Abstract
We present here the latest development of a machine-learning pipeline for premerger alerts from gravitational waves coming from binary neutron stars (BNSs). This work starts from the convolutional neural networks introduced in [Baltus et al., Phys. Rev. D 103, 102003 (2021)PRVDAQ2470-001010.1103/PhysRevD.103.102003] that searched for the early inspirals in simulated Gaussian noise colored with the design-sensitivity power-spectral density of LIGO. Our new network is able to search for any BNS with a chirp mass between 1 and 3 M⊙, it can take into account all the detectors available, and it can see the events even earlier than the previous one. We study the performance of our method in three different scenarios: colored Gaussian noise based on the O3 sensitivity, real O3 noise, colored Gaussian noise based on the predicted O4 sensitivity. We show that our network performs almost as well in non-Gaussian noise as in Gaussian noise: our method is robust with respect to glitches and artifacts present in real noise. Although it would not have been able to trigger on the BNSs detected during O3 because their signal-to-noise ratio was too weak, we expect our network to find around 3 BNSs during O4 with a time before the merger between 3 and 88 s in advance.
| Original language | English |
|---|---|
| Article number | 042002 |
| Pages (from-to) | 1-10 |
| Journal | Physical Review D |
| Volume | 106 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - 2 Aug 2022 |
Bibliographical note
Funding Information:The authors thank Thomas Dent and Srashti Goyal for their useful comments, as well as Maxime Fays, Vincent Boudart, Sarah Caudill and Chris Van Den Broeck for useful discussions. G. B. is supported by a FRIA grant from the Fonds de la Recherche Scientifique-FNRS, Belgium. J. R. C. acknowledges the support of the Fonds de la Recherche Scientifique-FNRS, Belgium, under Grant No. 4.4501.19. M. L. H. N., and J. J. are supported by the research program of the Netherlands Organisation for Scientific Research (NWO). The authors are grateful for computational resources provided by the LIGO Laboratory and supported by the National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation.
Publisher Copyright:
© 2022 American Physical Society.
Funding
The authors thank Thomas Dent and Srashti Goyal for their useful comments, as well as Maxime Fays, Vincent Boudart, Sarah Caudill and Chris Van Den Broeck for useful discussions. G. B. is supported by a FRIA grant from the Fonds de la Recherche Scientifique-FNRS, Belgium. J. R. C. acknowledges the support of the Fonds de la Recherche Scientifique-FNRS, Belgium, under Grant No. 4.4501.19. M. L. H. N., and J. J. are supported by the research program of the Netherlands Organisation for Scientific Research (NWO). The authors are grateful for computational resources provided by the LIGO Laboratory and supported by the National Science Foundation Grants No. PHY-0757058 and No. PHY-0823459. This material is based upon work supported by NSF’s LIGO Laboratory which is a major facility fully funded by the National Science Foundation.