Hydrodynamic Simulations ofrelativistic AGN jet feedback
Radio-mode Feedback in Evolving Galaxies
Alex Wagner aGeoff Bicknell b
Masayuki Umemura aRalph Sutherland b
a Center for Computational Sciences

University of Tsukubab Research School of Astronomy and Astrophysics

The Australian National University

Introduction|Radio mode feedbackHydra A

AGN Feedback on cluster scales
Heating of cooling flows

Croton et al (2006)
See also Benson et al (2003)
Lagos et al (2008)
and Fanidakis et al (2010)

Galaxy luminosity functions

Semi-analytic models require
AGN (radio-mode) feedback toexplain deficit of very brightgalaxies

4C 41.17 (z~3.8)

Spiderweb galaxy (z~2.2)

Reuland et al (2003)

200 kpc

Miley et al (2006)

200 kpc

Radio Galaxies

High z radio galaxies

Neutral gas (HI)v ~ several 100
km s-1(Morganti et al2005-2010)

Ionised Gas
v >1000 km s-1(Nesvadba et al2006-2010)

Outflows in radio galaxies

Neutral and ionised gas

Role of AGN feedback

deus ex machina➡ Anti-hierarchical galaxy and black hole growth (“downsizing”)➡ Bulge-SMBH coevolution• ESTABLISHMENT PHASE
At early times, AGN feedback may first induce, then shut off star-formation in
massive gas-rich galaxies, and establish an old population of stars. Stars must form
before potential gets too deep.
• MAINTENANCE PHASE
Cooling flows and late-time star-formation are continuously suppressed otherwise
too many blue and massive galaxies form. M-sigma relation is maintained.

RADIO MODE FEEDBACK - Key Questions‣ What jet powers affect star formation in what galaxies at what epochs in galaxy
formation?
‣ How does the jet interact with the ISM/IGM?
‣ Can we explain panchromatic morphology and spectra of radio galaxies?

Simulations|Feedback in two phase media

•Hemispherical isotropic distributionof clouds•Pressure equilibrium (except jet)•x=0 reflective, others are reflective•v = 0 everywhere, except at jet inlet

Grid, initial conditions andboundary conditions

FLASH 3.2 with Paramesh AMR

AGN Jet Feedback

The difference between auniform medium and a twophase medium.

AGN Jet Feedback

Jet propagation
Energy deposition

Cloud filling factor and maximum sizes
fV = 0.027
0.052
0.13
0.43
0.42
kmin = 204010

Dispersal of warmphase• Diffuse ablated material
reaches 1000 km s-1.
• Denser clumps move atseveral 100 km s-1.

AGN Jet Feedback

The dispersal of warm gas

Mechanical Advantage
Radial pressure gradientMechanical Advantage
Cloud Momentum

Mechanical Advantage
Cloud
Cloud
KineticMomentum
EnergyAGN Jet Feedback
Efficiencies• Black points: Maximum velocity
dispersion in clouds.
‣ Higher jet power→ Higher velocity dispersion‣ Higher ISM density→ Lower velocity dispersion‣ Lower filling factor→ Lower velocity dispersion(weak dependence)• Dashed line: M-σ relation forη = Pjet / Ledd.

Ledd = eta 4 pi G MBH
mp c / sigmaT
Eta is thus a scaling
for the relative powerof a galaxy to it’s size.

AGN Jet Feedback
Efficiencies• Black points: Critical η = ηcrit.
• Dashed line: Feedback
important in a galaxy with jetpower η only if
η > ηcrit.
‣ No substantial feedback forlog10(η) < -4.
‣ Low powered jets are only
important in less massivegalaxies.

AGN Jet Feedback
Efficiencies

A galaxy with many small
isolated clouds experiences
efficient cloud dispersion
compared to a galaxy withfewer but bigger cloudcomplexes.
Bigger cloud complexes may
be more easily triggered tocollapse.

If one neglects magnetic
fields, focus is on BonnorEbert sphereStar formation in the bulge

But bulge is highly
turbulent

Ebert (1955), Bonnor (1956), McCrea (1957)
Mcrit ≈ 1.5 × MBE ∝ T 2 p−1/2Rc � 25 pc

6M
�
10M⊙
Largest clouds:

Before Jet:

p= 107 K cm−3k

Mcrit ≈ 2.5 × 106 M⊙

After Jet:

p= 3 × 1010 K cm−3k

Mcrit ≈ 5.0 × 104 M⊙

Timescales:

tcollapse ∼(4πGρc )−1/2

tablate ∼Rc
2 vablate��−1/2nc≈ 2 × 106yr−3�300 cm� ��
Rvcablate4≈ 8 × 10yr
−125 pc
600 km s

Star formation in the bulge
Pjet = 1045 erg s-1

kmin = 10

fV = 0.13

Summary in words• Jet plasma floods through porous channels in two-phase ISM and inflates anenergy bubble.
• Pressure gradients in expanding bubble ablate and accelerate clouds tospeeds of warm-gas outflows observed in high-redshift galaxies.
• According to models so far, jets of all powers > 1043 erg s-1 are relevant toAGN feedback, if η > ηcrit. Details of the feedback efficiency depend on filling
factor and size distribution of clouds.
• Dependence of feedback efficiency is strongest on ISM density, Jet power,and size distribution of clouds.
• Star-formation: pressure triggered SF competes with cloud ablation.

Summary in images

jet plasma | warm clouds

density - all phases

temperature - all phasesradial negative pressure gradient

warm phase momentum
warm phase kinetic energy

Galaxy number density evolution in LX bins

BH mass function in z bins

Luminosity-dependent evolution of soft X-ray selected AGN

Vestergaard & Osmer (2009)of redshift in different luminosity classes and the sum over all luminosities with log Lx ≥ 42.
4.4) are overplotted with solid lines. b) The same as a), except that the soft X-ray emissivities
most curve (black) shows the sum of emissivities in all luminosity classes plotted.

Hasinger, Miyaji, & Schmidt (2005)

Fanidakis et al (2010)(See also Bower et al 2006)

minosity bin.

Apzp
KS–probMassive
brightgalaxies
have their stellar mass in(7.67 ± 1.28) × 10
4.90
0.65
−2.40.47, 0.77, 0.64
(1.59 ± 0.15) × 10
3.89
1.11
−1.80.55, 0.25, 0.39
placeatz≳1.
Massivegalaxies and massive black(1.83 ± 0.19) × 10
5.51
1.78
−1.80.05, 0.47, 0.07
(4.90 ±holes1.21) × 10 form
6.06
1.79
−0.4(*)
0.81, 0.98,0.62 massive counterparts:
earlier
thanless
e fit are labelled by (*). Units – A : h Mpc , L : 10 h erg s . The three values are
test for the z distribution and the 2D–KS
test for the (L formation,z) space respectively.
n, L , 1D–KS
Aniti-hierarchicalgalaxy0−6−6−8−11axb1c

2+1.21
−1.12
+0.43
−0.50
+0.38
−0.37
+1.18
−1.22

+0.12
−0.12
+0.22
−0.11
+0.14
−0.16
+0.59
−0.26

+1.0
−1.1+0.7
−1.1
+1.3
−1.4

03
70

−3x,∗

44−270−1 bxz) bins. This binata are scarce (ofins are large. Theorrects the binned

luminosity function of radio quasars from a sample in whichonly the optically brightest objects had redshifts (Schmidt1968).

Galaxy and Black Hole “Downsizing”

ties from binned
ues of individual
osity function. We

5.1. Using Vmax to derive the luminosity function
The derivation of a luminosity function from objects in a welldefined sample usually involves binning the observations inredshift and luminosity. If we make the bins in luminosity so

Merloni (2004)