47 research outputs found
Training and test dataset for CNN-LSTM model for GW waveform extraction
No full textThis repository contains training and test samples corresponding to the paper, 'Extraction of binary black hole gravitational wave signals from detector data using deep learning', Chatterjee et al., Phys. Rev. D 104, 064046 (2021)
Pre-merger sky localization results using GW-SkyLocator
No full textResults of pre-merger sky localization using GW-SkyLocator, a deep learning model for localizing gravitational waves from binary neutron star mergers up to 60 secs before merger. A description of the method and the method and the tests conducted can be found here: https://arxiv.org/abs/2301.03558. The histograms in the paper can be reproduced using the data provided here
Reconstruction of Binary Black Hole Harmonics in LIGO Using Deep Learning
No full textGravitational-wave signals from coalescing compact binaries in the LIGO and Virgo interferometers are primarily detected by the template-based matched filtering method. While this method is optimal for stationary and Gaussian data scenarios, its sensitivity is often affected by nonstationary noise transients in the detectors. Moreover, most of the current searches do not account for the effects of precession of black hole spins and higher-order waveform harmonics, focusing solely on the leading-order quadrupolar modes. This limitation impacts our search for interesting astrophysical sources, such as intermediate-mass black hole binaries and hierarchical mergers. Here we show, for the first time, that deep learning can be used for accurate waveform reconstruction of precessing binary black hole signals with higher-order modes. This approach can also be adapted into a rapid trigger generation algorithm to enhance online searches. Our model, tested on simulated injections in real LIGO noise from the third observing run (2019–2020) achieved a high degree of overlap with injected signals. This accuracy was consistent across a wide range of black hole masses and spin configurations chosen for this study. When applied to real gravitational-wave events, our model's reconstructions achieved between 85% and 98% overlap with those obtained by Coherent WaveBurst (unmodeled) and LALInference (modeled) analyses. These results suggest that deep learning is a potent tool for analyzing signals from a diverse catalog of compact binaries
Pre-merger sky localization of gravitational waves from binary neutron star mergers using deep learning
No full textThe simultaneous observation of gravitational waves (GW) and prompt
electromagnetic counterparts from the merger of two neutron stars can help
reveal the properties of extreme matter and gravity during and immediately
after the final plunge. Rapid sky localization of these sources is crucial to
facilitate such multi-messenger observations. Since GWs from binary neutron
star (BNS) mergers can spend up to 10-15 mins in the frequency bands of the
detectors at design sensitivity, early warning alerts and pre-merger sky
localization can be achieved for sufficiently bright sources, as demonstrated
in recent studies. In this work, we present pre-merger BNS sky localization
results using CBC-SkyNet, a deep learning model capable of inferring sky
location posterior distributions of GW sources at orders of magnitude faster
speeds than standard Markov Chain Monte Carlo methods. We test our model's
performance on a catalog of simulated injections from Sachdev et al. (2020),
recovered at 0-60 secs before merger, and obtain comparable sky localization
areas to the rapid localization tool BAYESTAR. These results show the
feasibility of our model for rapid pre-merger sky localization and the
possibility of follow-up observations for precursor emissions from BNS mergers.Comment: 7 pages, 5 figure
Navigating Unknowns: Deep Learning Robustness for Gravitational-wave Signal Reconstruction
No full textWe present a rapid and reliable deep-learning-based method for gravitational-wave (GW) signal reconstruction from elusive, generic binary black hole mergers in LIGO data. We demonstrate that our model, AWaRe , effectively recovers GWs with parameters it has not encountered during training. This includes features like higher black hole masses, additional harmonics, eccentricity, and varied waveform systematics, which introduce complex modulations in the waveform’s amplitudes and phases. The accurate reconstructions of these unseen signal characteristics demonstrate AWaRe 's ability to handle complex features in the waveforms. By directly incorporating waveform reconstruction uncertainty estimation into the AWaRe framework, we show that for real GW events, the uncertainties in AWaRe 's reconstructions align closely with those achieved by benchmark algorithms like BayesWave and coherent WaveBurst. The robustness of our model to real GW events and its ability to extrapolate to unseen data open new avenues for investigations in various aspects of GW astrophysics and data analysis, including tests of general relativity and the enhancement of current GW search methodologies
Pre-merger sky localization results using GW-SkyLocator
No full textResults of pre-merger sky localization using GW-SkyLocator, a deep learning model for localizing gravitational waves from binary neutron star mergers up to 60 secs before merger. A description of the method and the method and the tests conducted can be found here: https://arxiv.org/abs/2301.03558. The histograms in the paper can be reproduced using the data provided here
Pre-merger sky localization results using GW-SkyLocator
No full textResults of pre-merger sky localization using GW-SkyLocator, a deep learning model for localizing gravitational waves from binary neutron star mergers up to 60 secs before merger. A description of the method and the method and the tests conducted can be found here: https://arxiv.org/abs/2301.03558. The histograms in the paper can be reproduced using the data provided here
Pre-merger sky localization results using GW-SkyLocator
No full textResults of pre-merger sky localization using GW-SkyLocator, a deep learning model for localizing gravitational waves from binary neutron star mergers up to 60 secs before merger. A description of the method and the method and the tests conducted can be found here: https://arxiv.org/abs/2301.03558. The histograms in the paper can be reproduced using the data provided here
СРЕДСТВА СОЗДАНИЯ КОМИЧЕСКОГО В САТИРИЧЕСКИХ ЖУРНАЛАХ (НА ПРИМЕРЕ ЖУРНАЛА "ЧАЯН" ЗА 2018 ГОД)
No full textВ данной статье автор рассказывает о средствах создания комического в сатирическом татарском журнале Чаян". Автор поднимает проблему неизученности данного журнала, даже если журнал является ведущим сатирическим изданием Татарстана. Автор анализирует разные жанры, часто используемые на страницах издания и приходит к выводу, что даже через юмор можно показать ту или иную проблему общественности.In this article, the author talks about the means of creating a comic in the satirical Tatar magazine Chayan". The author raises the problem of the lack of research of this magazine, even if the magazine is the leading satirical publication of Tatarstan. The author analyzes the different genres often used on the pages of the publication and comes to the conclusion that even through humor, you can show a particular problem to the public.119-12
