Related papers: DNN-based Signal Processing for Liquid Argon Time …
Grand Unification of the strong, weak and electromagnetic interactions into a single unified gauge group is an extremely appealing idea which has been vigorously pursued theoretically and experimentally for many years. The detection of…
The ATLAS experiment measures the properties of particles that are products of proton-proton collisions at the LHC. The ATLAS detector will undergo a major upgrade before the high luminosity phase of the LHC. The ATLAS liquid argon…
Beam dump experiments proposed at the SPS are perfectly suited to explore the parameter space of models with long-lived particles, thanks to the combination of a large intensity with a high proton beam energy. In this paper, we study how…
Since the original detection of core-collapse supernova neutrinos in 1987, all large neutrino experiments seek to detect the neutrinos from the next nearby supernova. Among them, liquid argon time projection chambers (LArTPCs) offer a…
We describe a novel ionization signal capture and waveform digitization scheme for kiloton-scale liquid argon Time Projection Chamber (TPC) detectors. The scheme is based on a pixel-scale self-triggering `charge integrate/reset' block,…
LArIAT (Liquid Argon In A Testbeam) aims to characterize the response of a liquid argon time projection chamber (LArTPC) to the particles often seen as final-state products of ~1 GeV neutrino interactions in existing and planned detectors.…
We introduce a scheme based on machine learning and deep neural networks to model the environmental dependence of the electronic polarizability in insulating materials. Application to liquid water shows that training the network with a…
The Deep Underground Neutrino Experiment (DUNE) will be a premier facility for exploring long-standing questions about the boundaries of the standard model. Acting in concert with the liquid argon time projection chambers underpinning the…
This paper introduces a novel track-length extension fitting algorithm for measuring the kinetic energies of inelastically interacting particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable…
Training deep neural networks using simulations typically requires very large numbers of simulated events. This can be a large computational burden and a limitation in the performance of the deep learning algorithm when insufficient numbers…
We present the multiple particle identification (MPID) network, a convolutional neural network (CNN) for multiple object classification, developed by MicroBooNE. MPID provides the probabilities of $e^-$, $\gamma$, $\mu^-$, $\pi^\pm$, and…
The Deep Underground Neutrino Experiment (DUNE) is a leading-edge experiment designed to perform neutrino science and proton decay searches. In particular, the far detector will consist of four 10-kton Liquid Argon (LAr) Time Projection…
A novel concept for position-sensitive thermal neutron detectors based on thin-gap Resistive Plate Chambers (RPC) lined with a neutron converter layer is presented and its feasibility is discussed. Several detector architectures…
These proceedings review the two DUNE prototype detectors, namely Single- and Dual-Phase ProtoDUNEs. The detectors, both employing liquid argon Time Projection Chambers (LAr TPCs), are currently being built at CERN as part of the ProtoDUNE…
The dual-phase xenon time projection chamber (TPC) is a powerful technology to detect rare interactions such as scatters of dark matter particles on nuclei. In particular, the built-in gain of ionization signals in a dual-phase TPC makes it…
Liquid argon is being employed as a detector medium in neutrino physics and Dark Matter searches. A recent push to expand the applications of scintillation light in Liquid Argon Time Projection Chamber neutrino detectors has necessitated…
The Time Projection Chamber (TPC) is an ideal candidate to finely study the charged particle ionization in a gaseous medium. Large volumes TPCs can be read out with a suitable number of channels offering a complete 3D reconstruction of an…
The scintillation time response of liquid argon has a key role in the discrimination of electronic backgrounds in dark matter search experiments. However, its extraordinary rejection power can be affected by various detector effects such as…
Using deep neural networks for identifying physics objects at the Large Hadron Collider (LHC) has become a powerful alternative approach in recent years. After successful training of deep neural networks, examining the trained networks not…
The Argon Dark Matter experiment is a ton-scale double phase argon Time Projection Chamber designed for direct Dark Matter searches. It combines the detection of scintillation light together with the ionisation charge in order to…