6 citations · 6 across the 2 of their papers we have counts for
15 papers
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…
Data-driven extraction, phenomenology and modeling of eccentric harmonics in binary black hole merger waveforms
Tousif Islam, Tejaswi Venumadhav, Ajit Kumar Mehta +6
Newtonian and post-Newtonian (PN) calculations suggest that each spherical harmonic mode of the gravitational waveforms (radiation) emitted by eccentric binaries can be further dec…
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…
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…
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…
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…