From One to Many: A Deep Learning Coincident Gravitational-Wave Search

• URL: http://arxiv.org/abs/2108.10715v1
• Date: Tue, 24 Aug 2021 13:25:02 GMT
• Title: From One to Many: A Deep Learning Coincident Gravitational-Wave Search
• Authors: Marlin B. Sch\"afer (1 and 2), Alexander H. Nitz (1 and 2) ((1) Max-Planck-Institut f\"ur Gravitationsphysik (Albert-Einstein-Institut), (2) Leibniz Universit\"at Hannover)
• Abstract summary: We construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors.
• Score: 58.720142291102135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors. The most sensitive search algorithms convolve many different pre-calculated gravitational waveforms with the detector data and look for coincident matches between different detectors. Machine learning is being explored as an alternative approach to building a search algorithm that has the prospect to reduce computational costs and target more complex signals. In this work we construct a two-detector search for gravitational waves from binary black hole mergers using neural networks trained on non-spinning binary black hole data from a single detector. The network is applied to the data from both observatories independently and we check for events coincident in time between the two. This enables the efficient analysis of large quantities of background data by time-shifting the independent detector data. We find that while for a single detector the network retains $91.5\%$ of the sensitivity matched filtering can achieve, this number drops to $83.9\%$ for two observatories. To enable the network to check for signal consistency in the detectors, we then construct a set of simple networks that operate directly on data from both detectors. We find that none of these simple two-detector networks are capable of improving the sensitivity over applying networks individually to the data from the detectors and searching for time coincidences.

Related papers

• Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing [0.7305342793164903]
  We propose a model simplification method for two-stage object detectors. Our method reduces computation costs upto 61.2% with the accuracy loss within 2.1% on the DOTAv1.5 dataset.
  arXiv  Detail & Related papers  (2024-04-11T00:45:10Z)
• MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge [110.7678032481059]
  We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. Our results show that current machine learning search algorithms may already be sensitive enough in limited parameter regions to be useful for some production settings.
  arXiv  Detail & Related papers  (2022-09-22T16:44:59Z)
• Training Strategies for Deep Learning Gravitational-Wave Searches [43.55994393060723]
  We restrict our analysis to signals from non-spinning binary black holes. We systematically test different strategies by which training data is presented to the networks. We find that the deep learning algorithms can generalize low signal-to-noise ratio (SNR) signals to high SNR ones but not vice versa.
  arXiv  Detail & Related papers  (2021-06-07T16:04:29Z)
• Detecting residues of cosmic events using residual neural network [0.0]
  Residual networks have transformed many fields like image classification, face recognition and object detection with their robust structure. Deep learning networks are trained only once. When a new type of gravitational wave is to be detected, this turns out to be a drawback of deep learning. I aim to create a custom residual neural network for 1-dimensional time series inputs.
  arXiv  Detail & Related papers  (2021-01-01T08:44:58Z)
• Self-Supervised Person Detection in 2D Range Data using a Calibrated Camera [83.31666463259849]
  We propose a method to automatically generate training labels (called pseudo-labels) for 2D LiDAR-based person detectors. We show that self-supervised detectors, trained or fine-tuned with pseudo-labels, outperform detectors trained using manual annotations. Our method is an effective way to improve person detectors during deployment without any additional labeling effort.
  arXiv  Detail & Related papers  (2020-12-16T12:10:04Z)
• Single-stage intake gesture detection using CTC loss and extended prefix beam search [8.22379888383833]
  Accurate detection of individual intake gestures is a key step towards automatic dietary monitoring. We propose a single-stage approach which directly decodes the probabilities learned from sensor data into sparse intake detections.
  arXiv  Detail & Related papers  (2020-08-07T06:04:25Z)
• Suppress and Balance: A Simple Gated Network for Salient Object Detection [89.88222217065858]
  We propose a simple gated network (GateNet) to solve both issues at once. With the help of multilevel gate units, the valuable context information from the encoder can be optimally transmitted to the decoder. In addition, we adopt the atrous spatial pyramid pooling based on the proposed "Fold" operation (Fold-ASPP) to accurately localize salient objects of various scales.
  arXiv  Detail & Related papers  (2020-07-16T02:00:53Z)
• Detection of gravitational-wave signals from binary neutron star mergers using machine learning [52.77024349608834]
  We introduce a novel neural-network based machine learning algorithm that uses time series strain data from gravitational-wave detectors. We find an improvement by a factor of 6 in sensitivity to signals with signal-to-noise ratio below 25. A conservative estimate indicates that our algorithm introduces on average 10.2 s of latency between signal arrival and generating an alert.
  arXiv  Detail & Related papers  (2020-06-02T10:20:11Z)
• BiDet: An Efficient Binarized Object Detector [96.19708396510894]
  We propose a binarized neural network learning method called BiDet for efficient object detection. Our BiDet fully utilizes the representational capacity of the binary neural networks for object detection by redundancy removal. Our method outperforms the state-of-the-art binary neural networks by a sizable margin.
  arXiv  Detail & Related papers  (2020-03-09T08:16:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.