Note

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Targeted grid search with line-robust BSGL statisticΒΆ

Search for a monochromatic (no spindown) signal using a parameter space grid (i.e. no MCMC) and the line-robust BSGL statistic to distinguish an astrophysical signal from an artifact in a single detector.

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 import pyfstat
 import numpy as np
 import os

 label = "PyFstat_example_grid_search_BSGL"
 outdir = os.path.join("PyFstat_example_data", label)

 F0 = 30.0
 F1 = 0
 F2 = 0
 Alpha = 1.0
 Delta = 1.5

 # Properties of the GW data - first we make data for two detectors,
 # both including Gaussian noise and a coherent 'astrophysical' signal.
 depth = 70
 sqrtS = "1e-23"
 h0 = float(sqrtS) / depth
 cosi = 0
 IFOs = "H1,L1"
 sqrtSX = ",".join(np.repeat(sqrtS, len(IFOs.split(","))))
 tstart = 1000000000
 duration = 100 * 86400
 tend = tstart + duration
 tref = 0.5 * (tstart + tend)

 data = pyfstat.Writer(
     label=label,
     outdir=outdir,
     tref=tref,
     tstart=tstart,
     duration=duration,
     F0=F0,
     F1=F1,
     F2=F2,
     Alpha=Alpha,
     Delta=Delta,
     h0=h0,
     cosi=cosi,
     sqrtSX=sqrtSX,
     detectors=IFOs,
     SFTWindowType="tukey",
     SFTWindowBeta=0.001,
     Band=1,
 )
 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]
 extra_writer = pyfstat.Writer(
     label=label,
     outdir=outdir,
     tref=tref,
     F0=F0 + 0.01,
     F1=F1,
     F2=F2,
     Alpha=Alpha,
     Delta=Delta,
     h0=10 * h0,
     cosi=cosi,
     sqrtSX=0,  # don't add yet another set of Gaussian noise
     noiseSFTs=SFTs_H1,
     SFTWindowType="tukey",
     SFTWindowBeta=0.001,
 )
 extra_writer.make_data()

 # set up search parameter ranges
 dF0 = 0.0001
 DeltaF0 = 1000 * dF0
 F0s = [F0 - DeltaF0 / 2.0, F0 + DeltaF0 / 2.0, dF0]
 F1s = [F1]
 F2s = [F2]
 Alphas = [Alpha]
 Deltas = [Delta]

 # 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.
 searchF = pyfstat.GridSearch(
     label + "_twoF",
     outdir,
     os.path.join(outdir, "*" + label + "*sft"),
     F0s,
     F1s,
     F2s,
     Alphas,
     Deltas,
     tref,
     tstart,
     tend,
 )
 searchF.run()

 print("Plotting 2F(F0)...")
 searchF.plot_1D(xkey="F0")

 # second search: line-robust statistic BSGL activated
 searchBSGL = pyfstat.GridSearch(
     label + "_BSGL",
     outdir,
     os.path.join(outdir, "*" + label + "*sft"),
     F0s,
     F1s,
     F2s,
     Alphas,
     Deltas,
     tref,
     tstart,
     tend,
     BSGL=True,
 )
 searchBSGL.run()

 # The actual output statistic is log10BSGL.
 # The peak at the higher frequency from the "line artifact" should now
 # be massively suppressed.
 print("Plotting log10BSGL(F0)...")
 searchBSGL.plot_1D(xkey="F0")

Total running time of the script: ( 0 minutes 0.000 seconds)

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