A Stellar Feedback Origin for Neutral Hydrogen in High-Redshift Quasar-Mass Halos

2 MB·

by  C. -A. Faucher-Giguere

Mon.Not.R.Astron.Soc.000,000–000(0000) Printed12May2016 (MNLATEXstylefilev2.2)
A Stellar Feedback Origin for Neutral Hydrogen in
High-Redshift Quasar-Mass Halos
Claude-Andr´e Faucher-Gigu`ere,1∗ Robert Feldmann,2 Eliot Quataert,2 Duˇsan
Kereˇs,3 Philip F. Hopkins4 and Norman Murray5,6
1Department of Physics and Astronomy and Center for Interdisciplinary Exploration and Research in Astrophysics (CIERA),
6
Northwestern University, 2145 Sheridan Road, Evanston, IL 60208, USA: [email protected].
1
2Department of Astronomy and Theoretical Astrophysics Center, University of California, Berkeley, CA 94720-3411, USA.
0
3Department of Physics, Center for Astrophysics and Space Sciences, University of California, San Diego, 9500 Gilman Drive,
2
La Jolla, CA 92093.

y 4TAPIR, Mailcode 350-17, California Institute of Technology, Pasadena, CA 91125, USA.
a 5Canadian Institute for Theoretical Astrophysics, 60 St. George Street, University of Toronto, ON M5S 3H8, Canada.
M 6Canada Research Chair in Astrophysics.

1
1 12May2016

]
A
ABSTRACT
G Observations reveal that quasar host halos at z ∼ 2 have large covering fractions of
. cool dense gas ((cid:38) 60% for Lyman limit systems within a projected virial radius).
h
Most simulations have so far have failed to explain these large observed covering
p
fractions. We analyze a new set of 15 simulated massive halos with explicit stellar
-
o feedback from the FIRE project, covering the halo mass range M ≈2×1012−1013
h
r M at z = 2. This extends our previous analysis of the circum-galactic medium of
t (cid:12)
s high-redshift galaxies to more massive halos. AGN feedback is not included in these
a
simulations. We find Lyman limit system covering fractions consistent with those
[
observed around quasars. The large HI covering fractions arise from star formation-

2 driven galactic winds, including winds from low-mass satellite galaxies that interact
v with cosmological filaments. We show that it is necessary to resolve these satellite
8 galaxiesandtheirwindstoreproducethelargeLymanlimitsystemcoveringfractions
8 observed in quasar-mass halos. Our simulations predict that galaxies occupying dark
1
matter halos of mass similar to quasars but without a luminous AGN should have
7
Lyman limit system covering fractions comparable to quasars.
0
1. Key words: galaxies:formation—galaxies:evolution—galaxies:haloes—quasars:
0 absorption lines — intergalactic medium — cosmology: theory
6
1
v: 1 INTRODUCTION quasars (e.g., Prochaska et al. 2013). Driven by this explo-
i sion in high-quality observations, many groups have used
X Spectroscopicmeasurementsofgasflowsaroundgalaxiesus-
cosmological simulations to make predictions for circum-
ingsightlinestobackgroundquasarsprovideoneofthemost
r galactic medium (CGM) absorbers (e.g., Faucher-Gigu`ere
a directwaysofprobingthecosmologicalinflowsandgalactic
&Kereˇs2011;Kimmetal.2011;Fumagallietal.2011;Go-
outflows that regulate galaxy growth. Over the past sev-
erdtetal.2012;Stinsonetal.2012;Shenetal.2013;Hum-
eralyears,thistechniquehasbeenusedatbothlowredshift
mels et al. 2013; Suresh et al. 2015). Such comparisons are
andaroundthepeakofthecosmicstarformationhistoryat
particularlyvaluableasstate-of-the-artcosmologicalgalaxy
z(cid:38)2(e.g.,Adelbergeretal.2003;Hennawietal.2006;Stei-
formationmodelshavenowbroadlyconvergedontheirpre-
del et al. 2010; Tumlinson et al. 2011; Turner et al. 2014).
dictions for the global stellar properties of galaxy popula-
Thetechniquehasalsobeenappliedtoawiderangeoffore-
tionsbutdivergestronglyontheirpredictionsforgasprop-
ground objects, including dwarf galaxies (e.g., Bordoloi et
erties (Somerville & Dav´e 2015). Thus, CGM observations
al. 2014), damped Lyα absorbers (e.g., Rubin et al. 2015),
can break degeneracies between galaxy formation theories.
luminous red galaxies (LRGs; e.g., Gauthier et al. 2010),
∼ L(cid:63) star-forming galaxies (e.g., Rudie et al. 2012), and OurfocusinthisLetterisontheCGMofgalaxieslikely
to be traced by luminous quasars at z ∼ 2, which have a
characteristichalomassM ∼1012.5 M (e.g.,Whiteetal.
h (cid:12)
2012). Prochaska et al. (2013, hereafter PHS13) reported a
∗ [email protected] surprisinglyhighcoveringfractionfcov(>1017.2; <Rvir)≈
(cid:13)c 0000RAS
2 Faucher-Gigu`ere et al.
0.64+0.06ofLymanlimitsystems(LLSs;N >1017.2cm−2) FIREsuite.TheMassiveFIREhalosincludedinthisanalysis
−0.07 HI
within a projected virial radius of z ∼ 2−2.5 quasars (see arethehalosinthemassrangeM ≈2×1012−1013 M at
h (cid:12)
also Prochaska et al. 2014). The high covering fraction of z=2introducedinFeldmannetal.(2016).Thesehalosspan
cool gas in quasar halos is in contrast to the lower fraction awiderangeofaccretionhistoriesandenvironments.Asub-
f (1017.2; < R ) = 0.30±0.14 measured by Rudie et set of the MassiveFIRE simulations have been run at three
cov vir
al.(2012)aroundz∼2−2.5Lymanbreakgalaxies(LBGs) resolutionlevels,labeledLR(lowresolution),MR(medium
in the Keck Baryonic Structure Survey (KBSS). The LBGs resolution), and HR (high resolution). The HR simulations
in KBSS reside in dark matter halos of characteristic mass havea(zoom-inregion)gasparticlemassm =3.3×104M
b (cid:12)
M ≈1012 M (Adelberger et al. 2005), a factor ∼3 lower andaminimum(adaptive)gasgravitationalsoftening(cid:15) =9
h (cid:12) b
than luminous quasars. Using cosmological zoom-in simu- properpc.Thedarkmatterparticlemassandgravitational
lations of galaxy formation with stellar feedback but ne- softeninglengthsinthezoom-inregionsarem =1.7×105
dm
glecting the effects of active galactic nuclei (AGN), Fuma- M and (cid:15) = 143 proper pc, respectively. These HR res-
(cid:12) dm
galli et al. (2014) and Faucher-Gigu`ere et al. (2015, here- olution parameters are similar to the ‘z2h’ simulations and
after FG15) showed that the LLS covering fractions in the other LBG-mass halos analyzed in FG15. The MR simula-
simulationswerebroadlyconsistentwiththosemeasuredin tions have the same gravitational softening parameters but
LBG halos (see also Shen et al. 2013). Both studies how- higher gas and dark matter particle masses by a factor of 8
ever concluded that the most massive halos in their analy- inzoom-inregions.TheLRsimulationshavehigherzoom-in
ses could not explain the LLS covering fractions measured particlemassesbyanotherfactorof8,doubletheminimum
around quasars (but see Rahmati et al. 2015, which we dis- gravitational softening lengths of the MR and HR simula-
cuss further in §3.1). tions,andalowerstarformationdensitythresholdofn =1
H
In this work, we extend the FG15 analysis with a new cm−3. Our final compilation of covering fractions is based
set of 15 halos simulated to z = 2 with stellar feedback on HR-level simulations only and we focus on z=2−2.5.
physics from the FIRE (“Feedback In Realistic Environ- In FG15, we concluded that simulations only includ-
ments”) project and with masses representative of quasar ingstellarfeedbackfailedtoexplainthelargeLLScovering
hosts.1 ThesesimulationsarepartoftheMassiveFIREsim- fractions observed around z ∼ 2 quasars. That conclusion
ulation suite described in Feldmann et al. (2016). We use was primarily based on our analysis of the m14 simulation
these simulations to revisit the comparison with HI cover- (M (z = 2) ≈ 6×1012 M ). The m14 simulation had a
h (cid:12)
ing fractions measured around z ∼ 2 quasars. The simula- zoom-in gas particle mass m =4.4×106 M much larger
b (cid:12)
tions we analyze here do not include AGN but comparing thantheHR-levelLBG-masshalosincludedintheanalysis.
them observations of quasar-mass halos is useful because it Weshowin§3.2thatthem14simulationdidnothavesuffi-
canshowwhethertheobservedcoveringfractionsinquasar cientresolutiontoproduceconvergedCGMpredictionsand
halos require the presence of a luminous AGN or not. soweexcludeitfromourupdatedanalysis.Wealsoexclude
We describe our simulations and analysis methodology the m13 simulation analyzed in FG15 since its resolution
in §2, discuss our main results in §3, and conclude in §4. was closer to MR level than HR level.
Throughout, we assume a standard ΛCDM cosmology with
parameters consistent with the latest constraints (h ≈ 0.7,
Ω =1−Ω ≈0.27 and Ω ≈0.046; Planck 2015).
m Λ b
2.2 CGM analysis methodology
2 SIMULATIONS AND ANALYSIS Our analysis is similar to that performed in FG15; we only
METHODOLOGY summarize here the key points. To evaluate covering frac-
tions, the particle data are first projected onto a Cartesian
2.1 Zoom-in simulations
gridofsidelengthLcenteredonthehalowithN gridpoints
Oursimulationsimplementthesamestellarfeedbackphysics along each dimension. In this paper, we focus on LLS cov-
and numerical methods as the ones analyzed in Hopkins ering fractions within a projected virial radius, defined as
et al. (2014) and FG15; we refer to those papers for de- the fractions of projected pixels with HI column density
tails. Briefly, the simulations were run using the GIZMO NHI > 1017.2 cm−2. We use the Bryan & Norman (1998)
simulation code in P-SPH mode (Hopkins 2013, 2015). Gas virial radius definition. For the new massive halos, we use
is allowed to cool to T ∼ 10 K via atomic and molecu- L=600properkpcandN =600,correspondingtoaspatial
lar lines and star formation proceeds only in dense regions grid resolution of 1 proper kpc. Our LLS covering fractions
(n >5cm−3)thatarelocallyself-gravitating.Stellarfeed- arewellconvergedwithgridresolution.Toapproximateneu-
H
backismodeledbyimplementingenergy,momentum,mass, tralfractionsinself-shieldedgas,weusetheanalyticfitsto
and metal return from radiation, supernovae, stellar winds, radiative transfer calculations developed by Rahmati et al.
andphotoionizationfollowingSTARBURST99(Leithereret (2013). We neglect ionization of CGM gas by local sources.
al. 1999). During the course of the hydrodynamical calcu- This tends to overestimate HI covering fractions, but only
lation, ionization balance is computed using the ultraviolet slightly for LLSs around ordinary galaxies (e.g., Faucher-
background model of Faucher-Gigu`ere et al. (2009) and we Gigu`ere & Kereˇs 2011; Fumagalli et al. 2011). Hennawi &
apply an on-the-fly correction for self-shielded gas. Prochaska(2007)showedthattheclusteringofLLSsaround
Ouranalysisinthispapercombinesthesimulationspre- luminousquasarsishighlyanisotropic,consistentwithLLSs
viously analyzed in FG15 and new halos from the Massive- being photo-evaporated along the line of sight but largely
unaffected by the quasar radiation in the transverse direc-
tion. For our comparison with LLSs transverse to quasars,
1 Seeprojectwebsite:http://fire.northwestern.edu we thus also neglect local ionization effects.
(cid:13)c 0000RAS,MNRAS000,000–000
Neutral Hydrogen in Quasar-Mass Halos 3
Figure1.HIcolumndensity(top),gas-phasemetallicity(middle)andstellarmasssurfacedensity(bottom)mapsforthreerepresentative
MassiveFIREhalosatz=2(fromlefttoright:Mh(z=2)=(2.4,8.8,9.9)×1012 M(cid:12)).Thevirialradiusisindicatedbydashedcirclein
eachpanelandsolidcontoursindicateLymanlimitsystems.Thevectorsonmetallicitymapsindicateprojectedmass-weightedvelocities.
3 THE CGM OF HIGH-REDSHIFT MASSIVE sistent with observational constraints from high-dispersion
HALOS spectraofthez∼2quasarCGM(Lauetal.2015).Interest-
ingly,theoverallspatialdistributionofLLSscorrelateswith
3.1 Lyman limit system properties the spatial distribution of satellite galaxies, indicating that
satellites play an important role in shaping the HI distri-
Figure 1 shows HI column density, gas-phase metallicity,
bution in massive halos. As we showed for LGB-mass halos
and stellar surface density maps for three representative
in FG15, ejection of cool gas by both central and satellite
high-resolution halos from the MassiveFIRE sample. The
galaxies can interact with infalling large-scale structure fil-
halos are substantially filled with high-column and metal-
aments to enhance LLS covering fractions substantially.
enriched HI. The mean, median, and standard deviation of
log (Z/Z ), where Z is the HI-mass weighted metallicity, Figure 2 summarizes the LLS covering fractions eval-
10 (cid:12)
for LLS sight lines within a projected R (but excluding uated within a projected virial radius for the simulations
vir
the inner 20 proper kpc to minimize contamination from previously analyzed in FG15 and for the new MassiveFIRE
thecentralgalaxy)are−1.1,−0.9,and0.7,respectively(as- halosatz=2andz=2.5.Thesimulatedcoveringfractions
suming Z =0.14; Asplund et al. 2009). The projected gas arecomparedtotheaveragecoveringfractionsmeasuredby
(cid:12)
kinematics are complex (velocities up to ∼500 km s−1; see Rudie et al. (2012) around LBGs and by PHS13 in halos
Fig. 1) and it is not generally possible to use LLS metal- hosting quasars over matching redshift ranges. To facilitate
licity to cleanly separate cosmological inflows or galactic the comparison of our simulated halos with the Rudie et
winds in an instantaneous sense (see also Hafen et al., in al. and Prochaska et al. observational data points, we also
prep.). Overall, the metallicity and kinematic properties of show averages over the LBG- and quasar-mass halos in our
denseHIinoursimulatedmassivehalosappearbroadlycon- simulation sample. The open black circle averages over all
(cid:13)c 0000RAS,MNRAS000,000–000
4 Faucher-Gigu`ere et al.
1.0 etal.2005;Faucher-Gigu`ereetal.2011)whosepressuremay
increasethedensityofcoolgasand,moregenerally,change
Rudie+12 z 2 2.5 LBGs
∼ − how cosmological inflows and outflows propagate inside ha-
)vir0.8 Prochaska+13 z∼2−2.5 QSOs los.
R In their analysis of the EAGLE simulations, Rahmati
<
0.6 et al. (2015) were also able to reconcile the LLS covering
7.2; fractionspredictedbytheirsimulationswiththoseobserved
1
0 around quasars by PHS13. However, the agreement found
10.4
> by Rahmati et al. (2015) is due primarily to a different ef-
(
ov fect.Intheirsimulations,themedianLLScoveringfraction
fc0.2 withinR increasesbylessthan30%fromM =1012 M
vir h (cid:12)
to M =1013 M at z =2. Instead, Rahmati et al. (2015)
h (cid:12)
0.0 find agreement by assuming that all quasars in the PHS13
9.0 9.5 10.0 10.5 11.0 11.5 12.0 12.5 13.0
samplearehosted byhalos withmassM >1012.5 M and
log M (M ) h (cid:12)
10 h
fl followingthemassdistributionofthemostmassivehalosin
theEAGLEbox.TheythencomparedPHS13’sobservations
Figure2.Bluecircles:Lymanlimitsystemcoveringfractionsat
with simulated halos at fixed transverse proper projection,
z=2(large)andz=2.5(small)withinaprojectedvirialradius
rather than as a fraction of R . Since LLS covering frac-
forthesimulatedhalosanalyzedinFG15(HRresolutionorbetter vir
tions decrease with increasing fraction of R , Rahmati et
only). Green stars: Covering fractions at z = 2 (large) and z = vir
2.5 (small) for the MassiveFIRE halos. The open black symbols al.(2015)effectivelyobtainedhighcoveringfractionsbyas-
show averages over simulated LBG-mass halos and quasar-mass suming that many of PHS13’s sight lines probe a smaller
haloswiththeerrorbarsshowingthestandarddeviationsofthe fraction of the virial radius of foreground quasars than as-
simulateddatapointsincludedintheaverages.Wecomparethe sumedbyPHS13,whoreportedcoveringfractionsassuming
simulated covering fractions to LLS measurements transverse to a fixed virial radius appropriate for M ≈ 1012.5 M . This
h (cid:12)
LBGs at z ∼ 2−2.5 by Rudie et al. (2012) (black square) and highlights the fact that different state-of-the-art feedback
transverse to luminous quasars in the same redshift interval by
models still predict significantly different HI covering frac-
Prochaskaetal.(2013)(blacktriangle).
tions around massive high-redshift galaxies.
halosofmass1011.5 (cid:54)M (cid:54)1012.5 M (f =0.26±0.11)
h (cid:12) cov 3.2 Numerical convergence
andtheopenblackstaraveragesoverallMassiveFIREhalos
(M ≈1012−1013 M ; f =0.55±0.13). InFigure3,wecompareHImapsforHR,MR,andLRruns
h (cid:12) cov
Overall, we find good agreement between the simu- for a representative M (z = 2) = 3.6×1012 M halo. The
h (cid:12)
latedandobservedcoveringfractionsforboththeLBGand mapsshowthattheLLScoveringfractionsincreasesystem-
quasar samples, with the covering fractions increasing sys- atically with increasing resolution. This is confirmed more
tematically with increasing halo mass and from z = 2 to quantitatively by the bottom panels, which show the corre-
z = 2.5. We find much higher covering fractions than in spondingcoveringfractionsandstarformationrateswithin
FG15 in our more statistically robust and higher-resolution the halo for 100 time slices between z = 4 and z = 2. An
sample of quasar-mass halos. We explain this difference in important factor determining the high resolution needed to
thenextsection.Ourpredictedcoveringfractionsinquasar- obtainconvergedHIcoveringfractionsisthatitrequiresnot
mass halos are also higher by a factor ∼ 3 than those pre- only resolving the generation of galactic winds from central
dicted by the simulations of Fumagalli et al. (2014). Our galaxies, but also from lower mass satellites that are repre-
simulatedhalosincludestrongstellarfeedbackbutnoAGN sented by a smaller number of resolution elements.
feedback, suggesting that the high covering fractions mea- ThesystematicincreaseinpredictedLLScoveringfrac-
sured around quasars do not require a significant contri- tions with increased resolution is the most important fac-
bution from AGN feedback (although such feedback could tor driving the different conclusion that we reached previ-
certainly be important in real halos). ously (FG15) regarding quasar-mass halos. That analysis
While the covering fractions appear to increase was based primarily on the covering fractions of the m14
smoothly from the LBG mass regime to the quasar mass simulation.EventheLRversionofMF2hasslightlysmaller
regime,theincreaseisrathersteep:afactor>2increasein gas particle mass and minimum gas softening length than
coveringfractionforfactorof5increaseinhalomass(com- m14(m =2.1×106 vs.m =4.4×106,and(cid:15) =18proper
b b b
puted from the averages shown by the open black symbols pc vs. (cid:15) = 70 proper pc). The simulated LLS system cov-
b
in Fig. 2). A few effects likely contribute to this steep in- ering fractions increase slightly even from the SR run to
crease. First, low-mass main halos have small HI covering the HR run in Figure 3, indicating that covering fractions
fractions within their virial radii, as can be seen from the may not be fully converged even in our HR simulations for
open blue symbols in Figure 2. When such low-mass ha- quasar-masshalos.Westress,however,thatthemajorityof
los are the satellites of more massive main halos, they do theLBG-masshalosanalyzedinFG15hadresolutionsimilar
not contribute a large LLS cross section. Quasar-mass ha- totheHRrunsanalyzedhereandthatFG15demonstrated
los,however,canhavesatellitesthatareasmassiveasLBGs convergence of their HI covering fractions for those halos.
andwhichcaneachcontributeasignificantfractionoftheir Finally,itisworthnotingthatFigure1showsthatthe
virial cross section as LLSs. In addition, quasar hosting ha- denseHIdistributioninourmassivehalosisclumpy.Inde-
los are massive enough that they may be able to sustain a tail, the phase structure of the CGM probably depends not
quasi-statichotatmosphere(Birnboim&Dekel2003;Kereˇs only on the subgrid models for stellar feedback and resolu-
(cid:13)c 0000RAS,MNRAS000,000–000
Neutral Hydrogen in Quasar-Mass Halos 5
LR MR HR
Figure 3.Top: z=2HImapsforaMh(z=2)=3.6×1012 M(cid:12) MassiveFIREhalosimulatedatthreeresolutionlevels.Bottom: LLS
coveringfractionandstarformationratewithinavirialradiusfromz=4toz=2.TheLLScoveringfractionsincreasesystematically
withincreasingresolution(fromlefttoright),whiletheburstinessofthestarformationhistorydecreaseswithincreasingresolution.
tion parameters, but also on the properties of the hydrody- presence of a luminous AGN affects the properties of halo
namic solver employed (e.g., Kereˇs et al. 2012; Bird et al. gas on ∼ 100 proper kpc scales would be to obtain spec-
2013)andwhether“non-ideal”hydrodynamicaleffectssuch tra transverse to foreground galaxies that inhabit halos of
as magnetic forces and thermal conduction are included. similar mass but do not have a luminous AGN. Such halos
Such effects can, for example, significantly affect the sur- canbetracedbyhighlystar-formingsub-millimetergalaxies
vival of cool clouds in galactic winds (e.g., McCourt et al. (e.g.,Hickoxetal.2012;Narayananetal.2015)orbyz∼2
2015).ItisthusprudenttoregardtheCGMphasestructure galaxies selected based on their high stellar mass.
(including the detailed temperature distribution of galactic
winds)predictedbyoursimulationsasuncertain.Neverthe-
ACKNOWLEDGMENTS
less, our simulations provide a clear demonstration that an
explicit implementation of stellar feedback processes that We grateful for useful discussions with Xavier Prochaska,
successfully explains the stellar masses of galaxies without Joe Hennawi, Ali Rahmati, Zach Hafen, Daniel Angl´es-
any parameter tuning (Hopkins et al. 2014; Feldmann et Alca´zar, and Alexander Muratov. CAFG was supported
al. 2016) also predicts the presence of sufficient cool gas in by NSF grants AST-1412836 and AST-1517491, by NASA
galaxy halos to explain LLS covering fractions around both grant NNX15AB22G, and by STScI grants HST-AR-
LBGs and quasars at z∼2−2.5. 14293.001-A and HST-GO-14268.022-A. RF was supported
byNASAthroughHubbleFellowshipgrantHF-51304.01-A.
4 DISCUSSION AND CONCLUSIONS
EQ was supported by NASA ATP grant 12-ATP-120183,
OurcentralresultisthattheMassiveFIREsimulations,with a Simons Investigator award from the Simons Foundation,
strong stellar feedback but no AGN feedback, predict LLS andtheDavidandLucilePackardFoundation.DKwassup-
covering fractions within a projected virial radius in good ported by NSF grant AST-1412153. Support for PFH was
agreementwiththosemeasuredbyPHS13aroundluminous providedbyanAlfredP.SloanResearchFellowship,NASA
quasars. In our simulations, the covering fractions are high ATP grant NNX14AH35G, and NSF grants AST-1411920
in quasar-mass halos to large extent because stellar feed- and AST-1455342. The simulations analyzed in this paper
back drives galactic winds which interact with and expand were run on XSEDE computational resources (allocations
cosmological filaments. It is thus critical for simulations to TG-AST120025,TG-AST130039,andTG-AST140023)and
notonlyresolvethegenerationofgalacticwindsfromcentral onNASAHigh-EndComputingresources(allocationsSMD-
galaxiesbutalsothewindsfromsatellitegalaxiesembedded 14-5492, SMD-14-5189, and SMD-15-6530).
in associated large-scale structure.
Our results suggest that AGN feedback is not neces-
REFERENCES
sarytoexplainthelargecoveringfractionsobservedaround
quasars, though it is certainly possible that AGN feedback Adelberger,K.L.,Erb,D.K.,Steidel,C.C.,Reddy,N.A.,
significantly affects the CGM of real quasars (e.g., Johnson Pettini, M., & Shapley, A. E. 2005, ApJL, 620, L75
et al. 2015). One way to observationally test whether the Adelberger et al. 2003, ApJ, 584, 45
(cid:13)c 0000RAS,MNRAS000,000–000
6 Faucher-Gigu`ere et al.
Asplund,M.,Grevesse,N.,Sauval,A.J.,&Scott,P.2009, Stinson et al. 2012, MNRAS, 425, 1270
ARA&A, 47, 481 Suresh et al. 2015, MNRAS, 448, 895
Bird, S., Vogelsberger, M., Sijacki, D., Zaldarriaga, M., Tumlinson et al. 2011, Science, 334, 948
Springel, V., & Hernquist, L. 2013, MNRAS, 429, 3341 Turner, M. L., Schaye, J., Steidel, C. C., Rudie, G. C., &
Birnboim, Y., & Dekel, A. 2003, MNRAS, 345, 349 Strom, A. L. 2014, MNRAS, 445, 794
Bordoloi et al. 2014, ApJ, 796, 136 White et al. 2012, MNRAS, 424, 933
Bryan, G. L., & Norman, M. L. 1998, ApJ, 495, 80
Faucher-Gigu`ere, C.-A., & Kereˇs, D. 2011, MNRAS, 412,
L118
Faucher-Gigu`ere,C.-A.,Kereˇs,D.,&Ma,C.-P.2011,MN-
RAS, 417, 2982
Faucher-Gigu`ere,C.-A.,Lidz,A.,Zaldarriaga,M.,&Hern-
quist, L. 2009, ApJ, 703, 1416
Faucher-Gigu`ere et al. 2015, MNRAS, 449, 987
Feldmann, R., Hopkins, P. F., Quataert, E., Faucher-
Gigu`ere, C.-A., & Kereˇs, D. 2016, MNRAS, 458, L14
Fumagalli,M.,Hennawi,J.F.,Prochaska,J.X.,Kasen,D.,
Dekel,A.,Ceverino,D.,&Primack,J.2014,ApJ,780,74
Fumagalli,M.,Prochaska,J.X.,Kasen,D.,Dekel,A.,Cev-
erino, D., & Primack, J. R. 2011, MNRAS, 418, 1796
Gauthier, J.-R., Chen, H.-W., & Tinker, J. L. 2010, ApJ,
716, 1263
Goerdt,T.,Dekel,A.,Sternberg,A.,Gnat,O.,&Ceverino,
D. 2012, MNRAS, 424, 2292
Hennawi, J. F., & Prochaska, J. X. 2007, ApJ, 655, 735
Hennawi et al. 2006, ApJ, 651, 61
Hickox et al. 2012, MNRAS, 421, 284
Hopkins, P. F. 2013, MNRAS, 428, 2840
—. 2015, MNRAS, 450, 53
Hopkins et al. 2014, MNRAS, 445, 581
Hummels,C.B.,Bryan,G.L.,Smith,B.D.,&Turk,M.J.
2013, MNRAS, 430, 1548
Johnson,S.D.,Chen,H.-W.,&Mulchaey,J.S.2015,MN-
RAS, 452, 2553
Kereˇs, D., Katz, N., Weinberg, D. H., & Dav´e, R. 2005,
MNRAS, 363, 2
Kereˇs, D., Vogelsberger, M., Sijacki, D., Springel, V., &
Hernquist, L. 2012, MNRAS, 425, 2027
Kimm,T.,Slyz,A.,Devriendt,J.,&Pichon,C.2011,MN-
RAS, 413, L51
Lau, M. W., Prochaska, J. X., & Hennawi, J. F. 2015,
arXiv:1510.06018
Leitherer et al. 1999, ApJS, 123, 3
McCourt, M., O’Leary, R. M., Madigan, A.-M., &
Quataert, E. 2015, MNRAS, 449, 2
Narayanan et al. 2015, Nature, 525, 496
Planck 2015. 2015, arXiv:1502.01589
Prochaska, J. X., Hennawi, J. F., & Simcoe, R. A. 2013,
ApJL, 762, L19
Prochaska,J.X.,Lau,M.W.,&Hennawi,J.F.2014,ApJ,
796, 140
Rahmati, A., Pawlik, A. H., Raicevic, M., & Schaye, J.
2013, MNRAS, 430, 2427
Rahmati et al. 2015, MNRAS, 452, 2034
Rubin,K.H.R.,Hennawi,J.F.,Prochaska,J.X.,Simcoe,
R. A., Myers, A., & Lau, M. W. 2015, ApJ, 808, 38
Rudie et al. 2012, ApJ, 750, 67
Schaye et al. 2015, MNRAS, 446, 521
Shen, S., Madau, P., Guedes, J., Mayer, L., Prochaska,
J. X., & Wadsley, J. 2013, ApJ, 765, 89
Somerville, R. S., & Dav´e, R. 2015, ARA&A, 53, 51
Steidel et al. 2010, ApJ, 717, 289
(cid:13)c 0000RAS,MNRAS000,000–000