A team of scientists at Freie Universität Berlin has developed an Artificial Intelligence (AI) method that provides a fundamentally new solution of the "sampling problem" in statistical physics. The sampling problem is that important properties of materials and molecules can practically not be computed by directly simulating the motion of atoms in the computer because the required computational capacities are too vast even for supercomputers. The team developed a deep learning method that speeds up these calculations massively, making them feasible for previously intractable applications. "AI is changing all areas of our life, including the way we do science," explains Dr. Frank Noé, professor at Freie Universität Berlin and main author of the study. Several years ago, so-called deep learning methods bested human experts in pattern recognition—be it the reading of handwritten texts or the recognition of cancer cells from medical images. "Since these breakthroughs, AI research has skyrocketed. Every day, we see new developments in application areas where traditional methods have left us stuck for years. We believe our approach could be such an advance for the field of statistical physics." The results were published in Science.

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A benchmark for Monte Carlo simulation in gamma-ray spectrometry

M.C. Lépy, C. Thiam, M. Anagnostakis, R. Galea, D. Gurau, S. Hurtado, K. Karfopoulos, J. Liang, H. Liu, A. Luca, I. Mitsios, C. Potiriadis, M.I. Savva, T.T. Thanh, V. Thomas, R.W. Townson, T. Vasilopoulou, M. Zhang


Monte Carlo (MC) simulation is widely used in gamma-ray spectrometry, however, its implementation is not always easy and can provide erroneous results. The present action provides a benchmark for several MC software for selected cases. The examples are based on simple geometries, two types of germanium detectors and four kinds of sources, to mimic eight typical measurement conditions. The action outputs (input files and efficiency calculation results, including practical recommendations for new users) are made available on a dedicated webpage.

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York University researchers have made a precise measurement of the size of the proton—a crucial step towards solving a mystery that has preoccupied scientists around the world for the past decade.

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Neutrinos come in three flavours made up of a mix of three neutrino masses. While the differences between the masses are known, little information was available about the mass of the lightest species until now.

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Optimization of uranium separation using silica gel impregnated tri‐n‐butyl phosphate for alpha spectrometry analysis of soil samples

 Van Thang Nguyen, Xuan Son Vi, Nguyen Phong Thu Huynh, Cong Hao Le


Silica gel impregnated tri-n-butyl phosphate was used as a chromatography column for extraction of uranium radioisotopes from soil samples. Diluted HNO3 and H2SO4 were used as the mobile and striping phases, respectively. In these stages, concentrations of HNO3 and H2SO4 solutions for the uranium extraction yield were optimized. We found that 5 M HNO3 and 3 M H2SO4 were the best solutions for uranium separation. Effects of electrodeposition conditions on uranium deposition yield were considered. Results showed that current of 1.5 mA, deposition time of 75 min, pH 2 and electrode gap of 1.5 cm were optimal conditions for electrodeposition.

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A simple approach for developing model OF Si(Li) detector in Monte Carlo simulation

Huynh Dinh Chuong, Nguyen Thi Truc Linh, Le Thi Ngoc Trang, Vo Hoang Nguyen, Le Hoang Minh , Chau Thanh Tai, Tran Thien Thanh


In this paper, a simple approach for developing the model of a Si(Li) detector in Monte Carlo simulations is presented and validated. Experimental measurements using “point-like” standard radioactive sources including 133Ba, 137Cs, 152Eu, 154Eu, 241Am were performed for both configurations with and without collimator, respectively. The MCNP6 code was used for Monte Carlo simulation of photon transport inside the models constructed similar to these configurations. Firstly, an initial model of the Si(Li) detector was constructed based on the manufacturer's specifications, but the simulated efficiency shows a very high discrepancy from the experiment. Then, the critical geometric parameters of the model of Si(Li) detector were improved step-by-step to achieve the optimized model. For the optimized model, a good agreement was obtained between the experimental and simulated results. The relative deviations of experimental and simulated efficiencies are less than 4% with energies in the range of 12–60 keV for both configurations.

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