A Nuclear Emulsion Detector for the Muon Radiography of a Glacier Structure

Ariga, A.; Ariga, T.; Ereditato, A.; Käser, Samuel; Lechmann, Alessandro; Mair, David; Nishiyama, Ryuichi; Pistillo, C.; Scampoli, P.; Schlunegger, Fritz; Vladymyrov, Mykhailo

doi:10.3390/instruments2020007

Cited by 12 publications

(15 citation statements)

References 23 publications

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“…Further experiments have been conducted in the Caribbean, in France (Ambrosino et al, 2015;Jourde et al, 2013Jourde et al, , 2015Lesparre et al, 2012;Marteau et al, 2015) and in Italy on Etna (Lo Presti et al, 2018) and Stromboli (Tioukov et al, 2017). Recently, Barnaföldi et al (2012) used A. Lechmann et al: The effect of rock composition on muon tomography measurements this technology to examine karstic caves in the Hungarian mountains.…”

Section: Introductionmentioning

confidence: 99%

The effect of rock composition on muon tomography measurements

et al. 2018

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Abstract. In recent years, the use of radiographic inspection with cosmic-ray muons has spread into multiple research and industrial fields. This technique is based on the high-penetration power of cosmogenic muons. Specifically, it allows the resolution of internal density structures of large-scale geological objects through precise measurements of the muon absorption rate. So far, in many previous works, this muon absorption rate has been considered to depend solely on the density of traversed material (under the assumption of a standard rock) but the variation in chemical composition has not been taken seriously into account. However, from our experience with muon tomography in Alpine environments, we find that this assumption causes a substantial bias in the muon flux calculation, particularly where the target consists of high {Z2∕A} rocks (like basalts and limestones) and where the material thickness exceeds 300 m. In this paper, we derive an energy loss equation for different minerals and we additionally derive a related equation for mineral assemblages that can be used for any rock type on which mineralogical data are available. Thus, for muon tomography experiments in which high {Z2∕A} rock thicknesses can be expected, it is advisable to plan an accompanying geological field campaign to determine a realistic rock model.

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Section: Introductionmentioning

confidence: 99%

The effect of rock composition on muon tomography measurements

et al. 2018

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“…Being impossible to make justice of so many applications, we decided to elaborate only on a very recent development related to glaciology, probably less known because of its novelty 7 : the study of the bedrock profiles underneath alpine glaciers [63,64], using nuclear emulsion detectors installed in three observation points underneath the targets in a railway tunnel, with the methodology described in Ref. [65]. The AM measurement yields, for each detector, the average density ρ along a line of sight; therefore, the position of the ice-rock transition surface is derived from the formula ρ = ρ rock · x + ρ ice · (1 − x) (following Ref.…”

Section: Geosciencesmentioning

confidence: 99%

“…Emulsions were among the earliest particle detectors and contributed to seminal results, like the discovery of the charged pion [5]. A recent large-scale application was the OPERA neutrino experiment, from where some recent spin-offs to muography [78,65,34] originated.…”

Section: Nuclear Emulsion Detectorsmentioning

confidence: 99%

Atmospheric muons as an imaging tool

2020

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Imaging methods based on the absorption or scattering of atmospheric muons, collectively named under the neologism "muography", exploit the abundant natural flux of muons produced from cosmic-ray interactions in the atmosphere. Recent years have seen a steep rise in the development of muography methods in a variety of innovative multidisciplinary approaches to study the interior of natural or man-made structures, establishing synergies between usually disconnected academic disciplines such as particle physics, geology, and archaeology. Muography also bears promise of immediate societal impact through geotechnical investigations, nuclear waste surveys, homeland security, and natural hazard monitoring. Our aim is to provide an introduction to this vibrant research area, starting from the physical principles at the basis of the methods and reviewing several recent developments in the application of muography methods to specific use cases, without any pretence of exhaustiveness. We then describe the main detector technologies and imaging methods, including their combination with conventional techniques from other disciplines, where appropriate. Finally, we discuss critically some outstanding issues that affect a broad variety of applications, and the current state of the art in addressing them. a

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“…First, 3D tomographic images of the emulsion detector are acquired using automated scanning microscopes. Next, the position of silver grains ("hits") is located in the 3D image volumes, and finally tracks in the detector volume are reconstructed as sequence of hits [2], [9].…”

Section: Introductionmentioning

confidence: 99%

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

Vladymyrov

Ariga

2020

J. Inst.

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Efficient tracking algorithm is a crucial part of particle tracking detectors. While big work was done in designing plethora of various algorithms, they usually require tedious tuning for each use case. (Weakly) supervised Machine Learning-based approaches can leverage the actual raw data for maximal performance. Yet in realistic scenarios sufficient high-quality labeled data is not available. While sometimes training can be performed on simulated data, often appropriate simulation of detector noise is impossible, compromising this approach. Here we propose a novel fully unsupervised approach to track reconstruction. The introduced model for learning to disentangle the factors of variation in a geometrically meaningful way employs geometrical space invariances. We train it through constraints on the equivariance between the image space and the latent representation in a Deep Convolutional Autoencoder. Using experimental results on synthetic data we show requirement of the variety of the space transformations for meaningful disentanglement of factors of variation. We also demonstrate performance of our model on real data from tracking detectors.

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A Nuclear Emulsion Detector for the Muon Radiography of a Glacier Structure

Cited by 12 publications

References 23 publications

The effect of rock composition on muon tomography measurements

The effect of rock composition on muon tomography measurements

Atmospheric muons as an imaging tool

Novel tracking approach based on fully-unsupervised disentanglement of the geometrical factors of variation

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