Recovering Weak Signals with Normalizing Flows

• URL: http://arxiv.org/abs/2609.06382v1
• Date: Sun, 06 Sep 2026 04:44:34 GMT
• Title: Recovering Weak Signals with Normalizing Flows
• Abstract summary: We propose the use of normalizing flow models to reconstruct calibration-affected weak signals.<n>We provide a comprehensive theoretical overview of this normalizing flow-based recovery method and demonstrate its efficacy using simulated data.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In many scientific disciplines, weak signals of interest are obscured by dominant nuisance signals that are several orders of magnitude stronger. Recovering these weak signals requires subtracting the dominant ones; however, this calibration process inherently distorts or partially suppresses the underlying signal of interest. To address this problem, we propose the use of normalizing flow models to reconstruct calibration-affected weak signals. By leveraging the statistical invariance of the target signals and assuming minimal initial suppression, our framework effectively recovers the lost signal components. We provide a comprehensive theoretical overview of this normalizing flow-based recovery method and demonstrate its efficacy using simulated data.

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