Analysis tool for lunar crescent visibility criterion based on integrated lunar crescent database

Abstract

The analysis of lunar crescent visibility criteria is vital to provide a comparative insight into lunar crescent visibility criteria performance in predicting the visibility of a lunar crescent and suitability for Hijri calendar determination. While there have been attempts to measure the performance of lunar crescent visibility criteria, these works are in a singular analysis and not a comparative manner, not based on an integrated database of lunar crescent visibility under standardized calculated astrometry, and some are biased towards their lunar crescent visibility criterion. This warrants new research on methods to analyse lunar crescent visibility criteria. Therefore, this research endeavour to develop an analysis tool for lunar crescent visibility criteria using an integrated lunar crescent visibility database. Lunar and solar geometrical positions are calculated using the Skyfield Python library. 8290 lunar crescent visibility records are collected as a reference for analysis. The analysis tool called HilalPy was developed in the form of a Python library, as it enables ease of integration into other software or web pages and has easier deployment onto various operating systems. HilalPy uses descriptive statistics, contradiction rate percentage, and regression analysis as its base analysis, making the calculated result comparable to other lunar crescent visibility criteria. HilalPy is hoped to provide insight into the future development of lunar crescent visibility criteria, particularly for calendrical purposes.