Part 1:
Title: Finding Exotic Transients in the Era of Big Data
Speaker: Sebastian Gomez (Assistant Professor of Astronomy at The University of Texas at Austin)

Part 2: Title: Time-Series Modeling of High-Resolution Radio Spectra
Speaker: Josh Taylor (Research Associate, Oden Institute)

Venue: UT Austin (POB 6.304) - Breakfast tacos to be served!!
11 AM CT / 12PM ET

Part 1 Recording
Part 2 Recording

Part 1 Abstract: Time domain astronomy, or the study of the dynamic universe on human timescales, stands at the forefront of a revolution fueled by the advent of large surveys. We have recently experienced an unprecedented influx of observations that led to the discovery of exotic transients such as superluminous supernovae or tidal disruption events. The upcoming deployment of next-generation survey telescopes, such as the Vera C. Rubin Observatory and the Nancy Grace Roman Space Telescope, will increase our transient detection capabilities by two orders of magnitude. Developing machine learning techniques will prove to be not only useful, but necessary, to deal with the deluge of data we will obtain from these observatories, promising deeper insights into known cosmic phenomena and the exciting prospect of discovering entirely new classes of transients.

Part 2 Abstract: We present a modeling technique to characterize high-resolution radio spectra based on ARIMA (AutoRegressive Integrated Moving Average) modeling from statistical time series analysis. ARIMA isolates the dependence of a spectrum's shape upon both its signal and structured noise components, making fewer assumptions about a spectrum's velocity structure than standard Gaussian component fitting, and is intended to serve as a complement to the latter. Structural dependence modeling can: improve summary moment calculations, provide alternative approaches to signal noise estimation (which can be modeled channel-wise if desired), and help characterize the provenance of any observed structure in a cube's spectra (as signal, structured noise, or white noise). ARIMA modeling is computationally lightweight, backed by statistical theory and, as a first step to an analytical pipeline, can inform further downstream tasks such as identifying when Gaussian component fitting is appropriate.

Speaker Bios:

Sebastian Gomez: Since August 2025, I have been an Assistant Professor of Astronomy at The University of Texas at Austin. Previously, I was a Clay postdoctoral fellow at the Center for Astrophysics | Harvard & Smithsonian and an STScI postdoctoral fellow at the Space Telescope Science Institute, where I split my time working with exotic transients with the Transients Science @ Space Telescope group and the Roman Space Telescope. I graduated with a PhD in Astronomy & Astrophysics in 2021 from Harvard University, where I worked with the Berger Time Domain Group on the discovery and classification of exotic supernovae, particularly on the optical study of superluminous supernovae and tidal disruption events, as well as machine learning techniques to find these objects more efficiently. I am originally from Ciudad Juárez, México. I attended college nearby at The University of Texas at El Paso and finished with a degree in Physics in 2015. While at UTEP I focused on the study of X-ray binaries, especially on the discovery of new galactic black hole X-ray binaries. In my spare time I like to do archery and photography.

Josh Taylor is a Research Associate in the Oden Institute for Computational Engineering and Sciences at The University of Texas at Austin. His research advances unsupervised machine learning methods for use in unbiased, data-driven exploratory, summary, and inferential analysis of astrophysical data; in particular, of data arising from observations and simulations of the star formation process. Dr. Taylor received his PhD in statistics from Rice University.