2,529 research outputs found
Classification and Recovery of Radio Signals from Cosmic Ray Induced Air Showers with Deep Learning
Radio emission from air showers enables measurements of cosmic particle
kinematics and identity. The radio signals are detected in broadband Megahertz
antennas among continuous background noise. We present two deep learning
concepts and their performance when applied to simulated data. The first
network classifies time traces as signal or background. We achieve a true
positive rate of about 90% for signal-to-noise ratios larger than three with a
false positive rate below 0.2%. The other network is used to clean the time
trace from background and to recover the radio time trace originating from an
air shower. Here we achieve a resolution in the energy contained in the trace
of about 20% without a bias for of the traces with a signal. The
obtained frequency spectrum is cleaned from signals of radio frequency
interference and shows the expected shape.Comment: 20 pages, 13 figures, resubmitted to JINS
Gravitational Wave Data Analysis: Computing Challenges in the 3G Era
Cyber infrastructure will be a critical consideration in the development of next generation gravitational-wave detectors. The demand for data analysis computing in the 3G era will be driven by the high number of detections as well as the expanded search parameter space for compact astrophysical objects and the subsequent parameter estimation follow-up required to extract the nature of the sources. Additionally, there will be an increased need to develop appropriate and scalable computing cyberinfrastructure, including data access and transfer protocols, and storage and management of software tools, that have sustainable development, support, and management processes. This report identifies the major challenges and opportunities facing 3G gravitational-wave observatories and presents recommendations for addressing them. This report is the fourth in a six part series of reports by the GWIC 3G Subcommittee: i) Expanding the Reach of Gravitational Wave Observatories to the Edge of the Universe, ii) The Next Generation Global Gravitational Wave Observatory: The Science Book, iii) 3G R&D: R&D for the Next Generation of Ground-based Gravitational Wave Detectors, iv) Gravitational Wave Data Analysis: Computing Challenges in the 3G Era (this report), v) Future Ground-based Gravitational-wave Observatories: Synergies with Other Scientific Communities, and vi) An Exploration of Possible Governance Models for the Future Global Gravitational-Wave Observatory Network
Novel neural-network architecture for continuous gravitational waves
The high computational cost of wide-parameter-space searches for continuous gravitational waves (CWs) significantly limits the achievable sensitivity. This challenge has motivated the exploration of alternative search methods, such as deep neural networks (DNNs). Previous attempts [1,2] to apply convolutional image-classification DNN architectures to all-sky and directed CW searches showed promise for short, one-day search durations, but proved ineffective for longer durations of around ten days. In this paper, we offer a hypothesis for this limitation and propose new design principles to overcome it. As a proof of concept, we show that our novel convolutional DNN architecture attains matched-filtering sensitivity for a targeted search (i.e., single sky-position and frequency) in Gaussian data from two detectors spanning ten days. We illustrate this performance for two different sky positions and five frequencies in the 20-1000 Hz range, spanning the spectrum from an "easy"to the "hardest"case. The corresponding sensitivity depths fall in the range of 82-86/ Hz. The same DNN architecture is trained for each case, taking between 4-32 hours to reach matched-filtering sensitivity. The detection probability of the trained DNNs as a function of signal amplitude varies consistently with that of matched filtering. Furthermore, the DNN statistic distributions can be approximately mapped to those of the F-statistic under a simple monotonic function
Neural network time-series classifiers for gravitational-wave searches in single-detector periods
The search for gravitational-wave signals is limited by non-Gaussian
transient noises that mimic astrophysical signals. Temporal coincidence between
two or more detectors is used to mitigate contamination by these instrumental
glitches. However, when a single detector is in operation, coincidence is
impossible, and other strategies have to be used. We explore the possibility of
using neural network classifiers and present the results obtained with three
types of architectures: convolutional neural network, temporal convolutional
network, and inception time. The last two architectures are specifically
designed to process time-series data. The classifiers are trained on a month of
data from the LIGO Livingston detector during the first observing run (O1) to
identify data segments that include the signature of a binary black hole
merger. Their performances are assessed and compared. We then apply trained
classifiers to the remaining three months of O1 data, focusing specifically on
single-detector times. The most promising candidate from our search is
2016-01-04 12:24:17 UTC. Although we are not able to constrain the significance
of this event to the level conventionally followed in gravitational-wave
searches, we show that the signal is compatible with the merger of two black
holes with masses and at the luminosity distance of .Comment: 29 pages, 11 figures, submitted to CQ
Workshop proceedings: Information Systems for Space Astrophysics in the 21st Century, volume 1
The Astrophysical Information Systems Workshop was one of the three Integrated Technology Planning workshops. Its objectives were to develop an understanding of future mission requirements for information systems, the potential role of technology in meeting these requirements, and the areas in which NASA investment might have the greatest impact. Workshop participants were briefed on the astrophysical mission set with an emphasis on those missions that drive information systems technology, the existing NASA space-science operations infrastructure, and the ongoing and planned NASA information systems technology programs. Program plans and recommendations were prepared in five technical areas: Mission Planning and Operations; Space-Borne Data Processing; Space-to-Earth Communications; Science Data Systems; and Data Analysis, Integration, and Visualization
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