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Digvijay Wadekar

Publications and source records attributed to Digvijay Wadekar.

At least 19 recordsLinked to original sources
arXiv2026

Unified remnant models for aligned-spin, precessing, and eccentric binary black hole mergers

Using approximately $5000$ numerical-relativity (NR) simulations spanning mass ratios up to $q=128$ and $1200$ black-hole-perturbation-theory (BHPT) simulations extending to $q=1000$, we present fully analytic models for the remnant properties of quasi-circular binary black hole mergers. The models gwModelRemS (for final mass, final spin, peak luminosity, and recoil kick) and gwModelRemP (for final mass, final spin, and peak luminosity) describe nonprecessing and precessing binaries, respectively. Our approach combines analytic insights from post-Newtonian theory and point-particle limits with a data-driven fitting framework which also includes validation-guided AI-agent-assisted optimization. The resulting fits outperform existing analytic models and achieve accuracies comparable to data-driven models within their domains of validity. We also construct gwModelRemP\_flow, a normalizing-flow model for recoil kicks from precessing binaries, marginalized over the in-plane spin orientations. This model additionally utilizes an enlarged low-fidelity training set, beyond the available NR and BHPT simulations, constructed through a waveform-based data-augmentation procedure. We also include simple eccentric extensions through leading-order dependence on eccentricity and orbital anomaly. The models span the equal-mass to extreme-mass-ratio regimes and are publicly available through the gwModels package for applications in gravitational-wave astronomy, astrophysical population studies, and cosmology.

gr-qc↗
arXiv2026

The diffraction-lensing interpretation of GW231123 with astrophysical priors

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 merger that is diffraction-lensed by an isolated object of redshifted mass $\sim 1000\,\rm M_\odot$, modeled either as a point mass or as a spherically symmetric compact halo. Because diffraction-lensed events are also rare, hypothesis testing should quote the posterior odds ratio rather than the Bayes factor, which requires quantifying our prior belief in the two hypotheses. We adopt the GWTC-5 population distribution as our source parameter prior, and quantify the prior on the lens hypothesis through the lensing optical depth. For a point-mass lens, observational constraints on the abundance of black holes in the Universe yield an upper bound on the optical depth, and hence on the posterior odds, which do not rule out lensing. However, using a predicted mass function of intermediate-mass black holes formed in star clusters gives a low optical depth that strongly disfavors lensing. For a dark matter halo lens, standard collisionless cold dark matter does not form halos compact enough to give the required optical depth, but self-interacting dark matter with a large cross section at low velocities can trigger gravothermal collapse and form them, in which case the posterior odds are inconclusive. In either case, from a frequentist perspective, we show that detecting a lensed event with properties like GW231123 is unlikely. These conclusions apply to isolated lenses, while a lens embedded in an external gravitational potential could change the picture.

astro-ph.CO↗
arXiv2026

Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group

Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely available textbook at https://deeplearning4astro.com, curated from the NASA Cosmic Origins Artificial Intelligence and Machine Learning Science and Technology Interest Group (AI/ML STIG) lecture series. The book collects 23 chapters by 17 lecturers across six parts, moving from computational foundations and deep-learning architectures through generative modeling, simulation-based inference, reinforcement learning, and large-language-model agents to the practice of AI-laden science. Many include executable notebooks using astronomical data.

astro-ph.IM↗
arXiv2026

HI Simulations for Cosmology with the SKA Observatory

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 for upcoming SKAO cosmological surveys. Our aim is to assess how different physical and empirical assumptions reflect into predictions for key observables such as the cosmic HI density, the HI mass function, and the HI-halo mass relation. We consider both: (i) semi-analytical approaches that self-consistently evolve baryonic components within dark matter merger trees through physically motivated prescriptions and (ii) empirical schemes tailored to different observables and based on fast approximations designed for large ensemble studies. By comparing the predictions from the different methods considered, we find overall consistency in integrated quantities such as $\Omega_{\mathrm{HI}}$, yet systematic differences in the detailed shape and scatter of the HI--halo mass relation and its redshift evolution. Semi-analytical models offer physically grounded predictions but depend on assumed prescriptions, while empirical methods provide flexibility and computational efficiency at the expense of robustness in extrapolated regions of the parameter space. The increasing number of HI measurements from SKA precursors and pathfinders (including surveys with MeerKAT, ASKAP, and FAST) will provide critical observational constraints to refine and calibrate current simulation methodologies. In turn, increasingly realistic HI simulations play a key role in interpreting these data, guiding survey design and analysis strategies, in preparation for the advent of SKAO data.

astro-ph.CO↗
arXiv2026

Improving low-latency multi-messenger follow-up of neutron star-black hole mergers with mode-by-mode filtering

Rapid parameter estimation for neutron star-black hole (NSBH) mergers is essential for deciding whether, where, and how electromagnetic facilities should follow up gravitational-wave alerts. Current low-latency analyses typically use only the dominant quadrupole harmonic, leaving strong degeneracies among luminosity distance, inclination, and intrinsic binary parameters. We show that mode-by-mode filtering of the $(2,2)$, $(3,3)$, and $(4,4)$ signal-to-noise-ratio (SNR) time series enables low-latency marginalization over higher-order-mode information at a computational cost comparable to quadrupole-only analyses. Applied to simulated NSBH detections in a LIGO-Virgo network at design sensitivity, our method improves constraints on luminosity distance, viewing angle, localization volume, and source-frame secondary mass, thereby sharpening crucial estimates of electromagnetic detectability and host-galaxy association. We also validate the approach on public data for previously detected NSBH events, finding the largest improvement for the asymmetric, higher-SNR event GW190814.

gr-qc↗
arXiv2026

Predicting intermediate-mass black hole formation in star clusters with machine learning

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 regressors on synthetic catalogs generated with the {\sc Rapster} cluster evolution code, mapping observable cluster properties such as total mass and half-mass radius onto the mass of the heaviest black hole built up through repeated mergers. Applying these models to nearby globular and nuclear star clusters, we forecast the intermediate-mass black hole population that each system may host. Globular clusters are unlikely to contain black holes more massive than $\sim 100\,M_\odot$, with an occupation fraction near 0.02, although they can produce remnants within the upper mass gap with masses approaching $100\,M_\odot$. Among nuclear star clusters, a handful of cases, including NGC 5102 and NGC 5206, yield predicted central black hole masses above $100\,M_\odot$, which we contrast with kinematically inferred estimates. Where the observationally claimed masses exceed our predictions, the implication is that the assembly history involved processes beyond hierarchical mergers, most plausibly accretion of gas and stars. Finally, we employ a normalizing flow to quantify, for individual globular clusters, the likelihood that their initial conditions were favorable to a collisional runaway during the first few million years after formation.

astro-ph.GA↗
arXiv2026

gwBenchmarks: Stress-Testing LLM Agents on High-Precision Gravitational Wave Astronomy

Modern gravitational wave astronomy relies on modeling tasks that often require months of graduate-level effort, including building fast waveform surrogates from expensive numerical relativity simulations, modeling orbital dynamics of black holes, fitting merger remnant properties and constructing template banks. These problems demand extreme precision to support detection and parameter inference, with state-of-the-art models achieving $\lesssim 10^{-4}$ relative error. We study whether state-of-the-art LLM coding agents can perform such end-to-end scientific modeling, where success requires constructing models with stringent accuracy criteria and reasoning about physical systems. We introduce gwBenchmarks, a suite of eight tasks grounded in gravitational wave analytic calculations and numerical simulations collectively representing over $10^8$ core-hours of compute. The tasks span interpolation, regression, and high-dimensional time-series modeling, requiring a combination of numerical methods, machine learning, and physics-informed approaches. In preliminary experiments, agents frequently relied on proxy metrics, partial evaluation, or fabricated results to spuriously complete tasks. We therefore implement an external pre-defined framework to gauge agent progress. Evaluating twelve coding agents, we find no consistent winner. On the easiest task, multiple agents converge to the same cubic spline solution, with one rediscovering a coordinate transformation widely used in the literature. On harder tasks like analytic waveform modeling, all agents fall 1-2 orders of magnitude short of domain requirements and exhibit systematic failures, including metric misuse, constraint violations, and result fabrication. Our code, data, and website are publicly available.

gr-qc↗
arXiv2026

Discovery of Interpretable Surrogates via Agentic AI: Application to Gravitational Waves

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 model (LLM)-based workflow that constructs interpretable analytic surrogates directly from simulation data. Surrogate modeling is well suited to agentic workflows because candidate models can be quantitatively validated against ground-truth simulations at each iteration. As a demonstration, we build a surrogate for gravitational waveforms from eccentric binary black hole mergers. We show that providing the agent with a physics-informed domain ansatz substantially improves output model accuracy. The resulting analytic surrogate attains a median Advanced LIGO mismatch of $6.9\times10^{-4}$ together with an $\sim 8.4\times$ speedup in waveform evaluation, surpassing both symbolic regression and conventional machine learning baselines. Beyond producing an accurate model, the workflow identifies compact physical structure from the learned representation. As an astrophysical application, we use \texttt{GWAgent} to analyze the eccentricity of GW200129 and infer $e_{20\mathrm{Hz}}=0.099^{+0.063}_{-0.044}$. These results show that validation-constrained agentic workflows can produce accurate, fast, and interpretable surrogates for scientific simulations and inference.

gr-qc↗
arXiv2026

Not too close! Evaluating the impact of the baseline on the localization of binary black holes by next-generation gravitational-wave detectors

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 signal-to-noise ratios (SNRs). Accurate sky localization of these sources is crucial for several aspects of the science case of CE and ET. The localization of most binary black hole (BBH) signals, which will spend at most a few minutes within the XG detector's effective sensitivity band, will continue to rely primarily on timing triangulation across a network of detectors. A key design choice for triangulation is the baseline between instruments. We investigate how the baseline affects the localization capabilities of a two-detector CE network, analyzing both fixed-parameter injections and a realistic BBH population consistent with the latest GWTC-4 results. For detector-frame total masses up to $\sim\!100\,{\rm M}_\odot$, we find that baselines corresponding to light travel times of $8-11$ ms ($\sim\!2300-3300$ km) offer a reasonable compromise, producing predominantly unimodal or bimodal sky localizations suitable for electromagnetic follow-up and statistical host galaxy identification and galaxy cross-correlation studies. Shorter baselines significantly degrade localization, particularly for high SNR events. Crucially, we find that adding a third detector to the network eliminates localization multimodality for a substantial fraction of sources. A network with two CEs and LIGO-India provides unimodal posteriors for a good fraction of events, whereas two CEs plus ET would provide unimodal posteriors for essentially all of them. These considerations should be useful to inform the development of the XG detector network.

gr-qc↗
arXiv2026

GW190711_030756 and GW200114_020818: astrophysical interpretation of two asymmetric binary black hole mergers in the IAS catalog

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 of astrophysical origin $p_{\rm astro}=0.99$ and $0.71$, respectively, and signal-to-noise ratios of approximately $10.0$ and $13.4$. We employ numerical relativity surrogate models to infer both the source properties and the remnant properties of these two candidates. We find that both GW190711_030756 and GW200114_020818 are asymmetric-mass binaries, with inferred mass ratios of $0.35^{+0.32}_{-0.15}$ and $\leq 0.20$. In addition, GW200114_020818 is inferred to have a source-frame total mass of approximately $220M_{\odot}$ and highly spinning black holes, with primary (secondary) dimensionless spin magnitudes of $0.96^{+0.03}_{-0.07}$ ($0.84^{+0.13}_{-0.34}$), closely resembling GW231123_135430. We further find that GW200114\_020818 has a confidently negative effective inspiral spin of $\chi_{\rm eff}=-0.60^{+0.22}_{-0.13}$ and exhibits strong spin precession, characterized by an effective precession parameter of $\chi_{\rm p}=0.60^{+0.21}_{-0.19}$. GW200114_020818 (when considered alongside GW231123_135430) points towards an emerging population of massive, rapidly spinning BBH mergers. While GW231123_135430 is consistent with mergers in globular clusters, producing systems like GW200114_020818 in such environments remains difficult even under hierarchical merger scenarios. The probability that the remnant black hole of GW190711_030756 (GW200114_020818) is retained in its host environment is $0.079$ ($0.0002$), $0.62$ ($0.965$), and $0.997$ ($1$) if the merger occurred in a globular cluster, a nuclear star cluster, or an elliptical galaxy, respectively.

astro-ph.HE↗
arXiv2026

Kick matters: The impact of a new recoil model on the retention of hierarchical black-hole remnants in globular clusters

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 black hole will be retained in these environments and thus participate in subsequent hierarchical mergers is the recoil kick velocity. Analytic models for the recoil velocity are currently employed in nearly all population-synthesis frameworks. We instead use a state-of-the-art recoil-kick model gwModel_flow_prec developed from a combination of numerical-relativity and black-hole perturbation-theory data, together with data-driven techniques such as normalizing flows and the post-Newtonian structure of the kick. Employing both back-of-the-envelope estimates and detailed N-body as well as semi-analytical cluster simulations, we show that gwModel_flow_prec leads to a noticeable increase in the retention probability of hierarchical-merger remnants compared to the previously used analytic model and changes the mass and spin distribution of the black holes formed through hierarchical mergers. Additionally, we discuss the implications of our results in the context of massive binaries such as GW231123.

astro-ph.HE↗
arXiv2026

Searching for precessing binary systems with mode-by-mode filtering and marginalization

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 momentum, thereby neglecting spin-precession effects in the waveform, which can lead to potentially missing interesting signals. Precessing searches are challenging, because the extra degrees of freedom due to misaligned spins lead to: $(i)$ a much larger number of templates compared to the aligned-spin configurations, $(ii)$ an increased rate of background triggers. To address this, we develop novel precessing signal template banks using mode-by-mode filtering and marginalization methods. We use the precession harmonic decomposition from Fairhurst et al. (2019) and filter each precessing harmonic separately with the data. We then marginalize over the SNRs from different harmonics in our detection statistic. We also use machine learning methods to improve our search efficiency: $(i)$ we use singular value decomposition together with random forest regressor to reduce redundancy in the dominant precessing-harmonic templates; $(ii)$ we use normalizing flows to generate optimal prior samples for harmonic SNRs for the marginalized statistic. We show that marginalizing (instead of maximizing) over the harmonic mode SNRs increases the search sensitive volume by $\sim 10\%$. Results from searching in LVK data using this framework will be reported in a companion paper.

gr-qc↗
arXiv2026

Systematic biases in parameter estimation on LISA binaries. II. The effect of excluding higher harmonics for spin-aligned, high-mass binaries

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 signals susceptible to seemingly small waveform errors. Of particular concern for MBHBs are errors due to neglected higher-order modes. We extend Yi et al. [arXiv:2502.12237] to examine errors due to neglected higher-order modes for MBHBs with nonzero (aligned) progenitor spins and total mass up to $10^8\,M_\odot$. For these very massive systems, there can be regions of parameter space in which the $(\ell, |m|)=(2,\,2)$ modes are no longer dominant with respect to higher-order ones. We find that the extent of systematic bias can change significantly when varying the progenitor spins of the binary. We also find that for the heaviest, and therefore shortest, MBHB signals, slight systematic errors can cause severe misinference of the sky localization parameters. We propose an improved likelihood optimization scheme with respect to previous work as a way to predict these effects in a computationally efficient manner.

gr-qc↗
arXiv2026

Progenitor of the recoiling super-massive black hole RBH-1 identified using HST/JWST imaging

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 inferred velocity of $954^{+110}_{-126}\,\mathrm{km\,s^{-1}}$, likely ejected from a compact star-forming galaxy (denoted as GX) at $z \approx 0.96$. Assuming the runaway black hole was the outcome of the gravitational-wave-driven merger of two black holes, we use its measured runaway velocity together with gravitational-wave recoil predictions from numerical relativity and black hole perturbation theory to constrain the mass ratio and spin configuration of the progenitor SMBHs that overcame the final-parsec problem and merged $\sim 70$~Myr ago. We find that the progenitor binary must have been precessing, with a mass ratio $m_1/m_2\lesssim 6$, and that the more massive SMBH must have possessed a high spin (dimensionless spin magnitude $\sim 0.75$) in order to generate a recoil of this magnitude. This has important astrophysical implications as similar SMBH mergers can be an interesting source population for the upcoming LISA mission with signal-to-noise ratios $\gtrsim$ 1000. Furthermore, the progenitor SMBH properties imply that GX was likely formed through a major, gas-rich (``wet'') merger between two galaxies of comparable mass, with a mass ratio $\lesssim 4$.

astro-ph.HE↗
arXiv2026

Sample-efficient non-Gaussian noise reduction in gravitational wave data via learnable wavelets

We introduce $\texttt{WaveletNet}$, a wavelet-based neural network architecture to identify and reduce non-Gaussian noise in gravitational wave data. Traditionally, convolutional neural networks (CNNs) have been widely used as a flexible machine learning method to mitigate non-Gaussian noise. However, training CNNs requires many data samples, especially when the input data segments are long. Glitches that mimic high-mass black hole signals are empirically known to have a wavelet-like structure. We exploit this property in $\texttt{WaveletNet}$ by using simple neural networks to learn the best family of wavelets to model glitches in the LIGO-Virgo-KAGRA O3 data. Due to its simplicity, our framework is significantly more sample-efficient than CNNs. As a use case, we build upon the $\texttt{TIER}$ method and show how $\texttt{WaveletNet}$ can improve the performance of any search pipeline. We take potential GW candidates from the pipeline, and then downweight the candidates having noisy strain regions in their vicinity. We use our framework in a modular way: we provide an output score which can be added to the pipeline's existing detection statistic score for the candidates. We test our method using candidates from the $\texttt{IAS-HM}$ search pipeline and show that it improves the search sensitive volume by up to 15% for high-mass, asymmetric binaries.

astro-ph.IM↗
arXiv2025

Accurate models for recoil velocity distribution in black hole mergers with comparable to extreme mass-ratios and their astrophysical implications

Modeling the remnant recoil velocity (kick) distribution from binary black hole mergers is crucial for understanding hierarchical mergers in active galactic nuclei or globular clusters. Existing analytic models often show large discrepancies with numerical relativity (NR) data, while data-driven models are limited to mass ratios of q<=8 (aligned spins) and q<=4 (precessing spins) and break down when extrapolated outside their training ranges. Using ~5000 of NR simulations from the SXS and RIT catalogs up to q=128 and ~100 black hole perturbation theory simulations up to q=200, we present two classes of models: (i) gwModel_kick_q200 (gwModel_kick_q200_GPR), an analytic (Gaussian process regression) model for aligned-spin binaries. (ii) gwModel_kick_prec_flow, a normalizing-flow model for kick distribution from precessing binaries with isotropic spins. Our approach combines analytic insights from post-Newtonian theory with data-driven techniques to ensure correct limiting behavior and high accuracy across parameter space. Both gwModel_kick_q200 and gwModel_kick_prec_flow are valid from comparable to extreme mass ratios. Extensive validation shows all three models outperform existing ones within their respective domains. Finally, using both back-of-the-envelope estimates and 1404 detailed star cluster simulations incorporating our kick models, we find that the black hole retention probability in low mass globular clusters can vary noticeably when the gwModel_kick_prec_flow model is employed. The models are publicly available through the gwModels package.

gr-qc↗
arXiv2025

Generating optimal Gravitational-Wave template banks with metric-preserving autoencoders

Matched filtering for signal detection in noisy data requires template banks that capture variation in signal waveforms while minimizing computational cost. Dimensionality reduction of signal waveforms can be important for building efficient template banks. In various domains of physics, dimensionality reduction is very commonly performed using linear methods such as singular value decomposition (SVD). This can, however, introduce redundancies if the signals span curved, nonlinear manifolds in parameter space. Alternatively, autoencoders are a type of neural networks that can be used for non-linear dimensionality reduction. We use a variation of the autoencoder which preserves the metric in its latent space ($g_{ij}^{\text{latent}} \approx g_{ij}^{\text{physical}}$); this enables template banks to be constructed by simply placing a uniform grid in the autoencoder's low-dimensional latent space. We apply our method for creating geometric template banks for gravitational wave searches and show that our banks require fewer dimensions compared to using the SVD basis. Our method can also be useful for other applications requiring dimensionality reduction, such as gravitational waveform modeling, fast parameter estimation and model-independent tests of general relativity. Finally, we discuss extensions to other domains including cosmological parameter estimation, and we show tests of our method in extreme cases of periodic signal manifolds.

gr-qc↗
arXiv2025

The Denario project: Deep knowledge AI agents for scientific discovery

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 literature, developing research plans, writing and executing code, making plots, and drafting and reviewing a scientific paper. The system has a modular architecture, allowing it to handle specific tasks, such as generating an idea, or carrying out end-to-end scientific analysis using Cmbagent as a deep-research backend. In this work, we describe in detail Denario and its modules, and illustrate its capabilities by presenting multiple AI-generated papers generated by it in many different scientific disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, material science, mathematical physics, medicine, neuroscience and planetary science. Denario also excels at combining ideas from different disciplines, and we illustrate this by showing a paper that applies methods from quantum physics and machine learning to astrophysical data. We report the evaluations performed on these papers by domain experts, who provided both numerical scores and review-like feedback. We then highlight the strengths, weaknesses, and limitations of the current system. Finally, we discuss the ethical implications of AI-driven research and reflect on how such technology relates to the philosophy of science. We publicly release the code at https://github.com/AstroPilot-AI/Denario. A Denario demo can also be run directly on the web at https://huggingface.co/spaces/astropilot-ai/Denario, and the full app will be deployed on the cloud.

cs.AI↗