Stellar classification Dataset

Keywords: Stellar, classification

Background

Stellar classification is one of the famous kind of task in the astronomy. Stars can be classified independently based on several different features and they can be classified in an efficient way with the ANN as it has the capacity to learn in-depth regarding the datasets. Some of the major challenges that can be occurred during the classification are complex datasets and their non-linear relations. However, ANN will help in working on complex datasets because of several different hidden layers. Some of the important features that support predicting the class of the stars are alpha value, delta value, and redshift value. However, the author has built the ANN model in such a way that the model can handle both complex and non-linear kinds of data. Overall, after the application of ANN layers, it has learned some important features from the dataset and achieved an accuracy of around 75%. Finally, this ANN model was quite successful in predicting the class of the stars by learning from historical data.

Aim & Objectives

Aim
To Classify Stellar Data Using DL
Objective
Develop an ANN model to classify stars based on spectral data, enhancing understanding of stellar evolution.
Create an ANN system that analyzes spectral features to accurately categorize stars into different classes.
Evaluate the ANN's classification accuracy, contributing to improved insights into stellar properties and compositions.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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