Freiburg 2019 – scientific programme
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FM: Fall Meeting
FM 82: Quantum & Information Science: Neural Networks, Machine Learning, and Artificial Intelligence III
FM 82.5: Talk
Thursday, September 26, 2019, 15:15–15:30, 3044
DSEA+: Deconvolution by Machine Learning — •Tim Ruhe1, Mirko Bunse2, Kai Brügge1, and Tobias Hoinka1 — 1Lehrstuhl Experimentelle Physik 5, TU Dortmund — 2LS8, Fakultät Informatik, TU Dortmund
The reconstruction of an experimentally inaccessible quantity, e.g. a particle's energy, is a common challenge in particle- and astroparticle physics, where correlated observables are measured instead. The transfer from the variable of interest into an experimentally observable quantity is, however, usually governed by stochastical processes, leading to the Fredholm integral equation of the first kind. Additional smearing, stemming from particle propagation and the detector itself, complicate the problem even further. We present a novel machine learning-based approach, DSEA+, which sidesteps certain limitations of existing algorithms by interpreting deconvolution as a multinominal classification task. We discuss the algorithm and show results obtained from simulations provided by the FACT Open Data Project.