Search

After a successful three-year physics run underground at the LNGS in Italy and a significant detector overhaul at CERN, the ICARUS-T600 detector was installed at Fermilab. In 2020, the cryogenic commissioning began with detector cool down, liquid argon filling and recirculation. ICARUS then started its operation collecting the first neutrino events from the Booster Neutrino Beam (BNB) and the Neutrinos at the Main Injector (NuMI) beam off-axis, which were used to test the ICARUS event selection, reconstruction and analysis algorithms. ICARUS successfully completed its commissioning phase in June 2022, and started data taking for neutrino oscillation physics, aiming at first to either confirm or refute the claim by Neutrino-4 short-baseline reactor experiment. ICARUS will also perform measurements of neutrino cross-sections with the NuMI beam and several Beyond Standard Model searches. After the first year of operations, ICARUS will jointly search for evidence of sterile neutrinos with the Short-Baseline Near Detector (SBND), within the Fermilab Short-Baseline Neutrino (SBN) program. In this paper, preliminary technical results from the ICARUS data with the BNB and NuMI beams are presented to highlight the performance of all ICARUS subsystems and its capability to select and reconstruct neutrino events. [ABSTRACT FROM AUTHOR]

Deep learning algorithms often are developed and trained on a training dataset and deployed on test datasets. Any systematic difference between the training and a test dataset may severely degrade the final algorithm performance on the test dataset—what is known as the domain shift problem . This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation (DA) methods. However, these methods are often tailored for a specific downstream task, such as classification or semantic segmentation, and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image (UI2I) translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber (LArTPC) detector research when seeking a way to translate samples between two differently distributed LArTPC detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related performance degradation. Conversely, using the translation from the simulated data to a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. To evaluate the quality of the translations, we use both pixel-wise metrics and a downstream task to measure the effectiveness of UI2I methods for mitigating the domain shift problem. We adapted several popular UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of DA techniques for scientific datasets, the ‘Simple Liquid-Argon Track Samples’ dataset used in this study is also published.

We present a custom implementation of a 2D Convolutional Neural Network (CNN) as a viable application for real-time data selection in high-resolution and high-rate particle imaging detectors, making use of hardware acceleration in high-end Field Programmable Gate Arrays (FPGAs). To meet FPGA resource constraints, a two-layer CNN is optimized for accuracy and latency with KerasTuner, and network quantization is further used to minimize the computing resource utilization of the network. We use “High Level Synthesis for Machine Learning” (hls4ml) tools to test CNN deployment on a Xilinx UltraScale+ FPGA, which is an FPGA technology proposed for use in the front-end readout system of the future Deep Underground Neutrino Experiment (DUNE) particle detector. We evaluate network accuracy and estimate latency and hardware resource usage, and comment on the feasibility of applying CNNs for real-time data selection within the currently planned DUNE data acquisition system. This represents the first-ever exploration of employing 2D CNNs on FPGAs for DUNE.

The SBN program at Fermilab aims to measure ν μ → ν e neutrino oscillations at ∼ 1 km/GeV, resulting from the existence of a sterile neutrino, namely a fourth neutrino mass eigenstate at 1 eV that does not interact weakly but participate at the oscillation phenomenon with the three standard neutrinos. The SBN Far Detector, ICARUS T600, was run at LNGS Lab from 2010 to 2013 as the first and largest LArTPC ever operated. At Fermilab ICARUS is at the surface and therefore a Cosmic Ray Tagger system is necessary in order to reject the cosmic ray background. [ABSTRACT FROM AUTHOR]

We report the measurement of longitudinal electron diffusion coefficients in liquid argon for electric fields between 100 and 2000 V/cm with a gold photocathode as a bright electron source. The measurement principle, apparatus, and data analysis are described. In the region between 100 and 350 V/cm, our results show a discrepancy with the previous measurement [1] . In the region between 350 and 2000 V/cm, our results represent the world׳s best measurement. Over the entire measured electric field range, our results are systematically higher than the calculation of Atrazhev‐Timoshkin [2] . The quantum efficiency of the gold photocathode, the drift velocity and longitudinal diffusion coefficients in gas argon are also presented. [ABSTRACT FROM AUTHOR]