Occlusion sensitivity, not gradient-based saliency, best reveals how a convolutional autoencoder recognizes scintillator pulses; the paper ties the loss minimum to kernel size and estimates 30 ns minimum pulse separation.
Explainable AI for High Energy Physics
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abstract
Neural Networks are ubiquitous in high energy physics research. However, these highly nonlinear parameterized functions are treated as \textit{black boxes}- whose inner workings to convey information and build the desired input-output relationship are often intractable. Explainable AI (xAI) methods can be useful in determining a neural model's relationship with data toward making it \textit{interpretable} by establishing a quantitative and tractable relationship between the input and the model's output. In this letter of interest, we explore the potential of using xAI methods in the context of problems in high energy physics.
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physics.comp-ph 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
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Applicability Evaluation of Selected xAI Methods for Machine Learning Algorithms for Signal Parameters Extraction
Occlusion sensitivity, not gradient-based saliency, best reveals how a convolutional autoencoder recognizes scintillator pulses; the paper ties the loss minimum to kernel size and estimates 30 ns minimum pulse separation.