Details
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 023021 |
| Fachzeitschrift | Physical Review D |
| Jahrgang | 107 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 27 Jan. 2023 |
Abstract
We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.
ASJC Scopus Sachgebiete
- Physik und Astronomie (insg.)
- Kern- und Hochenergiephysik
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in: Physical Review D, Jahrgang 107, Nr. 2, 023021, 27.01.2023.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - First machine learning gravitational-wave search mock data challenge
AU - Schäfer, Marlin B.
AU - Zelenka, Ondřej
AU - Nitz, Alexander H.
AU - Wang, He
AU - Wu, Shichao
AU - Guo, Zong Kuan
AU - Cao, Zhoujian
AU - Ren, Zhixiang
AU - Nousi, Paraskevi
AU - Stergioulas, Nikolaos
AU - Iosif, Panagiotis
AU - Koloniari, Alexandra E.
AU - Tefas, Anastasios
AU - Passalis, Nikolaos
AU - Salemi, Francesco
AU - Vedovato, Gabriele
AU - Klimenko, Sergey
AU - Mishra, Tanmaya
AU - Brügmann, Bernd
AU - Cuoco, Elena
AU - Huerta, E. A.
AU - Messenger, Chris
AU - Ohme, Frank
N1 - Funding Information: We want to thank Narenraju Nagarajan and Pascal Müller for their valuable scientific input and contributions to the code of this challenge. We acknowledge the Max Planck Gesellschaft and the Atlas cluster computing team at Albert-Einstein Institut (AEI) Hannover for support. O. Z. thanks the Carl Zeiss Foundation for the financial support within the scope of the program line “Breakthroughs.” The MFCNN team members would like to acknowledge that the submission was supported by the Peng Cheng Laboratory Cloud Brain (No. PCL2021A13). Z. C. was supported by the National Key Research and Development Program of China Grant No. 2021YFC2203001, the NSFC (No. 11920101003 and No. 12021003), and CAS Project for Young Scientists in Basic Research YSBR-006. The Virgo-AUTh team members would like to acknowledge the support provided by the IT Center of the Aristotle University of Thessaloniki (AUTh) throughout the progress of this work, as results presented in this work have been produced, in part, using the AUTh High Performance Computing Infrastructure and Resources, and thank the COST network CA17137 “G2Net” for support. P. I. acknowledges support by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 759253. The cWB team gratefully acknowledges the computational resources provided by LIGO-Virgo. This material is based upon work supported by NSF’s LIGO Laboratory, which is a major facility fully funded by the National Science Foundation. This research has made use of data, software and/or web tools obtained from the Gravitational Wave Open Science Center, a service of LIGO Laboratory, the LIGO Scientific Collaboration and the Virgo Collaboration. The work by S. K. was supported by NSF Grants No. PHY 1806165 and No. PHY 2110060. This publication is based upon work from COST Action CA17137, supported by COST (European Cooperation in Science and Technology). E. A. H. is supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357, and the U.S. National Science Foundation Grants No. OAC-2209892 and No. OAC-1931561. C. M. is supported by the Science and Technology Research Council (Grant No. ST/V005634/1) and the European Cooperation in Science and Technology (COST) action CA17137. F. O. was supported by the Max Planck Society’s Independent Research Group Programme. This research has made use of data or software obtained from the Gravitational Wave Open Science Center (gw-openscience.org), a service of LIGO Laboratory, the LIGO Scientific Collaboration, the Virgo Collaboration, and KAGRA. LIGO Laboratory and Advanced LIGO are funded by the United States National Science Foundation (NSF) as well as the Science and Technology Facilities Council (STFC) of the United Kingdom, the Max-Planck-Society (MPS), and the State of Niedersachsen/Germany for support of the construction of Advanced LIGO and construction and operation of the GEO600 detector. Additional support for Advanced LIGO was provided by the Australian Research Council. Virgo is funded, through the European Gravitational Observatory (EGO), by the French Centre National de Recherche Scientifique (CNRS), the Italian Istituto Nazionale di Fisica Nucleare (INFN) and the Dutch Nikhef, with contributions by institutions from Belgium, Germany, Greece, Hungary, Ireland, Japan, Monaco, Poland, Portugal, Spain. The construction and operation of KAGRA are funded by Ministry of Education, Culture, Sports, Science and Technology (MEXT), and Japan Society for the Promotion of Science (JSPS), National Research Foundation (NRF) and Ministry of Science and ICT (MSIT) in Korea, Academia Sinica (AS) and the Ministry of Science and Technology (MoST) in Taiwan.
PY - 2023/1/27
Y1 - 2023/1/27
N2 - We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.
AB - We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge. For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The final of the 4 provided datasets contained real noise from the O3a observing run and signals up to a duration of 20 s with the inclusion of precession effects and higher order modes. We present the average sensitivity distance and run-time for the 6 entered algorithms derived from 1 month of test data unknown to the participants prior to submission. Of these, 4 are machine learning algorithms. We find that the best machine learning based algorithms are able to achieve up to 95% of the sensitive distance of matched-filtering based production analyses for simulated Gaussian noise at a false-alarm rate (FAR) of one per month. In contrast, for real noise, the leading machine learning search achieved 70%. For higher FARs the differences in sensitive distance shrink to the point where select machine learning submissions outperform traditional search algorithms at FARs ≥200 per month on some datasets. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings. To improve the state-of-the-art, machine learning algorithms need to reduce the false-alarm rates at which they are capable of detecting signals and extend their validity to regions of parameter space where modeled searches are computationally expensive to run. Based on our findings we compile a list of research areas that we believe are the most important to elevate machine learning searches to an invaluable tool in gravitational-wave signal detection.
UR - http://www.scopus.com/inward/record.url?scp=85147417756&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2209.11146
DO - 10.48550/arXiv.2209.11146
M3 - Article
AN - SCOPUS:85147417756
VL - 107
JO - Physical Review D
JF - Physical Review D
SN - 2470-0010
IS - 2
M1 - 023021
ER -