Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

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doi:10.1016/j.softx.2021.100658

Cited by 366 publications

(209 citation statements)

References 116 publications

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“…To assess the robustness of our method, we selected data from the second observing run of the Advanced GW detectors, without relying on any neutrino information. In particular, we chose a stretch of real data even containing glitches, taken during August 2017, when Virgo joined the run [45]. The period includes about 15 days of coincidence time among the three detectors and we used this dataset to generate about 2 years of time-shift data to train and test the neural network as noise class.…”

Section: Datamentioning

confidence: 99%

Deep learning for core-collapse supernova detection

et al. 2021

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The detection of gravitational waves from core-collapse supernova (CCSN) explosions is a challenging task, yet to be achieved, in which it is key the connection between multiple messengers, including neutrinos and electromagnetic signals. In this work, we present a method for detecting these kind of signals based on machine learning techniques. We tested its robustness by injecting signals in the real noise data taken by the Advanced LIGO-Virgo network during the second observing run, O2. We trained a newly developed Mini-Inception Resnet neural network using time-frequency images corresponding to injections of simulated phenomenological signals, which mimic the waveforms obtained in 3D numerical simulations of CCSNe. With this algorithm we were able to identify signals from both our phenomenological template bank and from actual numerical 3D simulations of CCSNe. We computed the detection efficiency versus the source distance, obtaining that, for signal to noise ratio higher than 15, the detection efficiency is 70% at a false alarm rate lower than 5%. We notice also that, in the case of the O2 run, it would have been possible to detect signals emitted at 1 kpc of distance, while lowering down the efficiency to 60%, the event distance reaches values up to 14 kpc.

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Section: Datamentioning

confidence: 99%

Deep learning for core-collapse supernova detection

et al. 2021

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“…Data products associated with the results of analyses in this paper can be found in [53]. The GW strain data for all events are available at the Gravitational Wave Open Science Center [54,55].…”

Section: Introductionmentioning

confidence: 99%

Tests of general relativity with binary black holes from the second LIGO-Virgo gravitational-wave transient catalog

Abbott¹,

Abbott²,

Abraham³

et al. 2021

Phys. Rev. D

462

439

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Gravitational waves enable tests of general relativity in the highly dynamical and strong-field regime. Using events detected by LIGO-Virgo up to 1 October 2019, we evaluate the consistency of the data with predictions from the theory. We first establish that residuals from the best-fit waveform are consistent with detector noise, and that the low-and high-frequency parts of the signals are in agreement. We then consider parametrized modifications to the waveform by varying post-Newtonian and phenomenological coefficients, improving past constraints by factors of ∼2; we also find consistency with Kerr black holes when we specifically target signatures of the spin-induced quadrupole moment. Looking for gravitational-wave dispersion, we tighten constraints on Lorentz-violating coefficients by a factor of ∼2.6 and bound the mass of the graviton to m g ≤ 1.76 × 10 −23 eV=c 2 with 90% credibility. We also analyze the properties of the merger remnants by measuring ringdown frequencies and damping times, constraining fractional deviations away from the Kerr frequency to δf 220 ¼ 0.03 þ0.38 −0.35 for the fundamental quadrupolar mode, and δf 221 ¼ 0.04 þ0.27 −0.32 for the first overtone; additionally, we find no evidence for postmerger echoes. Finally, we determine that our data are consistent with tensorial polarizations through a template-independent method. When possible, we assess the validity of general relativity based on collections of events analyzed jointly. We find no evidence for new physics beyond general relativity, for black hole mimickers, or for any unaccounted systematics.

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“…For this study, we use real data that were provided by LIGO detectors, which are freely available on the LIGO-Virgo Gravitational Wave Open Science Center (GWOSC), (accessed on 1 April 2019) [ 42 ]. Because of reasons that are presented at the end of Section 1 , we decided to use data of sixth science run (S6), recorded from 7 July 2009 to 20 October 2010.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

Morales

Antelis

Moreno

et al. 2021

Sensors

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In this work, we apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from real LIGO detectors. Here, we adopted a resampling white-box approach to advance towards a statistical understanding of uncertainties intrinsic to CNNs in GW data analysis. We used Morlet wavelets to convert strain time series to time-frequency images. Moreover, we only worked with data of non-Gaussian noise and hardware injections, removing freedom to set signal-to-noise ratio (SNR) values in GW templates by hand, in order to reproduce more realistic experimental conditions. After hyperparameter adjustments, we found that resampling through repeated k-fold cross-validation smooths the stochasticity of mini-batch stochastic gradient descent present in accuracy perturbations by a factor of 3.6. CNNs are quite precise to detect noise, 0.952 for H1 data and 0.932 for L1 data; but, not sensitive enough to recall GW signals, 0.858 for H1 data and 0.768 for L1 data—although recall values are dependent on expected SNR. Our predictions are transparently understood by exploring tthe distribution of probabilistic scores outputted by the softmax layer, and they are strengthened by a receiving operating characteristic analysis and a paired-sample t-test to compare with a random classifier.

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Open data from the first and second observing runs of Advanced LIGO and Advanced Virgo

Cited by 366 publications

References 116 publications

Deep learning for core-collapse supernova detection

Deep learning for core-collapse supernova detection

Tests of general relativity with binary black holes from the second LIGO-Virgo gravitational-wave transient catalog

Deep Learning for Gravitational-Wave Data Analysis: A Resampling White-Box Approach

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