• Aryan Anchan
• Shaun D souza
• Krish Sanghvi
• Aryan Bhanushali

This research aims to create a deep learning classification model for supernova classification using light curve data. The model is trained to accurately distinguish different types of supernovae by leveraging observational data from known supernovae of diverse classes. The challenge lies in identifying unique characteristics and patterns in the light curve data that correspond to specific types of supernovae. The initial objective is to construct a robust model that can generalize from the training data and effectively categorize supernovae into their respective classes. The successful outcome of this project will enhance our understanding of supernovae and provide valuable insights to astronomers for their research and analysis of these celestial events. Supernovae come in various types, and our goal is to classify and identify the pertinent ones. This problem is well-suited for supervised machine learning. We began by extracting insightful features from the data, guided by scientific principles. Subsequently, we selected different machine learning algorithms for supernova classification and assessed the outcomes. We have chosen the PLASTiCC dataset. This project involves a multi-step approach to advance our knowledge of supernovae. The process begins with the extraction of scientifically relevant features from raw light curve data and augmenting them. These features are carefully decided to capture the intrinsic properties of supernovae, allowing the model to make informed classifications.

Light curves, Classification, PLASTiCC, Supernovae

"Convolutional Supernovae Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.j751-j758, April-2024, Available :http://www.jetir.org/papers/JETIR2404991.pdf

"Convolutional Supernovae Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppj751-j758, April-2024, Available at : http://www.jetir.org/papers/JETIR2404991.pdf