When: September 24, 2019 @ 12:00pm

Where: PAB, 3rd floor, B305

Classification in Astronomy using ML.

Dr. Sharma is a postdoc at the Inter University Centre for Astronomy and Astrophysics in Pune, India. His Tuesday noon talk will be on “Classification in Astronomy using ML.” In his research, he applies machine learning and deep learning to the classification of stellar spectra and LMXB X-ray spectra.

Abstract: The huge data volume generated from the ongoing and upcoming astronomical survey programs has pushed the astronomers to shift from the traditional methods to more sophisticated and scalable approaches for data reduction and analysis without compromising with the precision and accuracy of the results. To address these concerns, Machine Learning (ML) algorithms have been considered in Astronomy, like other domains, and have been employed successfully for a wide range of astronomical problems in the last couple of decades. Classifying astronomical objects is one such field and in this talk, we will consider:
(1) stellar spectral classification using machine learning (ML) and deep learning (DL) techniques like ANN, RF, and CNN based on their spectra in the optical region of EM spectrum. We show that using CNNs, we are able to lower the RMS error up to 1.23 spectral sub-classes and apply the final model on stellar spectra from the SDSS.
(2) classification of X-ray spectra of Low Mass X-ray Binary (LMXB) systems which consist of a main-sequence star and a compact object that could be a Black Hole (BH) or a Neutron Star (NS). Using random forest (RF) algorithm, we are able to identify the type of compact object in the LMXB system (BH or NS) by feeding the X-ray energy spectrum to the model with an average accuracy of 87+/-13%.