The field of deep learning has become increasingly important for particle physics experiments, yielding a multitude of advances, predominantly in event classification and reconstruction tasks. Many of these applications have been adopted from other domains. However, data in the field of physics are unique in the context of machine learning, insofar as their generation process and the laws and symmetries they abide by are usually well understood. Most commonly used deep learning architectures fail at utilizing this available information. In contrast, more traditional likelihood-based methods are capable of exploiting domain knowledge, but they are often limited by computational complexity. In this contribution, a hybrid approach is presented that utilizes generative neural networks to approximate the likelihood, which may then be used in a traditional maximum-likelihood setting. Domain knowledge, such as invariances and detector characteristics, can easily be incorporated in this approach. The hybrid approach is illustrated by the example of event reconstruction in IceCube.
Abstract-Stereo vision is a methodology to obtain depth in a scene based on the stereo image pair. In this paper we introduce a Discrete Wavelet Transform (DWT) based methodology for a state-of-the-art disparity estimation algorithm, that resulted in significant performance improvement in terms of speed and computational complexity. In the initial stage of the proposed algorithm, we apply DWT to the input images, reducing the number of samples to be processed in subsequent stages by 50%, thereby decreasing computational complexity and improving processing speed. Subsequently the architecture has been designed based on this proposed methodology and prototyped on a Xilinx Virtex-7 FPGA. The performance of the proposed methodology has been evaluated against four standard Middlebury Benchmark image pairs viz. Tsukuba, Venus, Teddy and Cones. The proposed methodology results in improvement of about 44.4% cycles per frame, 52% frames per second and 61.5% and 59.6% LUT and register utilization respectively, compared with state-of-the-art designs.
The IceCube Neutrino Observatory provides the opportunity to perform unique measurements of cosmic-ray air showers with its combination of a surface array and a deep detector. Electromagnetic particles and low-energy muons (∼GeV) are detected by IceTop, while a bundle of high-energy muons ( 400 GeV) can be measured in coincidence in IceCube. Predictions of air-shower observables based on simulations show a strong dependence on the choice of the high-energy hadronic interaction model. By reconstructing different composition-dependent observables, one can provide strong tests of hadronic interaction models, as these measurements should be consistent with one another. In this work, we present an analysis of air-shower data between 2.5 and 80 PeV, comparing the composition interpretation of measurements of the surface muon density, the slope of the IceTop lateral distribution function, and the energy loss of the muon bundle, using the models Sibyll 2.1, QGSJet-II.04 and EPOS-LHC. We observe inconsistencies in all models under consideration, suggesting they do not give an adequate description of experimental data. The results furthermore imply a significant uncertainty in the determination of the cosmic-ray mass composition through indirect measurements.
The Wavelength-shifting Optical Module (WOM) is a novel optical sensor that uses wavelength shifting and light guiding to substantially enhance the photosensitive area of UV optical modules. It has been designed for the IceCube Upgrade, a seven-string extension of the IceCube detector planned for the 2022/2023 South Pole deployment season. The WOM consists of a hollow quartz cylinder coated in wavelength shifting paint which serves as detection area and has two photomultipliers (PMTs) attached to the end faces. The light-collecting tube increases the effective photocathode area of the PMTs without producing additional dark current, making it suitable for low-signal, low-noise applications. We report on the design and performance of the WOM with a focus on the 12 modules in production for deployment in the IceCube Upgrade. While the WOM will be deployed in IceCube, its design is applicable to any large-volume particle detector based on the detection of Cherenkov light.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.