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Identifying extragalactic explosions in TESS data in near real time


Term:

Spring

Department:

MKI: MIT Kavli Institute for Astrophysics and Space Research

Faculty Supervisor:

Prof. Deepto Chakrabarty

Faculty email:

deepto@mit.edu

Apply by:

Contact:

Dheeraj Pasham: dheeraj@space.mit.edu

Project Description

The Transiting Exoplanet Survey Satellite (TESS) is a mission designed to find exoplanets around stars within our own galaxy. But the data collected by TESS contains information about massive stars exploding and forming black holes, neutron stars merging with each other, and stars getting ripped apart by supermassive black holes in external galaxies. We are working on a software pipeline to identify these “needle in a haystack” events among hundreds of millions of astronomical objects that TESS observes. The work will involve designing and training a neural network using Tensorflow. The student is expected to write code in Python.

Pre-requisites

Experience with Python and Linux, and basic machine learning algorithms. Prior experience with Tensorflow would be highly beneficial but not necessary.