# Stellar Spectra Classification and Feature evaluation Based on Random Forest

## TL;DR

This paper presents a method for classifying stellar spectra using normalization and random forest algorithms, demonstrating effective performance on large spectral datasets and analyzing spectral features for improved understanding.

## Contribution

It introduces a novel classification scheme combining polynomial normalization with random forest, specifically addressing uncalibrated spectra from large sky surveys.

## Key findings

- RF achieves high classification accuracy on multiple spectral libraries.
- Normalization via polynomial fitting improves spectral data consistency.
- Spectral feature evaluation enhances understanding of classification results.

## Abstract

With the availability of multi-object spectrometers and the designing \& running of some large scale sky surveys, we are obtaining massive spectra. Therefore, it becomes more and more important to deal with the massive spectral data efficiently and accurately. This work investigated the classification problem of stellar spectra under the assumption that there is no perfect absolute flux calibration, for example, the spectra from Guoshoujing Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, LAMOST). The proposed scheme consists of the following two procedures: Firstly, a spectrum is normalized based on a 17th polynomial fitting; Secondly, a random forest (RF) is utilized to classifying the stellar spectra. The experiments on four stellar spectral libraries show that RF has a good classification performance. This work also studied the spectral feature evaluation problem based on RF. The evaluation is helpful in understanding the results of the proposed stellar classification scheme and exploring its potential improvements in future.