Commit 8175ff10 authored by Surabhi Sachdev's avatar Surabhi Sachdev

gstlal_inspiral_mass_model: modified the narrow bns distribution

according to the injection sets.
parent 32c706d7
Pipeline #73756 failed with stages
in 1 minute and 6 seconds
...@@ -91,8 +91,9 @@ for row in sngl_inspiral_table: ...@@ -91,8 +91,9 @@ for row in sngl_inspiral_table:
mchirps[row.template_id] = mchirp mchirps[row.template_id] = mchirp
if options.model == "narrow-bns": if options.model == "narrow-bns":
sigma = 0.04 # comes from component mass mean 1.33, std 0.09, and redshift 0.02
mean = 1.20 sigma = 0.055
mean = 1.18
prob[row.template_id] = 1. / (2 * numpy.pi * sigma**2)**.5 * numpy.exp(-(mchirp - mean)**2 / 2. / sigma**2) prob[row.template_id] = 1. / (2 * numpy.pi * sigma**2)**.5 * numpy.exp(-(mchirp - mean)**2 / 2. / sigma**2)
elif options.model == "broad-bns": elif options.model == "broad-bns":
...@@ -123,7 +124,8 @@ for row in sngl_inspiral_table: ...@@ -123,7 +124,8 @@ for row in sngl_inspiral_table:
# BNS portion # BNS portion
# #
# assume a 0.15 solar mass std deviation, this should capture both population distribution and snr effects # assume a 0.35 solar mass std deviation in component masses to make it very broad, this should capture both population distribution and snr effects. This gives mean-mchirp = 1.179 and sigma-mchirp =0.186. Taking z = 0.02, and making std extra broad, we use:
sigma = 0.25 sigma = 0.25
mean = 1.2 mean = 1.2
bnsprob = 1. / (2 * numpy.pi * sigma**2)**.5 * numpy.exp(-(mchirp - mean)**2 / 2. / sigma**2) bnsprob = 1. / (2 * numpy.pi * sigma**2)**.5 * numpy.exp(-(mchirp - mean)**2 / 2. / sigma**2)
......
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