Collaborative Research: Highly Principled Data Science for Multi-Domain Astronomical Measurements and Analysis

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Massive data resources are coming online in every conceivable area of human exploration, and particularly in fields that are heavily observation-based such as astronomy and astrophysics. To extract the most information from these data, scientists and statisticians need to conduct highly principled data science, by using methods that are scientifically justified, statistically principled, and computationally efficient. This project outlines plans to achieve this goal while addressing four specific challenges in astronomical data involving space, time and energy. The proposed research has the dual impact of more reliable statistical methods in astronomy and of new general statistical inference and computational methods. In addition to providing methods and free software, the investigators also plan to communicate to the astronomical community the benefit of principled statistical methods through workshops and sessions at conferences. A fundamental impact of the proposed research is the more general acceptance and use of principled methods among astronomers. The general methods for efficient modeling of scientific phenomena, science-driven classification and clustering, and for statistical computing, can also help to solve complex data challenges throughout the natural, social, medical, and engineering sciences.Striking advances in both space-based and terrestrial instrumentation continuously increase the quality and quantity of data available to astronomers. Observations are made across the electromagnetic spectrum and compiled into enormous catalogs of high-resolution, but heterogeneous spectrograph, imaging, and time series data. The proposed research aims to use such multi-domain astronomical measurements to better understand the physical environment, structure, and evolution of astronomical individual sources, clusters, and ultimately of the entire universe. There are four major projects. (1) The PIs will develop methodology to solve the instrument calibration problem, which is a fundamental challenge in astrophysics, by fitting scientifically motivated statistical models to data from multiple astronomical objects observed by multiple instruments. (2) The PIs propose a statistically and computationally efficient algorithm to detect the boundaries of a power law distribution prevalent in various areas of astronomy and of far-reaching importance. (3) The PIs will extend image-processing algorithms designed for detecting point sources to complex extended multi-scale structures via a post-hoc analysis, which makes the computation efficient. (4) With astronomical images exhibiting complex structure, the PIs propose to explore image segmentation methods to distinguish overlapping point sources; the algorithm achieves the flux-conserving property, which is crucial for giving physically meaningful estimates that existing methods lack. These projects all involve significant challenges in developing efficient statistical methods, designing fast computational algorithms, and balancing subtle trade-offs between complexity and practicality. With their extensive and successful track record, the PIs will address these challenges by developing inferential and efficient computational methods under highly-structured models that involve multi-scale structure and/or multiple levels of latent variables. The central theme of the proposed research is the integration and pursuit of three desiderata in each of its four projects: scientific justification, statistical principles, and computational efficiency. This triple-goal advances the development of specifically designed methods that leverage computationally efficient and statistically principled data-driven techniques which explicitly incorporate scientific understanding of the astronomical sources. This ensures that the statistical analyses enhance the scientists' ability to answer specific questions about the underlying astronomical and physical processes. This strategy requires state-of-the-art statistical inference, sophisticated scientific computing, and careful model-checking procedures, all of which have been the hallmark of the work by this team of investigators.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.