In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons, and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying deep learning on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, on a pixel-by-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

The NEXT collaboration is dedicated to the study of double beta decays of $$^{136}$$ 136 Xe using a high-pressure gas electroluminescent time projection chamber. This advanced technology combines exceptional energy resolution ( $$\le 1\%$$ ≤ 1 % FWHM at the $$Q_{\beta \beta }$$ Q β β value of the neutrinoless double beta decay) and powerful topological event discrimination. Building on the achievements of the NEXT-White detector, the NEXT-100 detector started taking data at the Laboratorio Subterráneo de Canfranc (LSC) in May of 2024. Designed to operate with xenon gas at 13.5 bar, NEXT-100 consists of a time projection chamber where the energy and the spatial pattern of the ionising particles in the detector are precisely retrieved using two sensor planes (one with photo-multiplier tubes and the other with silicon photo-multipliers). The detector has been operating at stable conditions using argon and xenon gases at $$\sim $$ ∼ 4 bar and drift fields of $$74\hbox { V cm}^{-1}$$ 74 V cm - 1 and $$118\hbox { V cm}^{-1}$$ 118 V cm - 1 , respectively. Alpha decays from the $$^{222}$$ 222 Rn chain have been used to test and monitor the stability of the detector, showing a constant electron lifetime in the drift volume. In this paper, in addition to reporting the results of the commissioning run, we provide a detailed description of the NEXT-100 detector, describe its assembly, and present the current estimation of the radiopurity budget.

The amount and complexity of data recorded by high energy physics experiments are rapidly growing, and with these grow the difficulties in visualizing such data. To study the physics of neutrinos, a type of elementary particle, scientists use liquid argon time projection chamber (LArTPC) detectors, among other technologies. LArTPCs have a very high spatial resolution and resolve many of the elementary particles that come out of a neutrino interacting within the argon in the detector. Visualizing these neutrino interactions is of fundamental importance to understanding the properties of neutrinos, but also monitoring and checking on the detector conditions and operations. From these ideas, we have developed TITUS, an event display that shows images recorded by these neutrino detectors. TITUS is a piece of software that reads data coming from LArTPC detectors (as well as the corresponding simulation) and allows users to explore such data in multiple ways. TITUS is flexible to enable fast prototyping and customization.

We investigate the performance of , a 7.5 GPU-accelerated photon propagation tool compared with a single-threaded simulation. We compare the simulations using an improved model of the gaseous time projection chamber. Performance results suggest that improves simulation speeds by between $$58.47\pm {0.02}$$ 58.47 ± 0.02 and $$181.39\pm {0.28}$$ 181.39 ± 0.28 times relative to a CPU-only simulation and these results vary between different types of GPU and CPU. A detailed comparison shows that the number of detected photons, along with their times and wavelengths, are in good agreement between and .