Parameter estimates in binary black hole collisions using neural networks

Carrillo, M.; Gracia-Linares, Miguel; González, José Antonio García-Cruces; Guzmán, F. S.

doi:10.1007/s10714-016-2136-0

Cited by 13 publications

(10 citation statements)

References 28 publications

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“…As an alternative to these conventional statistical methods, the deep learning; namely, the machine learning devising deep neural networks (NNs), has been successfully applied to a wide range of physics fields [70][71][72][73][74][75][76]. Several studies have already put the NN in motion in the context of gravitational wave data analysis of binary neutron star mergers [77][78][79][80]. Along these lines, in our previous publications [81,82] we proposed a novel approach to the EoS extraction based on the deep machine learning (see also refs.…”

Section: Jhep03(2021)273mentioning

confidence: 99%

Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation

Fujimoto

Fukushima

Murase

2021

J. High Energ. Phys.

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We discuss deep learning inference for the neutron star equation of state (EoS) using the real observational data of the mass and the radius. We make a quantitative comparison between the conventional polynomial regression and the neural network approach for the EoS parametrization. For our deep learning method to incorporate uncertainties in observation, we augment the training data with noise fluctuations corresponding to observational uncertainties. Deduced EoSs can accommodate a weak first-order phase transition, and we make a histogram for likely first-order regions. We also find that our observational data augmentation has a byproduct to tame the overfitting behavior. To check the performance improved by the data augmentation, we set up a toy model as the simplest inference problem to recover a double-peaked function and monitor the validation loss. We conclude that the data augmentation could be a useful technique to evade the overfitting without tuning the neural network architecture such as inserting the dropout.

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Section: Jhep03(2021)273mentioning

confidence: 99%

Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation

Fujimoto

Fukushima

Murase

2021

J. High Energ. Phys.

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“…In addition, there are no limitations in the size of the templates bank of GW signals, and even more, it is preferable to use large datasets to cover as deep a parameter space as possible. Because of this fact they sparked the interest of several authors, who have built deep-learning algorithms to demonstrate their power on specific examples, including CCSN [15][16][17] among others [18,[32][33][34].…”

Section: A Challenges and Milestones Of Deep Learningmentioning

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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“…One way to address this issue is to use the strain data where the GW is detected as input to post-processing steps to determine more physical information. In this line, recent works have proposed the use of machine learning algorithms to estimate astrophysical source parameters based on the observed strain data [35,36].…”

Section: Discussionmentioning

confidence: 99%

An independent search of gravitational waves in the first observation run of advanced LIGO using cross-correlation

Antelis

Moreno

2019

Gen Relativ Gravit

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This work describes a template-free method to search gravitational waves (GW) using data from the LIGO observatories simultaneously. The basic idea of this method is that a GW signal is present in a short-duration data segment if the maximum correlation-coefficient between the strain signals is higher than a significant threshold and its time difference is lower than the 10 ms of inter-observatory light propagation time. Hence, this method can be used to carry out blind searches of any types of GW irrespective of the waveform and of the source type and sky location. An independent search of injected and real GW signals from compact binary coalescences (CBC) contained in the first observation run (O1) of advanced LIGO was carried out to asses its performance. On the basis of the results, the proposed method was able to detect GW produced by binary systems without making any assumption about them.

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Parameter estimates in binary black hole collisions using neural networks

Cited by 13 publications

References 28 publications

Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation

Extensive studies of the neutron star equation of state from the deep learning inference with the observational data augmentation

Deep learning for core-collapse supernova detection

An independent search of gravitational waves in the first observation run of advanced LIGO using cross-correlation

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