arXiv subjects

Muhammed Saleem

Publications and source records attributed to Muhammed Saleem.

At least 19 recordsLinked to original sources

arXiv2026

Binary Neutron Star Merger Evolution and r-Process Enrichment in the Milky Way Disk

The origin of half of the rapid neutron-capture nucleosynthesis (r-process) elements in the Universe remains an open question. Binary neutron star (BNS) mergers have been shown to face difficulties in reproducing the observed r-process enrichment in Milky Way disk stars. However, their r-process enrichment efficiency may evolve with redshift beyond the star formation history, potentially due to evolution in the merger rate or the average r-process yield in the early Universe. In this paper, we explore the possibility that BNS mergers with an evolving enrichment efficiency could serve as the sole r-process production channel. By jointly comparing gravitational-wave observations from LIGO--Virgo--KAGRA, short gamma-ray bursts, Galactic neutron star populations, and stellar abundance measurements in Milky Way disk stars, we find that scenarios with additional evolution are strongly preferred over non-evolving scenarios, with Bayes factors exceeding $10^{20}$. We quantify the required evolution in both the merger rate and yield, and directly compare them with observations and theoretical predictions. We find that the evolved scenarios are in tension with short gamma-ray burst observations and predictions from multiple population synthesis models, while remaining consistent with current stochastic gravitational-wave background constraints. Our results provide a quantitative framework for evaluating whether BNS mergers with evolving enrichment efficiency can account for the observed r-process enrichment history of Milky Way disk stars.

astro-ph.HE↗

arXiv2025

Implications of GW241011 for rotating exotic compact objects

A number of theoretical proposals have been made for horizonless compact objects with masses and spins similar to those of black holes. While gravitational wave signatures from their mergers can resemble those of black holes, features like the spin-induced quadrupole moment may reveal their distinct nature. Using the tight bounds on the spin-induced quadrupole moment of GW241011, we place gravitational wave constraints on the nature of its primary. We find that large classes of exotic compact objects (including rotating boson stars) cannot explain its nature, however, models of sufficiently large compactness of $C \gtrsim 0.24$ may still be viable contenders.

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arXiv2025

Confronting general relativity with principal component analysis: Simulations and results from GWTC-3 events

We present a comprehensive assessment of multiparameter tests of general relativity (GR) in the inspiral regime of compact binary coalescences using principal component analysis (PCA). Our analysis is based on an extensive set of simulated gravitational-wave (GW) signals, including both general relativistic and non-GR sources, injected into zero-noise data colored by the noise power spectral densities of the LIGO and Virgo GW detectors at their designed sensitivities. We evaluate the performance of PCA-based methods in the context of two established frameworks: TIGER and FTI. For GR-consistent signals, we find that PCA enables stringent constraints on potential deviations from GR, even in the presence of multiple free parameters. Applying the method to simulated signals that explicitly violate GR, we demonstrate that PCA is effective at identifying such deviations. We further test the method using numerical relativity waveforms of eccentric binary black hole systems and show that missing physical effects--such as orbital eccentricity--can lead to apparent violations of GR if not properly included in the waveform models used for analysis. Finally, we apply our PCA-based test to selected real gravitational-wave events from GWTC-3, including GW190814 and GW190412. We present joint constraints from selected binary black hole events in GWTC-3, finding that the 90% credible bound on the most informative PCA parameter is $0.03^{+0.08}_{-0.08}$ in the TIGER framework and $-0.01^{+0.05}_{-0.04}$ in the FTI framework, both of which are consistent with GR. These results highlight the sensitivity and robustness of the PCA-based approach and demonstrate its readiness for application to future observational data from the fourth observing runs of LIGO, Virgo, and KAGRA.

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arXiv2025

Mergers Fall Short: Non-merger Channels Required for Galactic Heavy Element Production

Since the discovery of the binary neutron star merger GW170817 and its associated kilonova, neutron star mergers have been established as a key production channel for r-process elements in the Universe. However, various lines of evidence, including observations of r-process abundances inferred from stellar spectra of Milky Way disk stars, suggest that additional channels are needed to fully account for r-process element enrichment in the Milky Way. Neutron star-black hole mergers and fast-merging binary neutron star systems are among the leading alternative candidates. In this paper, we combine gravitational-wave observations from LIGO-Virgo-KAGRA with data from short gamma-ray bursts, Galactic pulsars, and Galactic Eu/Fe versus Fe/H abundance observations to assess the contribution of these mergers to r-process enrichment in the Galactic disk. Our analysis employs a unified, likelihood-based inference framework that consistently propagates uncertainties in merger rates, delay-time distributions, mass- and spin-dependent ejecta yields, and stellar abundance measurements. We find that neither neutron star-black hole mergers nor fast- merging binary neutron star populations can serve as the dominant additional channel without generating strong tension with existing observations and theoretical expectations. These results constrain the viable sources of Galactic r-process enrichment and underscore the necessity of non- merger production channels.

astro-ph.HE↗

arXiv2025

Progress toward the detection of the gravitational-wave background from stellar-mass binary black holes: a mock data challenge

While the third LIGO--Virgo gravitational-wave transient catalog includes 90 signals, it is believed that ${\cal O}(10^5)$ binary black holes merge somewhere in the Universe every year. Although these signals are too weak to be detected individually with current observatories, they combine to create a stochastic background, which is potentially detectable in the near future. LIGO--Virgo searches for the gravitational-wave background using cross-correlation have so far yielded upper limits. However, Smith \& Thrane (2017) showed that a vastly more sensitive ``coherent'' search can be carried out by incorporating information about the phase evolution of binary black hole signals. This improved sensitivity comes at a cost; the coherent method is computationally expensive and requires a far more detailed understanding of systematic errors than is required for the cross-correlation search. In this work, we demonstrate the coherent approach with realistic data, paving the way for a gravitational-wave background search with unprecedented sensitivity.

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arXiv2025

Coherence DeepClean: Toward autonomous denoising of gravitational-wave detector data

Technical and environmental noise in ground-based laser interferometers designed for gravitational-wave observations like Advanced LIGO, Advanced Virgo and KAGRA, can manifest as narrow (<1Hz) or broadband ($10'$s or even $100'$s of Hz) spectral lines and features in the instruments' strain amplitude spectral density. When the sources of this noise cannot be identified or removed, in cases where there are witness sensors sensitive to this noise source, denoising of the gravitational-wave strain channel can be performed in software, enabling recovery of instrument sensitivity over affected frequency bands. This noise hunting and removal process can be particularly challenging due to the wealth of auxiliary channels monitoring the interferometry and the environment and the non-linear couplings that may be present. In this work, we present a comprehensive analysis approach and corresponding cyberinfrastructure to promptly identify and remove noise in software using machine learning techniques. The approach builds on earlier work (referred to as DeepClean) in using machine learning methods for linear and non-linear regression of noise. We demonstrate how this procedure can be operated and optimized in a tandem fashion close to online data taking; it starts off with a coherence monitoring analysis that first singles out and prioritizes witness channels that can then be used by DeepClean. The resulting denoised strain by DeepClean reflects a 1.4\% improvement in the binary neutron star range, which can translate into a 4.3\% increase in the sensitive volume. This cyber infrastructure we refer to as Coherence DeepClean, or CDC, is a significant step toward autonomous operations of noise subtraction for ground-based interferometers.

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arXiv2024

A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run

This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGRA. The search targets unmodeled transients with durations of milliseconds to a few seconds in the 30-1500 Hz frequency band, without assumptions about the incoming signal direction, polarization, or morphology. Using the Gravitational Wave Anomalous Knowledge (GWAK) method, three compact binary coalescences (CBCs) identified by existing pipelines are successfully detected, along with a range of detector glitches. The algorithm constructs a low-dimensional embedded space to capture the physical features of signals, enabling the detection of CBCs, detector glitches, and unmodeled transients. This study demonstrates GWAK's ability to enhance gravitational-wave searches beyond the limits of existing pipelines, laying the groundwork for future detection strategies.

gr-qc↗

arXiv2024

Application of Non-Linear Noise Regression in the Virgo Detector

This work presents the first demonstration of non-linear noise regression in the Virgo detector using deep learning techniques. We use DeepClean, a convolutional autoencoder previously shown to be effective in denoising LIGO data, as our tool for modeling and subtracting environmental and technical noise in Virgo. The method uses auxiliary witness channels to learn correlated noise features and remove them from the strain data. For this study, we apply DeepClean to Virgo O3b data, using 225 witness channels selected across 13 targeted frequency bands. Our analysis confirms the presence of non-linear couplings in the subtracted noise, highlighting the importance of DeepClean-like tools in capturing such effects. We observe up to a 1.3 Mpc improvement in the binary neutron star inspiral range (~2.5% gain), and an average increase of 1.7% in the recovered signal-to-noise ratio for injected binary black hole signals. Parameter estimation studies further confirm that DeepClean does not introduce bias in the recovery of source parameters. These results demonstrate the robustness of DeepClean on Virgo data and support its adoption in real-time noise subtraction frameworks for future observing runs.

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arXiv2024

Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware

We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time parameter estimation for candidates detected by machine-learning based compact binary coalescence search, Aframe. We present details of our algorithm and optimizations done related to data-loading and pre-processing on accelerated hardware. We train our model using binary black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has $\sim 6$ million trainable parameters with training times $\lesssim 24$ hours. Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe + AMPLFI is able to pick up BBH candidates and infer parameters for real-time alerts from data acquisition with a net latency of $\sim 6$s.

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arXiv2024

A machine-learning pipeline for real-time detection of gravitational waves from compact binary coalescences

The promise of multi-messenger astronomy relies on the rapid detection of gravitational waves at very low latencies ($\mathcal{O}$(1\,s)) in order to maximize the amount of time available for follow-up observations. In recent years, neural-networks have demonstrated robust non-linear modeling capabilities and millisecond-scale inference at a comparatively small computational footprint, making them an attractive family of algorithms in this context. However, integration of these algorithms into the gravitational-wave astrophysics research ecosystem has proven non-trivial. Here, we present the first fully machine learning-based pipeline for the detection of gravitational waves from compact binary coalescences (CBCs) running in low-latency. We demonstrate this pipeline to have a fraction of the latency of traditional matched filtering search pipelines while achieving state-of-the-art sensitivity to higher-mass stellar binary black holes.

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arXiv2024

Joint gravitational wave-short GRB detection of Binary Neutron Star mergers with existing and future facilities

We explore the joint detection prospects of short gamma-ray bursts (sGRBs) and their gravitational wave (GW) counterparts by the current and upcoming high-energy GRB and GW facilities from binary neutron star (BNS) mergers. We consider two GW detector networks: (1) A four-detector network comprising LIGO Hanford, Livingston, Virgo, and Kagra, (IGWN4) and (2) a future five-detector network including the same four detectors and LIGO India (IGWN5). For the sGRB detection, we consider existing satellites Fermi and Swift and the proposed all-sky satellite Daksha. Most of the events for the joint detection will be off-axis, hence, we consider a broad range of sGRB jet models predicting the off-axis emission. Also, to test the effect of the assumed sGRB luminosity function, we consider two different functions for one of the emission models. We find that for the different jet models, the joint sGRB and GW detection rates for Fermi and Swift with IGWN4 (IGWN5) lie within 0.07-0.62$\mathrm{\ yr^{-1}}$ (0.8-4.0$\mathrm{\ yr^{-1}}$) and 0.02-0.14$\mathrm{\ yr^{-1}}$ (0.15-1.0$\mathrm{\ yr^{-1}}$), respectively, when the BNS merger rate is taken to be 320$\mathrm{\ Gpc^{-3}~yr^{-1}}$. With Daksha, the rates increase to 0.2-1.3$\mathrm{\ yr^{-1}}$ (1.3-8.3$\mathrm{\ yr^{-1}}$), which is 2-9 times higher than the existing satellites. We show that such a mission with higher sensitivity will be ideal for detecting a higher number of fainter events observed off-axis or at a larger distance. Thus, Daksha will boost the joint detections of sGRB and GW, especially for the off-axis events. Finally, we find that our detection rates with optimal SNRs are conservative, and noise in GW detectors can increase the rates further.

astro-ph.HE↗

arXiv2023

Effect of double spin-precession and higher harmonics on spin-induced quadrupole moment measurements

We investigate the prospect of performing a null test of binary black hole (BBH) nature using spin-induced quadrupole moment (SIQM) measurements. This is achieved by constraining a deviation parameter ($\delta\kappa$) related to the parameter ($\kappa$) that quantifies the degree of deformation due to the spin of individual binary components on leading (quadrupolar) spin-induced moment. Throughout the paper, we refer to $\kappa$ as the SIQM parameter and $\delta\kappa$ as the SIQM-deviation parameter. The test presented here extends the earlier SIQM-based null tests for BBH nature by employing waveform models that account for double spin-precession and higher modes. We find that waveform with double spin-precession gives better constraints for $\delta\kappa$, compared to waveform with single spin-precession. We also revisit earlier constraints on the SIQM-deviation parameter for selected GW events observed through the first three observing runs (O1-O3) of LIGO-Virgo detectors. Additionally, the effects of higher-order modes on the test are also explored for a variety of mass-ratio and spin combinations by injecting simulated signals in zero-noise. Our analyses indicate that binaries with mass-ratio greater than 3 and significant spin precession may require waveforms that account for spin-precession and higher modes to perform the parameter estimation reliably.

gr-qc↗

arXiv2023

GWAK: Gravitational-Wave Anomalous Knowledge with Recurrent Autoencoders

Matched-filtering detection techniques for gravitational-wave (GW) signals in ground-based interferometers rely on having well-modeled templates of the GW emission. Such techniques have been traditionally used in searches for compact binary coalescences (CBCs), and have been employed in all known GW detections so far. However, interesting science cases aside from compact mergers do not yet have accurate enough modeling to make matched filtering possible, including core-collapse supernovae and sources where stochasticity may be involved. Therefore the development of techniques to identify sources of these types is of significant interest. In this paper, we present a method of anomaly detection based on deep recurrent autoencoders to enhance the search region to unmodeled transients. We use a semi-supervised strategy that we name Gravitational Wave Anomalous Knowledge (GWAK). While the semi-supervised nature of the problem comes with a cost in terms of accuracy as compared to supervised techniques, there is a qualitative advantage in generalizing experimental sensitivity beyond pre-computed signal templates. We construct a low-dimensional embedded space using the GWAK method, capturing the physical signatures of distinct signals on each axis of the space. By introducing signal priors that capture some of the salient features of GW signals, we allow for the recovery of sensitivity even when an unmodeled anomaly is encountered. We show that regions of the GWAK space can identify CBCs, detector glitches and also a variety of unmodeled astrophysical sources.

astro-ph.IM↗

arXiv2023

Demonstration of Machine Learning-assisted real-time noise regression in gravitational wave detectors

Real-time noise regression algorithms are crucial for maximizing the science outcomes of the LIGO, Virgo, and KAGRA gravitational-wave detectors. This includes improvements in the detectability, source localization and pre-merger detectability of signals thereby enabling rapid multi-messenger follow-up. In this paper, we demonstrate the effectiveness of \textit{DeepClean}, a convolutional neural network architecture that uses witness sensors to estimate and subtract non-linear and non-stationary noise from gravitational-wave strain data. Our study uses LIGO data from the third observing run with injected compact binary signals. As a demonstration, we use \textit{DeepClean} to subtract the noise at 60 Hz due to the power mains and their sidebands arising from non-linear coupling with other instrumental noise sources. Our parameter estimation study on the injected signals shows that \textit{DeepClean} does not do any harm to the underlying astrophysical signals in the data while it can enhances the signal-to-noise ratio of potential signals. We show that \textit{DeepClean} can be used for low-latency noise regression to produce cleaned output data at latencies $\sim 1-2$\, s. We also discuss various considerations that may be made while training \textit{DeepClean} for low latency applications.

gr-qc↗

arXiv2022

Investigating the relation between gravitational wave tests of general relativity

Gravitational wave observations of compact binary coalescences provide precision probes of strong-field gravity. There is thus now a standard set of null tests of general relativity (GR) applied to LIGO-Virgo detections and many more such tests proposed. However, the relation between all these tests is not yet well understood. We start to investigate this by applying a set of standard tests to simulated observations of binary black holes in GR and with phenomenological deviations from GR. We consider four types of tests: residuals, inspiral-merger-ringdown consistency, parameterized phasing (two varieties), and modified dispersion relation. We also check the consistency of the unmodeled reconstruction of the waveforms with the waveform recovered using GR templates. These tests are applied to simulated observations similar to GW150914 with both large and small deviations from GR and similar to GW170608 just with small deviations from GR. We find that while very large deviations from GR are picked up with high significance by almost all tests, more moderate deviations are picked up by only a few tests, and some deviations are not recognized as GR violations by any test at the moderate signal-to-noise ratios we consider. Moreover, the tests that identify various deviations with high significance are not necessarily the expected ones. In particular, the parameterized tests recover PN test parameters much closer to zero than their true values in some cases. Additionally, we find that of the GR deviations we consider, the residuals test is only able to detect extreme deviations from GR that no longer look like binary black hole coalescences in GR. The reconstruction comparison shows more promise for detecting relatively small GR deviations in an unmodeled framework, at least for high-mass systems.

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arXiv2021

Inferring kilonova population properties with a hierarchical Bayesian framework I : Non-detection methodology and single-event analyses

We present ${\tt nimbus}$ : a hierarchical Bayesian framework to infer the intrinsic luminosity parameters of kilonovae (KNe) associated with gravitational-wave (GW) events, based purely on non-detections. This framework makes use of GW 3-D distance information and electromagnetic upper limits from multiple surveys for multiple events, and self-consistently accounts for finite sky-coverage and probability of astrophysical origin. The framework is agnostic to the brightness evolution assumed and can account for multiple electromagnetic passbands simultaneously. Our analyses highlight the importance of accounting for model selection effects, especially in the context of non-detections. We show our methodology using a simple, two-parameter linear brightness model, taking the follow-up of GW190425 with the Zwicky Transient Facility (ZTF) as a single-event test case for two different prior choices of model parameters -- (i) uniform/uninformative priors and (ii) astrophysical priors based on surrogate models of Monte Carlo radiative transfer simulations of KNe. We present results under the assumption that the KN is within the searched region to demonstrate functionality and the importance of prior choice. Our results show consistency with ${\tt simsurvey}$ -- an astronomical survey simulation tool used previously in the literature to constrain the population of KNe. While our results based on uniform priors strongly constrain the parameter space, those based on astrophysical priors are largely uninformative, highlighting the need for deeper constraints. Future studies with multiple events having electromagnetic follow-up from multiple surveys should make it possible to constrain the KN population further.

astro-ph.IM↗

arXiv2021

Population inference of spin-induced quadrupole moments as a probe for non-black hole compact binaries

Gravitational-wave (GW) measurements of physical effects such as spin-induced quadrupole moments can distinguish binaries consisting of black holes from non-black hole binaries. While these effects may be poorly constrained for single-event inferences with the second-generation detectors, combining information from multiple detections can help uncover features of non-black hole binaries. The spin-induced quadrupole moment has specific predictions for different types of compact objects, and a generalized formalism must consider a population where different types of compact objects co-exist. In this study, we introduce a hierarchical mixture-likelihood formalism to estimate the {\it fraction of non-binary black holes in the population}. We demonstrate the applicability of this method using simulated GW signals injected into Gaussian noise following the design sensitivities of the Advanced LIGO Advanced Virgo detectors. We compare the performance of this method with a traditionally-followed hierarchical inference approach. Both the methods are equally effective to hint at inhomogeneous populations, however, we find the mixture-likelihood approach to be more natural for mixture populations comprising compact objects of diverse classes. We also discuss the possible systematics in the mixture-likelihood approach, caused by several reasons, including the limited sensitivity of the second-generation detectors, specific features of the astrophysical population distributions, and the limitations posed by the waveform models employed. Finally, we apply this method to the LIGO-Virgo detections published in the second GW transient catalog (GWTC-2) and find them consistent with a binary black hole population within the statistical precision.

gr-qc↗

arXiv2021

Parametrized tests of post-Newtonian theory using principal component analysis

Searching for departures from general relativity (GR) in more than one post-Newtonian (PN) phasing coefficients, called a \emph{multi-parameter test}, is known to be ineffective given the sensitivity of the present generation of gravitational-wave (GW) detectors. Strong degeneracies in the parameter space make the outcome of the test uninformative. We argue that Principal Component Analysis (PCA) can remedy this problem by constructing certain linear combinations of the original PN parameters that are better constrained by gravitational-wave observations. By analyzing binary black hole events detected during the first and second observing runs (O1 and O2) of LIGO/Virgo, we show that the two dominant principal components can capture the essence of a multi-parameter test. Combining five binary black hole mergers during O1/O2, we find that the dominant linear combination of the PN coefficients obtained from PCA is consistent with GR within the 0.38 standard deviation of the posterior distribution. Furthermore, using a set of simulated \emph{non-GR} signals in the three-detector LIGO-Virgo network with designed sensitivities, we find that the method is capable of excluding GR with high confidence as well as recovering the injected values of the non-GR parameters with good precision.

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