We adopt a hybrid frequentist-Bayesian approach, creating a marginalized likelihood that integrates out nuisance parameters, improving the detection of dark matter signals.
Our methodology systematically addresses the three-peak analysis, allowing for robust exclusion limits that can be set by generic experiments in dark matter research.
This work enhances understanding of vector dark matter by considering the peaks arising from Earth's rotation, which previous analyses have overlooked.
By examining signal likelihood through this innovative lens, we can potentially identify and constrain dark photon signals more effectively in future experiments.
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