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Table of Contents
Inference of gravitational-wave signals
A Bayesian analysis of a gravitational-wave signal which is not accompanied by electromagnetic signals can be performed within nmma following two main steps:
1) setting up a config.ini file and running the command
nmma_gw_generation config.ini
2) perform the analysis or parameter estimation using:
nmma_gw_analysis <name_of_analysis>_data_dump.pickle
Below, we provide an example of a gravitational-wave inference setup using observational data of GW170817 and another example for an injection based analysis.
Inference of an observed GW signal: GW170817
1) We start to set up a config.ini file and adapt it to our specific case. An example is shown below:
################################################################################
## Data generation arguments
################################################################################
trigger_time = 1187008882.43
################################################################################
## Detector arguments
################################################################################
detectors = [H1, L1, V1]
psd_dict = {H1=data/GW170817/h1_psd.txt, L1=data/GW170817/l1_psd.txt, V1=data/GW170817/v1_psd.txt}
channel_dict = {H1=LOSC-STRAIN, L1=LOSC-STRAIN, V1=LOSC-STRAIN}
data_dict = {H1=data/GW170817/H-H1_LOSC_CLN_16_V1-1187007040-2048.gwf, L1=data/GW170817/L-
L1_LOSC_CLN_16_V1-1187007040-2048.gwf, V1=data/GW170817/V-V1_LOSC_CLN_16_V1-1187007040-2048.gwf}
duration = 128
################################################################################
## Job submission arguments
################################################################################
label = GW170817
outdir = outdir
################################################################################
## Likelihood arguments
################################################################################
distance-marginalization=False
phase-marginalization=False
time-marginalization=False
################################################################################
## Prior arguments
################################################################################
prior-file = GW170817.prior
################################################################################
## Waveform arguments
################################################################################
frequency-domain-source-model = lal_binary_neutron_star
waveform_approximant = IMRPhenomPv2_NRTidalv2
binary-type=BNS
################################################################################
## EOS arguments
################################################################################
with-eos=True
eos-data=./eos/eos_IST_sorted
Neos=9501
eos-weight=./eos/EOS_IST_sorted_weight.dat
The trigger time is the GPS time of the observed event. With regard to detector arguments, one has to specify which detectors should be used. For example, detectors = [H1, L1, V1] stand for LIGO detectors Hanford, Livingston and Virgo. For each detector. the noise power spectral density of gravitational wave detector needs to be provided. Within data_dict, one needs to provide the GW170817 data measured with each detector.
With regard to likelihood arguments, we can specify if want a marginal likelihood that has been integrated over the parameter space, e.g. for distance, phase, or time.
For inferring the source properties of GW170817, we will sample over a set of EOSs that were computed with Chiral Effective Field Theory. The EOS set 15nsat_cse_natural_R14 was used in the study of Huth et al. and can be downloaded there. In order to
