arXiv subjects

Dixeena Lopez

Publications and source records attributed to Dixeena Lopez.

4 recordsLinked to original sources

arXiv2024

Gravitational wave memory of compact binary coalescence in the presence of matter effects

Binary neutron stars (BNSs) and neutron star--black hole (NSBH) binaries are two of the most promising gravitational wave (GW) sources to probe matter effects. Upcoming observing runs of LIGO-Virgo-KAGRA detectors and future third generation detectors like Einstein Telescope and Cosmic Explorer will allow the extraction of detailed information on these matter effects from the GW signature of BNS and NSBH systems. One subtle effect which may be helpful to extract more information from the detection of compact binary systems is the nonlinear memory. In this work, we investigate the observational consequences of gravitational wave nonlinear memory in the presence of matter effects. We start by quantifying the impact of nonlinear memory on distinguishing BNS mergers from binary black holes (BBHs) or NSBH mergers. We find that for the third generation detectors, the addition of nonlinear memory to the GW signal model expands the parameter space where BNS signals become distinguishable from the BBH and NSBH signals. Using numerical relativity simulations, we also study the nonlinear memory generated from the postmerger phase of BNS systems. We find that it does not show a strong dependence on the equation of state of the NS. However, the amplitude of nonlinear memory from the BNS postmerger phase is much lower than the one from BBH systems of the same masses. Furthermore, we compute the detection prospects of nonlinear memory from the postmerger phase of NS systems by accumulating signal strength from a population of BNS mergers for the current and future detectors. Finally, we discuss the impact of possible linear memory from the dynamical ejecta of BNS and NSBH systems and its signal strength relative to the nonlinear memory. We find that linear memory almost always has a much weaker effect than nonlinear memory.

gr-qc↗

arXiv2022

Prospects for detecting and localizing short-duration transient gravitational waves from glitching neutron stars without electromagnetic counterparts

Neutron stars are known to show accelerated spin-up of their rotational frequency called a glitch. Highly magnetized rotating neutron stars (pulsars) are frequently observed by radio telescopes (and in other frequencies), where the glitch is observed as irregular arrival times of pulses which are otherwise very regular. A glitch in an isolated neutron star can excite the fundamental (f)-mode oscillations which can lead to gravitational wave generation. Electromagnetic observations of pulsars (and hence pulsar glitches) require the pulsar to be oriented so that the jet is pointed toward the detector, but this is not a requirement for gravitational wave emission which is more isotropic and not jetlike. Hence, gravitational wave observations have the potential to uncover nearby neutron stars where the jet is not pointed towards the Earth. In this work, we study the prospects of finding glitching neutron stars using a generic all-sky search for short-duration gravitational wave transients. The analysis covers the high-frequency range from $1-4$ kHz of LIGO-Virgo detectors for signals up to a few seconds. We set upper limits for the third observing run of the LIGO-Virgo detectors and present the prospects for upcoming observing runs of LIGO, Virgo, KAGRA, and LIGO India. We find the detectable glitch size will be around $10^{-5}$ Hz for the fifth observing run for pulsars with spin frequencies and distances comparable to the Vela pulsar. We also present the prospects of localizing the direction in the sky of these sources with gravitational waves alone, which can facilitate electromagnetic follow-up. We find that for the five detector configuration, the localization capability for a glitch size of $10^{-5}$ Hz is around $132\,\mathrm{deg}^{2}$ at $1σ$ confidence for $50\%$ of events with distance and spin frequency as that of Vela.

astro-ph.HE↗

arXiv2022

Utilizing Gaussian mixture models in all-sky searches for short-duration gravitational wave bursts

Coherent WaveBurst is a generic, multidetector gravitational wave burst search based on the excess power approach. The coherent WaveBurst algorithm currently employed in the all-sky short-duration gravitational wave burst search uses a conditional approach on selected attributes in the multidimensional event attribute space to distinguish between noisy events from that of astrophysical origin. We have been developing a supervised machine learning approach based on the Gaussian mixture modeling to model the attribute space for signals as well as noise events to enhance the probability of burst detection [Gayathri et al.Phys. Rev. D 102, 104023 (2020)]. We further extend the GMM approach to the all-sky short-duration coherent WaveBurst search as a postprocessing step on events from the first half of the third observing run (O3a). We show an improvement in sensitivity to generic gravitational wave burst signal morphologies as well as the astrophysical source such as core-collapse supernova models due to the application of our Gaussian mixture model approach to coherent WaveBurst triggers. The Gaussian mixture model method recovers the gravitational wave signals from massive compact binary coalescences identified by coherent WaveBurst targeted for binary black holes in GWTC-2, with better significance than the all-sky coherent WaveBurst search. No additional significant gravitational wave bursts are observed.

gr-qc↗

arXiv2020

Enhancing the sensitivity of transient gravitational wave searches with Gaussian Mixture Models

Identifying the presence of a gravitational wave transient buried in non-stationary, non-Gaussian noise which can often contain spurious noise transients (glitches) is a very challenging task. For a given data set, transient gravitational wave searches produce a corresponding list of triggers that indicate the possible presence of a gravitational wave signal. These triggers are often the result of glitches mimicking gravitational wave signal characteristics. To distinguish glitches from genuine gravitational wave signals, search algorithms estimate a range of trigger attributes, with thresholds applied to these trigger properties to separate signal from noise. Here, we present the use of Gaussian mixture models, a supervised machine learning approach, as a means of modelling the multi-dimensional trigger attribute space. We demonstrate this approach by applying it to triggers from the coherent Waveburst search for generic bursts in LIGO O1 data. By building Gaussian mixture models for the signal and background noise attribute spaces, we show that we can significantly improve the sensitivity of the coherent Waveburst search and strongly suppress the impact of glitches and background noise, without the use of multiple search bins as employed by the original O1 search. We show that the detection probability is enhanced by a factor of 10, leading enhanced statistical significance for gravitational wave signals such as GW150914.

gr-qc↗