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.

翻译：在现代天文学中，数据采集量已远超人工分析能力，必须借助先进的人工智能技术协助科学家完成最繁重的任务。当前向模型生成时间成为计算瓶颈时，人工智能能够优化仿真代码。PHOEBE作为食双星系统建模代码便是典型案例：虽然单个系统仿真可行，但针对大量参数组合分析观测数据却极为耗时。为此，我们提出采用全连接前馈人工神经网络，该网络基于PHOEBE生成的超百万条合成光变曲线数据集进行训练。通过优化神经网络架构，我们获得了包含六个隐藏层（每层512个节点）的模型，在精度与计算复杂度之间实现了最优平衡。大量测试使我们确立了该神经网络的适用范围，并量化了将其用于食双星系统分析时产生的系统误差与统计误差。研究结果揭示了稀释效应对食双星参数估计的关键影响，同时提出了在基于人工智能的模型中纳入这些效应的方法。相较于传统方法，该神经网络框架实现了超过四个数量级的加速，且在整个参数空间内系统误差不超过1%，通常可低至0.01%。