Challenge

Data Challenge

Challenge activity for the Open Data Workshop course.

Data files may be downloaded from DCC G2300818 using these links:

• challenge1.gwf
• challenge2.gwf
• challenge3.gwf
• challenge3_2048hz.gwf <-- Downsampled version of Challenge 3 file

Workshop participants may submit solutions via thinkific as individuals or in teams of up to 3 people. Check for the deadline date on the Thinkific website.

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 spin = 0 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=f'{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://dcc.ligo.org/public/0187/G2300818/001/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')