arXiv subjects

Archana Pai

Publications and source records attributed to Archana Pai.

At least 19 recordsLinked to original sources

arXiv2026

Integrated cosmological memory: A dark-siren method to probe dark energy

Gravitational-wave (GW) cosmology is currently bottlenecked by the scarcity of electromagnetic counterparts for bright sirens and the systematic uncertainties of galaxy catalogs for dark sirens. We propose a purely gravitational resolution using the Integrated Cosmological Memory (ICM)-the cumulative GW strain encoded in the spacetime geometry of an expanding Universe. While the GW transient emitted by the source provides the luminosity distance, the ICM accumulates a mathematically distinct integral of the cosmic expansion history. We demonstrate that extracting both observables from a single binary merger completely breaks the distance-redshift degeneracy within the gravitational sector. This establishes a novel, catalog-free dark siren framework for third-generation GW detector networks. Crucially, the resulting constraints on late-time dark energy are only weakly sensitive to the local expansion rate, providing a robust cosmological probe that can potentially mitigate the impact of the H0 tension.

astro-ph.CO↗

arXiv2026

Binary Black Hole inspirals cannot hide their eccentricity

The events detected by the LIGO Virgo KAGRA collaboration over a period of 10 years have yielded a treasure trove of signals from compact binary coalescences. None of these events have shown a confident signature of eccentricity. With upgrades to the existing network and potential next generation gravitational wave detectors, we will be able to see much further into the universe increasing the likelihood of detecting eccentric systems. We improve upon the phenomenological approach of providing eccentricity constraints using an effective chirp mass model in the time frequency domain. We introduce an improved pixel collection method along with a likelihood based sampling approach inspired by Bayesian parameter estimation. Our approach constructs a likelihood from the product of energies collected across different eccentric harmonics in the time frequency representation. This formulation enables coarse but meaningful constraints on orbital eccentricity. Additionally, we incorporate information from the energy ratios between eccentric harmonics, further refining the eccentricity estimates. We test our approach on 500 non spinning equal mass eccentric systems and demonstrate that we can constrain the eccentricity within 0.2 around the true value. Moreover, our approach can deliver these constraints in 5 minutes on a machine with 50 cores. These results demonstrate that our phenomenological approach provides fast and reasonably accurate eccentricity estimates, making it a promising tool for rapid gravitational wave data analysis.

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arXiv2025

Leveraging cross-detector parameter consistency measures to enhance sensitivities of gravitational wave searches

All-sky searches for generic short-duration astrophysical GW transients are often challenging because of noise transients. Developing novel signal-noise discriminators is crucial for GW transient searches with LIGO Scientific, Virgo, and KAGRA (LVK) detectors. In this work, we adapt a recently developed Jensen Shannon divergence (JSD)-based measure, which assesses the cross-detector parameter consistency to distinguish between weakly modeled or unmodelled astrophysical GW signals and loud noise triggers. We first extend a 2-detector JSD-based measure, developed in an earlier work, to a 3-detector network. We leverage this to modify the test statistic of the existing Coherent Waveburst (cWB)-Gaussian Mixture Modelling (GMM) algorithm for short-duration transients towards improving the search sensitivity to ad-hoc waveforms like Sine-Gaussians, Gaussian Pulses, and White Noise Bursts. We find that with the new method, which we term cWB-GMM-JSD, the sensitivity to the ad-hoc waveforms, given by $h_{\mathrm{rss50}}$, improves by $\sim 10-20 \%$ at an IFAR of 10 years for the 2-detector network consisting of LHO and LLO detectors, and by $\sim 5-10 \%$ at the same IFAR for the 3-detector network consisting of LHO, LLO and Virgo detectors. Finally, we apply the modified statistic in the revised data analysis pipeline on the publicly available data from the third observing run (O3) of the LIGO and Virgo detectors. Although we do not find any new event in the O3 data, we see a notable rise in the statistical significance of most of the known GW events, which further testifies to the enhancement in sensitivities.

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arXiv2025

Prospect of Constraining the EoS of Neutron Stars Using Post-Merger Signals

Post-merger gravitational-wave emission from a binary neutron star merger carries crucial information about the equation of state (EoS) of matter at high temperatures. Current gravitational wave detectors have limited sensitivities at post-merger frequencies in the range [1.5, 4] kHz. Therefore, valuable inferences can only be made after combining information from multiple (BNS) events. Criswell et al. [Phys. Rev. D 107, 043021 (2023)] carries out an injection study to infer the radius posterior for a $1.6 M_{\odot}$ NS by combining the information from injected BNS events via the hierarchical Bayesian inference (HBI) formulation. This formulation utilizes empirical relations that connect the peak frequency of the post-merger remnant with the chirp mass of the system, and the EoS proxy parameter $R_{1.6}$. In this work, we extend the HBI formulation to other EoS proxy parameters (i.e., the radius of (NS), $R_{1.X}$, with masses 1.2, 1.4, and 1.8 $M_{\odot}$) and combine the four $R_{1.X}$ posteriors through the piecewise polytropic EoS model to obtain measurable constraints on the EoS of the NS. We show that the NS radii can be constrained to within $\sim$1 km ( $\sim$0.55 km) assuming uniform (astrophysical) prior on $R_{1.X}$ for injections in the A+ noise. We also study systematic biases in the analysis coming from the limitations of empirical relations.

astro-ph.HE↗

arXiv2025

Improving the detection significance of gravitational wave transient searches with CNN models

Gravitational wave (GW) transient searches rely on signal-noise discriminators to distinguish astrophysical signals from noise artefacts. These discriminators are typically tuned towards expected signal morphologies, which may limit their effectiveness as detector sensitivity improves and more complex signals, such as from core collapse supernovae or compact binary mergers featuring precession, higher-order harmonics, or eccentricity, become detectable. In this work, we use a Convolutional Neural Network-based approach to classify noise transients from astrophysical transients, aiming to enhance the sensitivity of existing searches. We evaluate our method on two matched filter based searches, PyCBC-IMBH and PyCBC-HM tuned for Intermediate Mass Black Hole (IMBH) binary systems. Our approach improves the sensitive volume-time reach of these searches by approximately 30% at a false alarm rate of once per 100 years. Finally, we apply our method to the first four chunks of the first half of the third observation run and demonstrate a marked improvement in significance. In particular, we significantly improve the first IMBH binary GW event GW190521 with an IFAR exceeding 42000 years.

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arXiv2025

Gravitational physics in the context of Indian astronomy: A vision document

Contributions from the Indian gravity community have played a significant role in shaping several branches of astronomy and astrophysics. This document reviews some of the most important contributions and presents a vision for gravity research in the context of astronomy \& astrophysics in India. This is an expanded version of one of the chapters in the recently released Vision Document of the Astronomical Society of India.

astro-ph.IM↗

arXiv2024

Enhancing search pipelines for short gravitational wave transients with Gaussian mixture modelling

We present an enhanced method for the application of Gaussian Mixture Modelling (GMM) to the coherent WaveBurst (cWB) algorithm in the search for short-duration gravitational wave (GW) transients. The supervised Machine Learning method of GMM allows for the multi-dimensional distributions of noise and signal to be modelled over a set of representative attributes, which aids in the classification of GW signals against noise transients (glitches) in the data. We demonstrate that updating the approach to model construction eliminates bias previously seen in the GMM analysis, increasing the robustness and sensitivity of the analysis over a wider range of burst source populations. The enhanced methodology is applied to the generic burst all-sky short search in the LIGO-Virgo full third observing run (O3), marking the first application of GMM to the 3 detector Livingston-Hanford-Virgo network. For both 2- and 3- detector networks, we observe comparable sensitivities to an array of generic signal morphologies, with significant sensitivity improvements to waveforms in the low Quality factor parameter space at false alarm rates of 1 per 100 years. This proves that GMM can effectively mitigate blip glitches, which are one of the most problematic sources of noise for un-modelled GW searches. The cWB-GMM search recovers similar numbers of compact binary coalescence (CBC) events as other cWB post-production methods, and concludes on no new gravitational wave detection after known CBC events are removed.

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arXiv2024

Probing eccentric higher-order modes through an effective chirp mass model

The three observational runs of advanced LIGO and Virgo detectors have detected $\sim$ 90 compact binary coalescence events with most of them being quasicircular compact binary merger events. Astrophysical models predict compact binary mergers with appreciable nonzero eccentricity if in dense stellar environments like globular clusters, active galactic nuclei discs. Gravitational wave searches from eccentric systems are either limited to very low eccentricities or rely on model-agnostic time-frequency-based searches. Typically, gravitational waves from eccentric systems with moderate eccentricities carry non-negligible energy in the eccentric higher-order modes. In this work, using the effective chirp mass model [N. Bose and A. Pai, Phys. Rev. D 104, 124021 (2021)] developed using EccentricTD waveform, we develop a phenomenological scaling relation for the eccentric higher-order mode time-frequency track. Further, we incorporate this additional information from the higher-order eccentric mode for nonspinning, stellar mass eccentric binaries to improve the constraint on eccentricity.

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arXiv2024

Impact of Bayesian Priors on the Inferred Masses of Quasi-Circular Intermediate-Mass Black Hole Binaries

Observation of gravitational waves from inspiralling binary black holes has offered a unique opportunity to study the physical parameters of the component black holes. To infer these parameters, Bayesian methods are employed in conjunction with general relativistic waveform models that describe the source's inspiral, merger, and ringdown. The results depend not only on the accuracy of the waveform models but also on the underlying fiducial prior distribution used for the analysis. In particular, when the pre-merger phase of the signal is barely observable within the detectors' bandwidth, as is currently the case with intermediate-mass black hole binary signals in ground-based gravitational wave detectors, different prior assumptions can lead to different interpretations. In this study, we utilise the gravitational-wave inference library, $\texttt{Parallel Bilby}$, to evaluate the impact of mass prior choices on the parameter estimation of intermediate-mass black hole binary signals. While previous studies focused primarily on analysing event data, we offer a broader, more controlled study by using simulations. Our findings suggest that the posteriors in total mass, mass ratio and luminosity distance are contingent on the assumed mass prior distribution used during the inference process. This is especially true when the signal lacks sufficient pre-merger information and/or has inadequate power in the higher-order radiation multipoles. In conclusion, our study underscores the importance of thoroughly investigating similarly heavy events in current detector sensitivity using a diverse choice of priors. Absent such an approach, adopting a flat prior on the binary's redshifted total mass and mass ratio emerges as a reasonable choice, preventing biases in the detector-frame mass posteriors.

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arXiv2024

Deep TOV to characterize Neutron Stars

Astrophysical observations, theoretical models, and terrestrial experiments probe different regions of neutron star (NS) interior. Therefore, it is essential to consistently combine the information from these sources. This analysis requires multiple evaluations of Tolman Oppenheimer Volkoff equations which can become computationally expensive with a large number of observations. Further, multi-messenger astronomy requires rapid NS characterization via gravitational waves for efficient electromagnetic follow-up. In this work, we develop a novel neural network-based map from the EoS curve to the mass and radius of cold non-rotating NS. We estimate a speed-up of an order of magnitude when compared with the state-of-the-art RePrimAnd solver and an average error of 1e-3 when calculating the mass and radius of the neutron star. Additionally, we also develop neural network solvers for obtaining EoS curves from a physics conforming EoS model, FRZ$χ_{1.5}$. We utilize this efficient continuous map to measure the sensitivity of model parameters of FRZ$χ_{1.5}$ towards mass and radius. We show that 8 out of 18 parameters of this model are sensitive by at least three orders of magnitude higher than the remaining 10 parameters. This information will be useful in further speeding up, as well as probing the crucial parameter space, in the parameter estimation from astrophysical observations using this physics-conforming EoS model.

astro-ph.HE↗

arXiv2024

Unmasking noise transients masquerading as intermediate-mass black hole binaries

In the first three observation runs, ground-based gravitational wave (GW) detectors have observed close to 100 compact binary coalescence (CBC) events. The GW detection rates for CBCs are expected to increase with improvements in the sensitivity of the International Gravitational-Wave Observatory Network (IGWN). However, with improved sensitivity, non-Gaussian instrumental transients or ``glitches'' are expected to adversely affect GW searches and characterisation algorithms. The most detrimental effect is due to short-duration glitches, which mimic the morphology of short-duration GW transients, in particular Intermediate-mass black hole (IMBH) binaries. They can be easily misidentified as astrophysical signals by current searches, and if included in astrophysical analyses, glitches mislabelled as IMBH binaries can affect IMBH population studies. In this work, we introduce a new similarity metric that quantifies the consistency of astrophysical parameters across the detector network and helps to distinguish between IMBH binaries and short-duration, loud glitches which mimic such binaries. We develop this method using a simulated set of IMBH binary signals and a collection of noise transients identified during the third observing run of the Advanced LIGO and Advanced Virgo detectors.

gr-qc↗

arXiv2024

Science with the Daksha High Energy Transients Mission

We present the science case for the proposed Daksha high energy transients mission. Daksha will comprise of two satellites covering the entire sky from 1~keV to $>1$~MeV. The primary objectives of the mission are to discover and characterize electromagnetic counterparts to gravitational wave source; and to study Gamma Ray Bursts (GRBs). Daksha is a versatile all-sky monitor that can address a wide variety of science cases. With its broadband spectral response, high sensitivity, and continuous all-sky coverage, it will discover fainter and rarer sources than any other existing or proposed mission. Daksha can make key strides in GRB research with polarization studies, prompt soft spectroscopy, and fine time-resolved spectral studies. Daksha will provide continuous monitoring of X-ray pulsars. It will detect magnetar outbursts and high energy counterparts to Fast Radio Bursts. Using Earth occultation to measure source fluxes, the two satellites together will obtain daily flux measurements of bright hard X-ray sources including active galactic nuclei, X-ray binaries, and slow transients like Novae. Correlation studies between the two satellites can be used to probe primordial black holes through lensing. Daksha will have a set of detectors continuously pointing towards the Sun, providing excellent hard X-ray monitoring data. Closer to home, the high sensitivity and time resolution of Daksha can be leveraged for the characterization of Terrestrial Gamma-ray Flashes.

astro-ph.HE↗

arXiv2024

Daksha: On Alert for High Energy Transients

We present Daksha, a proposed high energy transients mission for the study of electromagnetic counterparts of gravitational wave sources, and gamma ray bursts. Daksha will comprise of two satellites in low earth equatorial orbits, on opposite sides of earth. Each satellite will carry three types of detectors to cover the entire sky in an energy range from 1 keV to >1 MeV. Any transients detected on-board will be announced publicly within minutes of discovery. All photon data will be downloaded in ground station passes to obtain source positions, spectra, and light curves. In addition, Daksha will address a wide range of science cases including monitoring X-ray pulsars, studies of magnetars, solar flares, searches for fast radio burst counterparts, routine monitoring of bright persistent high energy sources, terrestrial gamma-ray flashes, and probing primordial black hole abundances through lensing. In this paper, we discuss the technical capabilities of Daksha, while the detailed science case is discussed in a separate paper.

astro-ph.IM↗

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↗

arXiv2022

First gravitational-wave search for intermediate-mass black hole mergers with higher order harmonics

Current matched-filter searches for gravitational waves from binary black-hole mergers compare the calibrated detector data to waveform templates that omit the higher-order mode content of the signals predicted by General Relativity. However, higher-order emission modes become important for highly inclined asymmetric sources with masses above $\simeq 100 M_\odot$, causing current searches to be ill-suited at detecting them. We present a new gravitational-wave search that implements templates including higher-order modes, adapted signal-glitch discriminators, and trigger-ranking statistics to specifically target signals displaying strong higher modes, corresponding to nearly edge-on sources with total redshifted masses in the intermediate-mass black-hole range $M_T(1+z) \in (100,500) M_\odot$ and mass-ratios $q\in(1,10)$. Our search shows a volumetric sensitivity gain of up to $450\%$ to these signals compared to existing searches omitting higher-order modes. We deploy our search on public data from the third observing run of Advanced LIGO. While we find no statistically significant candidates beyond those already reported elsewhere, our search sets the stage to search for higher-mode rich signals in future observing runs. The efficient detection of such signals is crucial to performing detailed tests of General Relativity, observing strong-field phenomena, and maximizing the chances of observing the yet uncharted realm of intermediate-mass black hole binaries.

gr-qc↗

arXiv2022

Fishing massive black hole binaries with THAMES

Hierarchical mergers in a dense environment are one of the primary formation channels of intermediate-mass black hole (IMBH) binary system. We expect that the resulting massive binary system will exhibit mass asymmetry. The emitted gravitational-wave (GW) carry significant contribution from higher-order modes and hence complex waveform morphology due to superposition of different modes. Further, IMBH binaries exhibit lower merger frequency and shorter signal duration in the LIGO detector which increases the risk of them being misclassified as short-duration noisy glitches. Deep learning algorithms can be trained to discriminate noisy glitches from short GW transients. We present the $\mathtt{THAMES}$ -- a deep-learning-based end-to-end signal detection algorithm for GW signals from quasi-circular nearly edge-on, mass asymmetric IMBH binaries in advanced GW detectors. Our study shows that it outperforms matched-filter based $\mathtt{PyCBC}$ searches for higher mass asymmetric, nearly edge-on IMBH binaries. The maximum gain in the sensitive volume-time product for mass ratio $q \in (5, 10)$ is by a factor of 5.24 (2.92) against $\mathtt{PyCBC-IMBH}$ ($\mathtt{PyCBC-HM}$) search at a false alarm rate of 1 in 100 years. Compared to the broad $\mathtt{PyCBC}$ search this factor is $\sim100$ for the $q \in (10,18)$. One of the reasons for this leap in volumetric sensitivity is its ability to discriminate between signals with complex waveform morphology and noisy transients, clearly demonstrating the potential of deep learning algorithms in probing into complex signal morphology in the field of gravitational wave astronomy. With the current training set, $\mathtt{THAMES}$ slightly underperforms with respect to $\mathtt{PyCBC}$-based searches targeting intermediate-mass black hole binaries with mass ratio $q \in (5, 10)$ and detector frame total mass $M_T(1+z) \in (100,200)~M_\odot$.

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.

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arXiv2021

The Science Case for LIGO-India

The global network of gravitational-wave detectors has completed three observing runs with $\sim 50$ detections of merging compact binaries. A third LIGO detector, with comparable astrophysical reach, is to be built in India (LIGO-Aundha) and expected to be operational during the latter part of this decade. Multiple detectors operating at different parts of the globe will provide several pairs of interferometers with longer baselines and an increased network SNR. This will improve the sky localisation of GW events. Multiple detectors simultaneously in operation will also increase the baseline duty factor, thereby, leading to an improvement in the detection rates and, hence, the completeness of surveys. In this paper, we quantify the improvements due to the expansion of the LIGO Global Network (LGN) in the precision with which source properties will be measured. We also present examples of how this expansion will give a boost to tests of fundamental physics.

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