pyXSIM Example

To show how to make a set of photons from a 3D dataset using pyXSIM and yt for reading into SOXS, we’ll look at is that of thermal emission from a galaxy cluster. In this case, the gas in the core of the cluster is “sloshing” in the center, producing spiral-shaped cold fronts. The dataset we want to use for this example is available for download from the yt Project at this link.

First, import our necessary modules:

import yt
import pyxsim
import soxs

Next, we load the dataset with yt (this dataset does not have species fields, so we specify that the gas is fully ionized in this case so that the emission measure field can be computed correctly):

ds = yt.load(
    "GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150", default_species_fields="ionized"
)

yt : [INFO     ] 2025-11-05 22:40:06,483 Parameters: current_time              = 1.1835090993823291e+17
yt : [INFO     ] 2025-11-05 22:40:06,483 Parameters: domain_dimensions         = [16 16 16]
yt : [INFO     ] 2025-11-05 22:40:06,484 Parameters: domain_left_edge          = [-3.70272e+24 -3.70272e+24 -3.70272e+24]
yt : [INFO     ] 2025-11-05 22:40:06,484 Parameters: domain_right_edge         = [3.70272e+24 3.70272e+24 3.70272e+24]
yt : [INFO     ] 2025-11-05 22:40:06,484 Parameters: cosmological_simulation   = 0

Let’s use yt to take a slice of density and temperature through the center of the dataset so we can see what we’re looking at:

slc = yt.SlicePlot(
    ds, "z", [("gas", "density"), ("gas", "temperature")], width=(1.0, "Mpc")
)
slc.show()

yt : [INFO     ] 2025-11-05 22:40:06,780 xlim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO     ] 2025-11-05 22:40:06,781 ylim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO     ] 2025-11-05 22:40:06,781 xlim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO     ] 2025-11-05 22:40:06,782 ylim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO     ] 2025-11-05 22:40:06,783 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800
yt : [INFO     ] 2025-11-05 22:40:06,873 Making a fixed resolution buffer of (('gas', 'temperature')) 800 by 800

Ok, sloshing gas as advertised. Next, we’ll create a sphere object to serve as a source for the photons. Place it at the center of the domain with "c", and use a radius of 500 kpc:

sp = ds.sphere("c", (0.5, "Mpc"))

Now, we need to set up the emission model for our source. We said we were going to look at the thermal emission from the hot plasma, so we’ll do that here by using ThermalSourceModel. The first four arguments are the name of the underlying spectral model, the maximum and minimum energies, and the number of bins in the spectrum. We’ve chosen these numbers so that the spectrum has an energy resolution of about 1 eV. Setting thermal_broad=True turns on thermal broadening. This simulation does not include metallicity, so we’ll do something simple and say that it uses the above spectral model and the metallicity is a constant \(Z = 0.3~Z_\odot\):

source_model = pyxsim.CIESourceModel(
    "apec", 0.5, 9.0, 9000, thermal_broad=True, Zmet=0.3
)

pyxsim : [INFO     ] 2025-11-05 22:40:07,407 kT_min = 0.025 keV
pyxsim : [INFO     ] 2025-11-05 22:40:07,407 kT_max = 64 keV

We’re almost ready to go to generate the photons from this source, but first we should decide what our redshift, collecting area, and exposure time should be. Let’s pick big numbers, because remember the point of this first step is to create a Monte-Carlo sample from which to draw smaller sub-samples for mock observations. Note these are all (value, unit) tuples:

exp_time = (300.0, "ks")  # exposure time
area = (3.0, "m**2")  # collecting area
redshift = 0.2

So, that’s everything–let’s create the photons!

n_photons, n_cells = pyxsim.make_photons(
    "my_photons", sp, redshift, area, exp_time, source_model
)

pyxsim : [INFO     ] 2025-11-05 22:40:07,416 Cosmology: h = 0.71, omega_matter = 0.27, omega_lambda = 0.73
pyxsim : [INFO     ] 2025-11-05 22:40:07,416 Using emission measure field '('gas', 'emission_measure')'.
pyxsim : [INFO     ] 2025-11-05 22:40:07,416 Using temperature field '('gas', 'temperature')'.

pyxsim : [INFO     ] 2025-11-05 22:44:44,059 Finished generating photons.
pyxsim : [INFO     ] 2025-11-05 22:44:44,059 Number of photons generated: 21491649
pyxsim : [INFO     ] 2025-11-05 22:44:44,060 Number of cells with photons: 4057685

Ok, that was easy. Now we have a photon list that we can use to create events, using the project_photons() function. To be realistic, we’re going to want to assume foreground Galactic absorption, using the “TBabs” absorption model and assuming a foreground absorption column of \(N_H = 4 \times 10^{20}~{\rm cm}^{-2}\). Here we’ll just do a simple projection along the z-axis, reducing the exposure time, and centering the photons at RA, Dec = (30, 45) degrees:

n_events = pyxsim.project_photons(
    "my_photons", "my_events", "z", (30.0, 45.0), absorb_model="tbabs", nH=0.04
)

pyxsim : [INFO     ] 2025-11-05 22:44:44,065 Foreground galactic absorption: using the tbabs model and nH = 0.04.

pyxsim : [INFO     ] 2025-11-05 22:44:50,164 Detected 19318950 events.

We can then use this event list that we wrote as an input to the instrument simulator in SOXS. We’ll use a smaller exposure time (100 ks instead of 500 ks), and observe it with the Lynx calorimeter:

soxs.instrument_simulator(
    "my_events.h5",
    "evt.fits",
    (100.0, "ks"),
    "lynx_lxm",
    [30.0, 45.0],
    overwrite=True,
)

soxs : [INFO     ] 2025-11-05 22:44:50,365 Simulating events from 1 sources using instrument lynx_lxm for 100 ks.
soxs : [INFO     ] 2025-11-05 22:44:52,757 Scattering energies with RMF xrs_mucal_3.0eV.rmf.

soxs : [INFO     ] 2025-11-05 22:44:56,159 Detected 2636935 events in total.
soxs : [INFO     ] 2025-11-05 22:44:56,171 Adding background events.
soxs : [INFO     ] 2025-11-05 22:44:56,243 Adding in point-source background.
soxs : [INFO     ] 2025-11-05 22:44:56,483 Simulating events from 1 sources using instrument lynx_lxm for 100 ks.
soxs : [INFO     ] 2025-11-05 22:44:56,519 Scattering energies with RMF xrs_mucal_3.0eV.rmf.

soxs : [INFO     ] 2025-11-05 22:44:56,931 Detected 36105 events in total.
soxs : [INFO     ] 2025-11-05 22:44:56,932 Generated 36105 photons from the point-source background.
soxs : [INFO     ] 2025-11-05 22:44:56,932 Adding in astrophysical foreground.

soxs : [INFO     ] 2025-11-05 22:45:07,326 Adding in instrumental background.
soxs : [INFO     ] 2025-11-05 22:45:07,380 Making 75616 events from the galactic foreground.
soxs : [INFO     ] 2025-11-05 22:45:07,381 Making 8962 events from the instrumental background.
soxs : [INFO     ] 2025-11-05 22:45:07,502 Observation complete.
soxs : [INFO     ] 2025-11-05 22:45:07,502 Writing events to file evt.fits.

We can use the write_image() function in SOXS to bin the events into an image and write them to a file, restricting the energies between 0.5 and 2.0 keV:

soxs.write_image("evt.fits", "img.fits", emin=0.5, emax=2.0, overwrite=True)

We can show the resulting image:

fig, ax = soxs.plot_image(
    "img.fits", stretch="sqrt", cmap="afmhot", vmax=1000.0, width=0.05
)

We can also bin the events into a spectrum using write_spectrum() and write the spectrum to disk:

soxs.write_spectrum("evt.fits", "evt.pha", overwrite=True)

and plot the spectrum using plot_spectrum():

fig, ax, _ = soxs.plot_spectrum(
    "evt.pha", xmin=0.5, xmax=7.0, xscale="log", yscale="log"
)

Let’s zoom into the region of the spectrum around the iron line to look at the detailed structure afforded by the resolution of the calorimeter:

ax.set_xlim(5.4, 5.7)
fig