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Herwig, Christian (ORCID:0000000242806382)

Publications and source records attributed to Herwig, Christian (ORCID:0000000242806382).

Shedding light on the MiniBooNE excess with searches at the LHC

The origin of the excess of low-energy events observed by the MiniBooNE experiment remains a mystery, despite exhaustive investigations of backgrounds and a series of null measurements from complementary experiments. One intriguing explanation is the production of beyond-the-Standard-Model particles that could mimic the experimental signature of additional ν e appearance seen in MiniBooNE. In one proposed mechanism, muon neutrinos up-scatter to produce a new “dark neutrino” state that decays by emitting highly collimated electron-positron pairs. We propose high-energy neutrinos produced from W boson decays at the Large Hadron Collider as an ideal laboratory to study such models. Simple searches for a low-mass, boosted dilepton resonance produced in association with a high- p T muon from the W decay with run 2 data would already provide unique sensitivity to a range of dark neutrino scenarios, with prompt and displaced searches providing complementarity. Looking farther ahead, we show how the unprecedented sample of W boson decays anticipated at the HL-LHC, together with improved lepton acceptance would explore much of the parameter space most compatible with the MiniBooNE excess. Published by the American Physical Society 2024

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Differentiable Earth mover’s distance for data compression at the high-luminosity LHC

Abstract The Earth mover’s distance (EMD) is a useful metric for image recognition and classification, but its usual implementations are not differentiable or too slow to be used as a loss function for training other algorithms via gradient descent. In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations. We apply this differentiable approximation in the training of an autoencoder-inspired neural network (encoder NN) for data compression at the high-luminosity LHC at CERN The goal of this encoder NN is to compress the data while preserving the information related to the distribution of energy deposits in particle detectors. We demonstrate that the performance of our encoder NN trained using the differentiable EMD CNN surpasses that of training with loss functions based on mean squared error.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗