Fast and accurate bandpass integration of complex SEDs with neural networks

Abstract

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.