Arunima Banerjee
IISER Tirupati
Arunima Banerjee, an Associate Professor and the Chair at the Department of Physics, Indian Institute of Science Education and Research, Tirupati. Her research interest is Theoretical Astrophysics, focussing on Dynamics of Galaxies using high resolution N-body + hydrodynamical simulations and machine learning techniques. Earlier, she was a DST-INSPIRE Faculty Fellow at the Inter-University Centre for Astronomy & Astrophysics, Pune, India (2014–2017), before that a Postdoctoral Visiting Scientist at the National Centre for Radio Astrophysics, Tata Institute of Fundamental Research, Pune, India (2012–2014) and prior to that an Integrated Ph.D. student at the Indian Institute of Science, Bangalore, India, obtaining a Masters in Physics and a Doctorate in Astrophysics from the Department of Physics in 2007 and 2012, respectively. She did her Bachelor’s in Physics (Hons.) from St. Xavier’s College, University of Calcutta in 2004. She received the Best Thesis Award in Theoretical Physics 2012 from IISc, Bangalore, the Justice Oak Best Thesis Award 2012, and the K.D. Abhayankar Best Thesis Award 2012, both from the Astronomical Society of India. She is a member of the Astronomical Society of India, the International Astronomical Union, a Visiting Associate of IUCAA, Pune, and also serves as an Editorial Board of the Journal of Astronomy & Astrophysics, Indian Academy of Sciences.
Session 2B: Invited Lectures
Chairperson: Ekambaram Balaraman, IISER Tirupati ; Vasudharani Devanathan, IISER Tirupati
Identifying lopsidedness in spiral galaxies using a deep convolutional neural network
About 30% of disk galaxies show lopsidedness in their stellar disk. Although such a large-scale asymmetry in the disk can be primarily looked upon as a long-lived mode ($m=1$), the physical origin of the lopsidedness in the disk continues to be a puzzle. In this work, we develop an automated approach to identify lopsided galaxies from the SDSS DR18 using a Deep Convolutional Neural Network (DCNN) based on the publicly available AlexNet architecture. We select nearly face-on spiral galaxies from SDSS DR18 with the Petrosian 90% light radius (petroR90_i) greater than $20^{''}$. Based on the visual inspection, we choose 106 lopsided spiral galaxies and 105 symmetric spiral galaxies, as our training set. Our trained model achieves a testing accuracy of 92.8% at the end of 150 epochs. We then employ the trained model on a set of 813 face-on spiral galaxies from SDSS DR18 with $17^{''} \le petroR90\_i \le 20^{''} $ and identify 452 new lopsided spiral galaxies. We next investigate the cosmic web environments in which the galaxies are located, using the Hessian matrix of the density field. We find that 39% of the lopsided galaxies are located in sparser environments such as sheets and voids. This may provide interesting clues towards understanding the origin of lopsidedness in isolated galaxies, where distortion due to the tidal interactions is less frequent.