Hydrodynamical simulations of feedback from AGN

Patrick Yates¹, Stas Shabala¹, Martin Krause^1,2

¹University of Tasmania
²University of Hertfordshire

Honourable mentions: Jonathon Rogers, Jesse Swan, Ross Turner, Katie Vandorou

Hi, I'm Patrick Yates from the University of Tasmania, where I recently started a PhD with Stas Shabala.

AGN Feedback

Overview

Radio mode feedback required to explain evolution of SFR density with redshift
Cooling catastrophe
Truncation of star-formation in order to reproduce bi-modal galaxy population

Shabala & Alexander 2009, Apj, 699, 525

AGN feedback plays an important role in galaxy evolution
In this talk I will be focussing on radio-mode feedback, aka jet feedback
Plot shows model of the evolution of star formation rate density with redshift, black is without feedback, red is with. Feedback needed for model to agree with observations
Feedback balances with cooling flows and prevents the cooling catastrophe
Feedback can be responsible for truncating star formation, which explains the bi-modality of the galaxy population. Can explain "red and dead" galaxies, where star formation has ceased

AGN Feedback

Large-scale feedback

Jet inflates bubbles outside the galactic disk
Bubbles do work on hot gas halo, through uplifting gas and shock heating

Fabian et al. 2000, MNRAS, 318 (4), 65

I will briefly mention two areas where AGN feedback occurs. The first is large-scale feedback.
This is feedback that essentially affects everything outside the galactic disc
Feedback over a large range of scales, extending out to hundreds or thousands of kiloparsecs
As the jet propagates through the environment it shock-heats the gas and inflates bubbles outside the galactic disc
These bubbles do work by uplifting gas
On the left is a plot showing the bubbles in the Perseus cluster. Radio contours overlayed on top of X-ray data
On the right is a movie of density plot from a hydrodynamic sim of a jet, showing the jet shock-heating the environment and inflating a bubble

AGN Feedback

Galaxy-scale feedback

Jets drive supersonic turbulence, drive outflows, can trigger star formation through compression due to shocks and heating
This is feedback in the HI region

Mukherjee et al. 2016, MNRAS, 461 (1), 967

The other interesting area where radio-mode feedback occurs is in the interstellar medium
In this area the jets do a bunch of interesting things, including heating the ISM through driving supersonic turbulence and outflows
It is possible that jets in the ISM can also trigger star formation through compression due to shocks and heating
Plots density slices of hydrodynamic simulations of a jet in the ISM
Different panels correspond to different jet powers and initial ISM turbulence values - see the paper for more information!
Can see how the jet drives outflows and turbulence in all four cases

We can use numerical simulations to find out!

Can gain insight into how the physics of radio jets are impacted by:
- Environment
- Duty cycle and outburst count
Can compare with:
- Analytical models
- Observations
Simulations carried out using the PLUTO hydrodynamic code

Quantifying Environment

Gas density for two halo masses

Use the NFW dark matter density profile
Gas density profile found by assuming hydrostatic equilibrium (Makino et al. 1998)
Assume system is isothermal

$$ \rho_\mathrm{DM}(r) = \frac{\delta_\mathrm{c} \rho_{\mathrm{c}0} }{(r/r_\mathrm{s}) \left( 1 + \frac{r}{r_\mathrm{s}} \right)^2 } $$ $$ \rho_\mathrm{g}(r) = \rho_{\mathrm{g}0} \exp \left( - \frac{27b}{2} \right) \left( 1 + \frac{r}{r_\mathrm{s}} \right)^{27b / (2r / r_\mathrm{s})} $$

Injecting the jet

Gas is in hydrostatic equilibrium
Jet injected as a mass inflow boundary condition at $r \sim 1$ kpc
Non-relativistic jet

Gas density profile showing log of the gas density

Different Environments

Density and Morphology

Power: $10^{37}$ Watt
Mach Number: 25
Active Time: 40 Myr
Total Sim Time: 200 Myr

Cluster

Poor group

Different Environments

Energy Components

Environment affects how the injected energy is distributed
Greater kinetic energy in the $10^{12.5} M_\odot$ mass halo

Cluster

Poor group

Different Environments

Observations

Cluster

Poor group

Can "paint on" radio emission as synchrotron emission using pressure
Can construct size-luminosity diagrams

Different Outburst Count

Density and Morphology

Left: 1 outburst for 40 Myr
Right: 4 outbursts for 10 Myr
Both: Same energy injected

4 Outbursts

1 Outburst

Different Outburst Count

Energy Components

Outburst count does not significantly affect how injected energy is distributed

4 Outbursts

1 Outburst

Different Outburst Count

Preconditioning

Morphological differences show up clearly in plot of outburst tracers
Explains some of the variation in feedback efficiency

Poor group

Cluster

These plots show the jet material corresponding to outbursts 1 through 4 at the end of each outburst, for the poor group and cluster environments
Clearly see how the preconditioning of the environment affects subsequent outbursts
This likely explains some of the variation in feedback efficiency with outburst count

Invisible Lobes

Radio AGN which appear compact with FIRST / NVSS may have large "invisible lobes"
Invisible lobes are present in simulated radio emission plots
Multi-frequency surveys such as GLASS can provide further insight into invisible lobes

1 Outburst

4 Outbursts

Simulations I have carried out support the existence of invisible lobes
This is where large-scale structures in the cluster are below detection limit and so are not visible in radio observations
Example of this is shown in plots on the right - the first is a single outburst jet, the second is a four outburst jet, both 200 Myrs old in a poor group
Can see that surface brightness is well below 1 mJy for both plots in most regions of the jet, however there are these large-scale structures that are not visible in the radio emission
More insight into these invisible lobes will be provided with multi-frequency surveys such as GLASS, as well as semi-analytic dynamical models
Glass: 4cm wide, deep (?)

Dynamical Models

Jet simulations are computationally intensive
New semi-analytic model developed by Ross Turner can help with this
Semi-analytic dynamical model for expansion and radio emission + a numerical model for backflow
Get electron aging, spectral break frequencies and more!

Turner, Shabala+ in prep.

Full hydrodynamic simulations of jets are computationally intensive
Can combine semi-analytic dynamical models with numerical simulations
Use the numerical simulation for the backflow
Combine with semi-analytic model for expansion and radio emission
Can accurately predict electron aging, spectral break frequencies
Can also quantify fraction of radio galaxies for a given frequency and sensitivity that have large-scale structures that go undedetected - invisible lobes

Summary

The environment into which the jet is injected affects the jet morphology, dynamics and injected energy distribution
Preconditioning of the environment in earlier outbursts affects the jet dynamics and energy of subsequent outbursts
The feedback efficiency of the jet is greater in poor environments compared to rich environments ($\sim 80 \%$ vs $\sim 50 \%$)
Size vs luminosity tracks are very different between environments
Invisible lobes are produced for simulations in poor environments

Hydrodynamical simulations of feedback from AGN

AGN Feedback

Overview

AGN Feedback

Large-scale feedback

AGN Feedback

Galaxy-scale feedback

How does the large-scale environment affect feedback?

We can use numerical simulations to find out!

Quantifying Environment

Injecting the jet

Different Environments

Density and Morphology

Different Environments

Energy Components

Different Environments

Feedback Efficiency

Different Environments

Observations

Different Outburst Count

Density and Morphology

Different Outburst Count

Energy Components

Different Outburst Count

Feedback Efficiency

Different Outburst Count

Feedback Efficiency

Different Outburst Count

Preconditioning

How many compact sources have invisible lobes?

Invisible Lobes

Dynamical Models

Summary