The MiniBooNE experiment at Fermilab reports results from an analysis of ν¯e appearance data from 11.27×10²⁰ protons on target in the antineutrino mode, an increase of approximately a factor of 2 ...over the previously reported results. An event excess of 78.4±28.5 events (2.8σ) is observed in the energy range 200<EQEν<1250 MeV. If interpreted in a two-neutrino oscillation model, ν¯μ→ν¯e, the best oscillation fit to the excess has a probability of 66% while the background-only fit has a χ² probability of 0.5% relative to the best fit. The data are consistent with antineutrino oscillations in the 0.01<Δm²<1.0 eV² range and have some overlap with the evidence for antineutrino oscillations from the Liquid Scintillator Neutrino Detector. All of the major backgrounds are constrained by in situ event measurements so nonoscillation explanations would need to invoke new anomalous background processes. The neutrino mode running also shows an excess at low energy of 162.0±47.8 events (3.4σ) but the energy distribution of the excess is marginally compatible with a simple two neutrino oscillation formalism. Expanded models with several sterile neutrinos can reduce the incompatibility by allowing for CP violating effects between neutrino and antineutrino oscillations.
A search for sub-GeV dark matter produced from collisions of the Fermilab 8 GeV Booster protons with a steel beam dump was performed by the MiniBooNE-DM Collaboration using data from 1.86 × 1020 ...protons on target in a dedicated run. The MiniBooNE detector, consisting of 818 tons of mineral oil and located 490 meters downstream of the beam dump, is sensitive to a variety of dark matter initiated scattering reactions. Three dark matter interactions are considered for this analysis: elastic scattering off nucleons, inelastic neutral pion production, and elastic scattering off electrons. Multiple data sets were used to constrain flux and systematic errors, and time-of-flight information was employed to increase sensitivity to higher dark matter masses. No excess from the background predictions was observed, and 90% confidence level limits were set on the vector portal and leptophobic dark matter models. New parameter space is excluded in the vector portal dark matter model with a dark matter mass between 5 and 50 MeV c−2 . The reduced neutrino flux allowed to test if the MiniBooNE neutrino excess scales with the production of neutrinos. No excess of neutrino oscillation events were measured ruling out models that scale solely by number of protons on target independent of beam configuration at 4.6σ.
The MiniBooNE-DM Collaboration searched for vector-boson mediated production of dark matter using the Fermilab 8-GeV Booster proton beam in a dedicated run with 1.86×10^{20} protons delivered to a ...steel beam dump. The MiniBooNE detector, 490 m downstream, is sensitive to dark matter via elastic scattering with nucleons in the detector mineral oil. Analysis methods developed for previous MiniBooNE scattering results were employed, and several constraining data sets were simultaneously analyzed to minimize systematic errors from neutrino flux and interaction rates. No excess of events over background was observed, leading to a 90% confidence limit on the dark matter cross section parameter, Y=ε^{2}α_{D}(m_{χ}/m_{V})^{4}≲10^{-8}, for α_{D}=0.5 and for dark matter masses of 0.01<m_{χ}<0.3 GeV in a vector portal model of dark matter. This is the best limit from a dedicated proton beam dump search in this mass and coupling range and extends below the mass range of direct dark matter searches. These results demonstrate a novel and powerful approach to dark matter searches with beam dump experiments.
The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging ...capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The largest sample ever recorded of charged-current quasielastic (CCQE, nu sub( mu ) + p arrow right mu super(+) + n) candidate events is used to produce the minimally model-dependent, ...flux-integrated double-differential cross section d2sigma/dT sub( mu )dcosstraighttheta sub( mu ) for nu sub( mu ) CCQE for a mineral oil target. This measurement exploits the large statistics of the MiniBooNE antineutrino mode sample and provides the most complete information of this process to date. In order to facilitate historical comparisons, the flux-unfolded total cross section sigma(E sub(nu)) and single-differential cross section dsigma/dQ super(2) on both mineral oil and on carbon are also reported. The observed cross section is somewhat higher than the predicted cross section from a model assuming independently acting nucleons in carbon with canonical form factor values. The shape of the data are also discrepant with this model. These results have implications for intranuclear processes and can help constrain signal and background processes for future neutrino oscillation measurements.
The MiniBooNE Collaboration observes unexplained electronlike events in the reconstructed neutrino energy range from 200 to 475 MeV. With 6.46x10;{20} protons on target, 544 electronlike events are ...observed in this energy range, compared to an expectation of 415.2+/-43.4 events, corresponding to an excess of 128.8+/-20.4+/-38.3 events. The shape of the excess in several kinematic variables is consistent with being due to either nu_{e} and nuover _{e} charged-current scattering or nu_{mu} neutral-current scattering with a photon in the final state. No significant excess of events is observed in the reconstructed neutrino energy range from 475 to 1250 MeV, where 408 events are observed compared to an expectation of 385.9+/-35.7 events.
We report on the first measurement of flux-integrated single differential cross sections for charged-current (CC) muon neutrino ($\nu_{\mu}$) scattering on argon with a muon and a proton in the final ...state, 40Ar $(\nu_{\mu},μp)X$. The measurement was carried out using the Booster Neutrino Beam at Fermi National Accelerator Laboratory and the MicroBooNE liquid argon time projection chamber detector with an exposure of 4.59 × 1019 protons on target. Events are selected to enhance the contribution of CC quasielastic (CCQE) interactions. The data are reported in terms of a total cross section as well as single differential cross sections in final state muon and proton kinematics. We measure the integrated per-nucleus CCQE-like cross section (i.e., for interactions leading to a muon, one proton, and no pions above detection threshold) of (4.93 ± 0.76stat ± 1.29sys)×10-38 cm2, in good agreement with theoretical calculations. The single differential cross sections are also in overall good agreement with theoretical predictions, except at very forward muon scattering angles that correspond to low-momentum-transfer events.
We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the ...classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level.
The single-phase liquid argon time projection chamber (LArTPC) provides a large amount of detailed information in the form of fine-grained drifted ionization charge from particle traces. To fully ...utilize this information, the deposited charge must be accurately extracted from the raw digitized waveforms via a robust signal processing chain. Enabled by the ultra-low noise levels associated with cryogenic electronics in the MicroBooNE detector, the precise extraction of ionization charge from the induction wire planes in a single-phase LArTPC is qualitatively demonstrated on MicroBooNE data with event display images, and quantitatively demonstrated via waveform-level and track-level metrics. Improved performance of induction plane calorimetry is demonstrated through the agreement of extracted ionization charge measurements across different wire planes for various event topologies. In addition to the comprehensive waveform-level comparison of data and simulation, a calibration of the cryogenic electronics response is presented and solutions to various MicroBooNE-specific TPC issues are discussed. This work presents an important improvement in LArTPC signal processing, the foundation of reconstruction and therefore physics analyses in MicroBooNE.
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