Tom Theuns

The IGM in simulations

1

Tom Theuns Institute for Computational Cosmology Ogden Centre for Fundamental Physics Durham University, UK and University of Antwerp Belgium

Tom Theuns2

Menu:

•Simulated DLAs: column density and dynamics •The temperature-T relation (redux)

Tom Theuns3

OWLS project

Leiden:

MPE /IAC

ICC

Chicago

HITS

ICC-Durham

Wierma, Van de Voort

Tom Theuns4

Star formation

Stellar evolution

Subgrid physics in OWLS

Z+J(nu) dependent cooling

Galactic winds AGN feedback

Tom Theuns5

Track 11 elements that dominate cooling/heating for in photo-ionisation equilibrium with optically thin UV-X-ray

background

Tom Theuns6

Subgrid: the star formation implementationSchaye & Dalla Vecchia, 08

d⇢?dt

= f(⇢, T, Z,H2, ...)

d⇢?dt

⇠ ⇢

⌧d/ ⇢3/2

But code’s density is averaged on kilo-parsecs scales!

Tom Theuns

ΣSFR ∝ Σn

gas (n = 1.4 ± 0.15)

Subgrid: the star formation implementation (and the origin of the Kennicutt-Schmidt law)

Kennicutt ‘98

Local: same galaxyGlobal: different galaxies

Calzetti et al

Star formation guarantees the simulated galaxies follow the imposed

Kennicutt-Schmidt law

Schaye 04

Tom Theuns9

Subgrid: stellar evolution

Few+12, Tornatore+07,Oppenheimer+06,Kawata+13,Scannapieco+09

•Assume: stellar initial initial mass function (Chabrier) •Assume: stellar lifetimes •Assume: luminosities (BC models) •Assume: stellar yields •Type I SNe •Type II SNe •AGB stars

Tom Theuns10

Supernova feedback expels gas out of galaxy/halo

GIMIC simulation

Dark halos(const M/L)

galaxies

Subgrid: SN feedback

Crain+09

Tom Theuns11

Galactic winds: stochastic kinetic feedback, no hydro-decoupling, no switching off of cooling

At high z: Pettini et al 02

Tom Theuns12

Z=2 reference model

1012 solar mass halo

Tom Theuns13

Subgrid variations

Tom Theuns14

Post-processing OWLS for self-shielding to identify DLAs: ray-tracing

Tom Theuns15

Optically t

hick gas

has tiny

cross

section. Ray-t

racing in

efficien

t

Tom Theuns16

Random sight lines will almost always miss high-density region: bad!

reverse-ray tracing: start from high density regions: good! Urchin

Impose optically thin ionising background

Tom Theuns17depth into slice [kpc]

neut

ral f

ract

ion

Different spectral shapes

Urchin can take into account full spectral information. Since know optical depth, can switch from case-A to case-B

recombination rate

Tom Theuns18Ray-tracing spherically-symmetric (stacked) OWLS haloes

Analytical profileUrchin profile

total densityHI density

OWLS gas mass

Urchin tests

Tom Theuns19

Through Thick and Thin - Hi Absorption in Cosmological

Simulations

Gabriel Altay1, Tom Theuns1,2, Joop Schaye3, Neil H. M. Crighton4,5 and Claudio Dalla

Vecchia3,6

gabriel.altay@gmail.com

1Institute for Computational Cosmology, Department of Physics, Durham University ,

South Road, Durham, DH1 3LE, U.K.

2Department of Physics, University of Antwerp, Campus Groenenborger,

Groenenborgerlaan 171, B-2020 Antwerp, Belgium

3Leiden Observatory, Leiden University, P.O. Box 9513, 2300 RA Leiden, the Netherlands

4Department of Physics, University of Durham, South Road, Durham DH1 3LE, UK

5Max Planck Institute for Astronomy, Konigstuhl 17, D-69117 Heidelberg, Germany

6Max-Planck-Institut fur Extraterrestrische Physik, Giessenbachstraße, D-85478 Garching,

Germany

ABSTRACT

We investigate the column density distribution function of neutral hydro-

gen at redshift z = 3 using a cosmological simulation of galaxy formation from

the OverWhelmingly Large Simulations (OWLS) project. The base simulation

includes gravity, hydrodynamics, star formation, supernovae feedback, stellar

winds, chemodynamics, and element-by-element cooling in the presence of a uni-

form UV background. Self-shielding and formation of molecular hydrogen are

treated in post-processing, without introducing any free parameters, using an ac-

curate reverse ray-tracing algorithm and an empirical relation between gas pres-

sure and molecular mass fraction. The simulation reproduces the observed z = 3

abundance of Ly-� forest, Lyman Limit, and Damped Ly-� Hi absorption sys-

tems probed by quasar sight lines over ten orders of magnitude in column density.

Self-shielding flattens the column density distribution for NHI > 1018 cm�2, while

the transition to fully neutral gas and conversion of Hi to H2 steepen it around

column densities of NHI = 1020.3 cm�2 and NHI = 1021.5 cm�2, respectively.

Subject headings: Methods: numerical — Quasars: absorption lines — Galaxies:

formation — intergalactic medium — large-scale structure of Universe

arX

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802.3

698v1 [a

stro

-ph]

26 F

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Mon. Not. R. Astron. Soc. 000, 1–17 (200?) Printed 26 February 2008 (MN LATEX style file v2.2)

SPHRAY: A Smoothed Particle Hydrodynamics RayTracer for Radiative Transfer

Gabriel Altay1, Rupert A.C. Croft1, and Inti Pelupessy11 Carnegie Mellon University, Department of Physics, 5000 Forbes Avenue, Pittsburgh PA 15213, USA

Accepted 200? ???? ??. Received 2007 ???? ??; in original form 2007 xx

ABSTRACTWe introduce the publically available code SPHRAY , a Smoothed Particle Hydrody-namics (SPH) ray tracer designed to solve the 3D, time dependent, radiative transfer(RT) equation for cosmological density fields. The SPH nature of SPHRAYmakes the in-corporation of separate hydrodynamics and gravity solvers very natural. SPHRAY relieson a Monte Carlo (MC) ray tracing scheme that does not interpolate the SPH particlesonto a grid but instead integrates directly through the SPH kernels. Given an arbitrary(series of) SPH density field(s) and a description of the sources of ionizing radiation,the code will calculate the non-equilibrium ionization and temperature state of Hydro-gen (HI, HII) and Helium (HeI, HeII, HeIII). The sources of radiation can include pointlike objects, diffuse recombination radiation, and a background field from outside thecomputational volume. The MC ray tracing implementation allows for the quick intro-duction of new physics and is parallelization friendly. A quick Axis Aligned BoundingBox (AABB) test taken from computer graphics applications allows for the accel-eration of the raytracing component. We present the algorithms used in SPHRAYandverify the code by performing the test problems detailed in the recent Radiative Trans-fer Comparison Project of Iliev et. al. The source code for SPHRAYand example SPHdensity fields are made available on a companion website (www.sphray.org).

Key words: cosmology, theory, numerical methods, N-body, SPH, ray tracing, MonteCarlo, simulations, radiative transfer, reionization, Stromgren

1 INTRODUCTION

In numerical cosmology, prescriptions for the treatment ofgravity and hydrodynamics are well developed and havebeen validated against one another in several comparisonstudies (see Frenk et al., 1999; O’Shea et al., 2005; Heit-mann et al., 2005, 2007; Regan et al., 2007; Agertz et al.,2007; Price, 2007). The density and temperature fields theyproduce provide input for sub-resolution models of star for-mation and feedback via supernovae (e.g. Springel & Hern-quist, 2003) and black holes (e.g. Di Matteo et al., 2007).Numerical radiative transfer (RT) techniques, necessary tocalculate the interaction of the ionizing photons producedby these sources with the cosmological gas, have not yetreached the level of maturity attained by N-body and gasdynamics solvers. Flexible and accurate RT techniques, vali-dated against analytic solutions and in comparison projects,are necessary to properly interpret many observations andguide the development of theoretical models from cosmolog-ical through stellar scales. This is especially true for analy-

sis of upcoming 21 cm surveys such as 21CMA 1 (formerlyPAST), LOFAR 2, MWA 3, SKA 4; modeling absorptionlines in the spectra of high redshift quasars and gamma rayburst afterglows, and understanding the feedback processeswhich influence star and galaxy formation.

The introduction of 3D radiative transfer into cosmo-logical simulations is complicated by several issues. The spe-cific intensity Iν = I(x, n, ν, t) is a function of seven vari-ables leading to a solution space with high dimensionality.R-type ionization fronts can travel at nearly the speed oflight through underdense regions and many times the speedof sound in dense regions leading to radiative time scalesorders of magnitude smaller than dynamical time scales. Inaddition, radiative transfer and hydrodynamic processes arecoupled. For example, photo heating creates large pressuregradients near luminous sources and can modify star forma-tion rates while hydrodynamic temperature changes affect

1 http://21cma.bao.ac.cn/index.php2 www.lofar.org3 www.haystack.mit.edu/ast/arrays/mwa4 www.skatelescope.org

Urchin

Tom Theuns20

– 4 –

Fig. 1.— Hi column density distribution function, f(NHI, z), at z � 3; simulation results

are shown as curves and observational data as symbols. The low NHI curve is obtained

using mock spectra fitted with VPFIT. Self-shielding and H2 are unimportant in this

range. The high NHI curve is obtained by projecting the simulation box onto

a plane and includes self-shielding and H2. The gap around NHI � 1017 cm�2

separates low and high NHI. Poisson errors on the simulation curves are always

smaller than their thickness. We also show high-resolution observations of the Ly-�

forest (Kim et al. 2002, “Kim02”), LLSs (Peroux et al. 2005, “Per05”; O’Meara et al. 2007,

“Ome07”), analysis of SDSS DLA data (Noterdaeme et al. 2009, “NPLS09”), and power

law constraints (Prochaska et al. 2010, “POW10”, open circles are spaced arbitrarily along

power law segments and do not represent NHI bins or errors).

Column-density distribution function

log HI column density

co-m

ovin

g nu

mbe

r de

nsity

of l

ines

owls + urchin

Tom Theuns21

owls = hydrodynamical simulation (Schaye 2010) !urchin = reverse ray-tracer (Altay & TT, 2013)

Tom Theuns22

Tom Theuns23

– 4 –

Fig. 1.— Hi column density distribution function, f(NHI, z), at z � 3; simulation results

are shown as curves and observational data as symbols. The low NHI curve is obtained

using mock spectra fitted with VPFIT. Self-shielding and H2 are unimportant in this

range. The high NHI curve is obtained by projecting the simulation box onto

a plane and includes self-shielding and H2. The gap around NHI � 1017 cm�2

separates low and high NHI. Poisson errors on the simulation curves are always

smaller than their thickness. We also show high-resolution observations of the Ly-�

forest (Kim et al. 2002, “Kim02”), LLSs (Peroux et al. 2005, “Per05”; O’Meara et al. 2007,

“Ome07”), analysis of SDSS DLA data (Noterdaeme et al. 2009, “NPLS09”), and power

law constraints (Prochaska et al. 2010, “POW10”, open circles are spaced arbitrarily along

power law segments and do not represent NHI bins or errors).

DLAs

Tom Theuns24

Fig. 2.— f(NHI, z) - LLS and DLA range. In the left panel, we vary the amplitude of the UV backgroundand show the impact of neglecting H2 and self-shielding. In the right panel, we isolate the e�ects of H2

and illustrate the changes due to suppression of cooling by the UV background. On top of each panel, weshow the ratio of each model to our default model (solid red curve), which includes self-shielding and H2.The observational data are a subset of those in Figure 1 plus SDSS analysis from Prochaska & Wolfe 2009,“PW09”, in the right panel. Self-shielding becomes important for NHI � 1018 cm�2 leading to a flatteningof f(NHI, z). Suppression of cooling a�ects f(NHI, z) between 1019 cm�2 < NHI < 1021.5, while H2 becomesimportant above these column densities.

6

Dependence on physics/numerics (self-shielding, H2 formation, UV-bckg, …)

Tom Theuns25log HI column

log

ratio

com

pare

d to

Ref

ISM

Tom Theuns26

Outliers

no reionisation

Millenniumno feedback

other IMFAGN

Tom Theuns27

IncidenceLLS DLAs

cosmology

Tom Theuns28

Menu:

•Simulated DLAs: column density and dynamics •The temperature-T relation (redux)

Tom Theuns29

Rob Perry

small DLA

big DLA

DLA line widths

Tom Theuns30

Sample DLA - but without the damping wing (!)

Si abundance - from simulation

SiII/Si = HI/H from Urchin

Tom Theuns31

Tom Theuns32

zoomed-in

Si2 column density

contours

Tom Theuns33

w i d e l i n e s: velocity structure

narrow lines: temperature

Tom Theuns34

Neeleman (100 DLAs)Owls (1+2 sigma)

V90 statistics

Tom Theuns35

Si II components vs subfindTM structures

typical

unusual

Tom Theuns36

Dissected cumulatively in FoF mass

Tom Theuns37

V90 [km s-1]

ratio

v90

com

pare

d to

REF no

reionisation

no feedbackMillennium

Tom Theuns38

Problem?

Tom Theuns39

Sample DLA - but without the damping wing (!)

Si abundance - from simulation

SiII/Si = HI/H from Urchin

Tom Theuns40

Maximum Si II extent (“v100”)

Tom Theuns41

Menu:

•WDM and satellites (to follow Lya WDM) •Lya flux PDF (and inverted rho-T relation) •Galactic winds •Simulated DLAs and LLSs •The rho-T relation (redux)

Tom Theuns42log density

log

tem

pera

ture

Tom Theuns43

OWLS temperature-density relation

Tom Theuns44

Column density

Line

-wid

th

b-N from VPfit

Tom Theuns45

Antonella Garzilli

1.even narrow lines are broader than (T/m)1/2

2.what are broader lines: errors? 3.what are narrower lines: errors? metals?

therm

al broadeni

ng

Tom Theuns46

Don’t use VPfit: single max is single absorber (no noise!)

Tom Theuns47

transmissi

on

optical depth

Tempera

ture

velocity [km s-1]

Tom Theuns48

Antonella Garzilli

Use curvature of spectrum to obtain temperature

Obtain density from integrating optical depth over the line

Tom Theuns49

T=100K: still lines have width.

Jeans sm

oothing

Tom Theuns50

Percentiles

Thermal broadening + Jeans smoothing

Tom Theuns51

??

line clustering

Tom Theuns52

Summary