arXiv subjects

Sunil Choudhary

Publications and source records attributed to Sunil Choudhary.

7 recordsLinked to original sources

arXiv2026

SiGMa-Net II: Distinguishing Binary Black Holes from Glitches

With increasing sensitivity of the gravitational wave (GW) detectors, we expect a significant rise in the detectable GW events. To process, analyse and identify such large amounts of GW signals arising from mergers of Binary Black Holes (BBH), we need both speed and accuracy. In the search for (massive) BBH signals, the biggest hurdle is posed by the various non-gaussian noise transients called glitches. Compared to our previous work, which used a simple convolutional neural network to distinguish BBHs from Blip glitches, this work uses transfer learning with InceptionNetV3 to distinguish BBHs from six types of most popular glitches from the third observing run of LIGO. While the glitches are real and identified via GravitySpy, the BBH signals are simulated and then injected into the real detector noise for each of the two LIGO detectors. We generate Sine-Gaussian Projection (SGP) maps by cross-correlating data with Sine-Gaussian functions of varied quality factors ($Q$) and central frequencies ($f_0$) and projected on the $Q$ - $f_0$ plane. We find that SGP maps make it easier to distinguish BBHs from glitches that look very similar to BBHs in the Time-Frequency maps like the Blips, while also maintaining significant morphological differences between BBHs and the more frequent glitches - Scattered Light and Fast Scattering. Our network has an accuracy of $87%$, a TPR of 0.83 for an FPR of 0.1 on our test dataset. It is also robust, retaining its level of accuracy, when tested on real BBH events identified in the first three observing runs of LIGO. Our proposed method shows the viability of using the SGP maps and neural networks for fast identification of GW events improving the efficiency of standard search pipelines.

astro-ph.IM↗

arXiv2025

Improved Binary Black Hole Search Discriminator from the Singular Value Decomposition of Non-Gaussian Noise Transients

The sensitivity of current gravitational wave (GW) detectors to transient GW signals is severely affected by a variety of non-Gaussian and non-stationary noise transients, such as the blip, tomte, koi fish, and low-frequency blip 'glitches'. These glitches share some time-frequency resemblance with GW signals from binary black holes. In earlier works [Joshi et al., Phys. Rev. D 103, 044035 (2021); Choudhary et al., Phys. Rev. D 110, 044051 (2024)], the authors presented a method for constructing a $\chi^2$-distributed optimized statistic, based on the unified formalism of $\chi^2$ discriminators [Dhurandhar et al., Phys. Rev. D 96, 103018 (2017)], to distinguish the blip glitches from the compact binary coalescence (CBC) signals. Unlike past works, the new $\chi^2$ discriminator is constructed from the most significant singular vectors obtained from the singular value decomposition of different classes of glitches in real detector data. We find that the chi-square developed in this work performs as efficiently as in Choudhary et al. [Phys. Rev. D 110, 044051 (2024)], which used sine-Gaussian basis vectors. This result supports past empirical findings that these glitches are reasonably well-modeled by sine-Gaussians. It also introduces a method for constructing signal- and glitch-based $\chi^2$ discriminators by directly using real data containing the glitches and, thus, holds promise for extensions to glitches that are captured less well by sine-Gaussians or other analytical functions.

gr-qc↗

arXiv2024

Low-latency gravitational wave alert products and their performance at the time of the fourth LIGO-Virgo-KAGRA observing run

Multi-messenger searches for BNS and NSBH mergers are currently one of the most exciting areas of astronomy. The search for joint electromagnetic and neutrino counterparts to GWs has resumed with O4. To support this effort, public semi-automated data products are sent in near real-time and include localization and source properties to guide complementary observations. In preparation for O4, we have conducted a study using a simulated population of compact binaries and a MDC in the form of a real-time replay to optimize and profile the software infrastructure and scientific deliverables. End-to-end performance was tested, including data ingestion, running online search pipelines, performing annotations, and issuing alerts to the astrophysics community. We present an overview of the low-latency infrastructure and the performance of the data products that are now being released during O4 based on the MDC. We report the expected median latency for the preliminary alert of full bandwidth searches (29.5s) and show consistency and accuracy of released data products using the MDC. For the first time, we report the expected median latency for triggers from early warning searches (-3.1s), which are new in O4 and target neutron star mergers during inspiral phase. This paper provides a performance overview for LVK low-latency alert infrastructure and data products using the MDC and serves as a useful reference for the interpretation of O4 detections.

astro-ph.HE↗

arXiv2023

Applications of Deep Learning to physics workflows

Modern large-scale physics experiments create datasets with sizes and streaming rates that can exceed those from industry leaders such as Google Cloud and Netflix. Fully processing these datasets requires both sufficient compute power and efficient workflows. Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) can either improve or replace existing domain-specific algorithms to increase workflow efficiency. Not only can these algorithms improve the physics performance of current algorithms, but they can often be executed more quickly, especially when run on coprocessors such as GPUs or FPGAs. In the winter of 2023, MIT hosted the Accelerating Physics with ML at MIT workshop, which brought together researchers from gravitational-wave physics, multi-messenger astrophysics, and particle physics to discuss and share current efforts to integrate ML tools into their workflows. The following white paper highlights examples of algorithms and computing frameworks discussed during this workshop and summarizes the expected computing needs for the immediate future of the involved fields.

hep-ex↗

arXiv2022

Improved binary black hole searches through better discrimination against noise transients

Short-duration noise transients in LIGO and Virgo detectors significantly affect the search sensitivity of compact binary coalescence (CBC) signals, especially in the high mass region. In a previous work by the authors \cite{Joshi_2021}, a $\chi^2$ statistic was proposed to distinguish them, when modeled as sine-Gaussians, from non-spinning CBCs. The present work is an extension where we demonstrate the better noise-discrimination of an improved $\chi^2$ statistic -- called the optimized sine-Gaussian $\chi^2$ -- in real LIGO data. The extension includes accounting for the initial phase of the noise transients and use of a well-informed choice of sine-Gaussian basis vectors selected to discern how CBC signals and some of the most worrisome noise-transients project differently on them~\cite{sunil_2022}. To demonstrate this improvement, we use data with blip glitches from the third observational run (O3) of LIGO-Hanford and LIGO-Livingston detectors. Blips are a type of short-duration non-Gaussian noise disturbance known to adversely affect high-mass CBC searches. For CBCs, spin-aligned binary black hole signals were simulated using the \textsc{IMRPhenomPv2} waveform and injected into real LIGO data from the same run. We show that in comparison to the sine-Gaussian $\chi^2$, the optimized sine-Gaussian $\chi^2$ improves the overall true positive rate by around 6\% in a lower-mass bin ($m_1,m_2 \in [20,40]M_{\odot}$) and by more than 3\% in a higher-mass bin ($m_1,m_2 \in [60,80]M_{\odot}$). On the other hand, we see a larger improvement -- of more than 20\% -- in both mass bins in comparison to the traditional $\chi^2$.

gr-qc↗

arXiv2022

SiGMa-Net: Deep learning network to distinguish binary black hole signals from short-duration noise transients

Blip glitches, a type of short-duration noise transient in the LIGO--Virgo data, are a nuisance for the binary black hole (BBH) searches. They affect the BBH search sensitivity significantly because their time-domain morphologies are very similar, and that creates difficulty in vetoing them. In this work, we construct a deep-learning neural network to efficiently distinguish BBH signals from blip glitches. We introduce sine-Gaussian projection (SGP) maps, which are projections of GW frequency-domain data snippets on a basis of sine-Gaussians defined by the quality factor and central frequency. We feed the SGP maps to our deep-learning neural network, which classifies the BBH signals and blips. Whereas the BBH signals are simulated, the blips used are taken from real data throughout our analysis. We show that our network significantly improves the identification of the BBH signals in comparison to the results obtained using traditional-$χ^2$ and sine-Gaussian $χ^2$. For example, our network improves the sensitivity by 75% at a false-positive rate of $10^{-2}$ for BBHs with total mass in the range $[80,140]~M_{\odot}$ and SNR in the range $[3,8]$. Also, it correctly identifies 95% of the real GW events in GWTC-3. The computation time for classification is a few minutes for thousands of SGP maps on a single core. With further optimisation in the next version of our algorithm, we expect a further reduction in the computational cost. Our proposed method can potentially improve the veto process in the LIGO--Virgo GW data analysis and conceivably support identifying GW signals in low-latency pipelines.

gr-qc↗

arXiv2021

Gravitational waves from binary black hole mergers surrounded by scalar field clouds: Numerical simulations and observational implications

We show how gravitational-wave observations of binary black hole (BBH) mergers can constrain the physical characteristics of a scalar field cloud parameterized by mass $\tildeμ$ and strength $ϕ_0$ that may surround them. We numerically study the inspiraling equal-mass, non-spinning BBH systems dressed in such clouds, focusing especially on the gravitational-wave signals emitted by their merger-ringdown phase. These waveforms clearly reveal that larger values of $\tildeμ$ or $ϕ_0$ cause bigger changes in the amplitude and frequency of the scalar-field-BBH ringdown signals. We show that the numerical waveforms of scalar-field-BBHs can be modelled as chirping sine-Gaussians, with matches in excess of 95%. This observation enables one to employ computationally expensive Bayesian studies for estimating the parameters of such binaries. Using our chirping sine-Gaussian signal model we establish that observations of BBH mergers at a distance of 450 Mpc will allow to distinguish BBHs without any scalar field from those with a field strength $ϕ_0 \gtrsim 5.5\times 10^{-3}$, at any fixed value of $\tilde μ\in [0.3,0.8]$, with 90% confidence or better, in single detectors with Advanced LIGO/Virgo type sensitivities. This provides hope for the possibility of determining or constraining the mass of ultra-light bosons with gravitational-wave observations of BBH mergers.

gr-qc↗