Elements: Scalable Bayesian Software for Interpreting Astronomical Images

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The BLISS (Bayesian Light Source Separator) Project is an interdisciplinary research effort to develop a software tool that allows astronomers to make use of the latest advances in machine learning. By harnessing these advances, astronomers can rapidly analyze vast quantities of complex data to understand the nature of our universe. This project also engages and educates a wider audience through a workshop series that promotes technical proficiency in software development and machine learning.The software tool, developed as part of this project, will allow astronomers to more easily access Bayesian statistical methods to interpret image data from astronomical surveys. Bayesian methods excel at uncertainty quantification and data integration, two capabilities that will be critical in analyzing the deluge of data produced by next-generation astronomical surveys. One major barrier to the more widespread adoption of Bayesian analysis for interpreting astronomical images is computational: Bayesian inference is notoriously computationally demanding. A second major barrier is social: up to now, novel Bayesian methods have been developed in isolation by statisticians and have rarely been integrated into astronomy workflows because it is unclear to practitioners in either discipline how this can be accomplished. The BLISS Project addresses both these computational and community integration challenges. To overcome the computational challenges, BLISS leverages recent advances in Bayesian inference methodology, including the use of deep learning, variational inference, and GPU acceleration. To ensure immediate and sustainable community use, development of the BLISS is guided by needs identified by domain experts, who are themselves prepared to participate in BLISS's development and are enthusiastic about integrating BLISS into their teams' data analysis workflows.This project is supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer & Information Science & Engineering, the Division of Mathematical Sciences and the Division of Astronomical Sciences in the Directorate for Mathematical and Physical Sciences.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.