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Interaction-model dependence in calorimetric energy reconstruction methods due to non-linear material effects in modern neutrino detectors
One-line summary
A solar energy research paper on Interaction-model dependence in calorimetric energy reconstruction methods due to non-linear material effects in modern neutrino detectors.
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Original abstract
Neutrino oscillation experiments rely on high precision neutrino energy reconstruction. A common reconstruction technique in LAr-TPCs and scintillators is via the calorimetric sum of visible particles created in the interaction. However, non-linearities in the detector response, such as Birks quenching for scintillators and recombination effects for TPCs, lead to ambiguities in the reconstruction of visible hadronic energies. Interaction-model-dependent assumptions are required to resolve these ambiguities, which introduces a bias in reconstruction of neutrino energy. This introduces a systematic uncertainty separate from the well-studied bias due to interaction-model dependent modelling of missing energy caused by, for example, the production of final state neutrons. In this work, we evaluate the interaction-model dependence of the bias caused by these material effects across multiple tunes of the GENIE, NEUT, NuWro, and GiBUU neutrino interaction event generators for cases representative of calorimetric energy reconstruction at the T2K (ND280), NO$ν$A, MINER$ν$A, $μ$BooNE, and DUNE experiments. Using pure calorimetric reconstruction, our results show significant differences in the mean neutrino-energy reconstruction bias between models, at the level of $\sim$7-9\,MeV for scintillator detectors and $\sim$11-18\,MeV for argon-based detectors in the relevant energy range. The latter is shown to be reduced (down to $\sim$3.5\,MeV) when using an idealised hybrid energy reconstruction based on tracking and calorimetry. Overall, we conclude that neutrino-energy reconstruction bias due to material effects may imply non-negligible systematic uncertainties for neutrino oscillation and cross-section measurements, and discuss alternative analysis strategies to mitigate the issue.
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