Deep-learning continuous gravitational waves : Multiple detectors and realistic noise

The sensitivity of wide-parameter-space searches for continuous gravitational waves is limited by computational cost. Recently it was shown that deep neural networks (DNNs) can perform all-sky searches directly on (single-detector) strain data [C. Dreissigacker, Phys. Rev. D 100, 044009 (2019)PRVDAQ2470-001010.1103/PhysRevD.100.044009], potentially providing a low-computing-cost search method that could lead to a better overall sensitivity. Here we expand on this study in two respects: (i) using (simulated) strain data from two detectors simultaneously, and (ii) training for directed (i.e., single sky-position) searches in addition to all-sky searches. For a data time span of T=105 s, the all-sky two-detector DNN is about 7% less sensitive (in amplitude h0) at low frequency (f=20 Hz), and about 51% less sensitive at high frequency (f=1000 Hz) compared to fully-coherent matched-filtering (using weave). In the directed case the sensitivity gap compared to matched-filtering ranges from about 7%-14% at f=20 Hz to about 37%-49% at f=1500 Hz. Furthermore we assess the DNN's ability to generalize in signal frequency, spin down and sky-position, and we test its robustness to realistic data conditions, namely gaps in the data and using real LIGO detector noise. We find that the DNN performance is not adversely affected by gaps in the test data or by using a relatively undisturbed band of LIGO detector data instead of Gaussian noise. However, when using a more disturbed LIGO band for the tests, the DNN's detection performance is substantially degraded due to the increase in false alarms, as expected. © 2020 authors

Deep-learning continuous gravitational waves

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. © 2019 authors. Published by the American Physical Society

OctApps:a library of Octave functions for continuous gravitational-wave data analysis

Gravitational waves are minute ripples in spacetime, first predicted by Einstein's general theory of relativity in 1916. Gravitational waves from rapidly-rotating neutron stars, whose shape deviates from perfect axisymmetry, are a potential astrophysical source of gravitational waves, but which so far have not been detected. The search for this type of signals, also known as continuous waves, presents a significant data analysis challenge, as their weak signatures are expected to be buried deep within the instrumental noise of the LIGO and Virgo detectors. The OctApps library provides various functions, written in Octave, intended to aid research scientists who perform searches for continuous gravitational waves

A Fermi Gamma-Ray Burst Monitor Search for Electromagnetic Signals Coincident with Gravitational-wave Candidates in Advanced LIGO's First Observing Run

We present a search for prompt gamma-ray counterparts to compact binary coalescence gravitational wave (GW) candidates from Advanced LIGO's first observing run (O1). As demonstrated by the multimessenger observations of GW170817/GRB 170817A, electromagnetic and GW observations provide complementary information about the astrophysical source, and in the case of weaker candidates, may strengthen the case for an astrophysical origin. Here we investigate low-significance GW candidates from the O1 compact binary coalescence searches using the Fermi Gamma-Ray Burst Monitor (GBM), leveraging its all sky and broad energy coverage. Candidates are ranked and compared to background to measure the significance. Those with false alarm rates (FARs) of less than 10−5 Hz (about one per day, yielding a total of 81 candidates) are used as the search sample for gamma-ray follow-up. No GW candidates were found to be coincident with gamma-ray transients independently identified by blind searches of the GBM data. In addition, GW candidate event times were followed up by a separate targeted search of GBM data. Among the resulting GBM events, the two with the lowest FARs were the gamma-ray transient GW150914-GBM presented in Connaughton et al. and a solar flare in chance coincidence with a GW candidate

Fast and accurate sensitivity estimation for continuous-gravitational-wave searches

This paper presents an efficient numerical sensitivity-estimation method and implementation for continuous-gravitational-wave searches, extending and generalizing an earlier analytic approach by Wette [1]. This estimation framework applies to a broad class of F-statistic-based search methods, namely (i) semi-coherent StackSlide F-statistic (single-stage and hierarchical multistage), (ii) Hough number count on F-statistics, as well as (iii) Bayesian upper limits on F-statistic search results (coherent or semi-coherent). We test this estimate against results from Monte-Carlo simulations assuming Gaussian noise. We find the agreement to be within a few % at high detection (i.e., low false-alarm) thresholds, with increasing deviations at decreasing detection (i.e., higher false-alarm) thresholds, which can be understood in terms of the approximations used in the estimate. We also provide an extensive summary of sensitivity depths achieved in past continuous-gravitational-wave searches (derived from the published upper limits). For the F-statistic-based searches where our sensitivity estimate is applicable, we find an average relative deviation to the published upper limits of less than 10%, which in most cases includes systematic uncertainty about the noise-floor estimate used in the published upper limits

Fast and accurate sensitivity estimation for continuous-gravitational-wave searches

This paper presents an efficient numerical sensitivity-estimation method and implementation for continuous-gravitational-wave searches, extending and generalizing an earlier analytic approach by Wette [1]. This estimation framework applies to a broad class of F-statistic-based search methods, namely (i) semi-coherent StackSlide F-statistic (single-stage and hierarchical multistage), (ii) Hough number count on F-statistics, as well as (iii) Bayesian upper limits on F-statistic search results (coherent or semi-coherent). We test this estimate against results from Monte-Carlo simulations assuming Gaussian noise. We find the agreement to be within a few % at high detection (i.e., low false-alarm) thresholds, with increasing deviations at decreasing detection (i.e., higher false-alarm) thresholds, which can be understood in terms of the approximations used in the estimate. We also provide an extensive summary of sensitivity depths achieved in past continuous-gravitational-wave searches (derived from the published upper limits). For the F-statistic-based searches where our sensitivity estimate is applicable, we find an average relative deviation to the published upper limits of less than 10%, which in most cases includes systematic uncertainty about the noise-floor estimate used in the published upper limits

Acoustic waves in granular packings at low confinement pressure

Elastic properties of a granular packing show a nonlinear behavior determined by its discrete structure and nonlinear inter-grain force laws. Acoustic waves show a transition from constant, pressure-dependent sound speed to a shock-wave-like behavior with an amplitude-determined propagation speed. This becomes increasingly visible at low static confinement pressure as the transient regime shifts to lower wave amplitudes for lower static pressure. In microgravity, confinement pressure can be orders of magnitude lower than in a ground-based experiment. In addition, the absence of hydrostatic gradients allows for much more homogeneous and isotropic pressure distribution. We present a novel apparatus for acoustic wave transmission measurements at such low packing pressures. A pressure control loop is implemented by using a microcontroller that monitors static force sensor readings and adjusts the position of a movable wall with a linear-motor until the desired pressure is reached. Measurements of acoustic waves are possible using accelerometers embedded in the granular packing as well as piezos. For excitation, we use a voice-coil-driven wall, with a large variety of signal shapes, frequencies, and amplitudes. This enables experiments in both the linear and strongly nonlinear regimes

OctApps: a library of Octave functions for continuous gravitational-wave data analysis

[eng] Gravitational waves are minute ripples in spacetime, first predicted by Einstein's general theory of relativity in 1916. Gravitational waves from rapidly-rotating neutron stars, whose shape deviates from perfect axisymmetry, are a potential astrophysical source of gravitational waves, but which so far have not been detected. The search for this type of signals, also known as continuous waves, presents a significant data analysis challenge, as their weak signatures are expected to be buried deep within the instrumental noise of the LIGO and Virgo detectors. The OctApps library provides various functions, written in Octave, intended to aid research scientists who perform searches for continuous gravitational waves

First narrow-band search for continuous gravitational waves from known pulsars in advanced detector data

International audienceSpinning neutron stars asymmetric with respect to their rotation axis are potential sources of continuous gravitational waves for ground-based interferometric detectors. In the case of known pulsars a fully coherent search, based on matched filtering, which uses the position and rotational parameters obtained from electromagnetic observations, can be carried out. Matched filtering maximizes the signal-to-noise (SNR) ratio, but a large sensitivity loss is expected in case of even a very small mismatch between the assumed and the true signal parameters. For this reason, narrow-band analysis methods have been developed, allowing a fully coherent search for gravitational waves from known pulsars over a fraction of a hertz and several spin-down values. In this paper we describe a narrow-band search of 11 pulsars using data from Advanced LIGO’s first observing run. Although we have found several initial outliers, further studies show no significant evidence for the presence of a gravitational wave signal. Finally, we have placed upper limits on the signal strain amplitude lower than the spin-down limit for 5 of the 11 targets over the bands searched; in the case of J1813-1749 the spin-down limit has been beaten for the first time. For an additional 3 targets, the median upper limit across the search bands is below the spin-down limit. This is the most sensitive narrow-band search for continuous gravitational waves carried out so far