN SEQUENCE. log ( SFR [M yr ]) SFR [instant.] Illustris EAGLE SDSS Centrals Order of magnitude disagreement between the simulated SFMSs over the whole stellar mass range, although the slope is similar MUFASA Santa Cruz SAM Best-fit SFMS log ( M [M ]) Hahn, Starkenburg, Dickey, and IQ-collaboratory in prep.

10 FITTING THE STAR FORMATION MAIN SEQUENCE log ( SFR [M yr ]) SFR [instant.] SFR [ Myr] Illustris EAGLE log ( M [M ]) MUFASA Hahn, Starkenburg, Dickey, and IQ-collaboratory in prep. Santa Cruz SAM There is a low-sfr component at almost all masses There are sometimes transitioning, and sometimes high-sfr components

11 FITTING THE STAR FORMATION MAIN SEQUENCE GMM component fractions SFR [instant.] SFR [ Myr] SFMS other other quenched SFR = NSA SDSS 9.. Illustris 9 EAGLE 9 MUFASA 9 log M [M ] Santa Cruz SAM 9 There is a low-sfr component at almost all masses There are sometimes transitioning, and sometimes high-sfr components Hahn, Starkenburg, Dickey, and IQ-collaboratory in prep.

12 BUILDING MOCK GALAXY SPECTRA FSPS: Flexible Stellar Population Synthesis (Conroy+) set assumptions such that all simulated galaxies are treated equally Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

13 REMEASURING LINES, INDICES, BANDS, AND DERIVED PARAMETERS Test: remeasure Halpha luminosity for 5 SDSS galaxy spectra Use Halpha luminosity to estimate SFR Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

14 FITTING THE STAR FORMATION MAIN SEQUENCE SFR [Ha no Dust] PRELIMINARY log ( SFR [M yr ]) 8 SFR [UV no Dust] SDSS Centrals 8 Illustris 8 EAGLE 8 MUFASA 8 log M [M ] Santa Cruz SAM 8 SFR measurement based on Halpha or NUV luminosity No dust added to the spectra or corrected for Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

15 FITTING THE STAR FORMATION MAIN SEQUENCE. SFR [Ha no Dust].8 PRELIMINARY log ( SFR [M yr ]).6.. Illustris EAGLE SDSS Centrals MUFASA Santa Cruz SAM Best-fit SFMS log ( M [M ]) SFR measurement based on Halpha luminosity, no dust Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

16 FITTING THE STAR FORMATION MAIN SEQUENCE. SFR [Ha no Dust] PRELIMINARY log ( SFR [M yr ]) SFR [UV no Dust] Illustris EAGLE MUFASA Santa Cruz SAM log ( M [M ]) SFR measurement based on Halpha or NUV luminosity, no dust Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

17 FITTING THE STAR FORMATION MAIN SEQUENCE GMM component fractions SFR [Ha no Dust] SFR [UV no Dust] SFMS other other quenched SFR = PRELIMINARY NSA SDSS 9.. Illustris 9 EAGLE 9 MUFASA 9 log M [M ] Santa Cruz SAM 9 SFR measurement based on Halpha or NUV luminosity, no dust Halpha luminosity can be whereas UV has contribution from older stars Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

18 REMEASURING LINES, INDICES, BANDS, AND DERIVED PARAMETERS PRELIMINARY SFR measurement based on Halpha luminosity no Halpha line -> use Dn-SSFR relation: adds a quenched component? Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

19 REMEASURING LINES, INDICES, BANDS, AND DERIVED PARAMETERS ΔSFMS > dex Dn UVJ selection log(ssfr) < - yr - PRELIMINARY Starkenburg, Dickey, Hahn, and IQ-collaboratory in prep.

20 CONCLUSIONS The IQ (Isolated and Quenched galaxies) collaboratory aims to better understand and constrain the quenching processes. However, we first need to understand how to define quenching consistently between all simulations and observations. Gaussian Mixture Modeling is a flexible and data-driven approach to fit the SFMS. The amplitude of the SFMS differs by ~ dex between simulations. Consistent subpopulations are found for the hydro simulations, but differences with the SAM. We build mock galaxy spectra to compare observations for all galaxies, and remeasure spectral indices, lines, bands, and derived parameters. Different star formation and quenching indicators result in very different SFMS fits as well as quenched populations and fractions