Challenge

Data Challenge

GW Open Data Workshop #5

Challenge activity for the Open Data Workshop 2022: https://www.gw-openscience.org/odw/odw2022/

Data files may be downloaded using these links:

• challenge1.gwf
• challenge2.gwf
• challenge3.gwf
• challenge3-2048.gwf <-- Downsampled version of Challenge 3 file

Workshop participants may submit solutions as individuals or in teams of up to 3 people. Solutions are due before 23:00 UTC of Day 3+7, Wednesday June 1.

Challenges are ordered by difficulty. Entries will be rewarded a number of points that scales with the difficulty of the challenge.

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

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

3. For each simulated BBH you found, use bilby to compute a posterior distribution for the mass. You can fix the spin and mass ratio to make this run faster.

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)

To load data in google co-lab. Run this code, and then 'restart runtime', and run it again

! pip install -q lalsuite
! pip install -q gwpy
! pip install -q pycbc
# -- Click "restart runtime" in the runtime menu

# -- download data
! wget https://www.gw-openscience.org/s/workshop3/challenge/challenge3.gwf

# -- for gwpy 
from gwpy.timeseries import TimeSeries
gwpy_strain = TimeSeries.read('challenge3.gwf', channel="H1:CHALLENGE3")

# -- for pycbc
from pycbc import frame
pycbc_strain = frame.read_frame('challenge3.gwf', 'H1:CHALLENGE3')