Abstract
The domain of astrophysics involves the documentation of celestial bodies with the means of procuring supporting data which have evolved with technological advancements; the present procedure involving spectral data has received a healthy response with two recent prominent datasets. The collaboration of Computer Science and Astrophysics has been a blessing in disguise which has helped advance the research stagnant at the developing process. Observing an unexplored plethora of opportunities, the team has undertaken the task of inspecting the accuracy provided by the collected SDSS datasets to the sought-after problem of classification. The fundamentals of Astronomy within Astrophysics lie in the study of heavenly bodies under observation, and the query regarding the birth of Milky Ways has become prominent under curious scrutiny. The research implements comparative study between DR-16 and DR-17 datasets through usage of prominent machine learning algorithms—logistic regression, support vector machine, multilayer perceptron, and decision trees—to arrive at respective results. The mentioned models have been opted as they have a history of arriving at optimal solutions pertaining to the issue of classification. The efficiency of the process is promoted through data filtration. The filtered data is partitioned into training and testing sets, which is finally used to train the algorithm models, and the other half is utilized in the process of predicting the outcomes. Finally, the result with highest accuracy is declared as the most suitable algorithm.
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Bhuvaneshwari, R., Karthika Devi, M.S., Vishal, R., Sarvesh, E., Sanjeevaditya, T.M. (2023). CoSSC—Comparative Study of Stellar Classification Using DR-16 and DR-17 Datasets. In: Kumar, S., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Third Congress on Intelligent Systems. CIS 2022. Lecture Notes in Networks and Systems, vol 608. Springer, Singapore. https://doi.org/10.1007/978-981-19-9225-4_45
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