Building Machine Learning Challenges for Anomaly Detection in Science
Authors:
Elizabeth G. Campolongo,
Yuan-Tang Chou,
Ekaterina Govorkova,
Wahid Bhimji,
Wei-Lun Chao,
Chris Harris,
Shih-Chieh Hsu,
Hilmar Lapp,
Mark S. Neubauer,
Josephine Namayanja,
Aneesh Subramanian,
Philip Harris,
Advaith Anand,
David E. Carlyn,
Subhankar Ghosh,
Christopher Lawrence,
Eric Moreno,
Ryan Raikman,
Jiaman Wu,
Ziheng Zhang,
Bayu Adhi,
Mohammad Ahmadi Gharehtoragh,
Saúl Alonso Monsalve,
Marta Babicz,
Furqan Baig
, et al. (125 additional authors not shown)
Abstract:
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be c…
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Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not conform to the norms are an indication that the rules of science governing the data are incomplete, and something new needs to be present to explain these unexpected outliers. The challenge of finding anomalies can be confounding since it requires codifying a complete knowledge of the known scientific behaviors and then projecting these known behaviors on the data to look for deviations. When utilizing machine learning, this presents a particular challenge since we require that the model not only understands scientific data perfectly but also recognizes when the data is inconsistent and out of the scope of its trained behavior. In this paper, we present three datasets aimed at developing machine learning-based anomaly detection for disparate scientific domains covering astrophysics, genomics, and polar science. We present the different datasets along with a scheme to make machine learning challenges around the three datasets findable, accessible, interoperable, and reusable (FAIR). Furthermore, we present an approach that generalizes to future machine learning challenges, enabling the possibility of large, more compute-intensive challenges that can ultimately lead to scientific discovery.
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Submitted 29 March, 2025; v1 submitted 3 March, 2025;
originally announced March 2025.