BINARY BLACK HOLE SIGNALS IN LIGO OPEN DATA

This notebook is a simplified version of the notebook found here which describes the analysis used to process a gravitational wave signal. It will go through some typical signal processing tasks on strain time-series data associated with the LIGO Event data releases from the LIGO Open Science Center (LOSC).

Note: although I’ve tried to simplify this notebook a bit, it’s still highly technical. I do not in the slightest expect you to understand everything that’s going it with repect to the physics and data processing, but hopefully you should be able to understand some of the Python and get a good idea of how scientists use Python in their experiments!

Table of Contents

• Intro to gravitational-waves
• Download the data
• Set the event name to choose event and the plot type
• Read in the data
• Plot the ASD
• Binary Neutron Star detection range
• Whitening
• Spectrograms
• Waveform Template
• Matched filtering to find the signal
• Make sound Files
• Data segments

Intro to gravitational waves

Over a hundred years ago, Einstein published the Theory of General Relativity. This theory describes how what we experience as gravity is actually the bending of spacetime by objects with mass (i.e. rather that being a force as previously thought). This has many consequences, one of which is that objects with mass can emit gravitational waves. These are oscillations in spacetime produced by the movement of massive bodies, sort of like how a boat moving across a still lake will send out ripples across the surface. Unfortunately, these ripples are very very small and therefore very very hard to detect. It took scientists a hundred years to build an experiment sensitive enough to detect these gravitational waves (the LIGO detectors), and even then it’s still only able to detect waves emitted in one of the most violent events in the universe: two black holes crashing into each other.

The LIGO experiment consists of two interferometers, one in Livingston, LA, the other in Hanford, WA. Each interferometer has two arms, and along each arm is sent a laser beam. These two beams meet at the photodetector, where they produce an interference pattern. When a gravitational wave passes through the detector, this will cause spacetime to bend, slightly lengthening/shortening one of the arms with respect to the other and therefore changing the distance the laser beam has to travel. This will change the interference pattern at the photodetector.

Before scientists are able to see whether a gravitational wave has passed through the detectors, they need to process the raw data. Firstly, the data may contain noise, or unwanted data in the detected signal. Noise can come from many different sources. It could come from the experiment itself (e.g. the humming of the electricity in the wires could cause the laser beams to shake), it could come from outside sources (e.g. seismic activity could cause the experiment to shake), it could come from glitches in the experiment (e.g. the computer powering the experiment could freeze, causing it to miss some of the signal it’s supposed to be recording), etc. In this notebook, we’re going to have a look at some of the things that the LIGO scientists did to process the raw interferometer data in order to produce the gravitational wave signal.

Download the data on a computer with a python installation

First we need to get the necessary files, by downloading the zip file and unpacking it into single directory:

• LOSC_Event_tutorial.zip

Important Make sure that you move/copy the contents of the zip file once you have unpacked it to the same directory where this notebook is running.

This zip file contains:

• this IPython notebook LOSC_Event_tutorial.ipynb, and LOSC_Event_tutorial.py code.
• python code for reading LOSC data files: readligo.py.
• the event data files (32s sampled at 4096 Hz, in hdf5 format, for both LIGO detectors).
• waveform templates (32s sampled at 4096 Hz, in hdf5 format, for both plus and cross polarizations).
• a parameter file in json format

Set the event name to choose event and the plot type

#-- SET ME   Tutorial should work with most binary black hole events
#-- Default is no event selection; you MUST select one to proceed.
eventname = ''
eventname = 'GW150914' 
#eventname = 'GW151226' 
#eventname = 'LVT151012'
#eventname = 'GW170104'

# want plots?
make_plots = 1
plottype = "png"
#plottype = "pdf"

# Standard python numerical analysis imports:
import numpy as np
from scipy import signal
from scipy.interpolate import interp1d
from scipy.signal import butter, filtfilt, iirdesign, zpk2tf, freqz
import h5py
import json

# the IPython magic below must be commented out in the .py file, since it doesn't work there.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import matplotlib.pyplot as plt
import matplotlib.mlab as mlab

# LIGO-specific readligo.py 
import readligo as rl

# you might get a matplotlib warning here; you can ignore it.

/Users/aliceharpole/anaconda3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 88 from C header, got 96 from PyObject
  return f(*args, **kwds)

Read the event properties from a local json file (download in advance):

# Read the event properties from a local json file
fnjson = "BBH_events_v3.json"
try:
    events = json.load(open(fnjson,"r"))
except IOError:
    print("Cannot find resource file "+fnjson)
    print("You can download it from https://losc.ligo.org/s/events/"+fnjson)
    print("Quitting.")
    quit()

# did the user select the eventname ?
try: 
    events[eventname]
except:
    print('You must select an eventname that is in '+fnjson+'! Quitting.')
    quit()

# Extract the parameters for the desired event:
event = events[eventname]
fn_H1 = event['fn_H1']              # File name for H1 data
fn_L1 = event['fn_L1']              # File name for L1 data
fn_template = event['fn_template']  # File name for template waveform
fs = event['fs']                    # Set sampling rate
tevent = event['tevent']            # Set approximate event GPS time
fband = event['fband']              # frequency band for bandpassing signal
print("Reading in parameters for event " + event["name"])
print(event)

Reading in parameters for event GW150914
{'name': 'GW150914', 'fn_H1': 'H-H1_LOSC_4_V2-1126259446-32.hdf5', 'fn_L1': 'L-L1_LOSC_4_V2-1126259446-32.hdf5', 'fn_template': 'GW150914_4_template.hdf5', 'fs': 4096, 'tevent': 1126259462.44, 'utcevent': '2015-09-14T09:50:45.44', 'm1': 41.743, 'm2': 29.237, 'a1': 0.355, 'a2': -0.769, 'approx': 'lalsim.SEOBNRv2', 'fband': [43.0, 300.0], 'f_min': 10.0}

Read in the data

We will make use of the data, and waveform template, defined above.

#----------------------------------------------------------------
# Load LIGO data from a single file.
# FIRST, define the filenames fn_H1 and fn_L1, above.
#----------------------------------------------------------------
try:
    # read in data from H1 and L1, if available:
    strain_H1, time_H1, chan_dict_H1 = rl.loaddata(fn_H1, 'H1')
    strain_L1, time_L1, chan_dict_L1 = rl.loaddata(fn_L1, 'L1')
except:
    print("Cannot find data files!")
    print("You can download them from https://losc.ligo.org/s/events/"+eventname)
    print("Quitting.")
    quit()

/Users/aliceharpole/anaconda3/lib/python3.6/site-packages/h5py/_hl/dataset.py:313: H5pyDeprecationWarning: dataset.value has been deprecated. Use dataset[()] instead.
  "Use dataset[()] instead.", H5pyDeprecationWarning)

First look at the data from H1 and L1

He we’re going to look at the raw data from the Hanford (H1) and Livingston (L1) detectors. First we shall print the timeframe of the signal and look at the properties of the measured strain. Strain is the ratio of the change in length of the laser beam to the original length. If a gravitational wave has passed through the detector, then this will cause the strain to increase.

# both H1 and L1 will have the same time vector, so:
time = time_H1
# the time sample interval (uniformly sampled!)
dt = time[1] - time[0]

# Let's look at the data and print out some stuff:

print('time_H1: len, min, mean, max = ', \
    len(time_H1), time_H1.min(), time_H1.mean(), time_H1.max() )
print('strain_H1: len, min, mean, max = ', \
    len(strain_H1), strain_H1.min(),strain_H1.mean(),strain_H1.max())
print( 'strain_L1: len, min, mean, max = ', \
    len(strain_L1), strain_L1.min(),strain_L1.mean(),strain_L1.max())

#What's in chan_dict?  (See also https://losc.ligo.org/tutorials/)
bits = chan_dict_H1['DATA']
print("For H1, {0} out of {1} seconds contain usable DATA".format(bits.sum(), len(bits)))
bits = chan_dict_L1['DATA']
print("For L1, {0} out of {1} seconds contain usable DATA".format(bits.sum(), len(bits)))
 

time_H1: len, min, mean, max =  131072 1126259446.0 1126259461.999878 1126259477.9997559
strain_H1: len, min, mean, max =  131072 -7.044665943156067e-19 5.895522509246437e-23 7.706262192397465e-19
strain_L1: len, min, mean, max =  131072 -1.8697138664279764e-18 -1.0522332249909908e-18 -4.60035111311666e-20
For H1, 32 out of 32 seconds contain usable DATA
For L1, 32 out of 32 seconds contain usable DATA

# plot +- deltat seconds around the event:
# index into the strain time series for this time interval:
deltat = 5
indxt = np.where((time >= tevent-deltat) & (time < tevent+deltat))
print(tevent)

if make_plots:
    plt.figure()
    plt.plot(time[indxt]-tevent,strain_H1[indxt],'r',label='H1 strain')
    plt.plot(time[indxt]-tevent,strain_L1[indxt],'g',label='L1 strain')
    plt.xlabel('time (s) since '+str(tevent))
    plt.ylabel('strain')
    plt.legend(loc='lower right')
    plt.title('Advanced LIGO strain data near '+eventname)
    plt.savefig(eventname+'_strain.'+plottype)

1126259462.44

The data are dominated by low frequency noise; there is no way to see a signal here, without some signal processing.

Plot the Amplitude Spectral Density (ASD)

First, we’re going to plot these in what’s called the Fourier domain. This is a fancy way of saying that we’re going to plot the strain as a function of the frequency. A way to visualize the frequency content of the data is to plot the amplitude spectral density, ASD.

The ASDs are an estimate of the “strain-equivalent noise” of the detectors versus frequency, which limit the ability of the detectors to identify GW signals.

There’s a signal in these data! For the moment, let’s ignore that, and assume it’s all noise.

make_psds = 1
if make_psds:
    # number of sample for the fast fourier transform:
    NFFT = 4*fs
    Pxx_H1, freqs = mlab.psd(strain_H1, Fs = fs, NFFT = NFFT)
    Pxx_L1, freqs = mlab.psd(strain_L1, Fs = fs, NFFT = NFFT)

    # We will use interpolations of the ASDs computed above for whitening:
    psd_H1 = interp1d(freqs, Pxx_H1)
    psd_L1 = interp1d(freqs, Pxx_L1)

    # Here is an approximate, smoothed PSD for H1 during O1, with no lines. We'll use it later.    
    Pxx = (1.e-22*(18./(0.1+freqs))**2)**2+0.7e-23**2+((freqs/2000.)*4.e-23)**2
    psd_smooth = interp1d(freqs, Pxx)

if make_plots:
    # plot the ASDs, with the template overlaid:
    f_min = 20.
    f_max = 2000. 
    plt.figure(figsize=(10,8))
    plt.loglog(freqs, np.sqrt(Pxx_L1),'g',label='L1 strain')
    plt.loglog(freqs, np.sqrt(Pxx_H1),'r',label='H1 strain')
    plt.loglog(freqs, np.sqrt(Pxx),'k',label='H1 strain, O1 smooth model')
    plt.axis([f_min, f_max, 1e-24, 1e-19])
    plt.grid('on')
    plt.ylabel('ASD (strain/rtHz)')
    plt.xlabel('Freq (Hz)')
    plt.legend(loc='upper center')
    plt.title('Advanced LIGO strain data near '+eventname)
    plt.savefig(eventname+'_ASDs.'+plottype)

NOTE that we only plot the data between f_min = 20 Hz and f_max = 2000 Hz.

Below f_min, the data are not properly calibrated. That’s OK, because the noise is so high below f_min that LIGO cannot sense gravitational wave strain from astrophysical sources in that band.

The sample rate is fs = 4096 Hz (2^12 Hz), so the data cannot capture frequency content above the Nyquist frequency = fs/2 = 2048 Hz. That’s OK, because our events only have detectable frequency content in the range given by fband, defined above; the upper end will (almost) always be below the Nyquist frequency. We set f_max = 2000, a bit below Nyquist.

You can see strong spectral lines in the data; they are all of instrumental origin. Some are engineered into the detectors (mirror suspension resonances at ~500 Hz and harmonics, calibration lines, control dither lines, etc) and some (60 Hz and harmonics) are unwanted. We’ll return to these, later.

You can’t see the signal in this plot, since it is relatively weak and less than a second long, while this plot averages over 32 seconds of data. So this plot is entirely dominated by instrumental noise.

The smooth model is hard-coded and tuned by eye; it won’t be right for arbitrary times. We will only use it below for things that don’t require much accuracy.

Binary Neutron Star (BNS) detection range

A standard metric that LIGO uses to evaluate the sensitivity of our detectors, based on the detector noise ASD, is the BNS range.

This is defined as the distance to which a LIGO detector can register a BNS signal with a single detector signal-to-noise ratio (SNR) of 8, averaged over source direction and orientation. Here, SNR 8 is used as a nominal detection threshold: we need a ratio of at least this for the signal to be classed as a real detection.

We take each neutron star in the BNS system to have a mass of 1.4 times the mass of the sun, and negligible spin (i.e. they are not rotating very fast).

GWs (gravitational waves) from BNS mergers are like “standard sirens”; we know their amplitude (how strong they are) at the source (i.e. the point when and where they’re first emitted) from theoretical calculations. The amplitude falls off like 1/r (1 divided by the distance from the source to us), so their amplitude at the detectors on Earth tells us how far away they are. This is great, because it is hard, in general, to know the distance to astronomical sources.

The amplitude at the source is a pretty complex calculation that we won’t go into the details of here. This next bit is going to do some fancy physics to try and calculate the furthest a BNS can be and still be detectable by the LIGO detectors. The distances it gives us are in megaparsecs. These are strange units of length used by astronomers. 1 parsec is about 3.26 light years, so 1 megaparsec (1 million parsecs) is 3.26 million light years. That’s quite far away!

BNS_range = 1
if BNS_range:
    #-- compute the binary neutron star (BNS) detectability range

    #-- choose a detector noise power spectrum:
    f = freqs.copy()
    # get frequency step size
    df = f[2]-f[1]

    #-- constants
    # speed of light:
    clight = 2.99792458e8                # m/s
    # Newton's gravitational constant
    G = 6.67259e-11                      # m^3/kg/s^2 
    # one parsec, popular unit of astronomical distance (around 3.26 light years)
    parsec = 3.08568025e16               # m
    # solar mass
    MSol = 1.989e30                      # kg
    # solar mass in seconds (isn't relativity fun?):
    tSol = MSol*G/np.power(clight,3)     # s
    # Single-detector SNR for detection above noise background: 
    SNRdet = 8.
    # conversion from maximum range (horizon) to average range:
    Favg = 2.2648
    # mass of a typical neutron star, in solar masses:
    mNS = 1.4

    # Masses in solar masses
    m1 = m2 = mNS    
    mtot = m1+m2  # the total mass
    eta = (m1*m2)/mtot**2  # the symmetric mass ratio
    mchirp = mtot*eta**(3./5.)  # the chirp mass (FINDCHIRP, following Eqn 3.1b)

    # distance to a fiducial BNS source:
    dist = 1.0                           # in Mpc
    Dist =  dist * 1.0e6 * parsec /clight # from Mpc to seconds

    # We integrate the signal up to the frequency of the "Innermost stable circular orbit (ISCO)" 
    R_isco = 6.      # Orbital separation at ISCO, in geometric units. 6M for PN ISCO; 2.8M for EOB 
    # frequency at ISCO (end the chirp here; the merger and ringdown follow) 
    f_isco = 1./(np.power(R_isco,1.5)*np.pi*tSol*mtot)
    # minimum frequency (below which, detector noise is too high to register any signal):
    f_min = 20. # Hz
    # select the range of frequencies between f_min and fisco
    fr = np.nonzero(np.logical_and(f > f_min , f < f_isco))
    # get the frequency and spectrum in that range:
    ffr = f[fr]

    # In stationary phase approx, this is htilde(f):  
    # See FINDCHIRP Eqns 3.4, or 8.4-8.5 
    htilde = (2.*tSol/Dist)*np.power(mchirp,5./6.)*np.sqrt(5./96./np.pi)*(np.pi*tSol)
    htilde *= np.power(np.pi*tSol*ffr,-7./6.)
    htilda2 = htilde**2

    # loop over the detectors
    dets = ['H1', 'L1']
    for det in dets:
        if det is 'L1': sspec = Pxx_L1.copy()
        else:           sspec = Pxx_H1.copy()
        sspecfr = sspec[fr]
        # compute "inspiral horizon distance" for optimally oriented binary; FINDCHIRP Eqn D2:
        D_BNS = np.sqrt(4.*np.sum(htilda2/sspecfr)*df)/SNRdet
        # and the "inspiral range", averaged over source direction and orientation:
        R_BNS = D_BNS/Favg
        print(det+' BNS inspiral horizon = {0:.1f} Mpc, BNS inspiral range   = {1:.1f} Mpc'.format(D_BNS,R_BNS))

H1 BNS inspiral horizon = 169.4 Mpc, BNS inspiral range   = 74.8 Mpc
L1 BNS inspiral horizon = 147.1 Mpc, BNS inspiral range   = 64.9 Mpc

BBH range is » BNS range!

NOTE that, since mass is the source of gravity and thus also of gravitational waves, systems with higher masses (such as the binary black hole merger GW150914) are much “louder” and can be detected to much higher distances than the BNS range. We’ll compute the BBH range, using a template with specific masses, below.

Whitening

From the ASD above, we can see that the data are very strongly “colored” - noise fluctuations are much larger at low and high frequencies and near spectral lines, reaching a roughly flat (“white”) minimum in the band around 80 to 300 Hz.

We can “whiten” the data (dividing it by the noise amplitude spectrum, in the fourier domain), suppressing the extra noise at low frequencies and at the spectral lines, to better see the weak signals in the most sensitive band.

Whitening is always one of the first steps in astrophysical data analysis (searches, parameter estimation). Whitening requires no prior knowledge of spectral lines, etc; only the data are needed.

To get rid of remaining high frequency noise, we will also bandpass the data (this means we’ll remove the signal at all frequencies above some value).

The resulting time series is no longer in units of strain; now in units of “sigmas” away from the mean.

We will plot the whitened strain data, along with the signal template, after the matched filtering section, below.

# function to whiten data
def whiten(strain, interp_psd, dt):
    Nt = len(strain)
    freqs = np.fft.rfftfreq(Nt, dt)
    freqs1 = np.linspace(0,2048.,Nt/2+1)

    # whitening: transform to freq domain, divide by asd, then transform back, 
    # taking care to get normalization right.
    hf = np.fft.rfft(strain)
    norm = 1./np.sqrt(1./(dt*2))
    white_hf = hf / np.sqrt(interp_psd(freqs)) * norm
    white_ht = np.fft.irfft(white_hf, n=Nt)
    return white_ht

whiten_data = 1
if whiten_data:
    # now whiten the data from H1 and L1, and the template (use H1 PSD):
    strain_H1_whiten = whiten(strain_H1,psd_H1,dt)
    strain_L1_whiten = whiten(strain_L1,psd_L1,dt)
    
    # We need to suppress the high frequency noise (no signal!) with some bandpassing:
    bb, ab = butter(4, [fband[0]*2./fs, fband[1]*2./fs], btype='band')
    normalization = np.sqrt((fband[1]-fband[0])/(fs/2))
    strain_H1_whitenbp = filtfilt(bb, ab, strain_H1_whiten) / normalization
    strain_L1_whitenbp = filtfilt(bb, ab, strain_L1_whiten) / normalization

/Users/aliceharpole/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.
  """

Spectrograms

Now let’s plot a short time-frequency spectrogram around our event:

if make_plots:
    # index into the strain time series for this time interval:
    indxt = np.where((time >= tevent-deltat) & (time < tevent+deltat))

    # pick a shorter FTT time interval, like 1/8 of a second:
    NFFT = int(fs/8)
    # and with a lot of overlap, to resolve short-time features:
    NOVL = int(NFFT*15./16)
    # and choose a window that minimizes "spectral leakage" 
    # (https://en.wikipedia.org/wiki/Spectral_leakage)
    window = np.blackman(NFFT)

    # the right colormap is all-important! See:
    # http://matplotlib.org/examples/color/colormaps_reference.html
    # viridis seems to be the best for our purposes, but it's new; if you don't have it, you can settle for ocean.
    #spec_cmap='viridis'
    spec_cmap='ocean'

    # Plot the H1 spectrogram:
    plt.figure(figsize=(10,6))
    spec_H1, freqs, bins, im = plt.specgram(strain_H1[indxt], NFFT=NFFT, Fs=fs, window=window, 
                                            noverlap=NOVL, cmap=spec_cmap, xextent=[-deltat,deltat])
    plt.xlabel('time (s) since '+str(tevent))
    plt.ylabel('Frequency (Hz)')
    plt.colorbar()
    plt.axis([-deltat, deltat, 0, 2000])
    plt.title('aLIGO H1 strain data near '+eventname)
    plt.savefig(eventname+'_H1_spectrogram.'+plottype)

    # Plot the L1 spectrogram:
    plt.figure(figsize=(10,6))
    spec_H1, freqs, bins, im = plt.specgram(strain_L1[indxt], NFFT=NFFT, Fs=fs, window=window, 
                                            noverlap=NOVL, cmap=spec_cmap, xextent=[-deltat,deltat])
    plt.xlabel('time (s) since '+str(tevent))
    plt.ylabel('Frequency (Hz)')
    plt.colorbar()
    plt.axis([-deltat, deltat, 0, 2000])
    plt.title('aLIGO L1 strain data near '+eventname)
    plt.savefig(eventname+'_L1_spectrogram.'+plottype)

In the above spectrograms, you may see lots of excess power below ~20 Hz, as well as strong spectral lines at 500, 1000, 1500 Hz (also evident in the ASDs above). The lines at multiples of 500 Hz are the harmonics of the “violin modes” of the fibers holding up the mirrors of the Advanced LIGO interferometers.

Now let’s zoom in on where we think the signal is, using the whitened data, in the hope of seeing a chirp:

if make_plots:
    #  plot the whitened data, zooming in on the signal region:

    # pick a shorter FTT time interval, like 1/16 of a second:
    NFFT = int(fs/16.0)
    # and with a lot of overlap, to resolve short-time features:
    NOVL = int(NFFT*15/16.0)
    # choose a window that minimizes "spectral leakage" 
    # (https://en.wikipedia.org/wiki/Spectral_leakage)
    window = np.blackman(NFFT)

    # Plot the H1 whitened spectrogram around the signal
    plt.figure(figsize=(10,6))
    spec_H1, freqs, bins, im = plt.specgram(strain_H1_whiten[indxt], NFFT=NFFT, Fs=fs, window=window, 
                                            noverlap=NOVL, cmap=spec_cmap, xextent=[-deltat,deltat])
    plt.xlabel('time (s) since '+str(tevent))
    plt.ylabel('Frequency (Hz)')
    plt.colorbar()
    plt.axis([-0.5, 0.5, 0, 500])
    plt.title('aLIGO H1 strain data near '+eventname)
    plt.savefig(eventname+'_H1_spectrogram_whitened.'+plottype)

    # Plot the L1 whitened spectrogram around the signal
    plt.figure(figsize=(10,6))
    spec_H1, freqs, bins, im = plt.specgram(strain_L1_whiten[indxt], NFFT=NFFT, Fs=fs, window=window, 
                                            noverlap=NOVL, cmap=spec_cmap, xextent=[-deltat,deltat])
    plt.xlabel('time (s) since '+str(tevent))
    plt.ylabel('Frequency (Hz)')
    plt.colorbar()
    plt.axis([-0.5, 0.5, 0, 500])
    plt.title('aLIGO L1 strain data near '+eventname)
    plt.savefig(eventname+'_L1_spectrogram_whitened.'+plottype)

Loud (high SNR) signals may be visible in these spectrograms. Compact object mergers show a characteristic “chirp” as the signal rises in frequency. If you can’t see anything, try event GW150914, by changing the eventname variable in the first cell above.

Waveform Template

To do the next bit of analysis, we’re going to compare the data against a theoretical prediction of what we think it should look like. By doing some very complex simulations of black holes colliding, we can predict what the gravitational wave signal from such an event would look like. By carrying out a number of these simulations for a range of different black hole masses, we can then produce a library of waveform templates (where the waveform is the squiggly line indicating a gravitational wave signal that we’re looking for). By comparing the real signal to these templates and seeing which matches the best, we can then determine the properties of the system that produced the gravitational wave signal.

The results of a full LIGO-Virgo analysis of this BBH event include a set of parameters that are consistent with a range of parameterized waveform templates. Here we pick one for use in matched filtering (another type of sophisticated signal processing to remove noise).

# read in the template (plus and cross) and parameters for the theoretical waveform
try:
    f_template = h5py.File(fn_template, "r")
except:
    print("Cannot find template file!")
    print("You can download it from https://losc.ligo.org/s/events/"+eventname+'/'+fn_template)
    print("Quitting.")
    quit()

# extract metadata from the template file:
template_p, template_c = f_template["template"][...]
t_m1 = f_template["/meta"].attrs['m1']
t_m2 = f_template["/meta"].attrs['m2']
t_a1 = f_template["/meta"].attrs['a1']
t_a2 = f_template["/meta"].attrs['a2']
t_approx = f_template["/meta"].attrs['approx']
f_template.close()
# the template extends to roughly 16s, zero-padded to the 32s data length. The merger will be roughly 16s in.
template_offset = 16.

# whiten the templates:
template_p_whiten = whiten(template_p,psd_H1,dt)
template_c_whiten = whiten(template_c,psd_H1,dt)
template_p_whitenbp = filtfilt(bb, ab, template_p_whiten) / normalization
template_c_whitenbp = filtfilt(bb, ab, template_c_whiten) / normalization

# Compute, print and plot some properties of the template:

# constants:
clight = 2.99792458e8                # m/s
G = 6.67259e-11                      # m^3/kg/s^2 
MSol = 1.989e30                      # kg

# template parameters: masses in units of MSol:
t_mtot = t_m1+t_m2
# final BH mass is typically 95% of the total initial mass:
t_mfin = t_mtot*0.95
# Final BH radius, in km:
R_fin = 2*G*t_mfin*MSol/clight**2/1000.

# complex template:
template = (template_p + template_c*1.j) 
ttime = time-time[0]-template_offset

# compute the instantaneous frequency of this chirp-like signal:
tphase = np.unwrap(np.angle(template))
fGW = np.gradient(tphase)*fs/(2.*np.pi)
# fix discontinuities at the very end:
# iffix = np.where(np.abs(np.gradient(fGW)) > 100.)[0]
iffix = np.where(np.abs(template) < np.abs(template).max()*0.001)[0]
fGW[iffix] = fGW[iffix[0]-1]
fGW[np.where(fGW < 1.)] = fGW[iffix[0]-1]

# compute v/c:
voverc = (G*t_mtot*MSol*np.pi*fGW/clight**3)**(1./3.)

# index where f_GW is in-band:
f_inband = fband[0]
iband = np.where(fGW > f_inband)[0][0]
# index at the peak of the waveform:
ipeak = np.argmax(np.abs(template))

# number of cycles between inband and peak:
Ncycles = (tphase[ipeak]-tphase[iband])/(2.*np.pi)

print('Properties of waveform template in {0}'.format(fn_template))
print("Waveform family = {0}".format(t_approx))
print("Masses = {0:.2f}, {1:.2f} Msun".format(t_m1,t_m2))
print('Mtot = {0:.2f} Msun, mfinal = {1:.2f} Msun '.format(t_mtot,t_mfin))
print("Spins = {0:.2f}, {1:.2f}".format(t_a1,t_a2))
print('Freq at inband, peak = {0:.2f}, {1:.2f} Hz'.format(fGW[iband],fGW[ipeak]))
print('Time at inband, peak = {0:.2f}, {1:.2f} s'.format(ttime[iband],ttime[ipeak]))
print('Duration (s) inband-peak = {0:.2f} s'.format(ttime[ipeak]-ttime[iband]))
print('N_cycles inband-peak = {0:.0f}'.format(Ncycles))
print('v/c at peak = {0:.2f}'.format(voverc[ipeak]))
print('Radius of final BH = {0:.0f} km'.format(R_fin))

if make_plots:
    plt.figure(figsize=(10,16))
    plt.subplot(4,1,1)
    plt.plot(ttime,template_p)
    plt.xlim([-template_offset,1.])
    plt.grid()
    plt.xlabel('time (s)')
    plt.ylabel('strain')
    plt.title(eventname+' template at D_eff = 1 Mpc')
    
    plt.subplot(4,1,2)
    plt.plot(ttime,template_p)
    plt.xlim([-1.1,0.1])
    plt.grid()
    plt.xlabel('time (s)')
    plt.ylabel('strain')
    #plt.title(eventname+' template at D_eff = 1 Mpc')
    
    plt.subplot(4,1,3)
    plt.plot(ttime,fGW)
    plt.xlim([-1.1,0.1])
    plt.grid()
    plt.xlabel('time (s)')
    plt.ylabel('f_GW')
    #plt.title(eventname+' template f_GW')
    
    plt.subplot(4,1,4)
    plt.plot(ttime,voverc)
    plt.xlim([-1.1,0.1])
    plt.grid()
    plt.xlabel('time (s)')
    plt.ylabel('v/c')
    #plt.title(eventname+' template v/c')
    plt.savefig(eventname+'_template.'+plottype)

/Users/aliceharpole/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.
  """


Properties of waveform template in GW150914_4_template.hdf5
Waveform family = b'lalsim.SEOBNRv2'
Masses = 41.74, 29.24 Msun
Mtot = 70.98 Msun, mfinal = 67.43 Msun 
Spins = 0.35, -0.77
Freq at inband, peak = 43.05, 169.84 Hz
Time at inband, peak = -0.08, -0.02 s
Duration (s) inband-peak = 0.06 s
N_cycles inband-peak = 4
v/c at peak = 0.57
Radius of final BH = 199 km

These plots show the waveform template we’re going to use for our match filtering. As a function of time, they show the strain (and again the strain zoomed in to the actual event), the gravitational wave frequency and the relative velocity of the black holes divided by the speed of light.

Matched filtering to find the signal

Matched filtering is the optimal way to find a known signal buried in stationary, Gaussian noise. It is the standard technique used by the gravitational wave community to find GW signals from compact binary mergers in noisy detector data.

For some loud signals, it may be possible to see the signal in the whitened data or spectrograms. On the other hand, low signal-to-noise ratio (SNR) signals or signals which are of long duration in time may not be visible, even in the whitened data. LIGO scientists use matched filtering to find such “hidden” signals. A matched filter works by compressing the entire signal into one time bin (by convention, the “end time” of the waveform).

LIGO uses a rather elaborate software suite to match the data against a family of such signal waveforms (“templates”), to find the best match. This procedure helps to “optimally” separate signals from instrumental noise, and to infer the parameters of the source (masses, spins, sky location, orbit orientation, etc) from the best match templates.

A blind search requires us to search over many compact binary merger templates (e.g. 250,000) with different masses and spins, as well as over all times in all detectors, and then requiring triggers coincident in time and template between detectors. It’s an extremely complex and computationally-intensive “search pipeline”.

Here, we simplify things, using only one template (the one identified in the full search as being a good match to the data).

Assuming that the data around this event is fairly Gaussian and stationary, we’ll use this simple method to identify the signal (matching the template) in our 32 second stretch of data. The peak in the SNR vs time is a “single-detector event trigger”.

# -- To calculate the PSD of the data, choose an overlap and a window (common to all detectors)
#   that minimizes "spectral leakage" https://en.wikipedia.org/wiki/Spectral_leakage
NFFT = 4*fs
psd_window = np.blackman(NFFT)
# and a 50% overlap:
NOVL = NFFT/2

# define the complex template, common to both detectors:
template = (template_p + template_c*1.j) 
# We will record the time where the data match the END of the template.
etime = time+template_offset
# the length and sampling rate of the template MUST match that of the data.
datafreq = np.fft.fftfreq(template.size)*fs
df = np.abs(datafreq[1] - datafreq[0])

# to remove effects at the beginning and end of the data stretch, window the data
# https://en.wikipedia.org/wiki/Window_function#Tukey_window
try:   dwindow = signal.tukey(template.size, alpha=1./8)  # Tukey window preferred, but requires recent scipy version 
except: dwindow = signal.blackman(template.size)          # Blackman window OK if Tukey is not available

# prepare the template fft.
template_fft = np.fft.fft(template*dwindow) / fs

# loop over the detectors
dets = ['H1', 'L1']
for det in dets:

    if det is 'L1': data = strain_L1.copy()
    else:           data = strain_H1.copy()

    # -- Calculate the PSD of the data.  Also use an overlap, and window:
    data_psd, freqs = mlab.psd(data, Fs = fs, NFFT = NFFT, window=psd_window, noverlap=NOVL)

    # Take the Fourier Transform (FFT) of the data and the template (with dwindow)
    data_fft = np.fft.fft(data*dwindow) / fs

    # -- Interpolate to get the PSD values at the needed frequencies
    power_vec = np.interp(np.abs(datafreq), freqs, data_psd)

    # -- Calculate the matched filter output in the time domain:
    # Multiply the Fourier Space template and data, and divide by the noise power in each frequency bin.
    # Taking the Inverse Fourier Transform (IFFT) of the filter output puts it back in the time domain,
    # so the result will be plotted as a function of time off-set between the template and the data:
    optimal = data_fft * template_fft.conjugate() / power_vec
    optimal_time = 2*np.fft.ifft(optimal)*fs

    # -- Normalize the matched filter output:
    # Normalize the matched filter output so that we expect a value of 1 at times of just noise.
    # Then, the peak of the matched filter output will tell us the signal-to-noise ratio (SNR) of the signal.
    sigmasq = 1*(template_fft * template_fft.conjugate() / power_vec).sum() * df
    sigma = np.sqrt(np.abs(sigmasq))
    SNR_complex = optimal_time/sigma

    # shift the SNR vector by the template length so that the peak is at the END of the template
    peaksample = int(data.size / 2)  # location of peak in the template
    SNR_complex = np.roll(SNR_complex,peaksample)
    SNR = abs(SNR_complex)

    # find the time and SNR value at maximum:
    indmax = np.argmax(SNR)
    timemax = time[indmax]
    SNRmax = SNR[indmax]

    # Calculate the "effective distance" (see FINDCHIRP paper for definition)
    # d_eff = (8. / SNRmax)*D_thresh
    d_eff = sigma / SNRmax
    # -- Calculate optimal horizon distnace
    horizon = sigma/8

    # Extract time offset and phase at peak
    phase = np.angle(SNR_complex[indmax])
    offset = (indmax-peaksample)

    # apply time offset, phase, and d_eff to template 
    template_phaseshifted = np.real(template*np.exp(1j*phase))    # phase shift the template
    template_rolled = np.roll(template_phaseshifted,offset) / d_eff  # Apply time offset and scale amplitude
    
    # Whiten and band-pass the template for plotting
    template_whitened = whiten(template_rolled,interp1d(freqs, data_psd),dt)  # whiten the template
    template_match = filtfilt(bb, ab, template_whitened) / normalization # Band-pass the template
    
    print('For detector {0}, maximum at {1:.4f} with SNR = {2:.1f}, D_eff = {3:.2f}, horizon = {4:0.1f} Mpc' 
          .format(det,timemax,SNRmax,d_eff,horizon))

    if make_plots:

        # plotting changes for the detectors:
        if det is 'L1': 
            pcolor='g'
            strain_whitenbp = strain_L1_whitenbp
            template_L1 = template_match.copy()
        else:
            pcolor='r'
            strain_whitenbp = strain_H1_whitenbp
            template_H1 = template_match.copy()

        # -- Plot the result
        plt.figure(figsize=(10,8))
        plt.subplot(2,1,1)
        plt.plot(time-timemax, SNR, pcolor,label=det+' SNR(t)')
        #plt.ylim([0,25.])
        plt.grid('on')
        plt.ylabel('SNR')
        plt.xlabel('Time since {0:.4f}'.format(timemax))
        plt.legend(loc='upper left')
        plt.title(det+' matched filter SNR around event')

        # zoom in
        plt.subplot(2,1,2)
        plt.plot(time-timemax, SNR, pcolor,label=det+' SNR(t)')
        plt.grid('on')
        plt.ylabel('SNR')
        plt.xlim([-0.15,0.05])
        #plt.xlim([-0.3,+0.3])
        plt.grid('on')
        plt.xlabel('Time since {0:.4f}'.format(timemax))
        plt.legend(loc='upper left')
        plt.savefig(eventname+"_"+det+"_SNR."+plottype)

        plt.figure(figsize=(10,8))
        plt.subplot(2,1,1)
        plt.plot(time-tevent,strain_whitenbp,pcolor,label=det+' whitened h(t)')
        plt.plot(time-tevent,template_match,'k',label='Template(t)')
        plt.ylim([-10,10])
        plt.xlim([-0.15,0.05])
        plt.grid('on')
        plt.xlabel('Time since {0:.4f}'.format(timemax))
        plt.ylabel('whitened strain (units of noise stdev)')
        plt.legend(loc='upper left')
        plt.title(det+' whitened data around event')

        plt.subplot(2,1,2)
        plt.plot(time-tevent,strain_whitenbp-template_match,pcolor,label=det+' resid')
        plt.ylim([-10,10])
        plt.xlim([-0.15,0.05])
        plt.grid('on')
        plt.xlabel('Time since {0:.4f}'.format(timemax))
        plt.ylabel('whitened strain (units of noise stdev)')
        plt.legend(loc='upper left')
        plt.title(det+' Residual whitened data after subtracting template around event')
        plt.savefig(eventname+"_"+det+"_matchtime."+plottype)
                 
        # -- Display PSD and template
        # must multiply by sqrt(f) to plot template fft on top of ASD:
        plt.figure(figsize=(10,6))
        template_f = np.absolute(template_fft)*np.sqrt(np.abs(datafreq)) / d_eff
        plt.loglog(datafreq, template_f, 'k', label='template(f)*sqrt(f)')
        plt.loglog(freqs, np.sqrt(data_psd),pcolor, label=det+' ASD')
        plt.xlim(20, fs/2)
        plt.ylim(1e-24, 1e-20)
        plt.grid()
        plt.xlabel('frequency (Hz)')
        plt.ylabel('strain noise ASD (strain/rtHz), template h(f)*rt(f)')
        plt.legend(loc='upper left')
        plt.title(det+' ASD and template around event')
        plt.savefig(eventname+"_"+det+"_matchfreq."+plottype)

/Users/aliceharpole/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.
  """


For detector H1, maximum at 1126259462.4395 with SNR = 18.6, D_eff = 814.44, horizon = 1889.6 Mpc
For detector L1, maximum at 1126259462.4324 with SNR = 13.2, D_eff = 999.74, horizon = 1650.6 Mpc

Notes on these results

• We make use of only one template, with a simple ASD estimate. The full analysis produces a Bayesian posterior result using many nearby templates. It does a more careful job estimating the ASD, and includes effects of uncertain calibration.
• As a result, our parameters (SNR, masses, spins, D_eff) are somewhat different from what you will see in our papers.
• We compute an “effective distance” D_eff. Is is NOT an estimate of the actual (luminosity) distance, which depends also on the source location and orbit orientation.
• These distances are at non-zero redshift, so cosmological effects must be taken into account (neglected here). Since we estimate the BH masses using the phase evolution of the waveform, which has been redshifted, our masses are themselves “redshifted”. The true source masses must be corrected for this effect; they are smaller by a factor (1+z).

Make sound files

Let’s turn our data into sound! Here, we’re going to make wav (sound) files from the filtered, downsampled data, +-2s around the event.

# make wav (sound) files from the whitened data, +-2s around the event.

from scipy.io import wavfile

# function to keep the data within integer limits, and write to wavfile:
def write_wavfile(filename,fs,data):
    d = np.int16(data/np.max(np.abs(data)) * 32767 * 0.9)
    wavfile.write(filename,int(fs), d)

deltat_sound = 2.                     # seconds around the event

# index into the strain time series for this time interval:
indxd = np.where((time >= tevent-deltat_sound) & (time < tevent+deltat_sound))

# write the files:
write_wavfile(eventname+"_H1_whitenbp.wav",int(fs), strain_H1_whitenbp[indxd])
write_wavfile(eventname+"_L1_whitenbp.wav",int(fs), strain_L1_whitenbp[indxd])

# re-whiten the template using the smoothed PSD; it sounds better!
template_p_smooth = whiten(template_p,psd_smooth,dt)

# and the template, zooming in on [-3,+1] seconds around the merger:
indxt = np.where((time >= (time[0]+template_offset-deltat_sound)) & (time < (time[0]+template_offset+deltat_sound)))
write_wavfile(eventname+"_template_whiten.wav",int(fs), template_p_smooth[indxt])

/Users/aliceharpole/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:5: DeprecationWarning: object of type <class 'float'> cannot be safely interpreted as an integer.
  """

Listen to the whitened template and data

With good headphones, you may be able to hear a faint thump in the middle; that’s our signal!

from IPython.display import Audio

fna = eventname+"_template_whiten.wav"
print(fna)
Audio(fna)

GW150914_template_whiten.wav






            <audio  controls="controls" >
                <source 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type="audio/x-wav" />
                Your browser does not support the audio element.
            </audio>

fna = eventname+"_H1_whitenbp.wav"
print(fna)
Audio(fna)

GW150914_H1_whitenbp.wav






            <audio  controls="controls" >
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" type="audio/x-wav" />
                Your browser does not support the audio element.
            </audio>

Frequency shift the audio files

We can enhance this by increasing the frequency; this is the “audio” equivalent of the enhanced visuals that NASA employs on telescope images with “false color”.

The code below will shift the data up by 400 Hz. The resulting sound file will be noticibly more high-pitched, and the signal will be easier to hear.

# function that shifts frequency of a band-passed signal
def reqshift(data,fshift=100,sample_rate=4096):
    """Frequency shift the signal by constant
    """
    x = np.fft.rfft(data)
    T = len(data)/float(sample_rate)
    df = 1.0/T
    nbins = int(fshift/df)
    # print T,df,nbins,x.real.shape
    y = np.roll(x.real,nbins) + 1j*np.roll(x.imag,nbins)
    y[0:nbins]=0.
    z = np.fft.irfft(y)
    return z

# parameters for frequency shift
fs = 4096
fshift = 400.
speedup = 1.
fss = int(float(fs)*float(speedup))

# shift frequency of the data
strain_H1_shifted = reqshift(strain_H1_whitenbp,fshift=fshift,sample_rate=fs)
strain_L1_shifted = reqshift(strain_L1_whitenbp,fshift=fshift,sample_rate=fs)

# write the files:
write_wavfile(eventname+"_H1_shifted.wav",int(fs), strain_H1_shifted[indxd])
write_wavfile(eventname+"_L1_shifted.wav",int(fs), strain_L1_shifted[indxd])

# and the template:
template_p_shifted = reqshift(template_p_smooth,fshift=fshift,sample_rate=fs)
write_wavfile(eventname+"_template_shifted.wav",int(fs), template_p_shifted[indxt])

Listen to the frequency-shifted template and data

fna = eventname+"_template_shifted.wav"
print(fna)
Audio(fna)

GW150914_template_shifted.wav






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                <source 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type="audio/x-wav" />
                Your browser does not support the audio element.
            </audio>

fna = eventname+"_H1_shifted.wav"
print(fna)
Audio(fna)

GW150914_H1_shifted.wav






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" 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Construct a csv file containing the whitened data and template

# time vector around event
times = time-tevent
# zoom in on [-0.2,0.05] seconds around event
irange = np.nonzero((times >= -0.2) & (times < 0.05))
# construct a data structure for a csv file:
dat = [times[irange], strain_H1_whitenbp[irange],strain_L1_whitenbp[irange],
      template_H1[irange],template_L1[irange] ]
datcsv = np.array(dat).transpose()
# make a csv filename, header, and format
fncsv = eventname+'_data.csv'
headcsv = eventname+' time-'+str(tevent)+ \
    ' (s),H1_data_whitened,L1_data_whitened,H1_template_whitened,L1_template_whitened'
fmtcsv = ",".join(["%10.6f"] * 5)
np.savetxt(fncsv, datcsv, fmt=fmtcsv, header=headcsv)

print("Wrote whitened data to file {0}".format(fncsv))
print("You can download this file by clicking 'jupyter' in the top left corner, or using the 'data' menu in Azure.")

Wrote whitened data to file GW150914_data.csv
You can download this file by clicking 'jupyter' in the top left corner, or using the 'data' menu in Azure.

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