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Register a new userSolving inverse problems with the unfolding program TRUEE: Examples in astroparticle physics
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The unfolding program TRUEE is a software package for the numerical solution of inverse problems. The algorithm was first applied in the FORTRAN77 program RUN. RUN is an event-based unfolding algorithm which makes use of the Tikhonov regularization. It has been tested and compared to different unfolding applications and stood out with notably stable results and reliable error estimation. TRUEE is a conversion of RUN to C++, which works within the powerful ROOT framework. The program has been extended for more user-friendliness and delivers unfolding results which are identical to RUN. Beside the simplicity of the installation of the software and the generation of graphics, there are new functions, which facilitate the choice of unfolding parameters and observables for the user. In this paper, we introduce the new unfolding program and present its performance by applying it to two exemplary data sets from astroparticle physics, taken with the MAGIC telescopes and the IceCube neutrino detector, respectively.
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Cherenkov light induced by fast charged particles in transparent dielectric media such as air or water is exploited by a variety of experimental techniques to detect and measure extraterrestrial particles impinging on Earth. A selection of detection principles is discussed and corresponding experiments are presented together with breakthrough-results they achieved. Some future developments are highlighted.
The open science framework defined in the German-Russian Astroparticle Data Life Cycle Initiative (GRADLCI) has triggered educational and outreach activities at the Irkutsk State University (ISU), which is actively participated in the two major astroparticle facilities in the region: TAIGA observatory and Baikal-GVD neutrino telescope. We describe the ideas grew out of this unique environment and propose a new open science laboratory based on education and outreach as well as on the development and testing new methods and techniques for the multimessenger astronomy.
Partial differential equations are central to describing many physical phenomena. In many applications these phenomena are observed through a sensor network, with the aim of inferring their underlying properties. Leveraging from certain results in sampling and approximation theory, we present a new framework for solving a class of inverse source problems for physical fields governed by linear partial differential equations. Specifically, we demonstrate that the unknown field sources can be recovered from a sequence of, so called, generalised measurements by using multidimensional frequency estimation techniques. Next we show that---for physics-driven fields---this sequence of generalised measurements can be estimated by computing a linear weighted-sum of the sensor measurements; whereby the exact weights (of the sums) correspond to those that reproduce multidimensional exponentials, when used to linearly combine translates of a particular prototype function related to the Greens function of our underlying field. Explicit formulae are then derived for the sequence of weights, that map sensor samples to the exact sequence of generalised measurements when the Greens function satisfies the generalised Strang-Fix condition. Otherwise, the same mapping yields a close approximation of the generalised measurements. Based on this new framework we develop practical, noise robust, sensor network strategies for solving the inverse source problem, and then present numerical simulation results to verify their performance.
Many application domains, spanning from computational photography to medical imaging, require recovery of high-fidelity images from noisy, incomplete or partial/compressed measurements. State of the art methods for solving these inverse problems combine deep learning with iterative model-based solvers, a concept known as deep algorithm unfolding. By combining a-priori knowledge of the forward measurement model with learned (proximal) mappings based on deep networks, these methods yield solutions that are both physically feasible (data-consistent) and perceptually plausible. However, current proximal mappings only implicitly learn such image priors. In this paper, we propose to make these image priors fully explicit by embedding deep generative models in the form of normalizing flows within the unfolded proximal gradient algorithm. We demonstrate that the proposed method outperforms competitive baselines on various image recovery tasks, spanning from image denoising to inpainting and deblurring.
The Alpha Magnetic Spectrometer (AMS02) experiment will be installed in 2009 on the International Space Station (ISS) for an operational period of at least three years. The purpose of AMS02 experiment is to perform accurate, high statistics, long duration measurements in space of charged cosmic rays in rigidity range from 1 GV to 3 TV and of high energy photons up to few hundred of GeV. In this work we will discuss the experimental details and the physics capabilities of AMS02 on ISS.
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