Data Processing Challenges for Real-time Observational Astrophysics

Abstract: Astronomical transient and variable events comprise the things that go boom in the night, or otherwise vary in brightness or color over time, and are among the most powerful phenomena of the universe, providing a window into energy scales inaccessible to any laboratory on Earth. The fundamental physics determining the time-series light curves of these astronomical objects, which include exploding stars and black hole mergers, is key to understanding the nature of the dark energy driving the accelerating expansion of the universe, the dark matter guiding the formation and clustering of massive structures, and ultimately our place in the cosmos. During its ten-year mission beginning in 2025, the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory will observe hundreds of millions of such transient and variable sources, up from the mere millions known to date, by making a ten-year 3D movie of the night sky. In doing so, it will revolutionize astronomy with a deluge of data that could enable boundless discoveries, conditioned on meeting the challenges of the data’s nontrivial noise properties; the scale of the anticipated data is a direct corollary to the strategy of collecting less informative photometric data rather than high-fidelity, resource-intensive spectroscopy. In this talk, I will introduce problems and evolving solutions for several interesting aspects of the systems for processing and interpreting the anticipated data.

Bio: Alex Malz is a cosmostatistician and LINCC Frameworks Project Scientist with interests spanning extragalactic astronomy in the uncertainty-dominated regime, focusing on the use of large quantities of low-information-density photometry to infer the physics of the dark universe. He develops and applies principled mathematical tools to extract physical information from noisy data to address problems ranging from photometric redshift estimation to light curve classification; he is a member of the Rubin PZ Commissioning Team as well as the PLAsTiCC/ELAsTiCC Team. He also conducts research on approaches to optimize design decisions for astronomical surveys and analyses thereof for a variety of static and time-domain science goals in the contexts of the DESC, ISSC, TVS, AGN, and Galaxies Science Collaborations. Alex loves to hack and is passionate about open software development and collaboration-building.