1,758 research outputs found
Modelling and experiments of self-reflectivity under femtosecond ablation conditions
We present a numerical model which describes the propagation of a single
femtosecond laser pulse in a medium of which the optical properties dynamically
change within the duration of the pulse. We use a Finite Difference Time Domain
(FDTD) method to solve the Maxwell's equations coupled to equations describing
the changes in the material properties. We use the model to simulate the
self-reflectivity of strongly focused femtosecond laser pulses on silicon and
gold under laser ablation condition. We compare the simulations to experimental
results and find excellent agreement.Comment: 11 pages, 8 figure
Testing of High Voltage Surge Protection Devices for Use in Liquid Argon TPC Detectors
In this paper we demonstrate the capability of high voltage varistors and gas
discharge tube arrestors for use as surge protection devices in liquid argon
time projection chamber detectors. The insulating and clamping behavior of each
type of device is characterized in air (room temperature), and liquid argon
(90~K), and their robustness under high voltage and high energy surges in
cryogenic conditions is verified. The protection of vulnerable components in
liquid argon during a 150 kV high voltage discharge is also demonstrated. Each
device is tested for argon contamination and light emission effects, and both
are constrained to levels where no significant impact upon liquid argon time
projection chamber functionality is expected. Both devices investigated are
shown to be suitable for HV surge protection applications in cryogenic
detectors.Comment: 22 pages, 18 figures v2: reduced file size for journal submissio
Siamese Survival Analysis with Competing Risks
Survival analysis in the presence of multiple possible adverse events, i.e.,
competing risks, is a pervasive problem in many industries (healthcare,
finance, etc.). Since only one event is typically observed, the incidence of an
event of interest is often obscured by other related competing events. This
nonidentifiability, or inability to estimate true cause-specific survival
curves from empirical data, further complicates competing risk survival
analysis. We introduce Siamese Survival Prognosis Network (SSPN), a novel deep
learning architecture for estimating personalized risk scores in the presence
of competing risks. SSPN circumvents the nonidentifiability problem by avoiding
the estimation of cause-specific survival curves and instead determines
pairwise concordant time-dependent risks, where longer event times are assigned
lower risks. Furthermore, SSPN is able to directly optimize an approximation to
the C-discrimination index, rather than relying on well-known metrics which are
unable to capture the unique requirements of survival analysis with competing
risks
Shadowing in Inelastic Scattering of Muons on Carbon, Calcium and Lead at Low XBj
Nuclear shadowing is observed in the per-nucleon cross-sections of positive
muons on carbon, calcium and lead as compared to deuterium. The data were taken
by Fermilab experiment E665 using inelastically scattered muons of mean
incident momentum 470 GeV/c. Cross-section ratios are presented in the
kinematic region 0.0001 < XBj <0.56 and 0.1 < Q**2 < 80 GeVc. The data are
consistent with no significant nu or Q**2 dependence at fixed XBj. As XBj
decreases, the size of the shadowing effect, as well as its A dependence, are
found to approach the corresponding measurements in photoproduction.Comment: 22 pages, incl. 6 figures, to be published in Z. Phys.
Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber
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
A Proposal for a Three Detector Short-Baseline Neutrino Oscillation Program in the Fermilab Booster Neutrino Beam
A Short-Baseline Neutrino (SBN) physics program of three LAr-TPC detectors
located along the Booster Neutrino Beam (BNB) at Fermilab is presented. This
new SBN Program will deliver a rich and compelling physics opportunity,
including the ability to resolve a class of experimental anomalies in neutrino
physics and to perform the most sensitive search to date for sterile neutrinos
at the eV mass-scale through both appearance and disappearance oscillation
channels. Using data sets of 6.6e20 protons on target (P.O.T.) in the LAr1-ND
and ICARUS T600 detectors plus 13.2e20 P.O.T. in the MicroBooNE detector, we
estimate that a search for muon neutrino to electron neutrino appearance can be
performed with ~5 sigma sensitivity for the LSND allowed (99% C.L.) parameter
region. In this proposal for the SBN Program, we describe the physics analysis,
the conceptual design of the LAr1-ND detector, the design and refurbishment of
the T600 detector, the necessary infrastructure required to execute the
program, and a possible reconfiguration of the BNB target and horn system to
improve its performance for oscillation searches.Comment: 209 pages, 129 figure
Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering
We discuss a technique for measuring a charged particle's momentum by means
of multiple Coulomb scattering (MCS) in the MicroBooNE liquid argon time
projection chamber (LArTPC). This method does not require the full particle
ionization track to be contained inside of the detector volume as other track
momentum reconstruction methods do (range-based momentum reconstruction and
calorimetric momentum reconstruction). We motivate use of this technique,
describe a tuning of the underlying phenomenological formula, quantify its
performance on fully contained beam-neutrino-induced muon tracks both in
simulation and in data, and quantify its performance on exiting muon tracks in
simulation. Using simulation, we have shown that the standard Highland formula
should be re-tuned specifically for scattering in liquid argon, which
significantly improves the bias and resolution of the momentum measurement.
With the tuned formula, we find agreement between data and simulation for
contained tracks, with a small bias in the momentum reconstruction and with
resolutions that vary as a function of track length, improving from about 10%
for the shortest (one meter long) tracks to 5% for longer (several meter)
tracks. For simulated exiting muons with at least one meter of track contained,
we find a similarly small bias, and a resolution which is less than 15% for
muons with momentum below 2 GeV/c. Above 2 GeV/c, results are given as a first
estimate of the MCS momentum measurement capabilities of MicroBooNE for high
momentum exiting tracks
The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector
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.Comment: Preprint to be submitted to The European Physical Journal
Ionization Electron Signal Processing in Single Phase LArTPCs II. Data/Simulation Comparison and Performance in MicroBooNE
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.Comment: 54 pages, 36 figures; the first part of this work can be found at
arXiv:1802.0870
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