Abstract Details

Name: Bhavana D
Affiliation: Indian Institute of Space science and Technology
Conference ID: ASI2020_79
Title : Identification of brown dwarf candidates through photometric techniques
Authors and Co-Authors : Bhavana D. (IIST), Sarita Vig (IIST), Swarna K. Ghosh (TIFR), R. K. S. S. Gorthi (IIT-Tirupati)
Abstract Type : Poster
Abstract Category : Stars, ISM and Galaxy
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. The colours are selected based on spectroscopic features observed in the brown dwarf spectra. We initially check the efficiencies of these three classification techniques, both individually and collectively, on known brown dwarfs. This is followed by their application to three regions in the sky, namely Hercules (2 deg × 2 deg ), Serpens (9 deg × 4 deg ), and Lyra (2 deg × 2 deg ). Testing these algorithms on sets of objects that include known brown dwarfs show 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.