MOVING TOWARDS AUTOMATED INTERSTELLAR BOUNDARY EXPLORER DATA SELECTION WITH LOTUS

• 成果类型: Article
• 署名作者: Stricklin, Madeline A.; Beesley, Lauren J.; Weaver, Brian P.; Moran, Kelly R.; Osthus, Dave; Janzen, Paul H.; Meadors, Grant David; Reisenfeld, Daniel B.
• 署名单位: United States Department of Energy (DOE); Los Alamos National Laboratory; University of Montana System; University of Montana; United States Department of Energy (DOE); Los Alamos National Laboratory; United States Department of Energy (DOE); Los Alamos National Laboratory
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2109
• 发表日期: 2026-03
• 页码: 663-682
• 关键词: Classification; automation; Pattern recognition; random forests; heliophysics; Physics-informed machine learning

摘要

The Interstellar Boundary Explorer (IBEX) satellite collects data on energetic neutral atoms (ENAs) that provide insight into the heliosphere, the region surrounding our solar system and separating it from interstellar space. IBEX collects information on these particles and on extraneous background particles. While IBEX records how and when the different particles are observed, it does not distinguish between heliospheric ENA particles and incidental background particles. To address this issue, all IBEX data has historically been manually labeled as good ENA data, or bad background data. This manual culling process is incredibly time-intensive and contingent on subjective, manually-induced decision thresholds. In this paper, we develop a three-stage automated culling process, called LOTUS, that uses random forests to expedite and standardize the labeling process. In Stage 1, LOTUS uses random forests to obtain probabilities of observing true ENA particles on a per-observation basis. In Stage 2, LOTUS aggregates these probabilities to obtain predictions within small windows of time. In Stage 3, LOTUS refines these predictions. We compare the labels generated by LOTUS to those manually generated by the subject matter expert. We use various metrics to demonstrate that LOTUS is a useful automated process for supplementing and standardizing the manual culling process.