A
bstract
NEXT-100 is an electroluminescent high-pressure xenon gas time projection chamber that will search for the neutrinoless double beta (0
νββ
) decay of
136
Xe. The detector possesses two ...features of great value for 0
νββ
searches: energy resolution better than 1% FWHM at the
Q
value of
136
Xe and track reconstruction for the discrimination of signal and background events. This combination results in excellent sensitivity, as discussed in this paper. Material-screening measurements and a detailed Monte Carlo detector simulation predict a background rate for NEXT-100 of at most 4 × 10
−4
counts keV
−1
kg
−1
yr
−1
. Accordingly, the detector will reach a sensitivity to the 0
νββ
-decay half-life of 2.8 × 10
25
years (90% CL) for an exposure of 100 kg·year, or 6.0 × 10
25
years after a run of 3 effective years.
We report the first measurement of the double-differential and total muon neutrino charged current inclusive cross sections on argon at a mean neutrino energy of 0.8 GeV. Data were collected using ...the MicroBooNE liquid argon time projection chamber located in the Fermilab Booster neutrino beam and correspond to 1.6×1020 protons on target of exposure. The measured differential cross sections are presented as a function of muon momentum, using multiple Coulomb scattering as a momentum measurement technique, and the muon angle with respect to the beam direction. We compare the measured cross sections to multiple neutrino event generators and find better agreement with those containing more complete treatment of quasielastic scattering processes at low Q2. The total flux integrated cross section is measured to be 0.693±0.010(stat)±0.165(syst)×10−38 cm2.
Neutrinos, unlike the other fermions, could be Majorana particles, that is, truly neutral particles identical to their antiparticles. This would have deep consequences in particle physics and ...cosmology. A unique signature of Majorana neutrinos is the observation of neutrinoless double beta decays (ββ0ν). The discovery of neutrino oscillations — which has shown that neutrinos have masses — and the possible evidence of a ββ0ν signal in the Heildelberg-Moscow experiment have revolutionized the double beta decay comunity. A new generation of experiments with improved sensitivity is currently under design and construction. This paper reviews some of these proposals, with special emphasis in NEXT, that aims at building a 100-kg, high-pressure gas xenon TPC to be hosted in the new Canfranc Underground Laboratory (LSC), in Spain.
We have developed a convolutional neural network that can make a pixel-level prediction of objects in image data recorded by a liquid argon time projection chamber (LArTPC) for the first time. We ...describe the network design, training techniques, and software tools developed to train this network. The goal of this work is to develop a complete deep neural network based data reconstruction chain for the MicroBooNE detector. We show the first demonstration of a network's validity on real LArTPC data using MicroBooNE collection plane images. The demonstration is performed for stopping muon and a νμ charged-current neutral pion data samples.
The results of the measurements of the double-differential production cross-sections of pions,
d
2
σ
π
/
d
p
d
Ω
, in p–C and
π
±
–C interactions using the forward spectrometer of the HARP experiment ...are presented. The incident particles are 12
GeV/c protons and charged pions directed onto a carbon target with a thickness of 5% of a nuclear interaction length. For p–C interactions the analysis is performed using 100,035 reconstructed secondary tracks, while the corresponding numbers of tracks for
π
-
–C and
π
+
–C analyses are 106,534 and 10,122, respectively. Cross-section results are presented in the kinematic range
0.5
GeV
/
c
⩽
p
π
<
8
GeV
/
c
and
30
mrad
⩽
θ
π
<
240
mrad
in the laboratory frame. The measured cross-sections have a direct impact on the precise calculation of atmospheric neutrino fluxes and on the improved reliability of extensive air shower simulations by reducing the uncertainties of hadronic interaction models in the low energy range.
Measurements of the double-differential π
±
production cross-section in the range of momentum 100 MeV/c≤p< 800 MeV/c and angle 0.35 rad ≤θ< 2.15 rad in proton–beryllium, proton–aluminium and ...proton–lead collisions are presented. The data were taken with the HARP detector in the T9 beam line of the CERN PS. The pions were produced by proton beams in a momentum range from 3 GeV/c to 12.9 GeV/c hitting a target with a thickness of 5% of a nuclear interaction length. The tracking and identification of the produced particles was performed using a small-radius cylindrical time projection chamber (TPC) placed inside a solenoidal magnet. Incident particles were identified by an elaborate system of beam detectors. Results are obtained for the double-differential cross-sections d
2
σ/dpdθ at six incident proton beam momenta (3 GeV/c, 5 GeV/c, 8 GeV/c, 8.9 GeV/c (Be only), 12 GeV/c and 12.9 GeV/c (Al only)) and compared to previously available data.
We present the multiple particle identification (MPID) network, a convolutional neural network for multiple object classification, developed by MicroBooNE. MPID provides the probabilities that an ...interaction includes an e−, γ , μ−, π±, and protons in a liquid argon time projection chamber single readout plane. The network extends the single particle identification network previously developed by MicroBooNE Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber, R. Acciarri et al. J. Instrum. 12, P03011 (2017). MPID takes as input an image either cropped around a reconstructed interaction vertex or containing only activity connected to a reconstructed vertex, therefore relieving the tool from inefficiencies in vertex finding and particle clustering. The network serves as an important component in MicroBooNE's deep-learning-based ν e search analysis. In this paper, we present the network's design, training, and performance on simulation and data from the MicroBooNE detector.
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