Session DG: Mini-Symposium on Fundamental Symmetries (Many-body Systems) II

Measurements of beta-energy spectra in nuclear beta decay with a calorimetric technique

Searches for exotic tensor interactions in beta decay have motivated new measurements of beta-energy spectra in allowed Gamow-Teller transitions. Such measurements enable the most direct access to the Fierz interference term which constitutes a very sensitive probe to the presence of exotic couplings. We have implemented a calorimetric technique which makes use of high energy beams produced at the National Superconducting Cyclotron Laboratory. The technique eliminates the major instrumental effect related to the back-scattering of beta particles in surrounding matter and detectors but poses other specific challenges. We have performed measurements in the decays of $^6$He and $^{20}$F. In this contribution we describe the status of the data analysis with emphasis on the discussion of specific systematic effects.
  

In Situ Characterization of Beta Scattering at TRINAT

Recently, the TRINAT collaboration reported a 0.32\% measurement of the $\beta$ asymmetry parameter, $A_\beta$, in the isobaric analgue decay of polarized $^{37}$K [PRL {\bf 120} (2018)].  The statistical and systematic uncertainty components are equal at 0.23\%, with the largest systematic sources arising from backgrounds (0.14\%) and $\beta$ scattering (0.12\%).  We are able to test our GEANT4 modelling of $\beta$ scattering using a small subset of the data which we veto in the main analysis:  events in our $\beta$ telescopes which register two separate pixels in the double-sided Si-strip detector along with a signal in the plastic scinitillator are predominantly due to $\beta$s which backscatter out of the scintillator. 

We will show how well GEANT4 reproduces the scintillator backscatters and discuss its impact on the $A_\beta$ measurement.  $\beta$ scattering currently limits our precision to 0.12\% and is expected to be our dominant source in the next experiment with $^{37}$K.  We will discuss our plans to adapt our geometry to reduce scattering effects and how to better benchmark our simulations to improve this source of uncertainty, allowing us to reach better than 0.1\% precision.   

Deep Learning for Liquid Scintillator-Based Double-Beta Decay Searches

Liquid scintillator-based (LS) detectors are one of the leading technologies in the search for neutrinoless double-beta decay. They are currently limited by naturally occurring and spallation-induced  backgrounds. In the future, they will be limited by the neutrino-electron scattering of Boron-8 solar neutrinos. Here we use a convolutional neural network, a common algorithm from computer vision, to distinguish between signal and background events that would have made it through existing cuts. The network was trained on the MC events for different levels of photocathode coverage and quantum efficiency to demonstrate its performance under these parameters. The ultimate goal is to apply sophisticated machine learning techniques to reject backgrounds in real detector data.