SearcharxivSearch

arXiv · 2403.11392

Gravitational Wave Searches for Post-Merger Remnants of GW170817 and GW190425

Abstract

We present the results of two searches for gravitational waves from the post-merger remnants of the binary neutron star coalescence events GW170817 and GW190425. The searches are fully coherent over 1800~s of data from the 2nd (for GW170817) and 3rd (for GW190425) observing runs of the LIGO and Virgo observatories. The searches compute the matched filter $\mathcal{F}$-statistic, and use a piecewise model of the rapidly changing frequency evolution appropriate for young neutron stars. No detection is claimed. The peak root-sum-squared strain upper limit at 50\% detection probability ($h_{\text{rss}}^{50\%})$ of both searches occurs at 1700~Hz and is estimated at $1.64 \times 10^{-22}~\text{Hz}^{-1/2}$ for GW170817, and $1.0 \times 10^{-22}~\text{Hz}^{-1/2}$ for GW190425. This is the first gravitational wave search for a neutron star remnant of GW190425.

Explore related subjects

Keep this discovery

BibTeXRIS

Benjamin Grace, Karl Wette, Susan Scott. 2024-03-18. Gravitational Wave Searches for Post-Merger Remnants of GW170817 and GW190425. https://doi.org/10.1103/physrevd.110.083016

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Electrovacuum Black Hole Uniqueness

We prove the black hole uniqueness conjecture in the axially symmetric, stationary, electrovacuum setting, subject to the refined asymptotic analysis of the associated singular harmonic maps, which includes an analyticity hypothesis at the axes. More precisely, it is shown that any asymptotically flat solution of the Einstein--Maxwell equations in this class, with more than one black hole horizon component is either: Majumdar--Papapetrou, up to a duality rotation, in which case all logarithmic angle defects vanish, or every finite axis rod logarithmic angle defect is strictly negative and hence every interaction force is strictly attractive. The proof extends the singular harmonic map method used for vacuum Kerr uniqueness in [18].

gr-qc

Constraining Modified Mass-to-Horizon Cosmology Through Primordial Inflationary Observables

We investigate slow-roll inflation in a modified cosmological framework inspired by a generalized mass-to-horizon relation (MHR), $M=\gamma {c^2 L^n}/{G}$, where $n$ is a real parameter and $\gamma$ a dimensional constant. Using Padmanabhan's emergence paradigm, we derive the modified Friedmann equations for a flat FRW universe and analyze the dynamics of a canonical scalar field (inflaton) under the slow-roll approximation. We study the resulting inflationary phenomenology for power-law and Starobinsky potentials. For power-law potentials, the MHR modification fails to reconcile these models with current CMB constraints on $r$ and $n_s$. In contrast, Starobinsky inflation exhibits significant sensitivity to deviations from $n=1$. A perturbative analysis ($n=1+\Delta$) yields corrections to inflationary observables. We observe that the scalar power-spectrum normalization, under a fixed-Starobinsky prescription, imposes the stringent constraint $0.960 \lesssim n \lesssim 1.040$ for $N=60$ efolds. This is considerably tighter than spectral-index bounds. Our results establish inflation, particularly Starobinsky-like models, as a sensitive probe of generalized horizon thermodynamics and departures from standard MHR scaling.

gr-qc

Improving the Sensitivity of Gravitational Wave Detection with Weighted Conformal Prediction

In the last decade, kilometre-scale interferometric gravitational-wave detectors have observed hundreds of compact binary mergers, the majority of which are binary black holes. However, the data are noise-dominated, and multiple independent search algorithms (pipelines) are used to enhance sensitivity and improve robustness. Rather than the standard approach of selecting the most significant pipeline output, we combine the outputs from all pipelines using a conformal prediction-based framework to provide statistically rigorous confidence estimates for candidate events. While combining pipelines improves sensitivity and ranking robustness, it requires a principled statistical framework that remains valid as data properties evolve across observing runs. A key challenge is distribution shifts between simulated datasets used for training and calibration and the real, unlabelled, observations used for testing, which can invalidate coverage guarantees and bias confidence estimates. In this work, we address this challenge by incorporating likelihood-ratio reweighting into our conformal prediction framework to account for covariate shift. Using mock datasets containing simulated signals, we demonstrate that weighted conformal prediction restores well-calibrated coverage under covariate shift and increases the confidence of events near the detection threshold, recovering true signals that would otherwise be missed.

gr-qc