Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field

In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the design of future particle physics experiments and their data exploitation

Longitudinal kinematic imbalances in (anti-)neutrino interactions for improved measurements of nuclear removal energies and the axial vector form factor

Current and future accelerator neutrino oscillation experiments require an improved understanding of nuclear effects in neutrino-nucleus interactions. One important systematic uncertainty is given by potential mismodeling of the removal energy, which biases the reconstruction of the neutrino energy. In this manuscript, we introduce a novel observable for accelerator neutrino oscillation experiments, the visible longitudinal momentum imbalance, reconstructed in charged current quasi-elastic interactions from the outgoing charged lepton and nucleon. Minimally dependent on the neutrino energy and directly sensitive to the removal energy distribution, we demonstrate a method to constrain the latter. Further, we show how the use of the longitudinal imbalance in anti-neutrino interactions in a target containing hydrogen allows for an improved, high-purity selection of the interactions on hydrogen. This approach offers the potential for precise measurements of the nuclear axial vector form factor as well as of the anti-neutrino flux.Comment: 9 pages, 9 figure

Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectors

Deep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, we report on the performance of a graph neural network in assisting with particle flow event reconstruction. The three-dimensional reconstruction of particle tracks produced in neutrino interactions can be subject to ambiguities due to high multiplicity signatures in the detector or leakage of signal between neighboring active detector volumes. Graph neural networks potentially have the capability of identifying all these features to boost the reconstruction performance. As an example case study, we tested a graph neural network, inspired by the GraphSAGE algorithm, on a novel 3D-granular plastic-scintillator detector, that will be used to upgrade the near detector of the T2K experiment. The developed neural network has been trained and tested on diverse neutrino interaction samples, showing very promising results: the classification of particle track voxels produced in the detector can be done with efficiencies and purities of 94-96% per event and most of the ambiguities can be identified and rejected, while being robust against systematic effects

Demonstration of particle tracking with scintillating fibres read out by a SPAD array sensor and application as a neutrino active target

Scintillating fibre detectors combine sub-mm resolution particle tracking, precise measurements of the particle stopping power and sub-ns time resolution. Typically, fibres are read out with silicon photomultipliers (SiPM). Hence, if fibres with a few hundred mm diameter are used, either they are grouped together and coupled with a single SiPM, losing spatial resolution, or a very large number of electronic channels is required. In this article we propose and provide a first demonstration of a novel configuration which allows each individual scintillating fibre to be read out regardless of the size of its diameter, by imaging them with Single-Photon Avalanche Diode (SPAD) array sensors. Differently from SiPMs, SPAD array sensors provide single-photon detection with single-pixel spatial resolution. In addition, O(us) or faster coincidence of detected photons allows to obtain noise-free images. Such a concept can be particularly advantageous if adopted as a neutrino active target, where scintillating fibres alternated along orthogonal directions can provide isotropic, high-resolution tracking in a dense material and reconstruct the kinematics of low-momentum protons (down to 150 MeV/c), crucial for an accurate characterisation of the neutrino nucleus cross section. In this work the tracking capabilities of a bundle of scintillating fibres coupled to SwissSPAD2 is demonstrated. The impact of such detector configuration in GeV-neutrino experiments is studied with simulations and reported. Finally, future plans, including the development of a new SPAD array sensor optimised for neutrino detection, are discussed

The role of de-excitation in the final-state interactions of protons in neutrino-nucleus interactions

Present and next generation of long-baseline accelerator experiments are bringing the measurement of neutrino oscillations into the precision era with ever-increasing statistics. One of the most challenging aspects of achieving such measurements is developing relevant systematic uncertainties in the modeling of nuclear effects in neutrino-nucleus interactions. To address this problem, state-of-the-art detectors are being developed to extract detailed information about all particles produced in neutrino interactions. To fully profit from these experimental advancements, it is essential to have reliable models of propagation of the outgoing hadrons through nuclear matter able to predict how the energy is distributed between all the final-state observed particles. In this article, we investigate the role of nuclear de-excitation in neutrino-nucleus scattering using two Monte Carlo cascade models: NuWro and INCL coupled with the de-excitation code ABLA. The ablation model ABLA is used here for the first time to model de-excitation in neutrino interactions. As input to ABLA, we develop a consistent simulation of nuclear excitation energy tuned to electron-scattering data. The paper includes the characterization of the leading proton kinematics and of the nuclear cluster production during cascade and de-excitation. The observability of nuclear clusters as vertex activity and their role in a precise neutrino energy reconstruction is quantified.Comment: 14 pages, 13 figure

Two-particle correlations in azimuthal angle and pseudorapidity in inelastic p + p interactions at the CERN Super Proton Synchrotron

Results on two-particle ΔηΔϕ correlations in inelastic p + p interactions at 20, 31, 40, 80, and 158 GeV/c are presented. The measurements were performed using the large acceptance NA61/SHINE hadron spectrometer at the CERN Super Proton Synchrotron. The data show structures which can be attributed mainly to effects of resonance decays, momentum conservation, and quantum statistics. The results are compared with the Epos and UrQMD models.ISSN:1434-6044ISSN:1434-605

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Research and Development for Near Detector Systems Towards Long Term Evolution of Ultra-precise Long-baseline Neutrino Experiments

With the discovery of non-zero value of $\theta_{13}$ mixing angle, the next generation of long-baseline neutrino (LBN) experiments offers the possibility of obtaining statistically significant samples of muon and electron neutrinos and anti-neutrinos with large oscillation effects. In this document we intend to highlight the importance of Near Detector facilities in LBN experiments to both constrain the systematic uncertainties affecting oscillation analyses but also to perform, thanks to their close location, measurements of broad benefit for LBN physics goals. A strong European contribution to these efforts is possible

Hadron Production measurements at the NA61/SHINE experiment for the T2K Neutrino Flux Prediction

The largest source of uncertainty on the initial neutrino flux in modern accelerator neutrino ex- periments is the poor knowledge on the production of hadrons that decay into neutrinos. T2K is a long baseline neutrino experiment that aims to precisely measure the parameters of the PMNS ma- trix via the n m ! n e appearance and n m disappearance as well as to look for the first indication of CP violation in the leptonic sector. The required total systematic uncertainty on the neutrino flux as low as 5% can hopefully be achieved with high precision hadron production measurements, performed by the dedicated auxiliary NA61/SHINE experiment at the CERN SPS. Production of hadrons in 31 GeV/c proton interactions on carbon is measured with a thin target (4% of the nuclear interaction length) to study the primary interactions and with a T2K replica target (1.9 interaction length) to investigate re-interactions in the long target. The low statistic pilot data-set taken in 2007 was used to measure hadron multiplicities with the thin target and to demonstrate the capabilities of the spectrometer with the T2K replica target. High statistics 2009 and 2010 runs have been used to perform precise measurements. The latest 2009 results on charged pion, kaon and proton spectra are presented and experimental data are compared to model predictions. The re-weighting procedure used to tune the T2K neutrino flux is presented as well. This method will be very important also for the future neutrino long-baseline experiments for which a preci- sion of about 2% on the flux knowledge is required for the discovery of CP violation in the lepton sector