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CHALLENGE GW-ODW #2

Challenge activity for the Open Data Workshop 2019: https://indico.in2p3.fr/event/gw-odw2

Link to the Challenge spreadsheet: https://bit.ly/2WRxjay – Tab « Challenge »

Data files are available at https://dcc.ligo.org/LIGO-T1900135/public

Challenges are ordered by difficulty. You are rewarded a number of points that scales with the difficulty of the challenge. You can try to solve the challenge in the order you like. Starting with the most difficult is risky but you get a big reward if you succeed by the end of the session! Good luck to all!

Challenge 1 (1 point) -- Novice

Identify a loud binary black hole signal in white, Gaussian noise.

  • Use the data file "challenge1.gwf". The channel name is "H1:CHALLENGE1".
  • The data are white, Gaussian noise containing a simulated BBH signal.

  1. Load the data into memory. What are the sampling rate and duration of the data?

  2. Plot the data in the time-domain.

  3. Plot a spectrogram (or q-transform) of the data, and try to identify the signal.

  4. What is the time of the merger?

Challenge 2 (2 points) -- Rookie

Signal in colored, Gaussian noise.

  • Use the data file "challenge2.gwf", with channel name "H1:CHALLENGE2"
  • The data contain a BBH signal with m1=m2=30 solar masses, spin = 0.

  1. What is the approximative time of the merger? (Hint: a plot of the q-transform could help)

  2. Generate a time-domain template waveform using approximate "SEOBNRv4_opt". with the same parameters as above. Plot this waveform.

  3. Calculate a PSD of the data, and plot this on a log-log scale. Use axes ranging from 20 Hz up to the Nyquist frequency.

  4. Use the template waveform and PSD to calculate the SNR time series. Plot the SNR time-series.

  5. What is the matched filter SNR of the signal?

Challenge 3 (4 points) -- Intermediate

  • Use the data file "challenge3.gwf" with channel "H1:CHALLENGE3"
  • These are real LIGO data from O2, though we've adjusted the time labels and added some simulated signals.
  • The data contain a loud simulated signal with m1 = m2 = 10 solar masses.

  1. What is the merger time of this signal?

  2. What is the matched-filter SNR of this signal?

Challenge 4 (8 points) -- Advanced

  • Use the data file "challenge3.gwf" with channels "H1:CHALLENGE3" and "L1:CHALLENGE3".
  • These are real LIGO data from O2, though we've adjusted the time labels and added some simulated signals.
  • Any simulated signals have been added to both the H1 and L1 data
  • All simulated signals have 0 spin and m1=m2, with m1 somewhere in the range 10-50 solar masses
  1. Identify as many signals as you can. Watch out! These are real data, and so glitches may be present. Any correct detection is +1 point but any false alarms will count -1 point against your score. For each signal you find, list:
  • The merger time
  • The SNR
  • Your estimate of the component masses

  1. Identify as many glitches as you can. Make a spectrogram of each one.

  2. For each simulated BBH you found, use Bilby to compute a posterior distribution for the mass. You can fix the spin and sky location parameters to make this run faster. As long as you measure parameters of the signal phase such as the total mass, you can set the sky location arbitrarily.

Useful notes

Processing data from a local file with Bilby

sampling_rate=2048 #needs to be high enough for the signals found in steps above
duration=8 #needs to be long enough for the signals found in steps above
start_time=100 #needs to be set so that the segment defined by [start_time,start_time+duration] contains the signal

interferometers = bilby.gw.detector.InterferometerList([])
for ifo_name in ['H1','L1']:
    ifo=bilby.gw.detector.get_empty_interferometer(ifo_name)
    ifo.set_strain_data_from_frame_file('challenge3.gwf',sampling_rate, duration, start_time=start_time ,channel=ifo_name+':CHALLENGE3')
    interferometers.append(ifo)