A new veto for continuous gravitational wave searches

We present a new veto procedure to distinguish between continuous gravitational wave (CW) signals and the detector artifacts that can mimic their behavior. The veto procedure exploits the fact that a long-lasting coherent disturbance is less likely than a real signal to exhibit a Doppler modulation of astrophysical origin. Therefore, in the presence of an outlier from a search, we perform a multi-step search around the frequency of the outlier with the Doppler modulation turned off (DM-off), and compare these results with the results from the original (DM-on) search. If the results from the DM-off search are more significant than those from the DM-on search, the outlier is most likely due to an artifact rather than a signal. We tune the veto procedure so that it has a very low false dismissal rate. With this veto, we are able to identify as coherent disturbances >99.9% of the 6349 candidates from the recent all-sky low-frequency Einstein@Home search on the data from the Advanced LIGO O1 observing run [1]. We present the details of each identified disturbance in the Appendix.Comment: 10 pages, 6 figures, 2 table

An F-statistic based multi-detector veto for detector artifacts in continuous-wave gravitational wave data

Continuous gravitational waves (CW) are expected from spinning neutron stars with non-axisymmetric deformations. A network of interferometric detectors (LIGO, Virgo and GEO600) is looking for these signals. They are predicted to be very weak and retrievable only by integration over long observation times. One of the standard methods of CW data analysis is the multi-detector F-statistic. In a typical search, the F-statistic is computed over a range in frequency, spin-down and sky position, and the candidates with highest F values are kept for further analysis. However, this detection statistic is susceptible to a class of noise artifacts, strong monochromatic lines in a single detector. By assuming an extended noise model - standard Gaussian noise plus single-detector lines - we can use a Bayesian odds ratio to derive a generalized detection statistic, the line veto (LV-) statistic. In the absence of lines, it behaves similarly to the F-statistic, but it is more robust against line artifacts. In the past, ad-hoc post-processing vetoes have been implemented in searches to remove these artifacts. Here we provide a systematic framework to develop and benchmark this class of vetoes. We present our results from testing this LV-statistic on simulated data.Comment: 2 pages, 1 figure, to be published in Proceedings of Statistical Challenges in Modern Astronomy V, Springer 201

Fully coherent follow-up of continuous gravitational-wave candidates: an application to Einstein@Home results

We characterize and present the details of the follow-up method used on the most significant outliers of the Hough Einstein@Home all-sky search for continuous gravitational waves arXiv:1207.7176. This follow-up method is based on the two-stage approach introduced in arXiv:1303.2471, consisting of a semicoherent refinement followed by a fully coherent zoom. We quantify the efficiency of the follow-up pipeline using simulated signals in Gaussian noise. This pipeline does not search beyond first-order frequency spindown, and therefore we also evaluate its robustness against second-order spindown. We present the details of the Hough Einstein@Home follow-up (arXiv:1207.7176) on three hardware-injected signals and on the 8 most significant outliers of unknown origin.Comment: 8 pages, 3 figures, 3 table

Deep learning for clustering of continuous gravitational wave candidates II: identification of low-SNR candidates

Broad searches for continuous gravitational wave signals rely on hierarchies of follow-up stages for candidates above a given significance threshold. An important step to simplify these follow-ups and reduce the computational cost is to bundle together in a single follow-up nearby candidates. This step is called clustering and we investigate carrying it out with a deep learning network. In our first paper [1], we implemented a deep learning clustering network capable of correctly identifying clusters due to large signals. In this paper, a network is implemented that can detect clusters due to much fainter signals. These two networks are complementary and we show that a cascade of the two networks achieves an excellent detection efficiency across a wide range of signal strengths, with a false alarm rate comparable/lower than that of methods currently in use

Results from the First All-Sky Search for Continuous Gravitational Waves from Small-Ellipticity Sources

We present the results of an all-sky search for continuous gravitational-wave signals with frequencies in the 500-1700 Hz range targeting neutron stars with ellipticity of 10-8. The search is done on LIGO O2 data using the Falcon analysis pipeline. The results presented here double the sensitivity over any other result on the same data [B. P. Abbott (LIGO Scientific Collaboration and Virgo Collaboration), Phys. Rev. D 100, 024004 (2019)PRVDAQ2470-001010.1103/PhysRevD.100.024004, C. Palomba, Phys. Rev. Lett. 123, 171101 (2019)PRLTAO0031-900710.1103/PhysRevLett.123.171101]. The search is capable of detecting low-ellipticity sources up to 170 pc. We establish strict upper limits which hold for worst-case signal parameters. We list outliers uncovered by the search, including several which we cannot associate with any known instrumental cause. © 2020 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

Sensitivity improvements in the search for periodic gravitational waves using O1 LIGO data

We demonstrate a breakthrough in the capabilities of robust, broad-parameter space searches for continuous gravitational waves. With a large scale search for continuous gravitational waves on the O1 LIGO data, we prove that our Falcon search achieves the sensitivity improvements expected from the use of a long coherence length, while maintaining the computational expense within manageable bounds. On this data we set the most constraining upper limits in the gravitational wave amplitude in the band 100-200 Hz. We provide full outlier lists and upper limits near 0-spindown band suitable for analysis of signals with small spindown such as boson condensates around black holes.Comment: Updated paper titl

Adaptive clustering procedure for continuous gravitational wave searches

In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same root cause (be it a signal or a disturbance), based on a predefined cluster volume. In this paper, we present a procedure that adapts the cluster volume to the data itself and checks for consistency of such volume with what is expected from a signal. This significantly improves the noise rejection capabilities at fixed detection threshold, and at fixed computing resources for the follow-up stages, this results in an overall more sensitive search. This new procedure was employed in the first Einstein@Home search on data from the first science run of the advanced LIGO detectors (O1) [11].Comment: 11 pages, 9 figures, 2 tables; v1: initial submission; v2: journal review, copyedited version; v3: fixed typo in Fig