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:
[1]:
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):
[2]:
ds = yt.load(
"GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150", default_species_fields="ionized"
)
yt : [INFO ] 2024-06-19 16:05:03,987 Parameters: current_time = 1.1835090993823291e+17
yt : [INFO ] 2024-06-19 16:05:03,988 Parameters: domain_dimensions = [16 16 16]
yt : [INFO ] 2024-06-19 16:05:03,988 Parameters: domain_left_edge = [-3.70272e+24 -3.70272e+24 -3.70272e+24]
yt : [INFO ] 2024-06-19 16:05:03,988 Parameters: domain_right_edge = [3.70272e+24 3.70272e+24 3.70272e+24]
yt : [INFO ] 2024-06-19 16:05:03,989 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:
[3]:
slc = yt.SlicePlot(
ds, "z", [("gas", "density"), ("gas", "temperature")], width=(1.0, "Mpc")
)
slc.show()
yt : [INFO ] 2024-06-19 16:05:04,491 xlim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO ] 2024-06-19 16:05:04,491 ylim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO ] 2024-06-19 16:05:04,493 xlim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO ] 2024-06-19 16:05:04,493 ylim = -1542838790481162406985728.000000 1542838790481162406985728.000000
yt : [INFO ] 2024-06-19 16:05:04,498 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800
yt : [INFO ] 2024-06-19 16:05:04,840 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:
[4]:
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 \(Z = 0.3~Z_\odot\):
[5]:
source_model = pyxsim.CIESourceModel(
"apec", 0.5, 9.0, 9000, thermal_broad=True, Zmet=0.3
)
pyxsim : [INFO ] 2024-06-19 16:05:05,583 kT_min = 0.025 keV
pyxsim : [INFO ] 2024-06-19 16:05:05,583 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:
[6]:
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!
[7]:
n_photons, n_cells = pyxsim.make_photons(
"my_photons", sp, redshift, area, exp_time, source_model
)
pyxsim : [INFO ] 2024-06-19 16:05:05,596 Cosmology: h = 0.71, omega_matter = 0.27, omega_lambda = 0.73
pyxsim : [INFO ] 2024-06-19 16:05:05,597 Using emission measure field '('gas', 'emission_measure')'.
pyxsim : [INFO ] 2024-06-19 16:05:05,598 Using temperature field '('gas', 'temperature')'.
pyxsim : [INFO ] 2024-06-19 16:12:49,686 Finished generating photons.
pyxsim : [INFO ] 2024-06-19 16:12:49,687 Number of photons generated: 21420265
pyxsim : [INFO ] 2024-06-19 16:12:49,688 Number of cells with photons: 4054590
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:
[8]:
n_events = pyxsim.project_photons(
"my_photons", "my_events", "z", (30.0, 45.0), absorb_model="tbabs", nH=0.04
)
pyxsim : [INFO ] 2024-06-19 16:12:49,733 Foreground galactic absorption: using the tbabs model and nH = 0.04.
pyxsim : [INFO ] 2024-06-19 16:12:57,684 Detected 19255231 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:
[9]:
soxs.instrument_simulator(
"my_events.h5",
"evt.fits",
(100.0, "ks"),
"lynx_lxm",
[30.0, 45.0],
overwrite=True,
)
soxs : [INFO ] 2024-06-19 16:12:57,689 Making observation of source in evt.fits.
soxs : [INFO ] 2024-06-19 16:12:58,357 Detecting events from source my_events.
soxs : [INFO ] 2024-06-19 16:12:58,358 Applying energy-dependent effective area from xrs_mucal_3x10_3.0eV.arf.
soxs : [INFO ] 2024-06-19 16:13:00,364 Pixeling events.
soxs : [INFO ] 2024-06-19 16:13:00,782 Scattering events with a image-based PSF.
soxs : [INFO ] 2024-06-19 16:13:01,071 2630923 events were detected from the source.
soxs : [INFO ] 2024-06-19 16:13:01,158 Scattering energies with RMF xrs_mucal_3.0eV.rmf.
soxs : [INFO ] 2024-06-19 16:13:06,601 Adding background events.
soxs : [INFO ] 2024-06-19 16:13:06,694 Adding in point-source background.
soxs : [INFO ] 2024-06-19 16:13:06,903 Detecting events from source ptsrc_bkgnd.
soxs : [INFO ] 2024-06-19 16:13:06,903 Applying energy-dependent effective area from xrs_mucal_3x10_3.0eV.arf.
soxs : [INFO ] 2024-06-19 16:13:06,912 Pixeling events.
soxs : [INFO ] 2024-06-19 16:13:06,917 Scattering events with a image-based PSF.
soxs : [INFO ] 2024-06-19 16:13:06,927 31117 events were detected from the source.
soxs : [INFO ] 2024-06-19 16:13:06,928 Scattering energies with RMF xrs_mucal_3.0eV.rmf.
soxs : [INFO ] 2024-06-19 16:13:07,455 Generated 31117 photons from the point-source background.
soxs : [INFO ] 2024-06-19 16:13:07,455 Adding in astrophysical foreground.
soxs : [INFO ] 2024-06-19 16:13:24,495 Adding in instrumental background.
soxs : [INFO ] 2024-06-19 16:13:24,547 Making 71640 events from the galactic foreground.
soxs : [INFO ] 2024-06-19 16:13:24,548 Making 9075 events from the instrumental background.
soxs : [INFO ] 2024-06-19 16:13:24,574 Writing events to file evt.fits.
soxs : [INFO ] 2024-06-19 16:13:24,828 Observation complete.
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:
[10]:
soxs.write_image("evt.fits", "img.fits", emin=0.5, emax=2.0, overwrite=True)
We can show the resulting image:
[11]:
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:
[12]:
soxs.write_spectrum("evt.fits", "evt.pha", overwrite=True)
and plot the spectrum using plot_spectrum():
[13]:
fig, ax = soxs.plot_spectrum("evt.pha", xmin=0.5, xmax=7.0)
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:
[14]:
ax.set_xlim(5.4, 5.7)
fig
[14]:
