arXiv subjects

F. Hummer

Publications and source records attributed to F. Hummer.

3 recordsLinked to original sources

arXiv2026

An investigation of fast simulation techniques for pion showers using kernel density estimators with the CALICE AHCAL Technological Prototype

In this article, the development and investigation of fast hadron shower simulation methods is presented. A test beam dataset has been recorded in 2018 at CERN with the AHCAL Technological Prototype of the CALICE Collaboration, where the calorimeter prototype was exposed to electron, muon, and negatively charged pion beams of various initial energies. The pion shower dataset, covering energies between 10 GeV and 200 GeV, has been used to develop a data-driven fast simulation algorithm of the AHCAL response to pion showers. The resulting shower model demonstrates excellent agreement with measured shower observables. In addition, a method for simulating pion showers at arbitrary energies is introduced, based upon interpolation between simulated showers at neighbouring beam energies.

physics.ins-det↗

arXiv2024

Shower Separation in Five Dimensions for Highly Granular Calorimeters using Machine Learning

To achieve state-of-the-art jet energy resolution for Particle Flow, sophisticated energy clustering algorithms must be developed that can fully exploit available information to separate energy deposits from charged and neutral particles. Three published neural network-based shower separation models were applied to simulation and experimental data to measure the performance of the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL) technological prototype in distinguishing the energy deposited by a single charged and single neutral hadron for Particle Flow. The performance of models trained using only standard spatial and energy and charged track position information from an event was compared to models trained using timing information available from AHCAL, which is expected to improve sensitivity to shower development and, therefore, aid in clustering. Both simulation and experimental data were used to train and test the models and their performances were compared. The best-performing neural network achieved significantly superior event reconstruction when timing information was utilised in training for the case where the charged hadron had more energy than the neutral one, motivating temporally sensitive calorimeters. All models under test were observed to tend to allocate energy deposited by the more energetic of the two showers to the less energetic one. Similar shower reconstruction performance was observed for a model trained on simulation and applied to data and a model trained and applied to data.

physics.ins-det↗

arXiv2024

Software Compensation for Highly Granular Calorimeters using Machine Learning

A neural network for software compensation was developed for the highly granular CALICE Analogue Hadronic Calorimeter (AHCAL). The neural network uses spatial and temporal event information from the AHCAL and energy information, which is expected to improve sensitivity to shower development and the neutron fraction of the hadron shower. The neural network method produced a depth-dependent energy weighting and a time-dependent threshold for enhancing energy deposits consistent with the timescale of evaporation neutrons. Additionally, it was observed to learn an energy-weighting indicative of longitudinal leakage correction. In addition, the method produced a linear detector response and outperformed a published control method regarding resolution for every particle energy studied.

physics.ins-det↗