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Sayantan Auddy

Photo of Sayantan Auddy

Address:

4800 Oak Grove Drive

Pasadena, CA 91109

Curriculum Vitae:

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Member of:

Interstellar and Heliospheric Physics

NASA Postdoc

Biography

Sayantan (He/Him) is a computational astrophysicist. I have an amazing job where I spend my time thinking about the working of the cosmos. I enjoy the opportunity to solve research problems that help us to have better insights into the formation and evolution of stars and planets. I use supercomputers to run sophisticated numerical simulations to model interstellar systems that help to interpret observed data from large Telescopes. Additionally, with the aid of Machine Learning algorithms, we train models to understand and interpret data and bridge a bond between simulated models and real observed data

Education

Ph.D., Physics, University of Western Ontario, August 2018

Professional Experience

Assistant Research Scientist Johns Hopkins University June - August 2022

Postdoctoral Research Associate, Iowa State University, Nov 2020 - Jun 2022

Academia Sinica Fellow, Institute of Astronomy and Astrophysics, Academia Sinica, Jan-Oct 2020

Postdoctoral Fellow, Institute of Astronomy and Astrophysics, Academia Sinica, Nov 2018 - Jan 2020

Community Service

Public Talk at Astronomy on Tap, Taiwan, August 2019

I was one of the key organizers for the “Winter school on Astronomy and Workshop on Star Formation” for consecutive years 2019, 2017, and 2016 in India

“Looking into the Future”, Invited Talk at the Symposium of Ancient and Modern Science, Organized by Rotary Club and Divine Life, India, 2017

Research Interests

My primary research interest is to study the formation of stars and planets using sophisticated computer simulations. I also study and characterize exoplanets using state-of-the-art machine learning algorithms, more specifically deep neural networks and convolutional neural networks, along with disk-planet simulations

Selected Publications

  • “Basu, Takahiro Kudoh,2022 ApJL,928:L2, arXiv:2201.05620
  • “Using Bayesian Deep Learning to predict masses of exoplanets”, Sayantan Auddy, Ramit Dey, Min-Kai, Daniel Carrera, Jacob B Simon 2022 (accepted in ApJ) arXiv:2202.11730
  • ”DPNNet-2.0 Part I: Finding hidden planets from simulated images of protoplanetary disk gaps”, Sayantan Auddy, Ramit Dey, Min-kai Lin, et al., 2021, APJ 920, 3, arXiv:2107.09086
  • “A Machine Learning model to infer planet masses from gaps observed in protoplanetary disks”, Sayantan Auddy, Min-Kai Lin, 2020, ApJ 900:001 , arXiv:2007.13779
  • “The Transition from a Lognormal to a Power-Law Column Density Distribution in Molecular Clouds: An Imprint of the Initial Magnetic Field and Turbulence”; Sayantan Auddy, Shantanu Basu, Takahiro Kudoh, 2019, ApJL,881:L15, (arXiv:1907.09783)
  • “Magnetic Field Structure of Dense Cores using Spectroscopic Methods”; Sayantan Auddy, Philip Myers, Shantanu Basu, et al., 2019, ApJ 872:207. (arXiv:1901.09537)
  • “The Effect of Magnetic Fields and Ambipolar Diffusion on the Column Density Probability Distribution Function in Molecular Clouds”; Sayantan Auddy, Shantanu Basu, Takahiro Kudoh, 2017, MNRAS, 474, 400 (arXiv:1710.05427)
  • “A Magnetic Ribbon Model for Star Forming Filaments”; Sayantan Auddy, Shantanu Basu, Takahiro Kudoh, 2016, ApJ 831:46 (6pp). (arXiv:1609.02989)
  • “Analytic Models of Brown Dwarfs and Substellar Mass Limit”; Sayantan Auddy, Shantanu Basu and S.R. Valluri, 2016, Advances in Astronomy, Volume 2016 (2016), Article ID 5743272, 15 pages. (arXiv:1607.04338)
  • “From Molecular Clouds to the IMF: Spatial and Temporal Effects”; Shantanu Basu, Sayantan Auddy, 2017, Memorie della Societa Astronomica Italiana, arXiv:1710.06361