At the MIT Kavli Institute of astrophysics, I worked on training a real time neural network to predict the time of maximum light or epoch time of transients, objects that change in brightness in the night sky. Transients include supernovae, kilanovae, gamma-ray bursts, and more. What's the point of this? Transients are important objects in modern astronomy and have are involved indiscovery of the accelerating expansion of the universe, production of the universe’s heavy elements, the detection of gravitational waves and more. But with 10+ million transient alerts every night and limited resources for observation, it becomes important to determine and which transients should be observed at a given time. By predicting the time of maximum light, we learn when to observe a transient to capture the rise and fall of its lightcurve as well as observe early events.
  We fed the network light curve data obtained from two surveys TESS (Transiting Exoplanet Survey Satellite) and ZTF (Zwicky Transient Facility). The differences in the cadence, gaps, duration of each survey's observations provide a clearer picture of the transient when combined. My work also involved preprocessing the light curves to prepare it for the network. The model is trained to predict values at each timestep of the curve, making sure it can function on incomplete curves as seen to the right. It was a pleasure to learn more about astronomy, data science, and machine learning over this summer. Currently, the paper is in progress.