12,102 research outputs found
Towards a Reconfigurable Sense-and-Stimulate Neural Interface Generating Biphasic Interleaved Stimulus
Published versio
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
The IceCube Neutrino Observatory: Instrumentation and Online Systems
The IceCube Neutrino Observatory is a cubic-kilometer-scale high-energy
neutrino detector built into the ice at the South Pole. Construction of
IceCube, the largest neutrino detector built to date, was completed in 2011 and
enabled the discovery of high-energy astrophysical neutrinos. We describe here
the design, production, and calibration of the IceCube digital optical module
(DOM), the cable systems, computing hardware, and our methodology for drilling
and deployment. We also describe the online triggering and data filtering
systems that select candidate neutrino and cosmic ray events for analysis. Due
to a rigorous pre-deployment protocol, 98.4% of the DOMs in the deep ice are
operating and collecting data. IceCube routinely achieves a detector uptime of
99% by emphasizing software stability and monitoring. Detector operations have
been stable since construction was completed, and the detector is expected to
operate at least until the end of the next decade.Comment: 83 pages, 50 figures; updated with minor changes from journal review
and proofin
Ionization Electron Signal Processing in Single Phase LArTPCs I. Algorithm Description and Quantitative Evaluation with MicroBooNE Simulation
We describe the concept and procedure of drifted-charge extraction developed
in the MicroBooNE experiment, a single-phase liquid argon time projection
chamber (LArTPC). This technique converts the raw digitized TPC waveform to the
number of ionization electrons passing through a wire plane at a given time. A
robust recovery of the number of ionization electrons from both induction and
collection anode wire planes will augment the 3D reconstruction, and is
particularly important for tomographic reconstruction algorithms. A number of
building blocks of the overall procedure are described. The performance of the
signal processing is quantitatively evaluated by comparing extracted charge
with the true charge through a detailed TPC detector simulation taking into
account position-dependent induced current inside a single wire region and
across multiple wires. Some areas for further improvement of the performance of
the charge extraction procedure are also discussed.Comment: 60 pages, 36 figures. The second part of this work can be found at
arXiv:1804.0258
Observation results by the TAMA300 detector on gravitational wave bursts from stellar-core collapses
We present data-analysis schemes and results of observations with the TAMA300
gravitational-wave detector, targeting burst signals from stellar-core collapse
events. In analyses for burst gravitational waves, the detection and
fake-reduction schemes are different from well-investigated ones for a
chirp-wave analysis, because precise waveform templates are not available. We
used an excess-power filter for the extraction of gravitational-wave
candidates, and developed two methods for the reduction of fake events caused
by non-stationary noises of the detector. These analysis schemes were applied
to real data from the TAMA300 interferometric gravitational wave detector. As a
result, fake events were reduced by a factor of about 1000 in the best cases.
The resultant event candidates were interpreted from an astronomical viewpoint.
We set an upper limit of 2.2x10^3 events/sec on the burst gravitational-wave
event rate in our Galaxy with a confidence level of 90%. This work sets a
milestone and prospects on the search for burst gravitational waves, by
establishing an analysis scheme for the observation data from an
interferometric gravitational wave detector
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular
convolutional neural networks, have been investigated as a method to replace or
complement traditional matched filtering techniques that are used to detect the
gravitational-wave signature of merging black holes. However, to date, these
methods have not yet been successfully applied to the analysis of long
stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave
observatories. In this work, we critically examine the use of convolutional
neural networks as a tool to search for merging black holes. We identify the
strengths and limitations of this approach, highlight some common pitfalls in
translating between machine learning and gravitational-wave astronomy, and
discuss the interdisciplinary challenges. In particular, we explain in detail
why convolutional neural networks alone cannot be used to claim a statistically
significant gravitational-wave detection. However, we demonstrate how they can
still be used to rapidly flag the times of potential signals in the data for a
more detailed follow-up. Our convolutional neural network architecture as well
as the proposed performance metrics are better suited for this task than a
standard binary classifications scheme. A detailed evaluation of our approach
on Advanced LIGO data demonstrates the potential of such systems as trigger
generators. Finally, we sound a note of caution by constructing adversarial
examples, which showcase interesting "failure modes" of our model, where inputs
with no visible resemblance to real gravitational-wave signals are identified
as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.
Improving Photoelectron Counting and Particle Identification in Scintillation Detectors with Bayesian Techniques
Many current and future dark matter and neutrino detectors are designed to
measure scintillation light with a large array of photomultiplier tubes (PMTs).
The energy resolution and particle identification capabilities of these
detectors depend in part on the ability to accurately identify individual
photoelectrons in PMT waveforms despite large variability in pulse amplitudes
and pulse pileup. We describe a Bayesian technique that can identify the times
of individual photoelectrons in a sampled PMT waveform without deconvolution,
even when pileup is present. To demonstrate the technique, we apply it to the
general problem of particle identification in single-phase liquid argon dark
matter detectors. Using the output of the Bayesian photoelectron counting
algorithm described in this paper, we construct several test statistics for
rejection of backgrounds for dark matter searches in argon. Compared to simpler
methods based on either observed charge or peak finding, the photoelectron
counting technique improves both energy resolution and particle identification
of low energy events in calibration data from the DEAP-1 detector and
simulation of the larger MiniCLEAN dark matter detector.Comment: 16 pages, 16 figure
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