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Register a new userGeoneutrinos and geoscience: an intriguing joint-venture
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The review is conceived to help the reader to interpret present geoneutrino results in the framework of Earths energetics and composition. Starting from the comprehension of antineutrino production, propagation, and detection, the status of the geoneutrino field is presented through the description of the experimental and technological features of the Borexino and KamLAND ongoing experiments. The current understanding of the energetical, geophysical and geochemical traits of our planet is examined in a critical analysis of the currently available models. By combining theoretical models and experimental results, the mantle geoneutrino signal extracted from the results of the two experiments demonstrates the effectiveness in investigating deep earth radioactivity through geoneutrinos from different sites. The obtained results are discussed and framed in the puzzle of the diverse classes of formulated Bulk Silicate Earth models, analyzing their implications on planetary heat budget and composition. As a final remark, we present the engaging technological challenges and the future experiments envisaged for the next decade in the geoneutrino field.
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SNOLAB is one of the deepest underground laboratories in the world with an overburden of 2092 m. The SNO+ detector is designed to achieve several fundamental physics goals as a low-background experiment, particularly measuring the Earths geoneutrino flux. Here we evaluate the effect of the 2 km overburden on the predicted crustal geoneutrino signal at SNO+. A refined 3D model of the 50 x 50 km upper crust surrounding the detector and a full calculation of survival probability are used to model the U and Th geoneutrino signal. Comparing this signal with that obtained by placing SNO+ at sea level, we highlight a $1.4^{+1.8}_{-0.9}$ TNU signal difference, corresponding to the ~5% of the total crustal contribution. Finally, the impact of the additional crust extending from sea level up to ~300 m was estimated.
Despite the increasingly successful application of neural networks to many problems in the geosciences, their complex and nonlinear structure makes the interpretation of their predictions difficult, which limits model trust and does not allow scientists to gain physical insights about the problem at hand. Many different methods have been introduced in the emerging field of eXplainable Artificial Intelligence (XAI), which aim at attributing the networks prediction to specific features in the input domain. XAI methods are usually assessed by using benchmark datasets (like MNIST or ImageNet for image classification), or through deletion/insertion techniques. In either case, however, an objective, theoretically-derived ground truth for the attribution is lacking, making the assessment of XAI in many cases subjective. Also, benchmark datasets for problems in geosciences are rare. Here, we provide a framework, based on the use of additively separable functions, to generate attribution benchmark datasets for regression problems for which the ground truth of the attribution is known a priori. We generate a long benchmark dataset and train a fully-connected network to learn the underlying function that was used for simulation. We then compare estimated attribution heatmaps from different XAI methods to the ground truth in order to identify examples where specific XAI methods perform well or poorly. We believe that attribution benchmarks as the ones introduced herein are of great importance for further application of neural networks in the geosciences, and for accurate implementation of XAI methods, which will increase model trust and assist in discovering new science.
Seismic data quality is vital to geophysical applications, so methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the dataset without introducing pseudo-Gibbs artifacts when compared to other directional multiscale transform methods such as curvelets.
This paper describes the Borexino detector and the high-radiopurity studies and tests that are integral part of the Borexino technology and development. The application of Borexino to the detection and studies of geoneutrinos is discussed.
In the present paper, we consider the possibility of interaction between geoneutrinos and the geomagnetic field, by adopting an approach based on the Diracs equation with a non-minimal coupling that accounts for the magnetic interaction of the massive neutrinos. In our approach, we see that the magnetic interaction is controlled by a dimensionless parameter, $fsimeq 10^{-1}$, and we estimate the mean value of this interaction to be of the order of $10^{-14} MeV^{2}$.
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