The Structure of the Universe Joel Primack, UCSC
Friday January 12, 2018 Panofsky Auditorium
The Structure of the Universe Joel Primack, UCSC
ΛCDM - the Double Dark Theory of Cosmology Large Scale Structure of the Universe The Galaxy - Halo Connection How Galaxies Form - Hubble Space Telescope + Simulations Science and Technology Policy Cosmic Horizon (The Big Bang) Cosmic Background Radiation
Cosmic Dark Ages
Bright Galaxies Form Big Galaxies Form
Earth Forms Today
When we look Cosmic out in space we look back Spheres in time… of Time Matter and Energy Content of the Dark Universe Matter Ships
on a ΛCDM
Dark Double Energy Dark Ocean Imagine that the entire universe is an ocean of dark Theory energy. On that ocean sail billions of ghostly ships made of dark matter... Cosmic In lation: matter luctuations enter the horizon with about the same amplitude
scale factor a =1/(1+z)
M⦿,
4 Matter Distribution Agrees with Double Dark Theory! Planck Collaboration: Cosmological parameters
PlanckPlanck Collaboration: Collaboration: The ThePlanckPlanckmissionmission Planck Collaboration: The Planck mission 6000 6000 Angular scale 5000 90 500018 1 0.2 0.1 0.07 Planck Collaboration: The Planck mission
European 6000 ] 4000 2
] 4000 2 Double µ K [ µ K 3000 [ 3000Dark TT Space TT 5000 D
D 2000 2000Theory
Agency 1000 1000
] 4000 2 Cosmic 0 0 PLANCK 600 Variance600 60
µ K 60
[ 3000 300300 30 TT 30 TT 0 0 Satellite D 0 0 D D Fig. 7. Maximum posterior CMB intensity map at 50 resolution derived from the joint baseline-30 analysis of Planck, WMAP, and
-300 -300 408 MHz observations. A small strip of the Galactic plane, 1.6 % of the sky, is filled in by-30 a constrained realization that has the same statistical properties as the rest of the sky. 2000 -600 -60-60 Data -600 Temperature-Temperature 22 1010 3030 500500 10001000 15001500 20002000 25002500 Released 1000The Planck 2015 temperature power spectrum. At multipoles ` 30 we show the maximum likelihood frequency averaged Fig.Fig.Fig. 9. 9. 10.ThePlanckPlanck TT2015power temperature spectrum. power The points spectrum. in the At upper multipoles panel show` 30 the we maximum-likelihood show the maximum estimateslikelihood of frequency the primary averaged CMB temperature spectrum computed from the Plik cross-half-mission likelihood with foreground and other nuisance parameters deter- temperaturespectrum computed spectrum{ as computed described from in the the textPlik forcross-half-mission the best-fit foreground likelihood and nuisance with foreground parameters and of other the Planck nuisance+WP parameters+highL fit deter- listed February 9, mined from the MCMC analysis of the base ⇤CDM cosmology. In the multipole range 2 ` 29, we plot the power spectrum minedin Table from5. The the red MCMC line shows analysis the of best-fit the base base⇤⇤CDMCDM cosmology. spectrum. The In the lower multipole panel shows range the 2 residuals` 29, with we plot respect the powerto the theoretical spectrum 2015 estimatesestimates from the Commander component-separationcomponent-separation algorithm algorithm computed computed over over 94 94 % % of of the the sky. sky. The The best-fit best-fit base base⇤CDM⇤CDM theoretical theoretical spectrummodel. The fitted0 error to barsthe Planck are computedTT+lowP from likelihood the full covariance is plotted in matrix, the upper appropriately panel. Residuals weighted with across respect each to band this (see model Eqs. are36a shownand in spectrum fitted to the Planck TT+lowP likelihood is plotted in the upperFig. 8. Maximum panel. posterior Residuals amplitude Stokes Q ( withleft) and U ( respectright) maps derived to from thisPlanck observations model between are 30 and shown 353 GHz. in 36b), and include2 beam uncertainties10 and50 uncertainties500 in the foregroundThese1000 model mapS have been parameters. highpass-filtered with1500 a cosine-apodized filter between ` = 202000 and 40, and the a 17 % region of the Galactic2500 thethe lower lower panel. The error error bars bars show show 11 uncertainties.uncertainties. From FromPlanckPlanck Collaborationplane Collaboration has been replaced with a XIII constrained XIII( Gaussian2015(2015 realization). ). (Planck Collaboration IX 2015). From Planck Collaboration X Agrees with Double Dark Theory!±± Multipole moment,(2015). viewed as work in progress. Nonetheless, we find a high level of 8.2.2. Number of modes consistency in results between the TT and the full TT+TE+EE likelihoods. Furthermore, the cosmological parameters (which One way of assessing the constraining power contained in a par- 100 ticular measurement of CMB anisotropies is to determine the 140 do not depend strongly on ⌧) derived from the TE spectra have comparable errors to the TT-derived parameters, and they are e↵ective number of a`m modes that have been measured. This
The temperature angular power spectrum of the primary CMB from Planck] , showing a precise measurement of seven acoustic peaks,/ that Fig. 19. consistent to within typically 0.5 or better. Polarization-is equivalent to estimatingPolarization 2 times the square of the total S N
Temperature-Polarization 2 are well fit by a simple six-parameter ⇤CDM theoretical model (the model plotted80 is the one labelled [Planck+WPin the power+highL] spectra, a measure in Planck that contains all Collaborationthe available ] 70 2 XVI (2013)). The shaded area around the best-fit curve represents cosmic variance,µ K 16 including the sky cut used. The error bars on individual points 60
also include cosmic variance. The horizontal axis is logarithmic up to ` = 50, 5 and linear beyond. The vertical scale is `(` + 1)Cl/2⇡. The measured [ µ K 0
spectrum shown here is exactly the same as the one shown in Fig. 1 of Planck[10 Collaboration40 XVI (2013), but it has been rebinned to show better TE the low-` ` region. D EE -70 ` 20 Double Dark Theory C 0 Double Dark Theory -140 Angular scale tected by Planck over the entire sky, and which therefore con- 9010 18 1 0.2 0.1 tains both4 Galactic and extragalactic objects. No polarization in- EE `
6000TE ` 0 formation0 is provided for the sources at this time. The PCCS C 5000 D -10 di↵ers-4 from the ERCSC in its extraction philosophy: more e↵ort has been made on the completeness of the catalogue, without re- 30 500 1000 1500 2000 30 500 1000 1500 2000
] 4000 ducing notably the reliability of the detected sources, whereas 2 ` the ERCSC was built in the spirit of` releasing a reliable catalog µ K
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Fig. 10. Frequency-averaged TE (left) and EE (right) spectra (withoutsuitable fitting for quick for T– follow-upP leakage). (in The particular theoretical withTE theand short-livedEE spectra Fig.Fig. 10. 11.Frequency-averagedPlanck T E (left) andTEEE(leftspectra) and (right)EE (right computed) spectra as (withoutdescribed fitting in the for text.T– TheP leakage). red lines The show theoretical the polarizationTE and spectraEE spectra from plottedD in the upper panel of each plot are computed from the best-fitHerschel modeltelescope). of Fig. 9. Residuals The greater with amount respect of to data, this theoretical di↵erent selec- model plottedthe2000 base in⇤ theCDM upperPlanck panel+ ofWP each+highL plot aremodel, computedwhich from is fitted the to best-fit the TT model data only of Fig.. 9. Residuals with respect to this theoretical model areare shown shown in the lower panel panel in in each each plot. plot. The The error error bars bars show show 1tion 1 errors. processerrors. The The and green thegreen improvementslines lines in inthe the lower lower in thepanels panels calibration show show the and the best-fit map-best-fit temperature-to-polarization leakage model, fitted separately to the±± TE and EE spectra. From Planck Collaboration XIII (2015). temperature-to-polarization1000 leakage model, fitted separately to the makingTE and EE processingspectra. (references)From Planck help Collaboration the PCCS XIII to( improve2015). the performance (in depth and numbers) with respect to the previ- 0 2 10 50 500 1000 1500 2000 ous ERCSC. 4 cosmologicalcosmological informationMultipole if if we we assume assumemoment, that that the the anisotropies anisotropies are are andandThe 96 96 000 sources 000 modes, modes, were respectively. respectively. extracted4 fromFromFrom the this 2013 this perspective perspectivePlanck thefrequency the2015 2015 purelypurely Gaussian Gaussian (and hence hence ignore ignore all all non-Gaussian non-Gaussian informa- informa- mapsPlanckPlanck (Sect.datadata 6), constrain constrain which approximatelyinclude approximately data acquired 55 %55 more % over more modes more modes than than intwo in Fig.tiontion 20. coming comingThe temperature from lensing, angular the the power CIB, CIB, cross-correlations cross-correlationsspectrum of the CMB, with with other esti- other skythethe coverages.2013 2013 release. release. This Of Of implies course course this that this is the not is flux not the thedensities whole whole story, of story, most since since of matedprobes,probes, from etc.). etc.). the SMICA CarryingPlanck out outmap. this this procedure The procedure model for plotted for the the isPlanckPlanck the one20132013 la- somesome pieces pieces of of information information are are more more valuable valuable than than others, others, and and TT power spectrum data provided in Planck Collaboration XV thein sources fact Planck are anis able average to place of three considerably or more tighter di↵erent constraints observa- on belledTT [Planckpower+ spectrumWP+highL] data in provided Planck Collaboration in Planck Collaboration XVI (2013). The XV tionsin fact overPlanck a periodis able of to 15.5 place months. considerably The tighterMexican constraints Hat Wavelet on shaded((20142014 area)) and and aroundPlanck the Collaboration best-fit curve XVI represents XVI((20142014 cosmic),), yields yields variance, the the number number in- particularparticular parameters parameters (e.g., (e.g., reionization reionization optical optical depth depth or certain or certain 24 algorithm (Lopez-Caniego´ et al. 2006) has been selected as the cluding826826 000 000 the sky (which (which cut used. includes The error the the e ebars↵↵ectsects on of ofindividual instrumental instrumental points noise, do noise, not cos- in- cos- mic variance and masking). The 2015 TT data have increased baseline4 method for the production of the PCCS. However, one cludemic cosmic variance variance. and masking). The horizontal The axis 2015 is logarithmicTT data have up to increased` = 50, 4HereHere we we have have used used the the basic basic (and (and conservative) conservative) likelihood; likelihood; more more this value to 1 114 000, with TE and EE adding a further 60 000 additional methods, MTXF (Gonzalez-Nuevo´ et al. 2006) was andthis linear value beyond. to 1 114 The 000, vertical with scaleTE and is `EE(` +adding1)Cl/2 a⇡ further. The binning 60 000 modesmodes are are e↵ eectively↵ectively probed probed by byPlanckPlanckif oneif one includes includes larger larger sky skyfrac- frac- scheme is the same as in Fig. 19. implementedtions.tions. in order to support the validation and characteriza- tion of the PCCS.
8.1.1. Main catalogue The source selection for the PCCS is made on the basis17 of17 Signal-to-Noise Ratio (SNR). However, the properties of the The Planck Catalogue of Compact Sources (PCCS, Planck background in the Planck maps vary substantially depending on Collaboration XXVIII (2013)) is a list of compact sources de- frequency and part of the sky. Up to 217 GHz, the CMB is the
27 dark matter simulation - expanding with the universe
same simulation - not showingText expansion
8 Andrey Kravtsov CONSTRAINED LOCAL UNIVERSE SIMULATION Stefan Gottloeber, Anatoly Klypin, Joel Primack Visualization: Chris Henze (NASA Ames) Aquarius Simulation Volker Springel
Milky Way 100,000 light years
Milky Way Dark Matter Halo 1.5 million light years 10
Bolshoi Cosmological Simulation Anatoly Klypin & Joel Primack 8.6x109 particles 1/h kpc resolution Pleiades Supercomputer at NASA Ames Research Center
1 Billion Light Years 100 Million Light Years
1 Billion Light Years How the Halo of the Big Cluster Formed
100 Million Light Years Bolshoi-Planck Cosmological Simulation Merger Tree of a Large Halo
Peter Behroozi & Christoph Lee Structure Formation Methodology • Starting from the Big Bang, we simulate the evolution of a representative part of the universe according to the Double Dark theory to see if the end result matches what astronomers actually observe. • On the large scale the simulations produce a universe just like the one we observe. We’re always looking for new phenomena to predict — every one of which tests the theory! • But the way individual galaxies form is only partly understood because it depends on the interactions of the ordinary atomic matter as well as the dark matter and dark energy to form stars and super-massive black holes. We need help from observations. The Astrophysical Journal,795:104(20pp),2014November10 Whitaker et al. Two Key Discoveries About Galaxies The4 Astrophysical Journal,795:104(20pp),2014November10BEHROOZI, WECHSLER & CONROY Whitaker et al. (a) (b) Relationship(a) Between Galaxy Star-forming(b) Galaxies Lie z TheStellar Astrophysical Mass Journal,795:104(20pp),2014November10 and Halo Mass 8on42 a “Main 1 Sequence”0.5Whitaker 0.2 et al. 0 0.1 (a) (b) ] 3 0.1 0.01 / yr Mpc • O
0.001 z=0.1 0.01 z=1.0 z=2.0 Observations
Stellar Mass / Halo z=3.0 Full Star Formation History Best-Fit Model 0.0001 z=4.0 Cosmic SFR [M Time-Independent SFE Time-Independent SFE Mass and Time-Independent SFE 10 11 12 13 14 15 0.001 10 10 10 10 10 10 1 2 4 6 8 10 12 13.8 Halo Mass [M ] Time Since Big Bang [Gyr] O• Figure 1. Star formation rate as a function of stellar mass for star-forming galaxies. Open circles indicate the UV+IR SFRs from a stacking analysis, with a second-order polynomial fit above the mass completeness limits (solid vertical lines). Open squares signify measurements below the mass-completeness limits. Therunningmedians for individuallyFTheIG.3.— stellarLeft detected masspanel: objects to the halo instellar MIPS mass mass 24 µ ratio tom imaging halo at mass multiple with ratio S/N at> multiple3(shownasagray-scaledensityplotinthePanel(a),left)areindicatedwithfilledcirclesinthe redshifts as derived from observations (Behroozi et al. 2012) compared to a model which righthasFigure panelredshifts a time-independent 1. andStar are formationas color-coded derived rate star as byfrom aformation redshift.function observations of The efficiency stellar number mass (SFE). of forcompared star-forming star-forming Error barsto galaxies. galaxies show Open with 1 Scircles/uncertaintiesN > indicate3 detections the (Behroozi UV+IR in the SFRs 24 etµ from al.m imaging 2012). a stacking A and time-independent analysis, those with with S a/ secoN 4000 Å breaks, characterizedabove the mass-completeness by red14 rest-frame limits (TalU– etV al.colors2014). AmongMIPS imaging90%] completeness (3σ limit limits of derived10 µJy) by Tal and etHST al.∼ (2014/WFC3), calculatedJF 125W ] HST v4.0 catalogs. Of these, 39,106 star-forming galaxies are The mass• completeness limits in Figure 1 correspond to the O • 12 10 O ∼ and relatively bluethe10 rest-frameaboveUVJ-selected the mass-completenessV– star-formingJ colors. Following galaxies limits (Tal with et the al. masses two-2014). above Among theand H90%Fby160 completenessW comparing10imaging object limits (5σ derived detection26.9 by mag), Talin the et al. whereasCANDELS (2014), calculated the/deep other with three a color separationscompleteness definedthe UVJ in-selected Whitaker limits, star-forming 22,253 et al. have ( galaxies2012a S/N), with we> masses select1MIPS24 above theµfieldsm by havere-combined comparing shallower object subset MIPS detection of∼ imaging the in exposures the CANDELS (3σ thatlimits reach/deep of the with20 depthµ a Jy) of and detections11completeness (amongst limits, which 22,253 9,015 have have S S//NN >> 3)1MIPS24 and 35,916µm are re-combinedthe CANDELS9 subset/wide of the fields. exposures Although that reach the mass the depth∼ completeness of 58,973 star-forming galaxies at 0.5 densities in Section 4.2. (SFR*ND) of the SFR are indicated with solid circles when the data are detections (amongst10 whichdensities 9,015 in Section have4.2 S. /N > 3) and 35,916 are the CANDELSof the SFR10 are/wide indicated fields. with Although solid circles the when mass the data completeness are 15 10 complete both in stellar mass and SFR (above the shallower undetected in MIPS 24 µm photometry (S/N < 1). The full -4.0 in thecomplete deeper both GOODS-N in stellar mass and and GOODS-S SFR (above16 fields the shallower will extendlog -4.0 to sample of star-forming8 galaxies3. THE3. THE are STAR STAR considered FORMATION FORMATION in the SEQUENCE SEQUENCE stackinglog datadata 3σ 3MIPSσ6 MIPS 24 µ 24mµ detectionm detection limit). limit).16 We considerWe consider all MIPS all MIPS 10 lower stellarphotometry10 masses, in the we median adopt the for more the individual conservative UV+IR limits SFRs for analysis. AlthoughFigure we have11 2shows not removed 4 the star 6 formation sources 8 with sequence, 10 X-ray 12 log 13.8as a photometry in1 the/ 2 median 4 for the 6 individual 8 UV+IR 10 SFRs 12 13.8 Figure 1 shows the star formation sequence, log ΨΨasthe a shallowermeasurementsmeasurementsHST (filledWFC3 (filled circles), circles), imaging. even even those those galaxies galaxies intrinsically intrinsically M Time Since Big Bang [Gyr] Time Since Big Bang [Gyr] detections in the followingfunctionfunction of analysis, of log log M⋆,infourredshiftsbinsfrom⋆ we,infourredshiftsbinsfrom estimate the contributionzz 00.5to.5toFirst,faintfaint we in in the look the IR. IR. at Only Onlythe 1% measurements 1% of the of star-forming the star-forming for galaxies individual galaxies above above the galaxies. the z z 2.5.2.5. We We use use a a single single SFR SFR indicator, indicator, the UV+IR UV+IR== SFRs SFRs of active galactic nuclei= = (AGNs) to the median 24 µmflux The running2020µJyµJy limit median limit in each in each of redshift the redshift bin individual have bin have 24 µm UV+IR 24 photometryµm photometry measurements with with describeddescribed in in Section Section2.42.4,probingovertwodecadesinstellar,probingovertwodecadesinstellar S/SN/N< <1. 1. densities in Section 4.2FIG..4.—Left panel: Star formation rate as a function of halo massof and the cosmic SFR time, are weighted indicated by the number with density solid of circles dark matter when halos the at that data time. are Contours mass.mass. The The gray gray scale scale represents represents the the density density of points points for for star- star- ToTo leverage leverage the the additional additional decade decade lower lower in stellar in stellar mass mass showforming where galaxies 50 and 90% selected of all in stars Section were formed;2.5 with dashed S/N > line3MIPS showscomplete the median halo both mass in for stellar star formation mass as and a function SFR of (above time. Right thepanel: shallower Star formation forming galaxies selected in Section 2.5 with S/N > 3MIPS thatthat the the CANDELS CANDELSHSTHST/WFC3/WFC3 imaging imaging enables enables us to probe us to probe 3. THE STARrate as FORMATION a function of galaxy SEQUENCE stellar mass and time, weighted by thedata number 3σ densityMIPS of galaxies 24 µm at detection that time. Contours limit). and16 We dashed consider line are as all in top-left MIPS panel; dotted14 line shows current minimum stellar masses reached by observations. 16 14 EssentiallyEssentially identical identical to to the the publicly publicly released released catalogs catalogs available through through 16In the case of the 1.0 Assumptions: every halo hosts a galaxy, mass growth of galaxies is associated with that of halos Stellar-Halo Accretion Rate Coevolution 5 different, especially at lower masses where satellites tend to 2.3 Inferring Star Formation Rates From Halo have more stellar mass compared to centrals of the same halo Mass Accretion Rates mass (for a more general discussion see Rodr´ıguez-Puebla, AnumberofrecentstudiesexploringtheSHMRatdiffer- Drory & Avila-Reese 2012; Rodr´ıguez-Puebla, Avila-Reese ent redshifts have found that it evolves only slowly with &Drory2013;Reddicketal.2013;Watson&Conroy2013; time (see, e.g., Leauthaud et al. 2012; Hudson et al. 2013; Wetzel et al. 2013). Since we are interested in studying the Behroozi, Wechsler & Conroy 2013b, and references therein). connection between halo mass accretion and star formation For example, based on the observed evolution of the GSMF, in central galaxies, for our analysis we derive the SHMR for the star formation rate SFR, and the cosmic star formation central galaxies only. rate, Behroozi, Wechsler & Conroy (2013b) showed that this We model the GSMF of central galaxies by defining is the case at least up to z =4(cf.possibleincreasedevolu- P (M∗|Mvir)astheprobabilitydistributionfunctionthata tion at z>4; Behroozi & Silk 2015; Finkelstein et al. 2015). distinct halo of mass Mvir hosts a central galaxy of stellar Moreover, Behroozi, Wechsler & Conroy (2013a) showed mass M∗.ThentheGSMFforcentralgalaxiesasafunction 2 that assuming a time-independent ratio of galaxy specific of stellar mass is given by star formation rate (sSFR) to host halo specific mass accre- 2.2 The Galaxy Mass Function ∞ tion rate (sMAR), defined as the star formation efficiency ϵ, φ∗,cen(M∗)= P (M∗|Mvir)φh(Mvir)dMvir. (2) simply explains the cosmicWe star use formation the GSMF rate for since centralz =4. galaxies reported in ? and Z0 If we assume a time-independentobtained SHMR, from the? stargalaxy formation group catalog based on the SDSS efficiency is the slope of theDR7. SHMR, This catalog represents an updated version of ?. Here, φh(Mvir)isthehalomassfunctionandP (M∗|Mvir) is a log-normal distribution assumed to have a scatter of M˙ ∗/M∗ ∂ log M∗ ϵ = = . (3) ˙ vir σc =0.15 dex independent of halo mass. Such a value is Mvir/Mvir ∂ log M 2.3 Connecting Galaxies to Halos supported by the analysis of large group catalogs (Yang, This equation simply relates galaxy SFRs to their host Mo & van den Bosch 2009; Reddick et al. 2013), studies of We model the central GSMF by defining P (M∗|Mvir)asthe halo MARs without requiring knowledge of the underlying the kinematics of satellite galaxies (More et al. 2011), as well probability distribution function that a distinct halo of mass physics. (This is the main difference between the equilibrium as clustering analysis of large samples of galaxies (Shankar Mvir hosts a central galaxy of stellar mass M∗.Thenthe solution we present below and previous “bathtub” models.) et al. 2014; Rodr´ıguez-Puebla et al. 2015). Note that this GSMF for central galaxies as a function of stellar mass is Our primary motivation here is to understand whether halo scatter, σc,consistsofanintrinsiccomponentandamea- given by MARs are responsible for the mass and redshift dependence surement error component. At z =0,mostofthescatter ∞ of the SFR main sequence and its scatter. Similar models appears to be intrinsic, but that becomes less and less true φ∗,cen(M∗)= P (M∗|Mvir)φh(Mvir)dMvir. (2) have been explored in the past for differentZ0 purposes, includ- at higher redshifts (see, e.g., Behroozi, Conroy & Wechsler ing generating mock catalogs (Taghizadeh-Poppvir et al. 2015) 2010; Behroozi, Wechsler & Conroy 2013b; Leauthaud et al. Here, P (M∗|M )isalognormaldistributionswithascatter and understanding the differentaround clusteringM∗ assumed of quenched to be constant and with σc =0.15 dex. Such 2012; Tinker et al. 2013). Here, we do not deconvolve to re- star-forming galaxies (Beckeravalueissupportedtheanalysisofgenerallargegroupcat- 2015). move measurement error, as most of the observations that Using halo MARs, wealogs operationally(alias?),studiesonthekinematicsofsatellitegalaxies infer galaxy SFRs we will compare to include these errors in their measure- as follows. Let M∗ = M∗(M(Morevir(t),t et) be al. the 2011) stellar as well mass as of on a clustering analysis of large ments. central galaxy formed in asamples halo of ofmass galaxiesMvir(??t)attime. t. As2594 regardsA. Rodr´ıguez-Puebla the GSMF et al. of central galaxies, we here use In a time-independent SHMR, theEmphasis above reduces that the to modelM∗ = reproduces the observed the resultsTable 1. List of reported acronyms used in this in paper. Rodr´ıguez-Pueblaral halo finder etROCKSTAR al.(Behroozi, (2015). Wechsler In & Wua2013b;Behroozi et al. 2013c). Halo masses were defined using spherical overden-M∗(Mvir(t)). From this relationGSMF the at change redshift ofz ∼ stellar4. mass ART Adaptive refinement tree (simulation code). recent analysis of the SDSS DR7, Rodr´ıguez-Pueblasities according to the redshift-dependent et virial al. overdensity "vir(z) CSFR Cosmic star formation rate. given by the spherical collapse model (Bryan & Norman 1998), IMF Initial mass function. in time is simply with "vir 178 for large z and "vir 333 at z 0withour (2015)ISM derived theInterstellar total, medium. central, and satellite GSMF for stel- # . Like= the Bolshoi simulation (Klypin= et al. 2011= ), Bolshoi– GSMF Galaxy stellar mass9 function. M 12 ˙ Planck is complete down to haloes of maximum circular velocitydM∗ ∂ log M∗ dMvir lar massesMAR from MMass∗ =10 accretion rate, MMvir.⊙ to M∗ =10 M1 ⊙ based on the vmax 55 km s− . ∗ 2.4 Inferring Star Formation Rates From Halo SHARC Stellar halo accretion rate coevolution. ∼ = f , (4) E SHARC Equilibrium SHARC. In this paper, we calculate instantaneous halo MARs from the vir NYU-VAGC+ (Blanton+ et al. 2005) and using the 1/Vmax es- dt ∂ log M dt SDSS Sloan digital sky survey. Bolshoi–Planck simulation, as well as halo MARs averaged over Mass Accretion Rates SFR Star formation rate. the dynamical time (M˙ vir,dyn), defined as timator.SHMR The membershipStellar-to-halo mass relation. (central/satellite) for each galaxy where f∗ = M∗/Mvir is the stellar-to-halo mass ratio. ˙ dMvir Mvir(t) Mvir(t tdyn) sMAR Specific mass accretion rate, Mvir/Mvir. − − . (1) In recent analysis of the galaxy stellar mass functions, star was obtainedsSFR fromspecific an star formation updated rate, SFR/M . versiondt ofdyn = the Yangtdyn et al. ∗ ! " EquationDownloaded from (4) implies stellar-halo accretion rate coevolution, 1/2 The dynamical time of the halo is t (z) [G" (z)ρ ]− , which formation rates and cosmic star formation rates from z =0 (2007) group catalog presented in Yang et al. (2012).dyn = Thevir m the spherical overdensity criterion of Bryan & Norman (1998). We is 20 per cent of the Hubble time. Simulations (e.g. Dekel et al.SHARC. The left panel of Figure 4 shows the resulting ∼ to z =8combinedwiththegrowthhalosobtainedfromN- correspondingalso assume a Chabrier SHMR (2003) IMF. Finally, is shown Table 1 lists all as the the2009) black suggest that most curve star formation in results Fig- from cold gas flowing acronyms used in this paper. inward at about the virial velocity – i.e. roughly a dynamical timestellar-to-halo mass ratio, f∗,derivedforSDSScentralgalax- Figure 7. Upper Panel: Cosmic mass density as a function of after the gas enters. As instantaneous accretion rates for distinct body simulations, ? show that the M∗–Mvir relation evolves http://mnras.oxfordjournals.org/ z z ure 3, and the SHMR for all galaxieshaloes from near clusters Behroozi, can also be negative Wech- (Behroozi et al. 2014), .Cosmicstar-formationrateasafunctionof . 2STELLARHALOACCRETIONRATE ies (see Section 2.2). Consistent with previous studies, we using time-averaged accretion rates allows galaxies in these haloes slowly with redshift. Moreover,12 ? showed that when assum- COEVOLUTION (SHARC) sler & Conroy (2013a) is shown as theto continue red forming curve. stars. The dif- find that f∗ has a maximum of ∼ 0.03 at Mvir ∼ 10 M⊙, Fig. 1 shows the instantaneous and the dynamical-time-averaged ing that the ratio of galaxies specific star formation rates 2.1 The simulation ference between the two curves for halohalo MARs masses as a function of lower halo mass and than redshift, and Fig. 2 showsand it decreases at both higher and lower halo masses. The We generate12 our mock galaxy catalogues based on the N-body their respective scatters. Even before converting halo accretion rates (sSFR) to their host halos specific mass accretion rates Mvir ∼ 10 M⊙ reflects the fact that the SHMR of cen- rates averaged over a dynamical time instead. Additionally, Bolshoi–Planck simulation (Klypin et al. 2014). The Bolshoi– into SFRs (Section 2.3), it is evident that both the slope and disper-product f∗ × ϵ = dM∗/dM(sMAR),vir will be star shown formation as the effi blackciency ϵ,isindependentofred- Planck simulation is based onIs the !CDM Main cosmology with Sequence param- sion in halo MARs are already SFR very similar toControlled that of galaxy SFRs by Halo Mass Accretion? we show that a redshift-independent M∗–Mvir model ex- trals andeters consistent satellite with the latest galaxies results from the are Planck slightlyCollabora- on di theff mainerent sequence. as mentioned 12 curves in Figure 5 below. shift simply explains the cosmic star formation rate since tion (Planck Collaboration XIII 2015) and run using the ART code vir at University of California, Santa Cruz on December 10, 2015 plains the observed scatter of the SFR−M∗ in main sequence above.(Kravtsov, At Klypin halo & Khokhlov massesby1997 Aldo;Gottloeber&Klypin higher Rodríguez-Puebla, than2008). M ∼ 10 JoelM⊙ ,this Primack, PeterIn theBehroozi, more general Sandra case FaberM∗ = M MNRAS∗(Mvir(t) ,z2016), equation 1 3 2.2 Connecting galaxies to haloes z =4. The Bolshoi–Planck simulation has a volume of (250 h− Mpc) and galaxies. differencecontains 2048 is3 particles primarily of mass 1.9 10 due8 M .Haloes/subhaloes to the diThefferences abundance matching between technique is a simple the and powerful statis- × (4) generalizes to In this paperGalaxy-evolution we use5 these results by assuming that GSMFsand their used merger trees to were deriveHalo calculated with mass these the⊙ phase-space SHMRs, accretion tempo- tical Behroozi approach rates to connecting etz=0 galaxies al. toto 2013 haloes. 3 In its most simple the M∗–Mvir is independent of redshift. We use the relation dM∗ ∂M∗(Mvir(t),z) dMvir ∂M∗(Mvir(t),z) dz used (Moustakas et al. 2013). When comparing both GSMFs = obtained+ in Section 2.3 for, local(5) galaxies. Specifically, we dt ∂Mvir dt ∂z dt we find that the high mass-end from Rodr´ıguez-Puebla et al. infer galaxy star formation rates from halo mass accretion 3RESULTS (2015) is significantly different to the one derive in (Mous- where the first term is the contribution to the SFR from but if the M∗–Mvir relation is independentrates as follow. of redshift Let M then∗ = M the∗( Mvir(t),t)thestellarmassof Figures: SFR vs M∗;sSFRvsM∗;SFRDvszandcosmic takas et al. 2013). In contrast, when comparing Rodr´ıguez- stellarhalo mass MAR of and a central the second galaxy termacentralgalaxyformedinahaloofmass formed is the in changea halo of in mass the SHMR Mvir(t)attimet.If Puebla et al. (2015) GSMF with Bernardi et al. (2010) we with redshift. Although in this paper we assume a constant mass density vs z. Mvir(t) is M∗ = M∗(Mvir(t)) and theM ∗second–Mvir is term independent vanishes. of redshift then M∗ = M∗(Mvir(t)). find an excellent agreement, for a more general discussion FromSHMR, this relation the formalism star formation that weFrom rates describe this are relation given below simply appliesstar formation by to this rates are given simply by; see Rodr´ıguez-Puebla et al. (2015). In less degree, we also more general case. dM∗ d log M∗ dMvir find that the different values employed for the scatter of the The relation between stellar= massf∗ growth and observed, (3) 4DISCUSSION SHMR explain these differences. star formation rate is given bydt d log Mvir dt where f∗ = M∗/Mvir. We call thiswhere Stellar-Halof∗ = M ∗Accretion/Mvir.Moreover,fromtheaboveequation Rate Discuss about the star formation efficiency. For which galax- ⃝c 0000 RAS, MNRAS 000,000–000 Coevolution (SHARC) if true halo-by-halowe can deduce for that star-forming the star formation efficiency, ϵ,isjust, ies ϵ =1.Doweneedafigureofϵ vs Mvir? galaxies. sSFR d log M∗ = ϵ = . (4) Figure 1. Halo MARs from z 0 to 3, from the Bolshoi–Planck simulation. The instantaneous rate is shown in black, and the dynamically time averaged rate Stellar halo accretion rate coevolution 2595 = ˙ ˙ sMAR d log Mvir in red. The grey band is the 1σ (68 per cent) range of the instantaneous MARs. All the slopes are approximately the same 1.1 both for Mvir and Mvir,dyn. Figure 5. sSFRs using Eq. 4. 4.1 Implications for the bathtub model Scatter of halo mass accretion∼ rates Implied scatter of star formation rates While in the above analysis the term dMvir/dt refers to MNRAS 455, 2592–2606 (2016) Equation 3 is essentially the bathtub model. Differences be- the instantaneous mass accretion rates we also infer SFRs tween the observed SFRs and our models will give con- by using dMvir/dt averaged over a dynamical time scale as straints on the regime where the bathtub model is valid. measured from the simulations. As we will show below, we confirm the previous claim • For z>5ourSFRsareaboveobservations.Thismeans in ? that this modelConsistent reproduces the observed evolution of that at early epochs galaxies did not convert gas in stars as with the SFR−M∗ and cosmic star formation rate. Moreover, we fast as they receive it. This is a phase of gas accumulation observations! show that this is also true when using halo mass accretion where the bathtub is being fill with gas. ⃝c 20?? RAS, MNRAS 000,1–?? Downloaded from Figure 2. Scatter of halo MARs from z 0 to 3 from the Bolshoi–Planck Figure 6. Scatter of the SFRs using Eq. 3. = simulation. As in Fig. 1, scatter for the instantaneous rate is shown in black, ⃝c 20?? RAS, MNRAS 000,1–?? and that for the dynamically time averaged rate in red. http://mnras.oxfordjournals.org/ form, the cumulative halo and subhalo mass function1 and the cu- mulative GSMF are matched in order to determine the mass relation between haloes and galaxies. In order to assign galaxies to haloes in the Bolshoi–Planck simulation, in this paper we use a more gen- eral procedure for abundance matching. Recent studies have shown that the mean SHMRs of central and satellite galaxies are slightly different, especially at lower masses where satellites tend to have more stellar mass compared to centrals of the same halo mass (for a more general discussion see Rodr´ıguez-Puebla et al. 2012, 2013; at University of California, Santa Cruz on December 10, 2015 Reddick et al. 2013;Watson&Conroy2013;Wetzeletal.2013). Since we are interested in studying the connection between halo mass accretion and star formation in central galaxies, for our anal- ysis we derive the SHMR for central galaxies only. We model the GSMF of central galaxies by defining P (M Mvir) as the probability distribution function that a distinct halo of∗| mass Mvir hosts a central galaxy of stellar mass M . Then the GSMF for central galaxies as a function of stellar mass∗ is given by Figure 3. Upper panel: SHMR for SDSS galaxies. The red curve is for all ∞ SDSS galaxies, from Behroozi et al. (2013d) abundance matching using the φ ,cen(M ) P (M Mvir)φh(Mvir)dMvir. (2) ∗ ∗ = 0 ∗| Bolshoi simulation. The black curve is for SDSS central galaxies, using the ! abundance matching method of Rodr´ıguez-Puebla, Avila-Reese & Drory Here, φh(Mvir) is the halo mass function and P (M Mvir) is a log- (2013)appliedtotheBolshoi–Plancksimulation.Thelatteriswhatwe normal distribution assumed to have a scatter of ∗σ| 0.15 dex c = use in this paper, where we restrict attention to central galaxies. Bottom independent of halo mass. Such a value is supported by the anal- Panel: halo-to-stellar mass relations. The dotted vertical line and the blue ysis of large group catalogues (Yang, Mo & van den Bosch 2009; arrow indicate that galaxies below M 1010.5 M are considered as main ∗ = Reddick et al. 2013), studies of the kinematics of satellite galaxies sequence galaxies, while some higher mass galaxies⊙ are not on the main (More et al. 2011), as well as clustering analysis of large samples sequence. of galaxies (Shankar et al. 2014;Rodr´ıguez-Puebla et al. 2015). 12 Note that this scatter, σ c, consists of an intrinsic component and a to 10 M based on the NYU-VAGC (Blanton et al. 2005)and measurement error component. At z 0, most of the scatter ap- using the⊙ 1/V estimator. The membership (central/satellite) for = max pears to be intrinsic, but that becomes less and less true at higher each galaxy was obtained from an updated version of the Yang redshifts (see e.g. Behroozi, Conroy & Wechsler 2010;Leauthaud et al. (2007) group catalogue presented in Yang et al. (2012). The et al. 2012; Behroozi et al. 2013d; Tinker et al. 2013). Here, we corresponding SHMR is shown as the black curve in Fig. 3,and do not deconvolve to remove measurement error, as most of the the SHMR for all galaxies from Behroozi et al. (2013a) is shown observations that we will compare to include these errors in their as the red curve. The difference between the two curves for halo 12 measurements. masses lower than Mvir 10 M reflects the fact that the SHMR As regards the GSMF of central galaxies, we here use the results of centrals and satellite∼ galaxies are⊙ slightly different as mentioned 12 reported in Rodr´ıguez-Puebla et al. (2015). In a recent analysis of above. At halo masses higher than Mvir 10 M , this difference the SDSS DR7, Rodr´ıguez-Puebla et al. (2015) derived the total, is primarily due to the differences between∼ the GSMFs⊙ used to derive central, and satellite GSMF for stellar masses from M 109 M these SHMRs, Behroozi et al. (2013c) used Moustakas et al. (2013). ∗ = ⊙ When comparing both GSMFs, we find that the high-mass end from Rodr´ıguez-Puebla et al. (2015) is significantly different to the one 1 Typically defined at the time of subhalo accretion. derive in Moustakas et al. (2013). In contrast, when comparing MNRAS 455, 2592–2606 (2016) Is Main Sequence SFR Controlled by Halo Mass Accretion? by Aldo Rodríguez-Puebla, JoelStellar-Halo Primack, AccretionPeter Behroozi, Rate Coevolution Sandra Faber11 MNRAS 2016 SHARC correctly predicts star formation rates to z ~ 4 SHARC predicts “Age Matching” (blue galaxies in accreting halos) & “Galaxy SFR Conformity” at low z Consistent Open Questions: with observations! Extend SHARC to higher-mass galaxies Also take quenching into account Does SHARC correctly predict the growth rate of central galaxy stellar Stellar-Halo Accretion Rate Coevolution 9 mass from the accretion rate of their Stellar-Halo Accretion Rate Coevolution 9 halos? Test this in simulations! 9 9.5 10 10.5 Figure 8. Specific star formation rates as a function of redshift z for stellar masses M∗ =10, 10 , 10 and 10 M⊙ from time- independent SHMR model. The red and black curves are the sSFRs, from both dynamically-time-averaged and instantaneous mass accretion rates, respectively, with the gray band representing the dispersion in the latter. Both are corrected for mergers. The orange curve is the Speagle et al. (2014) summary of observed sSFRs onthemainsequence.ObservationsfromWhitakeretal.(2014We put SHARC in ), Ilbert et al. (2015) and Schreiber et al. (2015) are also included. “bathtub” equilibrium esting to discuss these differences in the light of the constant models of galaxy SHMR model. formation & predict First, the observed sSFRs of galaxies at z>4aresys- tematically lower than the time independent SHMR model mass loading and predictions. These differences increase at z =6.Thedis- metallicity evolution agreement between the constant SHMR predicted SFRs and the observations implies that the changing SHMR must be used, as in equation (5), at least at high redshift. Net mass loading factor η from an Figure 6. Left Panel: Net mass loading factor, η = ηw,ISM − ηr,ISM as a function of halo mass at z =0, 1, 4, and 6, obtained assuming equilibrium bathtub model (E+SHARC) z Between z =4andz =3theobservedstar-forming preventive feedback described by Eeff = Eh × Eq.Thecalculateddispersionisshownat =0and6.Right Panel: Net mass loading sequence is consistent with the SHARC predictions. Between factor and its dispersion as a function of galaxy stellar mass. z =2andz =0.5, the observed sSFRs are slightly above Figure 6. Left Panel: Net mass loading factor, η = ηw,ISM − ηr,ISM as a function of halo mass at z =0, 1, 4, and 6, obtained assuming the SHARC predictions. This departure occurs at the time preventive feedback12 described by Ee = Eh × Eq.Thecalculateddispersionisshownatz =0and6.Right Panel: Net mass loading than f∗ × ϵ in Mvir ∼ 10 M⊙ halos. Then theff mass loading In this Section we presented a simple framework that of the peak value of the cosmic star formation rate. factorfactor should and increase its dispersion at high redshift. as a function of galaxy stellarclarifies mass. how the net mass loading factor is connected to After the compilation carried out by Speagle et al. Halo mass quenching is more relevant for high mass preventive feedback in the context of the equilibrium time- (2014), new determinations of the sSFR have been pub- halos. This imposes the constraint that any functional form independent SHMR model. As long as the SFR is driven lished, particularly for redshifts z<2.5. In Figures 7 and 8, proposed for Eq should reproduce the12 fall off at higher masses by MAR these assumptions can be generalized in the same Figure 9. Scatter of the sSFR for main-sequencethan galaxiesf∗ × ϵ in pre-Mvir ∼ 10 M⊙ halos. Then the mass loading In this Section we presented a simple framework that of the term f∗ϵ.Giventheuncertainredshiftdependenceof framework, as we mention briefly in the discussion section. we reproduce new data published in Whitaker et al. (2014); dicted in our model. factor should increase at high redshift. clarifies how the net mass loading factor is connected to Ilbert et al. (2015) and Schreiber et al. (2015). This new Eq,wewillassumeforsimplicitythatitisindependentof set of data agrees better with our model between z =2 redshift. TheHalo functional mass quenching form Eq that is describes more therelevant fall-off for high mass preventive feedback in the context of the equilibrium time- and z =0.5, implying that the time-independent SHMR of f∗ϵ at z =0isgivenby 4.2 Scatter of the sSFR Main Sequencehalos. This imposes the constraint that any functional form independent SHMR model. As long as the SFR is driven (SHARC assumption) may be nearly valid across the wide 4SPECIFICSTARFORMATIONRATES proposed for Eq should reproduce−0.5 the fall off at higher masses by MAR these assumptions can be generalized in the same redshift range from z ∼ 4toz ∼ 0, a remarkable result. We now turn our discussion to the scatter of the star-forming Mvir FROM SHARC Eq(Mvir)=min(1, 0∗.85 12 ) . (16) However, it is not clear whether this is valid since the newer main sequence, displayed in Figure 9. Whenof using the termM˙ vir,thef ϵ.Giventheuncertainredshiftdependenceof„ 10 M⊙ « framework, as we mention briefly in the discussion section. 4.1 SHARC Compared with Observations observations have not been recalibrated as in Speagle et al. scatter is nearly independent of redshift andEq,wewillassumeforsimplicitythatitisindependentof it increases 12 (2014). very slowly with mass for z<2. TheNote valueredshift. that of at thez = The 0 scat- for functional halos more massive form E thanq that∼ 10 describesM⊙, theWe have fall-o nowff collected together all the tools needed to fol- Eeff ∼ ϵ × f∗/fb.Suchafall-off is thus necessary in or- low several aspects of galaxy evolution while galaxy stel- of f∗ϵ at z =0isgivenby ⃝c 000 der to make SHMR+equilibrium assumptions work, in other 9 10.5 0000 RAS, MNRAS ,000–000 lar masses are in the4SPECIFICSTARFORMATIONRATES range M∗ =10M⊙ to 10 M⊙. words, equation (12). The green long dashed-dotted−0. lines5 in We start by showing the evolution in the slope and zero- vir Figure 5 show Eq.AthigherredshiftsEeMff > ϵ×f∗/fb imply- point of the star-formingFROM main sequence SHARC inferred by the Eq(Mvir)=min(1, 0.85 12 ) . (16) ing that the mass-loading factor becomes„ 10 moreM⊙ important« at time-independent SHMR (SHARC model) in Figure 7. Re- high redshifts in high mass galaxies. call that when assuming4.1 a time-independentSHARC Compared SHMR, stellar with Observations Next, in equation (13) we use the functional forms de- mass12 growth can be inferred directly from halo mass accre- Note that at z = 0 for halos more massive than ∼ 10 M⊙, scribed in equations (15) and (16) to deduce a relation for tion rates via M˙ ∗ =Wef∗ × haveϵ × M now˙ vir,withthecorresponding collected together all the tools needed to fol- e ∗ b the netE ff mass∼ loadingϵ × f factor:/f .Suchafall-off is thus necessarySFR in = M or-˙ ∗/(1 − Rlow). Black several solid aspects lines show of results galaxy using evolution while galaxy stel- 9 10.5 der to make SHMR+equilibrium assumptions work,instantaneous in other mass accretion rates, M˙ vir,inequation(4). fb Eeff (Mvir,z) lar masses are in the range M∗ =10M⊙ to 10 M⊙. η = − 1 (1 − R). (17) Red solid lines show the SFRs when using mass accretion »words,f∗(Mvir equation) ϵ(Mvir) (12).– The green long dashed-dotted lines in We start by showing the evolution in the slope and zero- rates smoothed over a dynamical time scale, M˙ vir,dyn,in- Figure 5 show Eq.AthigherredshiftsEeff > ϵ×f∗/fb imply- The left hand panel of Figure 6 shows the net mass loading stead. The gray bandpoint indicates of the the intrinsic star-forming scatter around main sequence inferred by the factor,ingη = thatηw,ISM the− mass-loadingηr,ISM,asafunctionofhalomassat factor becomes morez = importantthe star-forming at maintime-independent sequence when using SHMRM˙ vir.Notethat (SHARC model) in Figure 7. Re- 0, 1, 4and6.Notethatthegenericredshiftevolutionofhigh redshifts in high mass galaxies. η is our model sSFRs werecall corrected that when in order assuming to take a into time-independent ac- SHMR, stellar governed byNext, the evolution in equation of Eeff .Forhaloslessmassivethan (13) we use the functionalcount forms the de- contribution of mergers to stellar mass growth, 11.5 mass growth can be inferred directly from halo mass accre- ∼ 10scribedM⊙,Figure6showsthatthemassloadingfactor in equations (15) and (16) to deduce a relationas explained for in §2.5. We show the resulting˙ sSFRs without˙ approximately scales as a power law with a power that is this merger correctiontion with rates the blackvia M and∗ = redf∗ dashed× ϵ × linesMvir,withthecorresponding −2.13 ˙ roughlythe independent net mass loading of redshift, factor:η ∝ Mvir .Equivalently, when using M˙ vir andSFRM˙ vir =,dynMrespectively.∗/(1 − R). Note Black that solid the lines show results using 9.7 we find that for galaxies with stellar mass below ∼ 10 M⊙ contribution from mergersinstantaneous becomes more mass important accretion for red- rates, M˙ vir,inequation(4). fb Eeff (Mvir,z) −1.07 the massη = loading factor scales as η ∝ M−∗ 1 (1.Massloading− R). shifts z<(17)0.5. Hereafter,Red solid we will lines focus show our discussion the SFRs on when using mass accretion factors are» predictedf∗(Mvir to) beϵ very(Mvir small) for halos– more massive the merger-corrected results, also shown as the solid lines in 12 ˙ vir dyn than ∼ 10 M⊙,especiallyatlowredshifts. Figure 8. rates smoothed over a dynamical time scale, M , ,in- The left hand panel of Figure 6 shows the net mass loading stead. The gray band indicates the intrinsic scatter around ⃝c 0000factor, RAS,η MNRAS= ηw,000ISM,000–000−ηr,ISM,asafunctionofhalomassatz = the star-forming main sequence when using M˙ vir.Notethat 0, 1, 4and6.Notethatthegenericredshiftevolutionofη is our model sSFRs were corrected in order to take into ac- governed by the evolution of Eeff .Forhaloslessmassivethan count the contribution of mergers to stellar mass growth, 11.5 ∼ 10 M⊙,Figure6showsthatthemassloadingfactor as explained in §2.5. We show the resulting sSFRs without approximately scales as a power law with a power that is this merger correction with the black and red dashed lines −2.13 roughly independent of redshift, η ∝ Mvir .Equivalently, when using M˙ vir and M˙ vir,dyn respectively. Note that the 9.7 we find that for galaxies with stellar mass below ∼ 10 M⊙ contribution from mergers becomes more important for red- −1.07 the mass loading factor scales as η ∝ M∗ .Massloading shifts z<0.5. Hereafter, we will focus our discussion on factors are predicted to be very small for halos more massive the merger-corrected results, also shown as the solid lines in 12 than ∼ 10 M⊙,especiallyatlowredshifts. Figure 8. ⃝c 0000 RAS, MNRAS 000,000–000 Does the Galaxy-Halo Connection Vary with Environment? Radu Dragomir, Aldo Rodriguez-Puebla, Joel Primack, Christoph Lee Does the Galaxy-Halo Connection Vary with Environment? 9 Figure 5. Two-point correlation function in five stellar mass bins. The solid lines show the predicted two-point correlation based on our stellar mass-to-Vmax relation from SHAM, while the circles with error bars show the same but for SDSS DR7 (Yang et al. 2012). mass projected two point correlation functions. In the case Here, !i = 1 if a galaxy is within the interval MX MX /2, ± of r-band, we compared to Zehavi et al. (2011) who used otherwise it is 0. Again, MX refers to Mu, Mg, Mr, Mi, Mz r-band magnitudes at z =0.1. We transformed our r-band and log M . The function fBP( 8) is the fraction of e↵ective ⇤ magnitudes to z =0.1 by finding the correlation between volume by a given overdensity bin for the BolshoiP simu- model magnitudes at z =0andatz =0.1 from the tables lation. In order to determine fBP( 8), we create a random 9 6 of the NYU-VAGC . For the projected two point correlation catalog of Nr 1 10 points in a box of side length iden- ⇠ ⇥ 1 function in stellar mass bins we compare with Yang et al. tical to the BolshoiP simulation, i.e., LBP =250h Mpc. (2012). Using Equation (10) allows us to calculate fBP( 8). 3.3 Measurements of the mock ugriz GLFs and the GSMF as a function of environment 4RESULTSONENVIRONMENTALDENSITY DEPENDENCE Our mock galaxy catalog is a volume complete sample down 1 In this section we present our determinations for the envi- to halos of maximum circular velocity Vmax 55 kms , ⇠ ronmental density dependence of the ugriz GLFs and the corresponding to galaxies brighter than Mr 5logh 14, ⇠ see Figure 3. This magnitude completeness is well above the GSMF from the SDSS DR7 and the BolshoiP. Here, we completeness of the SDSS DR7. Thus, galaxies selected in will investigate how well the assumption that the statisti- cal properties of galaxies are fully determined by Vmax can the absolute magnitude range 21.8 c 20?? RAS, MNRAS 000,1–17 10 Does the Galaxy-Halo Connection Vary with Environment? Figure 6. Comparison between the observed SDSS DR7 ugriz GLFs and GSMF, filled circles with error bars, and the ones predicted 1 based on the BolshoiPRadu simulation Dragomir, from SHAM, Aldo shadedRodriguez-Puebla, regions, at four environmental Joel Primack, densities in Christoph spheres of radius Lee 8 h Mpc. We also reproduce the best fitting Schechter functions to the r-band GLFs from the GAMA survey (McNaught-Roberts et al. 2014). Observe that SHAM predictions are in excellent agreement with observations, especially for the longest wavelength bands. r-Band Luminosity Function Stellar Mass Function density density ranges ranges Figure 7. Left Panel: Comparison between the observed r band GLF with environmental density in spheres of 8 h 1Mpc, filled circles with error bars, and the ones predicted based on the BolshoiP simulation from SHAM, shaded regions. The dashed lines show the best fitting Schechter functions to the r-band GLFs from the GAMA survey (McNaught-Roberts et al. 2014). Right Panel: Similar to the left panel but for the GSMF with environmental density. Here again the dashed lines are the best fitting Schechter functions. simplicity, we present only four overdensity bins in Figure dependence of the r-band GLF on environment over the red- 1 6. In Figure 7 we show the determinations in nine density shift range 0.04 c 20?? RAS, MNRAS 000,1–17 Astronaut Andrew Feustel installing Wide Field Camera Three on the last visit to Hubble Space Telescope in 2009 The infrared capabilities of WFC3 allow us to see the full stellar populations of forming galaxies candels.ucolick.org WFC3 ACS (blue 0.4 μm)(1+z) = 1.6 μm @ z = 3 (red 0.7 μm)(1+z) = 1.6 μm @ z = 2.3 Redshift COSMIC WEB This frame from the Bolshoi supercom- 69 4 3 2 10.50.20 ) puter simulation depicts the distribution of matter at 2 – redshift 3. Clusters of galaxies lie along the bright filaments. CANDELS Dark matter and cold gas flow along the filaments to supply × 10 3 galaxies with the material they need to form stars. 10 / CANDELS, ET AL. ) Cosmic 5 Cosmic dawn Cosmic high noon Star-formation rate UNIVERSITY OF NOTTINGHAMUNIVERSITY OF ( (solar masses/year/megaparsecs 0 BER Ö 012345 6 7 8 9 10 11 12 13 Time since Big Bang (billions of years) Big Now Bang 13.7 STARBIRTH RATE Using data from many surveys, including CANDELS, astronomers have plotted the rate of star formation through cosmic history. The rate climbed rapidly at cosmic dawn and peaked at cosmic high noon. GREGG DINDERMAN; SOURCE: KENNETH DUNCAN ANATOLY KLYPIN / STEFAN GOTTL STEFAN / KLYPIN ANATOLY S&T: At the present day, only a few galaxies lie between the present-day red ellipticals with old stars grew in size by peaks of the blue and red galaxies, in the so-called “green “dry” mergers — mergers between galaxies having older valley” (so named because green wavelengths are midway red stars but precious little star-forming cold gas. But between red and blue in the spectrum). A blue galaxy that the jury is still out on whether this mechanism works in is vigorously forming stars will become green within a detail to explain the observations. few hundred million years if star formation is suddenly quenched. On the other hand, a galaxy that has lots of old The Case of the Chaotic Blue Galaxies stars and a few young ones can also be green just through Ever since Hubble’s first spectacular images of distant the combination of the blue colors of its young stars and galaxies, an enduring puzzle has been why early star- the red colors of the old ones. The Milky Way probably forming galaxies look much more irregular and jumbled falls in this latter category, but the many elliptical galaxies than nearby blue galaxies. Nearby blue galaxies are around us today probably made the transition from blue relatively smooth. The most beautiful ones are elegant to red via a rapid quenching of star formation. CANDELS “grand-design” spirals with lanes of stars and gas, such as lets us look back at this history. M51. Smaller, irregular dwarf galaxies are also often blue. Most galaxies of interest to astronomers working on But at cosmic high noon, when stars were blazing CANDELS have a look-back time of at least 10 billion into existence at peak rates, many galaxies look distorted years, when the universe was only a few billion years old. or misshapen, as if galaxies of similar size are colliding. Because the most distant galaxies were relatively young at Even the calmer-looking galaxies are often clumpy and the time we observe them, we thought few of them would irregular. Instead of having smooth disks or spiral arms, have shut off star formation. So we expected that red gal- early galaxies are dotted with bright blue clumps of very axies would be rare in the early universe. But an impor- active star formation. Some of these clumps are over 100 tant surprise from CANDELS is that red galaxies with the times more luminous than the Tarantula Nebula in the same elliptical shapes as nearby red galaxies were already Large Magellanic Cloud, one of the biggest star-forming common only 3 billion years after the Big Bang — right regions in the nearby universe. How did the chaotic, dis- in the middle of cosmic high noon. ordered galaxies from earlier epochs evolve to become the Puzzlingly, however, elliptical galaxies from only familiar present-day spiral and elliptical galaxies? about 3 billion years after the Big Bang are only one- Because early galaxies appear highly distorted, astro- third the size of typical elliptical galaxies with the same physicists had hypothesized that major mergers — that is, stellar mass today. Clearly, elliptical galaxies in the early collisions of galaxies of roughly equal mass — played an universe must have subsequently grown in a way that important role in the evolution of many galaxies. Merg- increased their sizes without greatly increasing the num- ers can redistribute the stars, turning two disk galaxies ber of stars or redistributing the stars in a way that would into a single elliptical galaxy. A merger can also drive gas change their shapes. Many astronomers suspect that the toward a galaxy’s center, where it can funnel into a black SkyandTelescope.com June 2014 21 Most astronomers used to think (1) that galaxies form as disks, (2) that forming galaxies are pretty smooth, and (3) that galaxies generally grow in radius as they grow in mass. But CANDELS and other HST observations show that all these statements are questionable! (1) The majority of star-forming galaxies at z > 1 apparently have mostly elongated (prolate) stellar distributions rather than disks or spheroids, and our simulations may explain why. (2) A large fraction of star-forming galaxies at redshifts 1 < z < 3 are found to have massive stellar clumps; these originate from phenomena including mergers and disk instabilities in our simulations. (3) These phenomena also help to create compact stellar spheroidal galaxies (“nuggets”) through galaxy compaction (rapid inflow of gas to galaxy centers, where it forms stars). The Astrophysical Journal Letters,792:L6(6pp),2014September1 van der Wel et al. 1 0.75 0.5 0.25 0 1 Prolate Galaxies0.75 Dominate at High Redshifts & Low Masses 0.5 Prolate 0.25 Spheroidal Oblate 0 Figure 3. Reconstructed intrinsic shape distributions of star-forming galaxies in our 3D-HST/CANDELS sample in four stellar mass bins and five redshift bins. The model ellipticity and triaxiality distributions are assumed to be Gaussian, with the mean indicated by the filled squares, and the standard deviation indicated by the open vertical bars. The 1σ uncertainties on the mean and scatter are indicated by the error bars. Essentially all present-day galaxies have large ellipticities, and small triaxialities—they are almost all fairly thin disks. Toward higher redshifts low-mass galaxies become progressively more triaxial. High-mass galaxies always have rather low triaxialities, but they become thicker at z 2. ∼ (A color version of this figure is available in the online journal.) van der Wel+2014 Figure 4. Color bars indicate the fraction of the different types of shape defined in Figure 2 as a function of redshift and stellar mass. The negative redshift bins representSee the also SDSS Morphological results for z< 0.1; the Survey other bins of are Galaxies from 3D-HST z=1.5-3.6/CANDELS. Law, Steidel+ ApJ 2012 (A color version of this figure is available in the online journal.) When Did Round Disk Galaxies Form? T. M. Takeuchi+ ApJ 2015 Letter allows us to generalize this conclusion to include earlier Despite this universal dominance of disks, the elongatedness epochs. of many low-mass galaxies at z ! 1impliesthattheshapeof At least since z 2moststarformationisaccountedforby agalaxygenerallydiffersfromthatofadiskatearlystages 10 ∼ !10 M galaxies (e.g., Karim et al. 2011). Figures 3 and 4 in its evolution. According to our results, an elongated, low- show that⊙ such galaxies have disk-like geometries over the same mass galaxy at z 1.5 will evolve into a disk at later times, or, redshift range. Given that 90% of stars in the universe formed reversing the argument,∼ disk galaxies in the present-day universe over that time span, it follows that the majority of all stars in the do not initially start out disks.13 universe formed in disk galaxies. Combined with the evidence As can be seen in Figure 3, the transition from elongated that star formation is spatially extended, and not, for example, to disky is gradual for the population. This is not necessarily concentrated in galaxy centers (e.g., Nelson et al. 2012;Wuyts et al. 2012)thisimpliesthatthevastmajorityofstarsformedin 13 This evolutionary path is potentially interrupted by the removal of gas and disks. cessation of star formation. 4 Our cosmological zoom-in simulations often produce elongated galaxies like observed ones. The elongated stellar distribution follows the elongated inner dark matter halo. MNRAS 453, 408–413 (2015) doi:10.1093/mnras/stv1603 Formation of elongated galaxies with low masses at high redshift Formation of elongated galaxies with low masses at high redshift Daniel Ceverino, Joel Primack and Avishai Dekel DanielABSTRACT Ceverino,1,2‹ Joel Primack3 and Avishai Dekel4 1WeCentro report de Astrobiolog the identification´ıa (CSIC-INTA), of elongated Ctra de Torrej (triaxialon´ a Ajalvir, or prolate) km 4, E-28850 Torrejon´ de Ardoz, Madrid, Spain 2galaxiesAstro-UAM, in Universidad cosmological Autonoma simulations de Madrid, at Unidadz ~ 2. AsociadaThese are CSIC, E-28049 Madrid, Spain 3 Department of Physics, University of California, Santa9.5 Cruz, CA 95064, USA 4preferentiallyCenter for Astrophysics low-mass and Planetary galaxies Science, (M∗ ≤ Racah 10 InstituteM⊙), residing of Physics, in The Hebrew University, Jerusalem 91904, Israel Dark matter halos are elongated, especially ! near their centers. Initially stars follow the ! dark matter (DM) haloes with strongly elongated inner parts, a gravitationally dominant dark matter, as shown.! common feature of high-redshift DM haloes in the cold dark But later as the ordinary matter central density Acceptedmatter cosmology. 2015 July 14. A Received large population 2015 June 26; of in originalelongated form galaxies 2015 April 20 grows and it becomes gravitationally dominant, produces a very asymmetric distribution of projected axis ratios, the star and dark matter distributions both Downloaded from become disky — as observed by Hubble as observed in high-z galaxy surveys. This indicates that the Space Telescope (van der Wel+ ApJL Sept majority of the galaxies at high redshiftsABSTRACT are not discs or spheroids 2014).! but rather galaxies with elongatedWe morphologies report the identification of elongated (triaxial or prolate) galaxies in cosmological simula- tions at z 2. These are preferentially low-mass galaxies (M 109.5 M ), residing in dark ≃ ∗ ≤ matter (DM) haloes with strongly elongated inner parts, a common feature⊙ of high-redshift Nearby large galaxies are mostly disks and spheroids — but they start out looking more like pickles. http://mnras.oxfordjournals.org/ DM haloes in the ! cold dark matter cosmology. Feedback slows formation of stars at the centres of these haloes, so that a dominant and prolate DM distribution gives rise to galaxies elongated along the DM major axis. As galaxies grow in stellar mass, stars dominate the total mass within the galaxy half-mass radius, making stars and DM rounder and more oblate. A large population of elongated galaxies produces a very asymmetric distribution of projected axis ratios, as observed in high-z galaxy surveys. This indicates that the majority of the galaxies at high redshifts are not discs or spheroids but rather galaxies with elongated morphologies. Key words: galaxies: evolution – galaxies: formation. at University of California, Santa Cruz on October 16, 2015 cluded that the majority of the star-forming galaxies with stellar 1INTRODUCTION masses of M 109–109.5 M are elongated at z 1. At lower The intrinsic, three-dimensional (3D) shapes of today’s galaxies redshifts, galaxies∗ = with similar⊙ masses are mainly oblate,≥ disc-like can be roughly described as discs or spheroids, or a combination of systems. It seems that most low-mass galaxies have not yet formed the two. These shapes are characterized by having no preferential a regularly rotating stellar disc at z ! 1. This introduces an interest- long direction. Examples of galaxies elongated along a preferential ing theoretical challenge. In principle, these galaxies are gas-rich direction (prolate or triaxial) are rare at z 0(Padilla&Strauss and gas tends to settle in rotationally supported discs, if the angular 2008;Weijmansetal.2014). They are usually= unrelaxed systems, momentum is conserved (Fall & Efstathiou 1980;Blumenthaletal. such as ongoing mergers. However, at high redshifts, z 1–4, we 1986;Mo,Mao&White1998;Bullocketal.2001). At the same may witness the rise of the galaxy structures that we see= today at time, high-mass galaxies tend to be oblate systems even at high-z. the expense of other structures that may be more common during The observations thus suggest that protogalaxies may develop an those early and violent times. early prolate shape and then become oblate as they grow in mass. Observations trying to constrain the intrinsic shapes of the stellar Prolateness or triaxiality are common properties of dark mat- components of high-z galaxies are scarce but they agree that the ter (DM) haloes in N-body-only simulations (Jing & Suto 2002; distribution of projected axis ratios of high-z samples at z 1.5–4 Allgood et al. 2006; Bett et al. 2007;Maccioetal.` 2007;Maccio,` is inconsistent with a population of randomly oriented disc= galaxies Dutton & van den Bosch 2008;Schneider,Frenk&Cole2012, (Ravindranath et al. 2006;Lawetal.2012;Yuma,Ohta&Yabe and references therein). Haloes at a given mass scale are more pro- 2012). After some modelling, Law et al. (2012) concluded that late at earlier times, and at a given redshift more massive haloes the intrinsic shapes are strongly triaxial. This implies that a large are more elongated. For example, small haloes with virial masses 11 population of high-z galaxies are elongated along a preferential around Mv 10 M at redshift z 2areasprolateastoday’s direction. galaxy clusters.≃ Individual⊙ haloes are= more prolate at earlier times, van der Wel et al. (2014) looked at the mass and redshift de- when haloes are fed by narrow DM filaments, including mergers, pendence of the projected axis ratios using a large sample of star- rather than isotropically, as described in Vera-Ciro et al. (2011). The forming galaxies at 0 C ⃝ 2015 The Authors Published by Oxford University Press on behalf of the Royal Astronomical Society FormationIn hydro of sims, elongated dark-matter galaxies dominated with low galaxies masses are at Daniel Ceverino, Joel Primack and Avishai Dekel MNRAS 2015 high redshift prolateCeverino, Primack, DekelMNRAS 453, 408 (2015) 10 Stars M* <10 M☉ at z=2 Dark matter Also Tomassetti et al. 2016 MNRAS Simulated elongated galaxies are aligned with cosmic web filaments, become round after compaction 20 kpc (gas inflow fueling central starburst) Science and Technology Policy Partly inspired by Sid’s devotion to science and technology policy, I started Stanford Workshops on Political and Social Issues (SWOPSI) with Joyce Kobayashi and Bob Jaffe in 1969, and I helped to start the following spherically sensible programs the APS Forum on Physics and Society (FPS) 1972 the program of APS Studies on Public Policy Issues 1973 the Congressional Science and Technology Fellowship program 1973 the AAAS Program on Science and Human Rights 1976 the NSF Science for Citizens Program 1977 Later on I led the Federation of American Scientists project that helped convince the USSR to end its RORSAT program of nuclear reactor powered radar satellites 1987-90. I chaired the AAAS Committee on Science, Ethics, and Religion 2000-2002, led a POPA study in 2004, chaired FPS in 2005, … Science and Technology Policy After I received the APS Leo Szilard Lectureship Award in 2016, I was asked to run for two leadership positions, and I’m now Vice Chair of FPS again and President-Elect of Sigma Xi, the scientific research honor society, which publishes American Scientist magazine. In these new roles, I have been thinking of ways that we scientists can improve the public appreciation of scientific research, and the importance of taking scientific information into account in decision making. I want to share one of these ideas with you, and challenge you to think of additional ones. We can’t depend on just a few public spokespeople like Bill Nye and Neil deGrasse Tyson to explain and defend science. Sigma Xi could expand its Distinguished Lectureship program to include younger and more diverse scientists who are chosen for being especially effective at reaching a broad pubic audience. Going further, Sigma Xi could work with other scientific societies to create a speakers bureau to help find audiences and media opportunities for these science speakers, we could also curate best science videos for a broad audience.