727 research outputs found
Diving deeper into the underlying white shark behaviors at Guadalupe Island, Mexico
We thank grants and logistic support from Alianza WWF-Fundación Carlos Slim, Alianza WWF-Telcel, Annenberg Foundation, Pfleger Institute of Environmental Research (PIER), and Fundación Mundo Azul. HV, FGM, and RGA acknowledge support from SNI (CONACYT), and COFAA and EDI programs from Instituto Politécnico Nacional.Fine-scale movement patterns are driven by both biotic (hunting, physiological needs) and abiotic (environmental conditions) factors. The energy balance governs all movement-related strategic decisions. Marine environments can be better understood by considering the vertical component. From 24 acoustic trackings of 10 white sharks in Guadalupe Island, this study linked, for the first time, horizontal and vertical movement data and inferred six different behavioral states along with movement states, through the use of hidden Markov models, which allowed to draw a comprehensive picture of white shark behavior. Traveling was the most frequent state of behavior for white sharks, carried out mainly at night and twilight. In contrast, area-restricted searching was the least used, occurring primarily in daylight hours. Time of day, distance to shore, total shark length, and, to a lesser extent, tide phase affected behavioral states. Chumming activity reversed, in the short term and in a nonpermanent way, the behavioral pattern to a general diel vertical pattern.Publisher PDFPeer reviewe
The Type Ib SN 1999dn: One Year of Photometric and Spectroscopic Monitoring
Extensive optical and near-infrared (NIR) observations of the type Ib
supernova 1999dn are presented, covering the first year after explosion. These
new data turn this object, already considered a prototypical SNIb, into one of
the best observed objects of its class. The light curve of SN 1999dn is mostly
similar in shape to that of other SNeIb but with a moderately faint peak
M_V=-17.2 mag). From the bolometric light curve and ejecta expansion
velocities, we estimate that about 0.11 Msun of 56Ni were produced during the
explosion and that the total ejecta mass was 4-6 Msun with a kinetic energy of
at least 5x10^{51} erg. The spectra of SN 1999dn at various epochs are similar
to those of other Stripped Envelope (SE) SNe showing clear presence of H at
early epochs. The high explosion energy and ejected mass, along with the small
flux ratio [CaII]/[OI] measured in the nebular spectrum, together with the lack
of signatures of dust formation and the relatively high-metallicity environment
point toward a single massive progenitor (M_ZAMS>=23-25 Msun) for SN 1999dn.Comment: 15 pages, 11 figures. MNRAS accepted; This version matches the
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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
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
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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
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
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
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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