7 research outputs found
Efficient Antihydrogen Detection in Antimatter Physics by Deep Learning
Antihydrogen is at the forefront of antimatter research at the CERN
Antiproton Decelerator. Experiments aiming to test the fundamental CPT symmetry
and antigravity effects require the efficient detection of antihydrogen
annihilation events, which is performed using highly granular tracking
detectors installed around an antimatter trap. Improving the efficiency of the
antihydrogen annihilation detection plays a central role in the final
sensitivity of the experiments. We propose deep learning as a novel technique
to analyze antihydrogen annihilation data, and compare its performance with a
traditional track and vertex reconstruction method. We report that the deep
learning approach yields significant improvement, tripling event coverage while
simultaneously improving performance by over 5% in terms of Area Under Curve
(AUC)
Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar
The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world's oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance
Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar
The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world’s oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance
Learning in the Machine: To Share or Not to Share?
Weight-sharing is one of the pillars behind Convolutional Neural Networks and
their successes. However, in physical neural systems such as the brain,
weight-sharing is implausible. This discrepancy raises the fundamental question
of whether weight-sharing is necessary. If so, to which degree of precision? If
not, what are the alternatives? The goal of this study is to investigate these
questions, primarily through simulations where the weight-sharing assumption is
relaxed. Taking inspiration from neural circuitry, we explore the use of Free
Convolutional Networks and neurons with variable connection patterns. Using
Free Convolutional Networks, we show that while weight-sharing is a pragmatic
optimization approach, it is not a necessity in computer vision applications.
Furthermore, Free Convolutional Networks match the performance observed in
standard architectures when trained using properly translated data (akin to
video). Under the assumption of translationally augmented data, Free
Convolutional Networks learn translationally invariant representations that
yield an approximate form of weight sharing
Machine learning at the energy and intensity frontiers of particle physics
Our knowledge of the fundamental particles of nature and their interactions is summarized by the standard model of particle physics. Advancing our understanding in this field has required experiments that operate at ever higher energies and intensities, which produce extremely large and information-rich data samples. The use of machine-learning techniques is revolutionizing how we interpret these data samples, greatly increasing the discovery potential of present and future experiments. Here we summarize the challenges and opportunities that come with the use of machine learning at the frontiers of particle physics