305 citations · 1.1k across the 49 of their papers we have counts for
51 papers
How AI Agents Discover Scientific Equations: From Hydrotope Rediscovery to New Water-Wave Amplitudes
Zihan Zhou, Digvijay Wadekar, Matias Zaldarriaga
We study how AI agents discover and validate scientific formulas using a controlled case study of the hydrotope, a recently discovered geometric formula that combines the different…
Using the Circumgalactic Medium of Dwarf Galaxies as a Calorimetric Dark Matter Detector
Abby Mintz, Digvijay Wadekar, Romain Teyssier
Many well-motivated dark matter (DM) scenarios, including dark photon DM, axion-like particles, sterile neutrinos, and primordial black holes, can deposit energy into ordinary matt…
Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers
Tousif Islam, Digvijay Wadekar, Gaurav Khanna
Using approximately numerical-relativity (NR) simulations spanning mass ratios up to and black-hole-perturbation-theory (BHPT) simulations extending to $q=100…
The diffraction-lensing interpretation of GW231123 with astrophysical priors
Mark Ho-Yeuk Cheung, Digvijay Wadekar, Matias Zaldarriaga +3
GW231123, if unlensed, is a rare binary black hole merger with high masses and high spins for both progenitors. We show that the signal is better fitted by a lower-mass, lower-spin…
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
HI Simulations for Cosmology with the SKA Observatory
Tommaso Ronconi, Gabriella De Lucia, Marta Spinelli +15
We present a comparative overview of state-of-the-art methods for modelling the distribution of neutral hydrogen (HI) in the post-reionization Universe, developed in preparation fo…
Improving low-latency multi-messenger follow-up of neutron star-black hole mergers with mode-by-mode filtering
Francesco Iacovelli, Digvijay Wadekar, Javier Roulet +2
Rapid parameter estimation for neutron star-black hole (NSBH) mergers is essential for deciding whether, where, and how electromagnetic facilities should follow up gravitational-wa…
Predicting intermediate-mass black hole formation in star clusters with machine learning
Konstantinos Kritos, Digvijay Wadekar, Emanuele Berti
Whether intermediate-mass black holes reside in nearby star clusters has remained contested for decades. We address this question by training neural network and random forest regre…
Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves
Tousif Islam, Digvijay Wadekar, Tejaswi Venumadhav +4
Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present \texttt{GWAgent}, a large language mode…
gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy
Tousif Islam, Digvijay Wadekar, Zihan Zhou
Modern gravitational wave astronomy relies on modeling tasks that often require months of graduate-level effort, including building fast waveform surrogates from expensive numerica…
Not too close! Evaluating the impact of the baseline on the localization of binary black holes by next-generation gravitational-wave detectors
Francesco Iacovelli, Luca Reali, Emanuele Berti +3
Next-generation (XG) gravitational-wave detectors, such as Cosmic Explorer (CE) and the Einstein Telescope (ET), will observe compact binary coalescences at unprecedented rates and…
GW190711_030756 and GW200114_020818: astrophysical interpretation of two asymmetric binary black hole mergers in the IAS catalog
Tousif Islam, Tejaswi Venumadhav, Digvijay Wadekar +6
We provide a comprehensive analysis of GW190711_030756 and GW200114_020818, two of the most significant binary black hole merger candidates in the IAS catalog, with probabilities o…
Kick matters: The impact of a new recoil model on the retention of hierarchical black-hole remnants in globular clusters
Tousif Islam, Digvijay Wadekar, Konstantinos Kritos
In globular clusters, hierarchical mergers are among the most promising pathways to forming massive black holes such as GW231123. A key factor determining whether a merger-remnant…
Searching for precessing binary systems with mode-by-mode filtering and marginalization
Zihan Zhou, Digvijay Wadekar, Javier Roulet +8
Nearly all previous binary black hole searches in LIGO--Virgo--KAGRA (LVK) gravitational wave data have assumed that the component spins are aligned with the orbital angular moment…
Systematic biases in parameter estimation on LISA binaries. II. The effect of excluding higher harmonics for spin-aligned, high-mass binaries
Sophia Yi, Francesco Iacovelli, Emanuele Berti +4
The Laser Interferometer Space Antenna (LISA) will observe massive black hole binaries (MBHBs) with astoundingly high signal-to-noise ratio, leaving parameter estimation with these…
Sample-efficient non-Gaussian noise reduction in gravitational wave data via learnable wavelets
Arush Pimpalkar, Digvijay Wadekar, Mark Ho-Yeuk Cheung +1
We introduce , a wavelet-based neural network architecture to identify and reduce non-Gaussian noise in gravitational wave data. Traditionally, convolutional n…
Progenitor of the recoiling super-massive black hole RBH-1 identified using HST/JWST imaging
Tousif Islam, Tejaswi Venumadhav, Digvijay Wadekar
Using a combination of \textit{Hubble Space Telescope} and \textit{James Webb Space Telescope} imaging, a runaway supermassive black hole (RBH-1) was recently identified with an in…
Generating optimal Gravitational-Wave template banks with metric-preserving autoencoders
Giovanni Cabass, Digvijay Wadekar, Matias Zaldarriaga +1
Matched filtering for signal detection in noisy data requires template banks that capture variation in signal waveforms while minimizing computational cost. Dimensionality reductio…
Accurate models for recoil velocity distribution in black hole mergers with comparable to extreme mass-ratios and their astrophysical implications
Tousif Islam, Digvijay Wadekar
Modeling the remnant recoil velocity (kick) distribution from binary black hole mergers is crucial for understanding hierarchical mergers in active galactic nuclei or globular clus…
The Denario project: Deep knowledge AI agents for scientific discovery
Francisco Villaescusa-Navarro, Boris Bolliet, Pablo Villanueva-Domingo +33
We present Denario, an AI multi-agent system designed to serve as a scientific research assistant. Denario can perform many different tasks, such as generating ideas, checking the…
Data-driven extraction and phenomenology of eccentric harmonics in eccentric spinning binary black hole mergers
Tousif Islam, Tejaswi Venumadhav, Ajit Kumar Mehta +6
Newtonian and post-Newtonian (PN) calculations indicate that the phenomenology of eccentric binary black hole (BBH) merger waveforms is significantly more complex than that of thei…
Binary black hole population inference combining confident and marginal events from the search pipeline
Ajit Kumar Mehta, Digvijay Wadekar, Isha Anantpurkar +6
We present the population properties of binary black hole mergers identified by the pipeline (which incorporates higher-order modes in the search templates) du…
Improving gravitational wave search sensitivity with TIER: Trigger Inference using Extended strain Representation
Digvijay Wadekar, Arush Pimpalkar, Mark Ho-Yeuk Cheung +9
We introduce a machine learning (ML) framework called for improving the sensitivity of gravitational wave search pipelines. Typically, search pipelines only use a s…
Sampler-free gravitational wave inference using matrix multiplication
Jonathan Mushkin, Javier Roulet, Barak Zackay +5
Parameter estimation (PE) for compact binary coalescence (CBC) events observed by gravitational wave (GW) laser interferometers is a core task in GW astrophysics. We present a meth…