The discovery of the lepton-number-violating neutrinoless double-beta decay would determine the Majorana or Dirac nature of neutrinos, indicate the origin of neutrino mass, and provide a path to leptogenesis in the early universe. 76Ge- based searches using High-Purity Germanium (HPGe) detectors have proven to be very successful in searching for this ultra-rare decay, with previous-generation experiments the Majorana Demonstrator (MJD) and GERDA demonstrating the best energy resolution and lowest backgrounds in the field, respectively. This program is rapidly advancing, with LEGEND-200 now taking data, and LEGEND-1000, the proposed the ton-scale phase of the LEGEND program, under design. The low-background requirements of these experiments and well- understood signal formation microphysics in HPGe detectors lead to unique challenges in designing machine learning-based approaches to data analysis, and have led to the development of novel highly-interpretable methods. I’ll discuss successful preliminary deployments of machine learning-based analyses in MJD and GERDA, methods that have been developed for LEGEND-200 commissioning and data-taking, and the ongoing research on new methods for LEGEND-1000.
Bio:
Julieta Gruszko is originally from Buenos Aires Argentina and grew up in Poughkeepsie New York. She arrived at UNC Chapel Hill in January 2020 after spending 2 years as a Pappalardo Fellow at MIT working with Lindley Winslow and Joe Formaggio. She completed her PhD as a National Science Foundation Graduate Research Fellow in Jason Detwiler’s group at the University of Washington where she studied neutrinoless double-beta decay with the MAJORANA DEMONSTRATOR and received her BS in physics and BA in mathematics from University of Rochester. In her spare time she enjoys climbing cooking and both playing and listening to music. She is an avid reader podcast-listener and art-lover.