GWSkyNet: A Real-time Classifier for Public Gravitational-wave Candidates

Cabero, M.; Mahabal, A.; McIver, J.

doi:10.3847/2041-8213/abc5b5

Cited by 23 publications

(17 citation statements)

References 34 publications

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“…For science related to compact objects, ML algorithms have for example been developed to classify new pulsar candidates (Bethapudi & Desai 2018;Lin et al 2020;Balakrishnan et al 2021) as well as transient radio events such as fast radio bursts (Agarwal et al 2020). Other approaches have aimed at forecasting and analyzing gravitational-wave signals in real time (Cabero et al 2020;Gerosa et al 2020;Skliris et al 2020;Wei & Huerta 2020), interpreting gravitational-wave events in light of population synthesis (Wong & Gerosa 2019), or reconstructing the equation of state of a neutron star from observed quantities (Morawski & Bejger 2020).…”

Section: Machine-learning Setupmentioning

confidence: 99%

Analyzing the Galactic Pulsar Distribution with Machine Learning

Ronchi

Graber

García-García

et al. 2021

ApJ

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We explore the possibility of inferring the properties of the Galactic population of neutron stars through machine learning. In particular, in this paper we focus on their dynamical characteristics and show that an artificial neural network is able to estimate with high accuracy the parameters that control the current positions of a mock population of pulsars. For this purpose, we implement a simplified population-synthesis framework (where selection biases are neglected at this stage) and concentrate on the natal kick-velocity distribution and the distribution of birth distances from the Galactic plane. By varying these and evolving the pulsar trajectories in time, we generate a series of simulations that are used to train and validate a suitably structured convolutional neural network. We demonstrate that our network is able to recover the parameters governing the distribution of kick velocity and Galactic height with a mean relative error of about 10 −2 . We discuss the limitations of our idealized approach and study a toy problem to introduce selection effects in a phenomenological way by incorporating the observed proper motions of 216 isolated pulsars. Our analysis highlights that by increasing the sample of pulsars with accurate proper-motion measurements by a factor of ∼10, one of the future breakthroughs of the Square Kilometre Array, we might succeed in constraining the birth spatial and kick-velocity distribution of the neutron stars in the Milky Way with high precision through machine learning.

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Section: Machine-learning Setupmentioning

confidence: 99%

Analyzing the Galactic Pulsar Distribution with Machine Learning

Ronchi

Graber

García-García

et al. 2021

ApJ

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“…The noise events in the data set consist of 1267 glitches from the first two observing runs of Advanced LIGO and Virgo, identified in Cabero et al (2020) using catalogs of noise transients (Cabero et al 2019;Zevin et al 2017) and GW candidates from the second Open Gravitational-wave Catalog (Nitz et al 2019). To construct a sufficiently large and balanced data set, we use gravitational waveform models from the LALSuite package (LIGO Scientific Collaboration 2018) to simulate 1000 GW events for each astrophysical source type: BBH, BNS mergers, and NSBH mergers.…”

Section: Data Setmentioning

confidence: 99%

“…To supplement the predictions available on GraceDB, Cabero et al (2020) introduced a "real-versus-noise" binary classifier, known as GWSkyNet, which leverages OPAs to inform potential EM follow-up seconds after the OPA is published. GWSkyNet achieves a test set accuracy of 93.5% and correctly predicts 37 of the 40 O3a events published in the second Gravitational-Wave Transient Catalog (GWTC-2; Abbott et al 2021a), before the publication of this catalog.…”

Section: Introductionmentioning

confidence: 99%

GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts

Abbott,

Buffaz,

Vieira

et al. 2021

Preprint

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Compact object mergers which produce both detectable gravitational waves and electromagnetic emission can provide valuable insights into the neutron star equation of state, the tension in the Hubble constant, and the origin of the r -process elements. However, electromagnetic follow-up of gravitational wave sources is complicated by false positive detections, and the transient nature of the associated electromagnetic emission. GWSkyNet-Multi is a machine learning model that attempts facilitate EM follow-up by providing real-time predictions of the source of a gravitational wave detection. The model uses information from Open Public Alerts (OPAs) released by LIGO-Virgo within minutes of a gravitational wave detection. GWSkyNet was introduced in Cabero et al. ( 2020) as a binary classifier and uses the OPA skymaps to classify sources as either astrophysical or as glitches. In this paper, we introduce GWSkyNet-Multi, an extension of GWSkyNet which further distinguishes sources as binary black hole mergers, mergers involving a neutron star, or non-astrophysical glitches. GWSkyNet-Multi is a sequence of three one-versus-all classifiers trained using a class-balanced and physically-motivated source mass distribution. Training on this data set, we obtain test set accuracies of 93.7% for BBHversus-all, 94.4% for NS-versus-all, and 95.1% for glitch-versus-all. We obtain an overall accuracy of 93.4% using a hierarchical classification scheme. Furthermore, we correctly identify 36 of the 40 gravitational wave detections from the first half of LIGO-Virgo's third observing run (O3a) and present predictions for O3b sources. As gravitational wave detections increase in number and frequency, GWSkyNet-Multi will be a powerful tool for prioritizing successful electromagnetic follow-up.

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“…Techniques from machine learning (ML), which originally belong to the fields of information and computer sciences, have found applications in many-body systems during the last a few years. Such examples contain studies associated with condensed matter physics including the critical phenomena of certain models , the high energy particle physics and astrophysics covering the analysis of the data relevant to jets and gravitational wave [39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57], and the first principles material calculations like finding the density functionals . In some cases, the performance of these new approaches for exploring many-body physical systems is comparable with that of the traditional methods.…”

Section: Introductionmentioning

confidence: 99%

A universal neural network for learning phases

et al. 2021

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A universal supervised neural network (NN) relevant to compute the associated critical points of real experiments studying phase transitions is constructed. The validity of the built NN is examined by applying it to calculate the critical points of several three-dimensional (3D) and two-dimensional (2D) models, including the 3D classical O(3) model, the 3D 5state ferromagnetic Potts model, a 3D dimerized quantum antiferromagnetic Heisenberg model, and the 2D XY model. Particularly, although the considered NN is only trained once on a one-dimensional (1D) lattice with 120 sites, it has successfully determined the related critical points of the studied systems. Moreover, real configurations of states are not used in the testing stage. Instead, the employed configurations for the NN prediction are constructed on a 1D lattice of 120 sites and are based on the bulk quantities or the spin states of the considered models. As a result, our calculations are ultimately efficient in computation, and the application of the built NN in studying the phase transitions of physical systems is extremely broad. Considering the fact that the investigated systems vary dramatically from each other, it is amazing that the combination of these two strategies in the training and the testing stages leads to a highly universal supervised neural network for learning phases of 3D and 2D models. Based on the outcomes presented in this study, it is favorably probable that much simpler but elegant machine learning techniques can be constructed for fields of many-body systems other than the critical phenomena.

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GWSkyNet: A Real-time Classifier for Public Gravitational-wave Candidates

Cited by 23 publications

References 34 publications

Analyzing the Galactic Pulsar Distribution with Machine Learning

Analyzing the Galactic Pulsar Distribution with Machine Learning

GWSkyNet-Multi: A Machine Learning Multi-Class Classifier for LIGO-Virgo Public Alerts

A universal neural network for learning phases

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