Measurement of Apparent Magnitude and Effective Temperature with Amateur Telescopes

TL;DR

This study presents algorithms using amateur telescope images to measure star magnitudes and temperatures, achieving reasonable accuracy with machine learning and simple fitting methods.

Contribution

It introduces a novel approach combining machine learning and simple fitting for star measurement using amateur telescopes, with calibration for improved accuracy.

Findings

Effective temperature measurement error around 9%

Apparent magnitude error nearly 0.1

Algorithms are effective with minimal calibration

Abstract

In the present study, we developed algorithms that are capable of measuring apparent magnitudes and the effective temperature of stars using raw images shot with amateur telescopes. The regularized Radial Basis Function (RBF) network, one of the machine learning algorithms, was employed to measure the effective temperature, and the simple function fitting method was adopted to measure the apparent magnitude. The achieved results are satisfying. After the white balance and noise cancellation process was simply calibrated, it was demonstrated that the measurements of the effective temperature had mean fraction errors at around 9%, and the measurements of the magnitudes had absolute error at nearly 0.1.