Speaker
Description
An essential part of any analysis using radio detector data is robust and accurate reconstruction of the physical parameters governing the observed signals. However, most current reconstruction methods ignore bin-to-bin noise correlations, which limits reconstruction resolution and prevents reliable event-by-event uncertainty estimates. In this talk, we present a likelihood-based approach to reconstruction that correctly models the band-limited/correlated nature of radio detector noise. This enables correct event-by-event uncertainty estimates, improves reconstruction accuracy, and has applications in detector optimization. We demonstrate the method on simulated in-ice neutrino and cosmic-ray air-shower events. The reconstruction code is available through the open-source software NuRadioReco.