# A Classifier to Detect Elusive Astronomical Objects through Photometry

## TL;DR

This paper explores machine learning techniques, including neural networks and k-nearest neighbors, to identify elusive brown dwarf objects in the sky through photometric data, demonstrating high efficiency especially with ensemble classifiers.

## Contribution

It introduces the use of ensemble machine learning classifiers for detecting brown dwarf candidates in astronomical photometric data, showing improved detection efficiency over individual methods.

## Key findings

- High completeness in detecting known brown dwarfs in tested regions
- Ensemble classifiers outperform individual methods in identifying brown dwarf candidates
- Successful application to multiple sky regions including Lyra, Hercules, and Serpens.

## Abstract

The application of machine learning principles in the photometric search of elusive astronomical objects has been a less-explored frontier of research. Here we have used three methods: the Neural Network and two variants of k-Nearest Neighbour, to identify brown dwarf candidates using the photometric colours of known brown dwarfs. We initially check the efficiencies of these three classification techniques, both individually and collectively, on known objects. This is followed by their application to three regions in the sky, namely Hercules (2 deg x 2 deg), Serpens (9 deg x 4 deg) and Lyra (2 deg x 2 deg). Testing these algorithms on sets of objects that include known brown dwarfs shows a high level of completeness. This includes the Hercules and Serpens regions where brown dwarfs have been detected. We use these methods to search and identify brown dwarf candidates towards the Lyra region. We infer that the collective method of classification, also known as ensemble classifier, is highly efficient in the identification of brown dwarf candidates.