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Many models of physics beyond the Standard Model include towers of particleswhose masses follow an approximately periodic pattern with little spacingbetween them. These resonances might be too weak to detect individually, butcould be discovered as a group by looking for periodic signals in kinematicdistributions. The continuous wavelet transform, which indicates how much agiven frequency is present in a signal at a given time, is an ideal tool forthis. In this paper, we present a series of methods through which continuouswavelet transforms can be used to discover periodic signals in kinematicdistributions. Some of these methods are based on a simple test statistic,while others make use of machine learning techniques. Some of the methods aremeant to be used with a particular model in mind, while others aremodel-independent. We find that continuous wavelet transforms can give boundscomparable to current searches and, in some cases, be sensitive to signals thatwould go undetected by standard experimental strategies.

Searching for periodic signals in kinematic distributions using continuous wavelet transforms