Javier Roulet

Digvijay Wadekar, Tejaswi Venumadhav, Ajit Kumar Mehta et al.

Searches for gravitational wave events use models, or templates, for the signals of interest. The templates used in current searches in the LIGO-Virgo-Kagra (LVK) data model the dominant quadrupole mode $(\ell,|m|)=(2,2)$ of the signals, and omit sub-dominant higher-order modes (HM) such as $(\ell,|m|)=(3,3)$, $(4,4)$, which are predicted by general relativity. This omission reduces search sensitivity to black hole mergers in interesting parts of parameter space, such as systems with high masses and asymmetric mass-ratios. We develop a new strategy to include HM in template banks: instead of making templates containing a combination of different modes, we separately store normalized templates corresponding to $(2,2)$, $(3,3)$ and $(4,4)$ modes. To model aligned-spin $(3,3)$, $(4,4)$ waveforms corresponding to a given $(2,2)$ waveform, we use a combination of post-Newtonian formulae and machine learning tools. In the matched filtering stage, one can filter each mode separately with the data and collect the timeseries of signal-to-noise ratios (SNR). This leads to a HM template bank whose matched-filtering cost is just $\approx 3\times$ that of a quadrupole-only search (as opposed to $\approx\! 100 \times$ in previously proposed HM search methods). Our method is effectual and generally applicable for template banks constructed with either stochastic or geometric placement techniques. New GW candidate events that we detect using our HM banks and details for combining the different SNR mode timeseries are presented in accompanying papers: Wadekar et al. [1] and [2] respectively. Additionally, we discuss non-linear compression of $(2,2)$-only geometric-placement template banks using machine learning algorithms.

Tousif Islam, Digvijay Wadekar, Tejaswi Venumadhav et al.

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.