Search

Machine learning can be a powerful tool to discover new signal types in astronomical data. We here apply it to search for long-duration transient gravitational waves triggered by pulsar glitches, which could yield physical insight into the mostly unknown depths of the pulsar. Current methods to search for such signals rely on matched filtering and a brute-force grid search over possible signal durations, which is sensitive but can become very computationally expensive. We develop a method to search for post-glitch signals on combining matched filtering with convolutional neural networks, which reaches similar sensitivities to the standard method at false-alarm probabilities relevant for practical searches, while being significantly faster. We specialize to the Vela glitch during the LIGO-Virgo O2 run, and set upper limits on the gravitational-wave strain amplitude from the data of the two LIGO detectors for both constant-amplitude and exponentially decaying signals., Comment: 19 pages, 9 figures. Comments welcome

Pulsars are rotating neutron stars that emit periodic electromagnetic radiation. While pulsars generally slow down as they lose energy, some also experience glitches: spontaneous increases of their rotational frequency. According to several models, these glitches can also lead to the emission of long-duration transient gravitational waves (GWs). We present detection prospects for such signals by comparing indirect energy upper limits on GW strain for known glitches with the sensitivity of current and future ground-based GW detectors. We first consider the optimistic case of generic constraints based on the glitch size and find that realistic matched-filter searches in the fourth LIGO-Virgo-KAGRA observing run(O4) could make a detection, or set constraints below these indirect upper limits, for equivalents of 36 out of 726 previously observed glitches, and 74 in the O5 run. With the third-generation Einstein Telescope or Cosmic Explorer, 35-40% of glitches would be accessible. When specialising to a scenario where transient mountains produce the post-glitch GW emission, following the Yim & Jones model, the indirect upper limits are stricter. Out of the smaller set of 119 glitches with measured healing parameter, as needed for predictions under that model, only 6 glitches would have been within reach for O4 and 14 for O5, with a similar percentage as before with third generation detectors. We also discuss how this model matches the observed glitch population., Comment: 16 pages, 9 figures, matches version accepted by MNRAS

Pulsars are spinning neutron stars which emit an electromagnetic beam. We expect pulsars to slowly decrease their rotational frequency. However, sudden increases of the rotational frequency have been observed from different pulsars. These events are called "glitches" and they are followed by a relaxation phase with timescales from days to months. Gravitational wave (GW) emission may follow these peculiar events. We give an overview of the setup for an analysis of GW data from the Advanced LIGO and Virgo third observing run (O3) arXiv:2112.10990 searching for transient GW signals lasting hours to months after glitches in known pulsars. The search method consists of placing a template grid in frequency-spindown space with fixed grid spacings. Then, for each point we compute the transient F-statistic which is maximized over a set of transient parameters like the duration and start time of the potential signals. A threshold on the detection statistic is then set, and we search for peaks over the parameter space for each candidate., Comment: 5 pages, submitted to Proceedings of TAUP 2021

Searches for gravitational-wave signals are often based on maximizing a detection statistic over a bank of waveform templates, covering a given parameter space with a variable level of correlation. Results are often evaluated using a noise-hypothesis test, where the background is characterized by the sampling distribution of the loudest template. In the context of continuous gravitational-wave searches, properly describing said distribution is an open problem: current approaches focus on a particular detection statistic and neglect template-bank correlations. We introduce a new approach using extreme value theory to describe the distribution of the loudest template's detection statistic in an arbitrary template bank. Our new proposal automatically generalizes to a wider class of detection statistics, including (but not limited to) line-robust statistics and transient continuous-wave signal hypotheses, and improves the estimation of the expected maximum detection statistic at a negligible computing cost. The performance of our proposal is demonstrated on simulated data as well as by applying it to different kinds of (transient) continuous-wave searches using O2 Advanced LIGO data. We release an accompanying Python software package, distromax, implementing our new developments., Comment: 24 pages, 23 figures, comments welcome. Package freely available in https://github.com/Rodrigo-Tenorio/distromax