Simulating X-ray Observations with yt

John ZuHone NASA/Goddard Space Flight Center

yt is a Python-based platform for analysis and visualization of astrophysical simulation data

Turk et al. 2011, ApJS, 192, 9 Turk & Smith 2011, arXiv:1112.4482

!yt is designed to address physical,

not computational, questions

“What is the average mass weighted temperature of the gas within a sphere of radius 100 kpc, centered at the maximum gas density? Oh, and I want it in keV.”

from yt.mods import * from yt.utilities.physical_constants import kboltz !ds = load("IsolatedGalaxy/galaxy0030/galaxy0030") !sp = ds.h.sphere("max", (100, “kpc”)) !T = sp.quantities[“WeightedAverageQuantity”](“temperature”, “cell_mass”) !print (kboltz*T).in_units(“keV”)

Fully-SupportedMostly-

SupportedIn Progress

Enzo FLASH Nyx

Orion In-Memory

Athena ART

Ramses

Gadget Hydra Cactus

PDKGRAV FITS Images

Formation of a Galaxy Cluster: Sam Skillman

Bolatto et al. 2013, Nature, 499, 450

Method: PHOX

• Method developed by Veronica Biffi, Klaus Dolag (http://www.mpa-garching.mpg.de/~kdolag/Phox/)

• Biffi, V., Dolag, K., Bohringer, H., & Lemson, G. 2012, MNRAS, 420, 3545

• Biffi, V., Dolag, K., Bohringer, H. 2013, MNRAS, 428, 1395

Three Steps:

1. Generate a very large number of photons from an appropriate spectral model for each cell

2. Project photons along a chosen line of sight, Doppler and cosmologically shift their energies. Apply galactic absorption.

3. Convolve photons with instrument models.

Step 1

• First, we define a spectral model.

• There are interfaces within the code to use:

• PyXspec (https://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/)

• AtomDB (http://www.atomdb.org)

• There is flexibility to include other model sources

Step 1• In the first step we generate a lot of photons, many

more than would be in a typical observation (at least ~10x more)

• To make this precise, we specify a very large collecting area and a very long exposure time, along with a source distance

• These photons become a Monte-Carlo sample which will be used to make the actual observation

• Typically, we will store them to disk, also saving the positions and velocities of the gas they originated from

Three Steps:1. Generate a very large number of photons

from an appropriate spectral model for each cell

2. Project photons along a chosen line of sight, Doppler and cosmologically shift their energies. Apply galactic absorption. Correct for exposure time and effective area.

3. Convolve photons with instrument models.

Step 2

• Using the saved positions, energies, and velocities, we can project them along a line of sight, and use the gas velocities to Doppler-shift them.

• We also apply cosmological redshift for distant sources, and galactic foreground absorption (tbabs, wabs, etc.)

• Here is where we use the actual effective area (constant or from an ARF) and exposure time of the desired observation

Three Steps:1. Generate a very large number of photons

from an appropriate spectral model for each cell

2. Project photons along a chosen line of sight, Doppler and cosmologically shift their energies. Apply galactic absorption. Correct for exposure time and effective area.

3. Convolve photons with instrument models.

Step 3

• The photon simulator module provides a way to simply convolve with a ARF/RMF pair, to get a quick-and-dirty observation

• If you want to accurately simulate a particular detector, you can export the generated events to files that can be read in by instrument simulators

Step 3

• SIMX: http://hea-www.harvard.edu/simx/

• Not a full raytrace, but a predefined set of PSFs, vignetting information, and instrumental responses and outputs to make the simulation.

• yt exports SIMPUT files of (x,y,E) that can be read in by SIMX

• http://hea-www.harvard.edu/heasarc/formats/simput-1.1.0.pdf

Advantages

• Most expensive step (generating the photons) happens in 3D, and only needs to be done (in most cases) ONCE.

• Different projections, different exposure times, different instruments simulated from the same set of photons (computationally cheaper)

• It runs in parallel using MPI

A Couple of Examples

Sloshing Cluster Core

Density Temperature

Athena MHD dataset, T ~ 2.5 keV

Sloshing Cluster CoreSXI 100 ks exposure, z = 0.01

(reblocked by 4x)

10.5 2 5

0.01

0.1

110

norm

aliz

ed c

ount

s/s/

keV

SXS spectrum

E (keV)

Sloshing Cluster Core

AGN-Blown Bubbles

Dataset created from scratch “in

memory”: 4 keV β-model

cluster with bubbles

AGN-Blown Bubbles

SXI 100 ks exposure

z = 0.02 (reblocked by 4x)

To Get yt

• http://yt-project.org/#getyt

• I recommend using the install script:

1. wget http://hg.yt-project.org/yt/raw/yt/doc/install_script.sh

2. bash install_script.sh

3. source YT_DEST/bin/activate

To Get HelpEmail Me: jzuhone@milkyway.gsfc.nasa.gov !Photon Simulator Documentation: http://yt-project.org/doc/analyzing/analysis_modules/photon_simulator.html !Website: http://yt-project.org !Mailing List (yt-users): http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org