Localizing Simulated Events with BAYESTAR

This tutorial will show you how to create a collection of sky maps for simulated binary neutron star mergers using BAYESTAR.

Some of the command-line tools used in this tutorial come from LALSuite or python-ligo-lw. If you followed the Quick Start installation instructions, then these tools should have been installed automatically with the other dependencies of ligo.skymap.

For each command below, we show some of the most useful options for customizing their behavior. Inline comments look `# like this` \ and should be ignored by your shell.

1. Draw Injections

Draw a sample of injections, or simulated events, from an astrophysical distribution using the lalapps_inspinj tool from LALSuite. This tool has many options to control the distribution of each parameter. To see all options, run lalapps_inspinj --help.

Run the following command to generate a handful of BNS events:

lalapps_inspinj \
`# Write output to inj.xml.` \
-o inj.xml \
`# Mass distribution.` \
`# In this example, the masses are pinned to 1.4 and 1.4 Msun.` \
--m-distr fixMasses --fixed-mass1 1.4 --fixed-mass2 1.4 \
`# Coalescence time distribution: adjust time step, start, and stop` \
`# time to control the number of injections.` \
--t-distr uniform --time-step 7200 \
--gps-start-time 1000000000 \
--gps-end-time 1000086400 \
`# Distance distribution: uniform in Euclidean volume.` \
`# WARNING: distances are in kpc.` \
--d-distr volume \
--min-distance 1 --max-distance 600e3 \
`# Sky position and inclination distribution: isotropic.` \
--l-distr random --i-distr uniform \
`# Write a table of CBC injections to inj.xml.` \
--f-lower 30 --disable-spin \
--waveform TaylorF2threePointFivePN

The output is saved in LIGO-LW format to inj.xml.

2. Get Noise Curves

Create discretely sampled noise PSDs for all of your detectors using bayestar-sample-model-psd:

bayestar-sample-model-psd \
`# Write output to psd.xml.` \
-o psd.xml \
`# Specify noise models for desired detectors.` \
--H1=aLIGOZeroDetHighPower \
--L1=aLIGOZeroDetHighPower \
--I1=aLIGOZeroDetHighPower \
--V1=AdvVirgo \
--K1=KAGRA \
`# Optional: apply scale factor to selected PSDs to increase or` \
`# decrease their sensitivity. The PSD is multiplied by a factor of one ` \
`# over scale squared; the horizon distance is multiplied by the scale.` \

The output is saved in LIGO-LW format to psd.xml.

3. Run Matched Filter Pipeline

Run a simulated matched-filter search pipeline to add measurement error and determine which injections are “detected” in coincidence by two or more of your simulated GW observatories. Run the following command:

bayestar-realize-coincs \
`# Write output to coinc.xml.` \
-o coinc.xml \
`# Use the injections and noise PSDs that we generated.` \
inj.xml --reference-psd psd.xml \
`# Specify which detectors are in science mode.` \
--detector H1 L1 V1 I1 K1 \
`# Optionally, add Gaussian noise (rather than zero noise).` \
--measurement-error gaussian-noise \
`# Optionally, adjust the detection threshold: single-detector` \
`# SNR, network SNR, and minimum number of detectors above` \
`# threshold to form a coincidence.` \
--snr-threshold 4.0 \
--net-snr-threshold 12.0 \
--min-triggers 2 \
`# Optionally, save triggers that were below the single-detector` \
`# threshold.` \

The output is saved in LIGO-LW format to coinc.xml.


Finally, make sky maps for your simulated events using bayestar-localize-coincs. If you are working on a computing cluster that uses the HTCondor job scheduler, then you can add the --condor-submit to automatically submit the BAYESTAR jobs to the cluster. Run this command:

# Explicitly set the number of OpenMP threads
# instead of using all available cores.

# Run BAYESTAR on all coincident events in coinc.xml.
bayestar-localize-coincs coinc.xml \
`# Optional: submit jobs to Condor` \
`# instead of running BAYESTAR locally.` \

The output is saved in the current working directory to FITS files named 0.fits, 1.fits, etc.

5. Find Injections (Optional)

Optionally, if you want to generate P-P plots, you need to convert the coinc.xml file to SQLite using the ligolw_sqlite tool from python-ligo-lw. Run the following command:

ligolw_sqlite --preserve-ids --replace --database coinc.sqlite coinc.xml

The output is saved in SQLite format as coinc.sqlite.

6. Analyze Sky Maps

Use the ligo-skymap-stats tool to gather summary statistics including credible areas for each sky map:

ligo-skymap-stats \
`# Write output to bayestar.tsv.` \
-o bayestar.tsv \
`# Include this option to enable P-P plots.` \
--database coinc.sqlite \
`# Read all sky maps in this directory.` \
*.fits \
`# Optional: calculate the 50% and 90% credible areas.` \
--contour 50 90 \
`# Optional: calculate the probability contained within the smallest` \
`# credible regions of 10 and 100 deg2.` \
--area 10 100 \
`# Optional: count the number of disjoint patches on the sky.` \
`# WARNING: this option makes the script very slow!` \
--modes \
`# Optional, but highly recommended: analyze sky maps using multiple` \
`# threads. In this example, we use 8 worker processes.` \
-j 8

The output is saved in tab-separated value format as bayestar.tsv.

7. Make Summary Plots

Lastly, make summary graphs including histograms and P-P plots using ligo-skymap-plot-stats:

ligo-skymap-plot-stats bayestar.tsv