Offline Localization (bayestar-localize-coincs)

Produce GW sky maps for all coincidences in search pipeline output database in LIGO-LW XML, LIGO-LW SQLite, or PyCBC HDF5 format.

The distance prior is controlled by the --prior-distance-power argument. If you set --prior-distance-power to k, then the distance prior is proportional to r^k. The default is 2, uniform in volume.

If the --min-distance argument is omitted, it defaults to zero. If the --max-distance argument is omitted, it defaults to the SNR=4 horizon distance of the most sensitive detector.

A FITS file is created for each sky map, having a filename of the form X.fits where X is the integer LIGO-LW row ID of the coinc. The OBJECT card in the FITS header is also set to the integer row ID.

usage: bayestar-localize-coincs [-h] [--f-low Hz]
                                [--f-high-truncate F_HIGH_TRUNCATE]
                                [--waveform WAVEFORM] [--min-distance Mpc]
                                [--max-distance Mpc]
                                [--prior-distance-power -1|2] [--cosmology]
                                [--enable-snr-series]
                                [--rescale-loglikelihood RESCALE_LOGLIKELIHOOD]
                                [--mcmc] [--chain-dump] [--seed SEED]
                                [--version]
                                [-l CRITICAL|ERROR|WARNING|INFO|DEBUG|NOTSET]
                                [-d X1 [X1 ...]] [--keep-going]
                                [--pycbc-sample PYCBC_SAMPLE]
                                [--coinc-event-id [COINC_EVENT_ID ...]]
                                [--output OUTPUT] [--condor-submit]
                                INPUT.{hdf,xml,xml.gz,sqlite}
                                [INPUT.{hdf,xml,xml.gz,sqlite} ...]

Positional Arguments

INPUT.{hdf,xml,xml.gz,sqlite}

Input LIGO-LW XML file, SQLite file, or PyCBC HDF5 files. For PyCBC, you must supply the coincidence file (e.g. “H1L1-HDFINJFIND.hdf” or “H1L1-STATMAP.hdf”), the template bank file (e.g. H1L1-BANK2HDF.hdf), the single-detector merged PSD files (e.g. “H1-MERGE_PSDS.hdf” and “L1-MERGE_PSDS.hdf”), and the single-detector merged trigger files (e.g. “H1-HDF_TRIGGER_MERGE.hdf” and “L1-HDF_TRIGGER_MERGE.hdf”), in any order.

Default: -

Named Arguments

--version

show program’s version number and exit

-l, --loglevel

Default: INFO

-d, --disable-detector

disable certain detectors

--keep-going, -k

Keep processing events if a sky map fails to converge

Default: False

--pycbc-sample

(PyCBC only) sample population

Default: 'foreground'

--coinc-event-id

run on only these specified events

--output, -o

output directory

Default: '.'

--condor-submit

submit to Condor instead of running locally

Default: False

waveform options

Options that affect template waveform generation

--f-low

Low frequency cutoff

Default: 30

--f-high-truncate

Truncate waveform at this fraction of the maximum frequency of the PSD

Default: 0.95

--waveform

Template waveform approximant: e.g., TaylorF2threePointFivePN

Default: 'o2-uberbank'

posterior options

Options that affect the BAYESTAR posterior

--min-distance

Minimum distance of prior in megaparsecs

--max-distance

Maximum distance of prior in megaparsecs

--prior-distance-power

Distance prior: -1 for uniform in log, 2 for uniform in volume

Default: 2

--cosmology

Use cosmological comoving volume prior

Default: False

--enable-snr-series, --disable-snr-series

Enable input of SNR time series

Default: True

--rescale-loglikelihood

Rescale log likelihood by the square of this factor to account for excess technical noise from search pipeline

Default: 0.83

BAYESTAR MCMC options

BAYESTAR options for MCMC sampling

--mcmc

Use MCMC sampling instead of Gaussian quadrature

Default: False

--chain-dump

For MCMC methods, dump the sample chain to disk

Default: False

random number generator options

Options that affect the Numpy pseudo-random number genrator

--seed

Pseudo-random number generator seed [default: initialized from /dev/urandom or clock]