TY - JOUR

T1 - Deep-learning continuous gravitational waves

AU - Sharma, Rahul

AU - Messenger, Chris

AU - Zhao, Ruining

AU - Prix, Reinhard

AU - Dreissigacker, C.

PY - 2019/8/15

Y1 - 2019/8/15

N2 - We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018)10.1103/PhysRevD.97.044039; H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)10.1103/PhysRevLett.120.141103]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the Weave pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)10.1103/PhysRevD.97.123016]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an "easy" search using T=105 s of data, and a "harder" search using T=106 s. The detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105 s at 20 Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106 s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.

AB - We present a first proof-of-principle study for using deep neural networks (DNNs) as a novel search method for continuous gravitational waves (CWs) from unknown spinning neutron stars. The sensitivity of current wide-parameter-space CW searches is limited by the available computing power, which makes neural networks an interesting alternative to investigate, as they are extremely fast once trained and have recently been shown to rival the sensitivity of matched filtering for black-hole merger signals [D. George and E. A. Huerta, Phys. Rev. D 97, 044039 (2018)10.1103/PhysRevD.97.044039; H. Gabbard, M. Williams, F. Hayes, and C. Messenger, Phys. Rev. Lett. 120, 141103 (2018)10.1103/PhysRevLett.120.141103]. We train a convolutional neural network with residual (shortcut) connections and compare its detection power to that of a fully coherent matched-filtering search using the Weave pipeline [K. Wette, S. Walsh, R. Prix, and M. A. Papa, Phys. Rev. D 97, 123016 (2018)10.1103/PhysRevD.97.123016]. As test benchmarks we consider two types of all-sky searches over the frequency range from 20 to 1000 Hz: an "easy" search using T=105 s of data, and a "harder" search using T=106 s. The detection probability pdet is measured on a signal population for which matched filtering achieves pdet=90% in Gaussian noise. In the easiest test case (T=105 s at 20 Hz) the DNN achieves pdet∼88%, corresponding to a loss in sensitivity depth of ∼5% versus coherent matched filtering. However, at higher frequencies and for longer observation times the DNN detection power decreases, until pdet∼13% and a loss of ∼66% in sensitivity depth in the hardest case (T=106 s at 1000 Hz). We study the DNN generalization ability by testing on signals of different frequencies, spindowns and signal strengths than they were trained on. We observe excellent generalization: only five networks, each trained at a different frequency, would be able to cover the whole frequency range of the search.

UR - http://www.scopus.com/inward/record.url?scp=85072228949&partnerID=8YFLogxK

U2 - 10.1103/PhysRevD.100.044009

DO - 10.1103/PhysRevD.100.044009

M3 - Article

AN - SCOPUS:85072228949

VL - 100

JO - Physical Review D

JF - Physical Review D

SN - 2470-0010

IS - 4

M1 - 044009

ER -