Christian Herwig

TL;DR: It is demonstrated that a neural network autoencoder model can be implemented in a radiation tolerant ASIC to perform lossy data compression alleviating the data transmission problem while preserving critical information of the detector energy profile.

TL;DR: Preliminary results by training a surrogate machine-learning model on real accelerator data to emulate the Booster environment and using this surrogate model to train the neural network for its regulation task are demonstrated.

TL;DR: In this paper, a method for precisely regulating the gradient magnet power supply (GMPS) at the Fermilab Booster accelerator complex using a neural network trained via reinforcement learning is described.

TL;DR: For example, the Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020 as discussed by the authors , which organized several topical workshops dedicated to physics, accelerator technology, and detector R&D.

TL;DR: The Light Dark Matter Experiment (LDMX) as mentioned in this paper is an electron-beam missing-momentum experiment that provides unique sensitivity to light dark matter in the sub-GeV range.