发表机构
Institute of Theoretical Astrophysics, University of Oslo（奥斯陆大学理论天体物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究提出基于机器学习的方法对光谱能量分布进行带通积分，开发计算方法并封装在pyfine中，通过测试案例表明该方法能快速准确预测积分值，运行快、内存开销小，在特定情况下优势明显。
AI 中文摘要
本文提出了一种基于机器学习的新方法，用于对光谱能量分布（SED）进行快速准确的带通积分。积分算子的非局部性保证了待近似目标函数的平滑性，使其成为神经网络的一个绝佳应用案例。为此开发的计算方法已封装在pyfine中（FINE：通过神经网络进行快速函数插值）。这个新的Python包使相关代码可供科学界的每个人使用且易于使用。该方法通过两个分别具有3个和9个自由参数的不同带通积分测试案例进行了演示，分析了精度和计算性能。结果表明，该方法能够在参数空间的采样区域内，以分别低至0.002%和0.08%的最大相对残差预测带通积分值，运行时间快，内存开销小。当给定一组自由参数值来计算SED函数形式成本较高时，pyfine的使用特别有效。然而，在两个测试案例中的第一个案例中，即采用简单修正黑体SED时，神经网络的测量运行时间比优化的多线程版本的积分“暴力”精确计算快两个数量级。
英文摘要
The paper presents a novel ML-based approach for fast and accurate bandpass integration of spectral energy distributions (SEDs). The non-locality of the integral operator involved guarantees the smoothness of the target function to be approximated, making it an excellent use-case for neural networks. The computational method developed for this work has been wrapped within pyfine (FINE: Fast Function Interpolation via NEural NEtworks). This new Python package makes the relevant code available and easy to use by everyone within the scientific community. The method is demonstrated with two different bandpass integration test cases with 3 and 9 free parameters, respectively, where both accuracy and computational performance are analyzed. The results show that the method is capable of predicting values of the bandpass integral with maximum relative residuals as low as 0.002% and 0.08% respectively, within the sampled region of the parameter space, fast run times, and very little memory overhead. The usage of pyfine is especially effective when the computation of an SED functional form, given a set of values for its free parameters, is expensive. However, already in the first of the two test cases, where a simple modified black body SED was employed, the neural network's measured run times resulted faster by two orders of magnitudes than an optimized multithreaded version of the ``brute force'' exact calculation of the integral.
