Computing for the DUNE Long-Baseline Neutrino Oscillation Experiment

The modern security landscape for distributed computing in High Energy Physics (HEP) includes a wide range of threats employing different attack vectors. The nature of these threats is such that the most effective method for dealing with them is to work collaboratively, both within the HEP community and with partners further afield - these can, and should, include institutional and campus security teams. In parallel with this work, an appropriate technology stack is essential, incorporating current work on Big Data analytics. The work of the Worldwide LHC Computing Grid (WLCG) Security Operations Center (SOC) Working Group (WG) [1] is to pursue these goals to form a reference design (or guidelines) for WLCG sites of different types. The strategy of the group is to identify necessary components - starting with threat intelligence (MISP [2]) and network intrusion detection (Bro [3]), building a working model over time. We present on the progress of the working group thus far, in particular on the programme of workshops now underway. These workshops give an opportunity to engage with sites to allow the development of advice and procedures for deployment, as well as facilitating wider discussions on how to best work with trust groups at different levels. These trust groups vary in scope but can include institutes, National Grid Infrastructures and the WLCG as a whole.

A 10 kilo-tonne dual-phase liquid argon TPC is one of the detector options considered for the Deep Underground Neutrino Experiment (DUNE). The detector technology relies on amplification of the ionisation charge in ultra-pure argon vapour and offers several advantages compared to the traditional single-phase liquid argon TPCs. A 4.2 tonne dual-phase liquid argon TPC prototype, the largest of its kind, with an active volume of \ hree has been constructed and operated at CERN. In this paper we describe in detail the experimental setup and detector components as well as report on the operation experience. We also present the first results on the achieved charge amplification, prompt scintillation and electroluminescence detection, and purity of the liquid argon from analyses of a collected sample of cosmic ray muons.

The Deep Underground Neutrino Experiment (DUNE) far detectors will operate as long-running liquid argon time projection chambers with minimal opportunities for intervention. Consequently, consistent and reliable calibration techniques are essential. As a proof of concept, two 207Bi radioactive sources were deployed in the ProtoDUNE Horizontal Drift (HD) detector at CERN to study the detector's response to the ∼1 MeV decay electrons. DUNE's data acquisition system supports real-time data processing and can identify localized activity (known as Trigger Primitives, TP) within the continuous data stream. This Trigger Primitive Generation enables faster data analysis through the complementary TP dataset. This proceeding presents an evaluation of energy reconstruction using raw data and examines the precision of applying these methods to TPs with results based on data collected during the late 2024 operational period of ProtoDUNE HD.

This paper presents a quantum convolutional neural network (QCNN) for the classification of high energy physics events. The proposed model is tested using a simulated dataset from the Deep Underground Neutrino Experiment. The proposed quantum architecture demonstrates an advantage of learning faster than the classical convolutional neural networks (CNNs) under a similar number of parameters. In addition to the faster convergence, the QCNN achieves a greater test accuracy compared to CNNs. Based on our results from numerical simulations, it is a promising direction to apply QCNN and other quantum machine learning models to high energy physics and other scientific fields.

The Deep Underground Neutrino Experiment (DUNE) will be the next generation long-baseline neutrino experiment. The far detector is designed as a complex of four LAr-TPC (Liquid Argon Time Projection Chamber) modules with 17 kt of liquid argon each. The development and validation of the first far detector technology is pursued through ProtoDUNE Single Phase (ProtoDUNE-SP), a 770 t LAr-TPC at CERN Neutrino Platform. Crucial in DUNE is the photon detection system that will ensure the trigger of non-beam events — proton decay, supernova neutrino burst and BSM searches — and will improve the timing and calorimetry for neutrino beam events. Doping liquid argon with xenon is a known technique to shift the light emitted by argon (128 nm) to a longer wavelength (178 nm) to ease its detection. The largest xenon doping test ever performed in a LAr-TPC was carried out in ProtoDUNE-SP. From February to May 2020, a gradually increasing amount of xenon was injected to also compensate for the light loss due to air contamination. The response of such a large TPC has been studied using the ProtoDUNE-SP Photon Detection System (PDS) and a dedicated setup installed before the run. With the first it was possible to study the light collection efficiency with respect to the track position, while with the second it was possible to distinguish the xenon light (178 nm) from the LAr light (128 nm). The light shifting mechanism proved to be highly efficient even at small xenon concentrations (<20 ppm in mass) furthermore it allowed recovering the light quenched by pollutants. The light collection improved far from the detection plane, enhancing the photon detector response uniformity along the drift direction and confirming a longer Rayleigh scattering length for 178 nm photons, with respect to 128 nm ones. The charge collection by the TPC was monitored proving that xenon up to 20 ppm does not impact its performance.

ProtoDUNE Single-Phase is a 700-ton liquid argon detector operated in the CERN Neutrino Platform from 2018 to 2020. It is part of the Deep Underground Neutrino Experiment (DUNE), a long-baseline neutrino oscillation experiment with a 40 kT liquid argon far detector to be built at the Sanford Underground Research Facility and a near detector, with both argon and non-argon detector technologies, to be hosted at the Fermi National Accelerator Laboratory. A critical uncertainty to understand in the neutrino oscillation program of DUNE is the uncertainty on final state interactions, either reaction or elastic, of various hadrons on argon since the scattering of neutrino-induced hadrons off argon bias the hadron's measured energy. It can also prevent algorithms from identifying the hadron's particle type. Protons, kaons, and pions from the beam are especially important for the DUNE neutrino program as they represent common final state particles in neutrino interactions off a nucl eus. Therefore, ProtoDUNE is analyzing the test beam data to measure cross sections of pions, protons, and kaons on argon, aiming to tune parameters that model charged particle scattering off argon. This talk will discuss the data-taking program for ProtoDUNE and an overview of the status and results of measuring cross sections of pions, protons, and kaons on argon. It will conclude with a brief overview of how these measurements can be used for future liquid argon neutrino detectors.

This paper describes a novel application and evaluation of programmable networking in High-Energy Physics (HEP): a complete parser for the custom packet format used by Fermilab’s DUNE experiment. Notably, this parser is implemented on a Tofino programmable network switch and evaluated on the FABRIC testbed by using network traffic generated by the ICEBERG DUNE prototype. The parsed network traffic consists of Jumbo Ethernet frames that contain digitizations of sensor readings from ICEBERG’s detector.

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components.

Computing infrastructure at the CERN neutrino platform prototypes experiments

Data processing in high energy physics experiments is a multi-tiered process in which raw detector signals are first processed locally into physics objects, and then collated into event records which can be scrutinized by a fast online trigger system. The resulting selection of events are reconstructed and pass through a number of software filters before arriving at a final offline analysis where hard physical constants are extracted. Although sophisticated statistical data analysis techniques are routinely employed high energy physics, the use of statistical signal processing in the field is has until now been rare. Our paper will begin with an overview of a typical high energy physics data acquisition system, outlining the technologies and tradeoffs involved at each stage. We will then proceed to argue that the dominant roles of model dependence and systematic errors in final physics analyses render statistical signal processing techniques largely inapplicable at this level. We observe, however, that at the low-level pattern recognition and event reconstruction levels, statistical signal processing techniques have been making inroads in high energy physics for a number of years, and examples from the literature will be cited. The viability of the technique for second level triggers will be assessed. Parallels to other other approaches, such as neural networks, will also be drawn. It will be argued that the falling cost of computing hardware favors the growth of statistical signal processing methods in high energy physics

Analytical calculations in high energy physics by computer

In the past few years a wide variety of applications of neural networks to pattern recognition in experimental high-energy physics has appeared. The neural network solutions are in general of high quality, and, in a number of cases, are superior to those obtained using "traditional'' methods. But neural networks are of particular interest in high-energy physics for another reason as well: much of the pattern recognition must be performed online, that is, in a few microseconds or less. The inherent parallelism of neural network algorithms, and the ability to implement them as very fast hardware devices, may make them an ideal technology for this application.

Particle tracking in physical systems is a well known simulation challenge in many domains. In particular, High Energy Physics (HEP) demand efficient simulations of charged particles moving throughout complex detector geometries in a magnetic field. Quantized State Systems (QSS) is a modern family of hybrid numerical methods that provides attractive performance features for these problems. Its state-of-the-art implementation is the general-purpose QSS Solver toolkit. Meanwhile, Geant4 is the most widely used platform for computational particle physics, embedding vast amounts of physics domain knowledge. Yet, Geant4 relies rigidly on classic discrete time numerical methods. In this work we present a robust co-simulation technique to apply QSS in the simulation of HEP experiments, thus leveraging the best of both toolkits. We obtained speedups of up to three times in synthetic, yet representative scenarios, and a competitive performance in a difficult benchmark modeled after the Compact Muon Solenoid (CMS) particle detector at CERN.

Software and Computing (S&C) are essential to all High Energy Physics (HEP) experiments and many theoretical studies. The size and complexity of S&C are now commensurate with that of experimental instruments, playing a critical role in experimental design, data acquisition/instrumental control, reconstruction, and analysis. Furthermore, S&C often plays a leading role in driving the precision of theoretical calculations and simulations. Within this central role in HEP, S&C has been immensely successful over the last decade. This report looks forward to the next decade and beyond, in the context of the 2021 Particle Physics Community Planning Exercise ("Snowmass") organized by the Division of Particles and Fields (DPF) of the American Physical Society.

Computers in High-Energy Physics