Exploring the FastIC+ ASIC Readout for Event Classification in Heterostructured Scintillators

Abstract

To improve coincidence time resolution (CTR) in time-of-flight positron emission tomography, heterostructured scintillators have been proposed as a novel approach combining high detection efficiency and fast timing. These structures typically use alternating micrometer-scale layers of materials with complementary properties like BGO and EJ-232 plastic scintillator. Through energy sharing, timing performance is enhanced via the fast plastic while maintaining detection efficiency from BGO. However, the random nature of energy sharing increases readout signal complexity. Previous studies used an event classification method to extract energy deposition per material, but this is not feasible for system-level use. To address this, a machine learning-based classification method was recently introduced.

Authors 7

1. G. Terragni

   European Organization for Nuclear Research

   Affiliation as printed

   CERN, Geneva,Genève,Switzerland

2. David Mazzanti

   Universitat de Barcelona

   Affiliation as printed

   University of Barcelona,Barcelona,Spain

3. J. Alozy

   European Organization for Nuclear Research

   Affiliation as printed

   CERN, Geneva,Genève,Switzerland

4. David Gascon

   Universitat de Barcelona

   Affiliation as printed

   University of Barcelona,Barcelona,Spain

5. Karl‐Josef Langen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Aachen,North Rhine-Westphalia,Germany

6. K. Ziemons Aachen

   FH Aachen

   Affiliation as printed

   FH Aachen - University of Applied Sciences,Aachen,North Rhine-Westphalia,Germany

7. E. Auffray

   European Organization for Nuclear Research

   Affiliation as printed

   CERN, Geneva,Genève,Switzerland