Optimizing Photometric Light Curve Analysis: Evaluating Scipy's Minimize Function for Eclipse Mapping of Cataclysmic Variables

TL;DR

This paper evaluates various optimization methods in Python's Scipy library for eclipse mapping of cataclysmic variables, focusing on maximizing entropy under chi-squared constraints to improve light curve analysis.

Contribution

It provides a comprehensive assessment of optimization techniques for eclipse mapping, including implementation details and analysis of factors affecting performance and image quality.

Findings

Nelder-Mead and SLSQP outperform CG in accuracy and speed

Factors like data points and resolution significantly impact results

Code analysis reveals potential improvements in efficiency

Abstract

With a particular focus on Scipy's minimize function the eclipse mapping method is thoroughly researched and implemented utilizing Python and essential libraries. Many optimization techniques are used, including Sequential Least Squares Programming (SLSQP), Nelder-Mead, and Conjugate Gradient (CG). However, for the purpose of examining photometric light curves these methods seek to solve the maximum entropy equation under a chi-squared constraint. Therefore, these techniques are first evaluated on two-dimensional Gaussian data without a chi-squared restriction, and then they are used to map the accretion disc and uncover the Gaussian structure of the Cataclysmic Variable KIC 201325107. Critical analysis is performed on the code structure to find possible faults and design problems. Additionally, the analysis shows how several factors impacting computing time and image quality are included including the variance in Gaussian weighting, disc image resolution, number of data points in the light curve, and degree of constraint.