Details
| Original language | English |
|---|---|
| Article number | 124071 |
| Number of pages | 10 |
| Journal | Physical Review D |
| Volume | 110 |
| Issue number | 12 |
| Publication status | Published - 31 Dec 2024 |
Abstract
The sensitivity of wide parameter-space searches for continuous gravitational waves (CWs) is limited by their high computational cost. Deep learning is being studied as an alternative method to replace various aspects of a CW search. In previous work [Phys. Rev. D 108, 063021 (2023)PRVDAQ2470-001010.1103/PhysRevD.108.063021], new design principles were presented for deep neural network (DNN) search of CWs and such DNNs were trained to perform a targeted search with matched-filtering sensitivity. In this paper, we adapt these design principles to build a DNN architecture for wide parameter-space searches in 10 days of data from two detectors (H1 and L1). We train a DNN for each of the benchmark cases: six all-sky searches and eight directed searches at different frequencies in the search band of 20-1000 Hz. We compare our results to the DNN sensitivity achieved from Dreissigacker and Prix [Phys. Rev. D 102, 022005 (2020)PRVDAQ2470-001010.1103/PhysRevD.102.022005] and find that our trained DNNs are more sensitive in all the cases. The absolute improvement in detection probability ranges from 6.5% at 20 Hz to 38% at 1000 Hz in the all-sky cases and from 1.5% at 20 Hz to 59.4% at 500 Hz in the directed cases. An all-sky DNN trained on the entire search band of 20-1000 Hz shows a high sensitivity at all frequencies, providing a proof of concept for training a single DNN to perform the entire search. We also study the generalization of the DNN performance to signals with different signal amplitude, frequency, and the dependence of the DNN sensitivity on sky position.
ASJC Scopus subject areas
- Physics and Astronomy(all)
- Nuclear and High Energy Physics
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In: Physical Review D, Vol. 110, No. 12, 124071, 31.12.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Large-kernel convolutional neural networks for wide parameter-space searches of continuous gravitational waves
AU - Joshi, Prasanna M.
AU - Prix, Reinhard
N1 - Publisher Copyright: © 2024 authors. Published by the American Physical Society. Published by the American Physical Society under the terms of the "https://creativecommons.org/licenses/by/4.0/"Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. Open access publication funded by the Max Planck Society.
PY - 2024/12/31
Y1 - 2024/12/31
N2 - The sensitivity of wide parameter-space searches for continuous gravitational waves (CWs) is limited by their high computational cost. Deep learning is being studied as an alternative method to replace various aspects of a CW search. In previous work [Phys. Rev. D 108, 063021 (2023)PRVDAQ2470-001010.1103/PhysRevD.108.063021], new design principles were presented for deep neural network (DNN) search of CWs and such DNNs were trained to perform a targeted search with matched-filtering sensitivity. In this paper, we adapt these design principles to build a DNN architecture for wide parameter-space searches in 10 days of data from two detectors (H1 and L1). We train a DNN for each of the benchmark cases: six all-sky searches and eight directed searches at different frequencies in the search band of 20-1000 Hz. We compare our results to the DNN sensitivity achieved from Dreissigacker and Prix [Phys. Rev. D 102, 022005 (2020)PRVDAQ2470-001010.1103/PhysRevD.102.022005] and find that our trained DNNs are more sensitive in all the cases. The absolute improvement in detection probability ranges from 6.5% at 20 Hz to 38% at 1000 Hz in the all-sky cases and from 1.5% at 20 Hz to 59.4% at 500 Hz in the directed cases. An all-sky DNN trained on the entire search band of 20-1000 Hz shows a high sensitivity at all frequencies, providing a proof of concept for training a single DNN to perform the entire search. We also study the generalization of the DNN performance to signals with different signal amplitude, frequency, and the dependence of the DNN sensitivity on sky position.
AB - The sensitivity of wide parameter-space searches for continuous gravitational waves (CWs) is limited by their high computational cost. Deep learning is being studied as an alternative method to replace various aspects of a CW search. In previous work [Phys. Rev. D 108, 063021 (2023)PRVDAQ2470-001010.1103/PhysRevD.108.063021], new design principles were presented for deep neural network (DNN) search of CWs and such DNNs were trained to perform a targeted search with matched-filtering sensitivity. In this paper, we adapt these design principles to build a DNN architecture for wide parameter-space searches in 10 days of data from two detectors (H1 and L1). We train a DNN for each of the benchmark cases: six all-sky searches and eight directed searches at different frequencies in the search band of 20-1000 Hz. We compare our results to the DNN sensitivity achieved from Dreissigacker and Prix [Phys. Rev. D 102, 022005 (2020)PRVDAQ2470-001010.1103/PhysRevD.102.022005] and find that our trained DNNs are more sensitive in all the cases. The absolute improvement in detection probability ranges from 6.5% at 20 Hz to 38% at 1000 Hz in the all-sky cases and from 1.5% at 20 Hz to 59.4% at 500 Hz in the directed cases. An all-sky DNN trained on the entire search band of 20-1000 Hz shows a high sensitivity at all frequencies, providing a proof of concept for training a single DNN to perform the entire search. We also study the generalization of the DNN performance to signals with different signal amplitude, frequency, and the dependence of the DNN sensitivity on sky position.
UR - http://www.scopus.com/inward/record.url?scp=85213880791&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2408.07070
DO - 10.48550/arXiv.2408.07070
M3 - Article
AN - SCOPUS:85213880791
VL - 110
JO - Physical Review D
JF - Physical Review D
SN - 2470-0010
IS - 12
M1 - 124071
ER -