Noise reduction in muon tomography for detecting high density objects

The muon tomography technique, based on multiple Coulomb scattering of cosmic ray muons, has been proposed as a tool to detect the presence of high density objects inside closed volumes. In this paper a new and innovative method is presented to handle the density fluctuations (noise) of reconstructed images, a well known problem of this technique. The effectiveness of our method is evaluated using experimental data obtained with a muon tomography prototype located at the Legnaro National Laboratories (LNL) of the Istituto Nazionale di Fisica Nucleare (INFN). The results reported in this paper, obtained with real cosmic ray data, show that with appropriate image filtering and muon momentum classification, the muon tomography technique can detect high density materials, such as lead, albeit surrounded by light or medium density material, in short times. A comparison with algorithms published in literature is also presented

Monitoring Spent Nuclear Fuel in a Dry Cask Using Momentum Integrated Muon Scattering Tomography

Nuclear materials accountability and nonproliferation are among the critical tasks to be addressed for the advancement of nuclear energy in the United States. Monitoring spent nuclear fuel is important to continue reliable stewardship of SNF storage. Cosmic ray muons have been acknowledged a promising radiographic tool for monitoring SNF due to their highly penetrative nature and high energy. Cosmic ray muons are more suitable and have been used for imaging large and dense objects. Despite their potential in various applications, the wide application of cosmic ray muons is limited by the naturally low intensity at sea level. To efficiently utilize cosmic ray muons in engineering applications, trajectory and momentum must be measured. Although various studies demonstrate that there is significant potential for measuring momentum in muon applications, it is still difficult to measure both muon scattering angle and momentum in the field. To fill this critical gap, a muon spectrometer using multilayer pressurized gas Cherenkov radiators was proposed. However, existing muon tomographic algorithms were developed assuming monoenergetic muon scattering and are not optimized for a measured polyenergetic momentum spectrum. In this work, we develop and evaluate a momentum integrated muon scattering tomography algorithm. We evaluate the algorithm on its capability to identify a missing fuel assembly from a SNF dry cask. Our results demonstrate that image resolution using MMST is significantly improved when measuring muon momentum and it can reduce monitoring time by a factor of 10 when compared to that of a conventional muon imaging technique in terms of systematically finding a missing FA.Comment: Transaction of American Nuclear Societ

Cosmic ray muons for spent nuclear fuel monitoring

There is a steady increase in the volume of spent nuclear fuel stored on-site (at reactor) as currently there is no permanent disposal option. No alternative disposal path is available and storage of spent nuclear fuel in dry storage containers is anticipated for the near future. In this dissertation, a capability to monitor spent nuclear fuel stored within dry casks using cosmic ray muons is developed. The motivation stems from the need to investigate whether the stored content agrees with facility declarations to allow proliferation detection and international treaty verification. Cosmic ray muons are charged particles generated naturally in the atmosphere from high energy cosmic rays. Using muons for proliferation detection and international treaty verification of spent nuclear fuel is a novel approach to nuclear security that presents significant advantages. Among others, muons have the ability to penetrate high density materials, are freely available, no radiological sources are required and consequently there is a total absence of any artificial radiological dose. A methodology is developed to demonstrate the applicability of muons for nuclear nonproliferation monitoring of spent nuclear fuel dry casks. Purpose is to use muons to differentiate between spent nuclear fuel dry casks with different amount of loading, not feasible with any other technique. Muon scattering and transmission are used to perform monitoring and imaging of the stored contents of dry casks loaded with spent nuclear fuel. It is shown that one missing fuel assembly can be distinguished from a fully loaded cask with a small overlapping between the scattering distributions with 300,000 muons or more. A Bayesian monitoring algorithm was derived to allow differentiation of a fully loaded dry cask from one with a fuel assembly missing in the order of minutes and negligible error rate. Muon scattering and transmission simulations are used to reconstruct the stored contents of sealed dry casks from muon measurements. A combination of muon scattering and muon transmission imaging can improve resolution and thus a missing fuel assembly can be identified for vertical and horizontal dry casks. The apparent separation of the images reveals that the muon scattering and transmission can be used for discrimination between casks, satisfying the diversion criteria set by IAEA

Methodologies for imaging a used nuclear fuel dry storage cask with cosmic ray muon computed tomography

Muons interact with matter via two major interaction mechanisms: ionization and radioactive process, and multiple Coulomb scattering leading to energy loss and trajectory deflection, respectively. For a monoenergetic muon beam crossing an object, the scattering angle follows a Gaussian distribution with a zero mean value and a variance that depends on the atomic number of the material object it traversed. Thus, the measured scattering angle may be used to reconstruct the geometrical and material information of the contents inside the dry storage cask. In traditional X-ray computed tomography, the projection information used to reconstruct the attenuation map of the imaged objects is the negative natural logarithm of the transmission rate of the X-rays, which is equal to the linear summation of the X-ray attenuation coefficients along the incident path. Similarly, the variance of the muon scattering angle is also the linear integral of the scattering density of the objects crossed by the muons. Thus, a muon CT image can be built by equating scattering density with attenuation coefficient. However, muon CT faces some unique challenges including: 1) long measurement times due to low cosmic muon flux, 2) insufficiently accurate muon path models, and 3) the inability to precisely measuring muon momentum. In this work, three different muon path models, two different projection methods, and two different reconstruction methods were investigated for use in muon CT of dry storage casks. The investigation was conducted in a validated Geant4 workspace, both in an ideal case and with relevant engineering restrictions considered. The results of these investigations and the expected benefits for fuel cask monitoring are reported herein

A compact, high resolution tracker for cosmic ray muon scattering tomography using semiconductor sensors

© 2018 IOP Publishing Ltd and Sissa Medialab. A semiconductor tracker for muon scattering tomography is presented. The tracker contains silicon strip sensors with an 80 μm pitch, precision mechanics and integrated cooling. The electronic readout of the sensors is performed by a scalable, inexpensive, flexible, FPGA-based system, which is demonstrated to achieve an event rate of 30 kHz. The tracker performance is compared with a Geant4 simulation. A scattering angle resolution compatible with 1.5 mrad at the 4 GeV average cosmic ray muon energy is demonstrated. Images of plastic, iron and lead samples are obtained using an Angle Statistics Reconstruction algorithm. The images demonstrate good contrast between low and high atomic number materials

Statistical unfolding of elementary particle spectra: Empirical Bayes estimation and bias-corrected uncertainty quantification

We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a particle detector. This important statistical inverse problem arising in data analysis at the Large Hadron Collider at CERN consists in estimating the intensity function of an indirectly observed Poisson point process. Unfolding typically proceeds in two steps: one first produces a regularized point estimate of the unknown intensity and then uses the variability of this estimator to form frequentist confidence intervals that quantify the uncertainty of the solution. In this paper, we propose forming the point estimate using empirical Bayes estimation which enables a data-driven choice of the regularization strength through marginal maximum likelihood estimation. Observing that neither Bayesian credible intervals nor standard bootstrap confidence intervals succeed in achieving good frequentist coverage in this problem due to the inherent bias of the regularized point estimate, we introduce an iteratively bias-corrected bootstrap technique for constructing improved confidence intervals. We show using simulations that this enables us to achieve nearly nominal frequentist coverage with only a modest increase in interval length. The proposed methodology is applied to unfolding the $Z$ boson invariant mass spectrum as measured in the CMS experiment at the Large Hadron Collider.Comment: Published at http://dx.doi.org/10.1214/15-AOAS857 in the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org). arXiv admin note: substantial text overlap with arXiv:1401.827

ADVANCED APPLICATIONS OF COSMIC-RAY MUON RADIOGRAPHY

The passage of cosmic-ray muons through matter is dominated by the Coulomb interaction with electrons and atomic nuclei. The muons interaction with electrons leads to continuous energy loss and stopping through the process of ionization. The muon\u27s interaction with nuclei leads to angular diffusion. If a muon stops in matter, other processes unfold, as discussed in more detail below. These interactions provide the basis for advanced applications of cosmic-ray muon radiography discussed here, specifically: 1) imaging a nuclear reactor with near horizontal muons, and 2) identifying materials through the analysis of radiation lengths weighted by density and secondary signals that are induced by cosmic-ray muon trajectories. We have imaged a nuclear reactor, type AGN-201m, at the University of New Mexico, using data measured with a particle tracker built from a set of sealed drift tubes, the Mini Muon Tracker (MMT). Geant4 simulations were compared to the data for verification and validation. In both the data and simulation, we can identify regions of interest in the reactor including the core, moderator, and shield. This study reinforces our claims for using muon tomography to image reactors following an accident. Warhead and special nuclear materials (SNM) imaging is an important thrust for treaty verification and national security purposes. The differentiation of SNM from other materials, such as iron and aluminum, is useful for these applications. Several techniques were developed for material identification using cosmic-ray muons. These techniques include: 1) identifying the radiation length weighted by density of an object and 2) measuring the signals that can indicate the presence of fission and chain reactions. By combining the radiographic images created by tracking muons through a target plane with the additional fission neutron and gamma signature, we are able to locate regions that are fissionable from a single side. The following materials were imaged with this technique: aluminum, concrete, steel, lead, and uranium. Provided that there is sufficient mass, U-235 could be differentiated from U-238 through muon induced fission

An integrated geological-geophysical approach to subsurface interface reconstruction of muon tomography measurements in high alpine regions

Muon tomography is an imaging technique that emerged in the last decades. The principal concept is similar to X-ray tomography, where one determines the spatial distribution of material densities by means of penetrating photons. It differs from this well-known technology only by the type of particle. Muons are continuously produced in the Earth’s atmosphere when primary cosmic rays (mostly protons) interact with the atmosphere’s molecules. Depending on their energies these muons can penetrate materials up to several hundreds of metres (or even kilometres). Consequently, they have been used for the imaging of larger objects, including large geological objects such as volcanoes, caves and fault systems. This research project aimed at applying this technology to an alpine glacier in Central Switzerland to determine its bedrock geometry, and if possible, to gain information on the bedrock erosion mechanism. To this end, two major experimental studies have been conducted with the aim to reconstruct bedrock geometries of two different glaciers. Given this framework, I present in this thesis my contribution to the project in which I worked for 5 years. Most of the technological know-how of muon tomography still lies within physics institutes who were the key drivers in the development of this method. As the geophysical/geological community is nowadays an important user of this technology, it is important that also non-physicists familiarise themselves with the theory and concepts behind muon tomography. This can be seen as an effective method to bring more geoscientists to utilize this new technology for their own research. The first part of this thesis is designed to tackle this problem with a review article on the principles of muon tomography and a guide to best practice. A second important aspect is the reconstruction of the bedrock topography given muon flux measurements at various locations. Many to-date reconstruction algorithms include supplementary geological information such as density and/or compositional measurements only on the side. A probabilistic framework was successfully set up that allows for such additional data to be included into the inversion. This may be used to better constrain the bedrock geometry. Moreover, this flexible framework allows also for the inclusion of modelling errors in the physical models which may result in a more reliable estimate of the mean and standard deviation of the bedrock position. The third article is concerned with the determination of the effect of rock composition on the muon flux measurements. Researchers in the community use a made-up rock, called “standard-rock” in their calculations. Hitherto, it was unclear in which geological settings this is a valid assumption and in which the induced error becomes too large. Simulations that use this fantasy rock are performed and compared to simulations that use a more realistic rock model. It was found that for felsic rocks the standard-rock approximation is valid over all thickness ranges, while for mafic rocks and limestones this can lead to a serious bias if the rock is thicker than 300m

Artificial intelligence for improved fitting of trajectories of elementary particles in inhomogeneous dense materials immersed in a magnetic field

In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more traditional Bayesian filtering methods, drastically improving the reconstruction of the interacting particle kinematics. We show that a specific form of neural network, inherited from the field of natural language processing, is very close to the concept of a Bayesian filter that adopts a hyper-informative prior. Such a paradigm change can influence the design of future particle physics experiments and their data exploitation