Catalog of Observed Gravitational-wave Mergers

Information about the growing catalog of gravitational-wave mergers can be accessed through the pycbc.catalog package.

Which mergers do we have information about?

import pycbc.catalog

c = pycbc.catalog.Catalog(source='gwtc-2')

# Names of mergers in the catalog
print(c.names)

# Approximate GPS time of the mergers
print([c[m].time for m in c])

$ python ../examples/catalog/what.py
dict_keys(['GW190408_181802-v1', 'GW190412-v3', 'GW190413_052954-v1', 'GW190413_134308-v1', 'GW190421_213856-v1', 'GW190424_180648-v1', 'GW190425-v2', 'GW190426_152155-v1', 'GW190503_185404-v1', 'GW190512_180714-v1', 'GW190513_205428-v1', 'GW190514_065416-v1', 'GW190517_055101-v1', 'GW190519_153544-v1', 'GW190521-v3', 'GW190521_074359-v1', 'GW190527_092055-v1', 'GW190602_175927-v1', 'GW190620_030421-v1', 'GW190630_185205-v1', 'GW190701_203306-v1', 'GW190706_222641-v1', 'GW190707_093326-v1', 'GW190708_232457-v1', 'GW190719_215514-v1', 'GW190720_000836-v1', 'GW190727_060333-v1', 'GW190728_064510-v1', 'GW190731_140936-v1', 'GW190803_022701-v1', 'GW190814-v2', 'GW190828_063405-v1', 'GW190828_065509-v1', 'GW190909_114149-v1', 'GW190910_112807-v1', 'GW190915_235702-v1', 'GW190924_021846-v1', 'GW190929_012149-v1', 'GW190930_133541-v1'])
[1238782700.3, 1239082262.2, 1239168612.5, 1239198206.7, 1239917954.3, 1240164426.1, 1240215503.0, 1240327333.3, 1240944862.3, 1241719652.4, 1241816086.8, 1241852074.8, 1242107479.8, 1242315362.4, 1242442967.4, 1242459857.5, 1242984073.8, 1243533585.1, 1245035079.3, 1245955943.2, 1246048404.6, 1246487219.3, 1246527224.2, 1246663515.4, 1247608532.9, 1247616534.7, 1248242632.0, 1248331528.5, 1248617394.6, 1248834439.9, 1249852257.0, 1251009263.8, 1251010527.9, 1252064527.7, 1252150105.3, 1252627040.7, 1253326744.8, 1253755327.5, 1253885759.2]

Plotting some key statistics from the catalog

import matplotlib.pyplot as pp
import pycbc.catalog


c = pycbc.catalog.Catalog(source='gwtc-2')
mchirp, elow, ehigh = c.median1d('mchirp', return_errors=True)
spin = c.median1d('chi_eff')

pp.errorbar(mchirp, spin, xerr=[-elow, ehigh], fmt='o', markersize=7)
pp.xlabel('Chirp Mass')
pp.xscale('log')
pp.ylabel('Effective Spin')
pp.show()

(Source code, png, hires.png, pdf)

_images/stat.png

Accessing data around each event

Data around each event can also be easily accessed for any detector.

import matplotlib.pyplot as pp
import pycbc.catalog


m = pycbc.catalog.Merger("GW170817", source='gwtc-1')

fig, axs = pp.subplots(2, 1, sharex=True, sharey=True)
for ifo, ax in zip(["L1", "H1"], axs):
    pp.sca(ax)
    pp.title(ifo)
    # Retreive data around the BNS merger
    ts = m.strain(ifo).time_slice(m.time - 15, m.time + 6)

    # Whiten the data with a 4s filter
    white = ts.whiten(4, 4)

    times, freqs, power = white.qtransform(.01, logfsteps=200,
                                        qrange=(110, 110),
                                        frange=(20, 512))
    pp.pcolormesh(times, freqs, power**0.5, vmax=5)

pp.yscale('log')
pp.ylabel("Frequency (Hz)")
pp.xlabel("Time (s)")
pp.show()

(Source code, png, hires.png, pdf)

_images/data.png