Markov-Chain Monte Carlo Localization (bayestar-mcmc)

Markov-Chain Monte Carlo sky localization.

usage: bayestar-mcmc [-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]
                     [--seed SEED] [--version]
                     [-l CRITICAL|ERROR|WARNING|INFO|DEBUG|NOTSET]
                     [--pycbc-sample PYCBC_SAMPLE]
                     [--coinc-event-id [COINC_EVENT_ID ...]] [--output OUTPUT]
                     [--condor-submit] [--ra DEG] [--dec DEG] [--distance Mpc]
                     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

--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

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]

fixed parameter options

Options to hold certain parameters constant

--ra

Right ascension

--dec

Declination

--distance

Luminosity distance