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Probing gas flows near galaxies: a spotlight on Lyman Limit Systems

Michele Fumagalli
With Xavier Prochaska, John O'Meara, Joe Hennawi, Tom Theuns, Avishai Dekel, and many others...


Why Lyman Limit Systems (a.k.a LLSs)?


In this talk
The clustering of LLSs in "statistical" samples provides solid empirical evidence of the association between LLSs and galaxies LLSs appear to be generally metal poor, i.e. log Z/ZO ~ - 2 (but more work on the robustness of ionisation correction is ongoing) In summary, the study of LLSs provides interesting new metrics to constrain models for feedback and accretion in simulations So far, there is no empirical evidence against the association between LLSs and cold accretion as put forward by theory


Halo gas, Absorption Lines, and LLS
Absorption lines, and particularly LLSs, offer a powerful tool to investigate the properties of gas around galaxies

O'Meara et al. 2012


Halo gas, Absorption Lines, and LLS
Already from the first LLS surveys, it became evident that LLSs are distinct from the IGM and trace the galaxy population
Sargent et al. 1989: "we believe that most of the LLSs are produced by galaxies"

Mage survey

Number of LLSs

MF et al. 2013


Halo gas, Absorption Lines, and LLS
Toy models can account for redshift evolution of LLSs up to z~3.5
gas

Number of LLSs

DM halos

MF et al. 2013


Predictions from simulations
Modern hydrodynamic simulations make quantitative predictions of the distribution of LLSs around galaxies
virial radius
van de Voort et al. 2012 See also Faucher-Giguere et al. 2011,2015... Shen et al. 2013 MF et al. 2011,2014

LLSs


Predictions from simulations
To zeroth order, the bulk of LLSs is associated with accretion with covering fractions between 0.1-0.3 (with weak mass dependence)


What's next (observationally)?
... besides tightening theoretical predictions...

1) Establish a solid connection between galaxies and LLSs 2) Define/quantify metrics that can be used to constrain models
Rudie et al. 2012; Crighton et al. 2013

MF et al. 2011,2014


Method #1: galaxy/LLSs pairs
MUSE/KCWI will soon help shrink the error bars
(PI programmes + GTO)
Quasar Q0956+122 with "pristine" LLS at z = 3.09 in queue for MUSE observations

First 2/5 h of data


Method #2: LLS autocorrelation
We can leverage quasar pairs to map the neutral hydrogen distribution around high-redshift galaxies

MF et al. 2014; MF et al. 2015 in prep


Method #2: LLS autocorrelation
The basic idea behind the experiment is to probe the correlation length of LLSs, which should correspond to the size of halos
Dark matter halos NH

N
H
Projected Separation

NHI UVB

Redshift


Method #2: LLS autocorrelation
The basic idea behind the experiment is to probe the correlation length of LLSs, which should correspond to the size of halos

1-halo term
Projected autocorrelation

2-halo term

Dark matter halos

Dark matter halos


Method #2: LLS autocorrelation
The basic idea behind the experiment is to probe the correlation length of LLSs, which should correspond to the size of halos
N
H

N
H
Projected Separation

NHI UVB

Dark matter halos + LLSs
Redshift


Method #2: LLS autocorrelation
The basic idea behind the experiment is to probe the correlation length of LLSs, which should correspond to the size of halos
NH

N
H
Projected Separation

1-halo term:
R < RCGM P (LLS,LLS) ~ f
NHI UVB

c

Dark matter halos + LLSs
Redshift


Method #2: LLS autocorrelation
The basic idea behind the experiment is to probe the correlation length of LLSs, which should correspond to the size of halos
N
H

N
H
Projected Separation

2-halo term:
NHI UVB

R > RCGM P ~ fc x DM

Dark matter halos + LLSs
Redshift


Method #2: LLS autocorrelation
The basic idea behind the experiment is to probe the correlation length of LLSs, which should correspond to the size of halos

1-halo term
Projected autocorrelation fc ~ 0.25

2-halo term

fc ~ 0.15 fc ~ 0.10 LLSs LLS autocorrelation Dark matter halos MF et al. 2014

Dark matter halos


Method #2: LLS autocorrelation
A preliminary measurement against 50 quasar pairs at z~3 reveals that LLSs are clustered, in line with theoretical expectations


Method #2: LLS autocorrelation
We can construct more advanced models with simulations including radiative transfer post-processing
Eagle 25 Mpc box 4 Mpc zoom-in

Projected Separation

20 00

km

s

-1

Projected Separation

Simulations courtesy of Eagle team; RT courtesy of T. Theuns


Method #2: LLS autocorrelation
We can construct more advanced models with simulations


Method #3: LLS metallicity distribution
We can exploit large samples of LLSs to map the imprint of feedback (or lack thereof) onto halo gas. The HD-LLS sample with 157 new LLSs

Prochaska et al. 2015


Method #3: LLS metallicity distribution
Ions are only tracers of the underlying metallicity. We are working on the delicate business of ionisation corrections.
log NHI< 19 All LLSs

MF+2013 & Cooper et al. 2015

MF et al. 2013; MF et al. 2015 (in prep) See also recent work by Lehner et al. 2013; Cooper et al. 2015


Method #3: LLS metallicity distribution
Metallicity distributions may become an interesting constraint for feedback models (maybe with some surprises?)
log NHI< 19 All LLSs

pPDF (x Nsys)

[X/H]

MF et al. 2015 (in prep)


Method #3: LLS metallicity distribution
Metallicity distributions may become an interesting constraint for feedback models (maybe with some surprises?)

MF et al. 2011


Method #3: LLS metallicity distribution
Metallicity distributions may become an interesting constraint for feedback models (maybe with some surprises?)

Shen et al. 2012


Summary
The clustering of LLSs in "statistical" samples provides solid empirical evidence of the association between LLSs and galaxies LLSs appear to be generally metal poor, i.e. log Z/ZO ~ - 2 (but more work on the robustness of ionisation correction is ongoing) In summary, the study of LLSs provides interesting new metrics to constrain models for feedback and accretion in simulations So far, there is no empirical evidence against the association between LLSs and cold accretion as put forward by theory