The Eclipsing Binaries via Artificial Intelligence. II. Need for Speed in PHOEBE Forward Models

Submitted to ApJS ABSTRACT In modern astronomy, the quantity of data collected has vastly exceeded the capacity for manual analysis, necessitating the use of advanced artificial intelligence (AI) techniques to assist scientists with the most labor-intensive tasks. AI can optimize simulation codes where computational bottlenecks arise from the time required to generate forward models. One such example is PHOEBE, a modeling code for eclipsing binaries (EBs), where simulating individual systems is feasible, but analyzing observables for extensive parameter combinations is highly time-consuming. To address this, we present a fully connected feedforward artificial neural network (ANN) trained on a dataset of over one million synthetic light curves generated with PHOEBE. Optimization of the ANN architecture yielded a model with six hidden layers, each containing 512 nodes, provides an optimized balance between accuracy and computational complexity. Extensive testing enabled us to establish ANN's applicability limits and to quantify the systematic and statistical errors associated with using such networks for EB analysis. Our findings demonstrate the critical role of dilution effects in parameter estimation for EBs, and we outline methods to incorporate these effects in AI-based models. This proposed ANN framework enables a speedup of over four orders of magnitude compared to traditional methods, with systematic errors not exceeding 1%, and often as low as 0.01%, across the entire parameter space. INTRODUCTION number of EBs are found in triple and multiple systems (Conroy et al. 2014; Orosz 2015), hosting circumbinary Fundamental stellar properties are inferred predominantly planets (Welsh et al. 2015), and featuring mass from the study of eclipsing binary stars (EBs) transfer and apsidal motion (Hambleton et al. 2013); (Torres et al. 2010). Their favorable orbital alignment these broaden the domains of study while retaining the with the line of sight, and consequent eclipses, make same tractable modeling principles. In particular, we them ideal astrophysical laboratories: a simple geometry can probe stellar interiors by studying tidally induced coupled with well-understood dynamical laws allow oscillations and gravity-mode pulsations in detached binaries us to obtain fundamental parameters without a-priori (Huber 2015); ubiquitous contact binaries are still assumptions (Prša 2018). Many of the phenomena being observed in hot that, we need samplers such as Markov Chain Monte Jupiters have their foundations in EB studies, e.g., the Carlo (MCMC, Foreman-Mackey et al. 2017) to provide Rossiter-McLaughlin effect, tidal distortions of the host heuristic parameter posteriors. This entails hundreds of star, irradiation effects, Roche lobe overflow and wind thousands if not millions of forward-model runs, which outflows, gravity darkening, apsidal motion, third body puts a hard limit on the number of systems we can solve dynamics, etc. (Barclay et al. 2012).