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-inclination deg]
                     [--max-inclination deg] [--min-distance Mpc]
                     [--max-distance Mpc] [--prior-distance-power -1|2]
                     [--cosmology] [--enable-snr-series] [--seed SEED]
                     [--version] [-l CRITICAL|ERROR|WARNING|INFO|DEBUG|NOTSET]
                     [--pycbc-sample PYCBC_SAMPLE]
                     [--coinc-event-id [COINC_EVENT_ID [COINC_EVENT_ID ...]]]
                     [--output OUTPUT] [--condor-submit] [--ra DEG]
                     [--dec DEG] [--distance Mpc]
                     [INPUT.{hdf,xml,xml.gz,sqlite} ...]

Positional Arguments


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


show program’s version number and exit

-l, --loglevel

Default: INFO


(PyCBC only) sample population

Default: “foreground”


run on only these specified events

--output, -o

output directory

Default: “.”


submit to Condor instead of running locally

Default: False

waveform options

Options that affect template waveform generation


Low frequency cutoff

Default: 30


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

Default: 0.95


Template waveform approximant: e.g., TaylorF2threePointFivePN

Default: “o2-uberbank”

prior options

Options that affect the BAYESTAR likelihood


Minimum inclination in degrees

Default: 0.0


Maximum inclination in degrees

Default: 90.0


Minimum distance of prior in megaparsecs


Maximum distance of prior in megaparsecs


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

Default: 2


Use cosmological comoving volume prior

Default: False

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

Enable input of SNR time series

Default: True

random number generator options

Options that affect the Numpy pseudo-random number genrator


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

fixed parameter options

Options to hold certain parameters constant


Right ascension




Luminosity distance