1,781 research outputs found
A deep proper-motion survey of the nearby open cluster Blanco 1
We provide two comprehensive catalogues of positions and proper motions in the area of open cluster Blanco 1. The main catalogue, CtlgM, contains 6271 objects down to V∼ 18.5 and covers a circular ∼11 deg2 area. The accuracy of CtlgM proper motions, at about 0.3-0.5 mas yr−1 for well-measured stars, permits an excellent segregation between the cluster and field stars. The vector-point diagram of proper motions indicates an estimated total of ∼165 cluster members among the stars in our sample, while 314 stars with σμ < 2.5 mas yr−1 have membership probabilities Pμ≥ 1 per cent. We also explored the astrometric potential of the Catalogue of Objects and Measured Parameters from All Sky Surveys (COMPASS) data base in order to obtain additional proper motions for fainter stars in the area of Blanco 1. This effort produced the second catalogue of proper motions, CtlgD, containing 11 598 objects down to V∼ 21. A total of 4273 objects are common between the two catalogues. The accuracy of proper motions in CtlgD ranges from 1.0 to 6 mas yr−1. A combination of both proper-motion catalogues was instrumental in confirming that Blanco 1 contains a large population of M dwarfs (∼150 down to M5 V - the limit of our survey). In many respects, Blanco 1 is a scaled down ‘twin' of the Pleiades. The noted discrepancy between the distance from a new Hipparcos parallax of Blanco 1 and the cluster's photometric distance, at least partially, might be due to the apparent correlation between parallax and proper motion in right ascension for the ensemble of cluster member
Assisted Diagnosis of Parkinsonism Based on the Striatal Morphology
Parkinsonism is a clinical syndrome characterized by the progressive loss of striatal dopamine. Its diagnosis
is usually corroborated by neuroimaging data such as DaTSCAN neuroimages that allow visualizing
the possible dopamine deficiency. During the last decade, a number of computer systems have been
proposed to automatically analyze DaTSCAN neuroimages, eliminating the subjectivity inherent to the
visual examination of the data. In this work, we propose a computer system based on machine learning
to separate Parkinsonian patients and control subjects using the size and shape of the striatal region,
modeled from DaTSCAN data. First, an algorithm based on adaptative thresholding is used to parcel
the striatum. This region is then divided into two according to the brain hemisphere division and characterized
with 152 measures, extracted from the volume and its three possible 2-dimensional projections.
Afterwards, the Bhattacharyya distance is used to discard the least discriminative measures and, finally,
the neuroimage category is estimated by means of a Support Vector Machine classifier. This method was
evaluated using a dataset with 189 DaTSCAN neuroimages, obtaining an accuracy rate over 94%. This
rate outperforms those obtained by previous approaches that use the intensity of each striatal voxel as
a feature.This work was supported by the MINECO/
FEDER under the TEC2015-64718-R project, the
Ministry of Economy, Innovation, Science and
Employment of the Junta de Andaluc´ıa under the
P11-TIC-7103 Excellence Project and the Vicerectorate
of Research and Knowledge Transfer of the
University of Granada
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Granger causality-based information fusion applied to electrical measurements from power transformers
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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
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
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
Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC
The low-noise operation of readout electronics in a liquid argon time
projection chamber (LArTPC) is critical to properly extract the distribution of
ionization charge deposited on the wire planes of the TPC, especially for the
induction planes. This paper describes the characteristics and mitigation of
the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase
LArTPC comprises two induction planes and one collection sense wire plane with
a total of 8256 wires. Current induced on each TPC wire is amplified and shaped
by custom low-power, low-noise ASICs immersed in the liquid argon. The
digitization of the signal waveform occurs outside the cryostat. Using data
from the first year of MicroBooNE operations, several excess noise sources in
the TPC were identified and mitigated. The residual equivalent noise charge
(ENC) after noise filtering varies with wire length and is found to be below
400 electrons for the longest wires (4.7 m). The response is consistent with
the cold electronics design expectations and is found to be stable with time
and uniform over the functioning channels. This noise level is significantly
lower than previous experiments utilizing warm front-end electronics.Comment: 36 pages, 20 figure
Design and construction of the MicroBooNE Cosmic Ray Tagger system
The MicroBooNE detector utilizes a liquid argon time projection chamber
(LArTPC) with an 85 t active mass to study neutrino interactions along the
Booster Neutrino Beam (BNB) at Fermilab. With a deployment location near ground
level, the detector records many cosmic muon tracks in each beam-related
detector trigger that can be misidentified as signals of interest. To reduce
these cosmogenic backgrounds, we have designed and constructed a TPC-external
Cosmic Ray Tagger (CRT). This sub-system was developed by the Laboratory for
High Energy Physics (LHEP), Albert Einstein center for fundamental physics,
University of Bern. The system utilizes plastic scintillation modules to
provide precise time and position information for TPC-traversing particles.
Successful matching of TPC tracks and CRT data will allow us to reduce
cosmogenic background and better characterize the light collection system and
LArTPC data using cosmic muons. In this paper we describe the design and
installation of the MicroBooNE CRT system and provide an overview of a series
of tests done to verify the proper operation of the system and its components
during installation, commissioning, and physics data-taking
Measurement of cosmic-ray reconstruction efficiencies in the MicroBooNE LArTPC using a small external cosmic-ray counter
The MicroBooNE detector is a liquid argon time projection chamber at Fermilab
designed to study short-baseline neutrino oscillations and neutrino-argon
interaction cross-section. Due to its location near the surface, a good
understanding of cosmic muons as a source of backgrounds is of fundamental
importance for the experiment. We present a method of using an external 0.5 m
(L) x 0.5 m (W) muon counter stack, installed above the main detector, to
determine the cosmic-ray reconstruction efficiency in MicroBooNE. Data are
acquired with this external muon counter stack placed in three different
positions, corresponding to cosmic rays intersecting different parts of the
detector. The data reconstruction efficiency of tracks in the detector is found
to be , in good agreement with the Monte Carlo reconstruction
efficiency . This analysis represents
a small-scale demonstration of the method that can be used with future data
coming from a recently installed cosmic-ray tagger system, which will be able
to tag of the cosmic rays passing through the MicroBooNE
detector.Comment: 19 pages, 12 figure
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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