MCMC search with fully coherent BSGL statistic

Targeted MCMC search for an isolated CW signal using the fully coherent line-robust BSGL-statistic.

 import pyfstat
 import numpy as np
 import os

 label = os.path.splitext(os.path.basename(__file__))[0]
 outdir = os.path.join("PyFstat_example_data", label)

 # Properties of the GW data - first we make data for two detectors,
 # both including Gaussian noise and a coherent 'astrophysical' signal.
 data_parameters = {
     "sqrtSX": 1e-23,
     "tstart": 1000000000,
     "duration": 100 * 86400,
     "detectors": "H1,L1",
     "SFTWindowType": "tukey",
     "SFTWindowBeta": 0.001,
 }
 tend = data_parameters["tstart"] + data_parameters["duration"]
 mid_time = 0.5 * (data_parameters["tstart"] + tend)

 # Properties of the signal
 depth = 10
 signal_parameters = {
     "F0": 30.0,
     "F1": -1e-10,
     "F2": 0,
     "Alpha": np.radians(83.6292),
     "Delta": np.radians(22.0144),
     "tref": mid_time,
     "h0": data_parameters["sqrtSX"] / depth,
     "cosi": 1.0,
 }

 data = pyfstat.Writer(
     label=label, outdir=outdir, **data_parameters, **signal_parameters
 )
 data.make_data()

 # Now we add an additional single-detector artifact to H1 only.
 # For simplicity, this is modelled here as a fully modulated CW-like signal,
 # just restricted to the single detector.
 SFTs_H1 = data.sftfilepath.split(";")[0]
 data_parameters_line = data_parameters.copy()
 signal_parameters_line = signal_parameters.copy()
 data_parameters_line["detectors"] = "H1"
 data_parameters_line["sqrtSX"] = 0  # don't add yet another set of Gaussian noise
 signal_parameters_line["F0"] += 1e-6
 signal_parameters_line["h0"] *= 10.0
 extra_writer = pyfstat.Writer(
     label=label,
     outdir=outdir,
     **data_parameters_line,
     **signal_parameters_line,
     noiseSFTs=SFTs_H1,
 )
 extra_writer.make_data()

 # The predicted twoF, given by lalapps_predictFstat can be accessed by
 twoF = data.predict_fstat()
 print("Predicted twoF value: {}\n".format(twoF))

 # MCMC prior ranges
 DeltaF0 = 1e-5
 DeltaF1 = 1e-13
 theta_prior = {
     "F0": {
         "type": "unif",
         "lower": signal_parameters["F0"] - DeltaF0 / 2.0,
         "upper": signal_parameters["F0"] + DeltaF0 / 2.0,
     },
     "F1": {
         "type": "unif",
         "lower": signal_parameters["F1"] - DeltaF1 / 2.0,
         "upper": signal_parameters["F1"] + DeltaF1 / 2.0,
     },
 }
 for key in "F2", "Alpha", "Delta":
     theta_prior[key] = signal_parameters[key]

 # MCMC sampler settings - relatively cheap setup, may not converge perfectly
 ntemps = 2
 log10beta_min = -0.5
 nwalkers = 50
 nsteps = [100, 100]

 # we'll want to plot results relative to the injection parameters
 transform_dict = dict(
     F0=dict(subtractor=signal_parameters["F0"], symbol="$f-f^\\mathrm{s}$"),
     F1=dict(
         subtractor=signal_parameters["F1"], symbol="$\\dot{f}-\\dot{f}^\\mathrm{s}$"
     ),
 )

 # first search: standard F-statistic
 # This should show a weak peak from the coherent signal
 # and a larger one from the "line artifact" at higher frequency.
 mcmc_F = pyfstat.MCMCSearch(
     label=label + "_twoF",
     outdir=outdir,
     sftfilepattern=os.path.join(outdir, "*{}*sft".format(label)),
     theta_prior=theta_prior,
     tref=mid_time,
     minStartTime=data_parameters["tstart"],
     maxStartTime=tend,
     nsteps=nsteps,
     nwalkers=nwalkers,
     ntemps=ntemps,
     log10beta_min=log10beta_min,
     BSGL=False,
 )
 mcmc_F.transform_dictionary = transform_dict
 mcmc_F.run(
     walker_plot_args={"plot_det_stat": True, "injection_parameters": signal_parameters}
 )
 mcmc_F.print_summary()
 mcmc_F.plot_corner(add_prior=True, truths=signal_parameters)
 mcmc_F.plot_prior_posterior(injection_parameters=signal_parameters)

 # second search: line-robust statistic BSGL activated
 mcmc_F = pyfstat.MCMCSearch(
     label=label + "_BSGL",
     outdir=outdir,
     sftfilepattern=os.path.join(outdir, "*{}*sft".format(label)),
     theta_prior=theta_prior,
     tref=mid_time,
     minStartTime=data_parameters["tstart"],
     maxStartTime=tend,
     nsteps=nsteps,
     nwalkers=nwalkers,
     ntemps=ntemps,
     log10beta_min=log10beta_min,
     BSGL=True,
 )
 mcmc_F.transform_dictionary = transform_dict
 mcmc_F.run(
     walker_plot_args={"plot_det_stat": True, "injection_parameters": signal_parameters}
 )
 mcmc_F.print_summary()
 mcmc_F.plot_corner(add_prior=True, truths=signal_parameters)
 mcmc_F.plot_prior_posterior(injection_parameters=signal_parameters)