Physics

{We consider the problem of estimating causal influences between observed processes from time series possibly corrupted by errors in the time variable (dating errors) which are typical in palaeoclimatology, planetary science and astrophysics. "Causality ratio" based on the Wiener -- Granger causality is proposed and studied for a paradigmatic class of model systems to reveal conditions under which it correctly indicates directionality of unidirectional coupling. It is argued that in case of {\it a priori} known directionality, the causality ratio allows a characterization of dating errors and observational noise. Finally, we apply the developed approach to palaeoclimatic data and quantify the influence of solar activity on tropical Atlantic climate dynamics over the last two millennia. A stronger solar influence in the first millennium A.D. is inferred. The results also suggest a dating error of about 20 years in the solar proxy time series over the same period.

For a better understanding of granular flow problems such as silo blockage, avalanche triggering, mixing and segregation, it is essential to have a `microscopic' view of individual particles. In order to cope with the difficulty arising from the opacity of granular materials, such as sands, powders and grains, a small scale bi-static radar system operating at 10 \,GHz (X-band) was recently introduced to trace a sub-centimeter particle in three dimensions. Similar to a moving target indicator radar, the relative movement of the tracer with respect to each of the three receiving antennae is obtained via comparing the phase shift of the electromagnetic wave traveling through the target area with an IQ-Mixer. From the azimuth and tilt angles of the receiving antennae obtained in the calibration, the target trajectory in a three-dimensional Cartesian system is reconstructed. Using a free-falling sphere as a test case, we discuss the accuracy of this radar system and possible ways to enhance it by IQ mismatch corrections.

In this work, we analyzed time-series of background radiation inside a multilayer interleaving structure, geomagnetic activity and cosmic-ray activity using the Pearson correlation coefficient and a new correlation measure based on the one-dimensional component of the fourth order cumulant. The new method is proposed based on the fact that the cumulant of a random process is zero if it is of Gaussian nature. The results show that this methodology is useful for detecting correlations between the analyzed variables.

In liquid argon time projection chambers exposed to neutrino beams and running on or near surface levels, cosmic muons and other cosmic particles are incident on the detectors while a single neutrino-induced event is being recorded. In practice, this means that data from surface liquid argon time projection chambers will be dominated by cosmic particles, both as a source of event triggers and as the majority of the particle count in true neutrino-triggered events. In this work, we demonstrate a novel application of deep learning techniques to remove these background particles by applying semantic segmentation on full detector images from the SBND detector, the near detector in the Fermilab Short-Baseline Neutrino Program. We use this technique to identify, at single image-pixel level, whether recorded activity originated from cosmic particles or neutrino interactions.

The projected discovery and exclusion capabilities of particle physics and astrophysics/cosmology experiments are often quantified using the median expected p -value or its corresponding significance. We argue that this criterion leads to flawed results, which for example can counterintuitively project lessened sensitivities if the experiment takes more data or reduces its background. We discuss the merits of several alternatives to the median expected significance, both when the background is known and when it is subject to some uncertainty. We advocate for standard use of the "exact Asimov significance" Z A detailed in this paper.

Much research in recent years has focused on using empirical machine learning approaches to extract useful insights on the structure-property relationships of superconductor material. Notably, these approaches are bringing extreme benefits when superconductivity data often come from costly and arduously experimental work. However, this assessment cannot be based solely on an open black-box machine learning, which is not fully interpretable, because it can be counter-intuitive to understand why the model may give an appropriate response to a set of input data for superconductivity characteristic analyses, e.g., critical temperature. The purpose of this study is to describe and examine an alternative approach for predicting the superconducting transition temperature T c from SuperCon database obtained by Japan's National Institute for Materials Science. We address a generative machine-learning framework called Variational Bayesian Neural Network using superconductors chemical elements and formula to predict T c . In such a context, the importance of the paper in focus is twofold. First, to improve the interpretability, we adopt a variational inference to approximate the distribution in latent parameter space for the generative model. It statistically captures the mutual correlation of superconductor compounds and; then, gives the estimation for the T c . Second, a stochastic optimization algorithm, which embraces a statistical inference named Monte Carlo sampler, is utilized to optimally approximate the proposed inference model, ultimately determine and evaluate the predictive performance.

The cross-spectrum method consists in measuring a signal c(t) simultaneously with two independent instruments. Each of these instruments contributes to the global noise by its intrinsec (white) noise, whereas the signal c(t) that we want to characterize could be a (red) noise. We first define the real part of the cross-spectrum as a relevant estimator. Then, we characterize the probability density function (PDF) of this estimator knowing the noise level (direct problem) as a Variance-Gamma (V Γ ) distribution. Next, we solve the "inverse problem" thanks to Bayes' theorem to obtain an upper limit of the noise level knowing the estimate. Checked by massive Monte Carlo simulations, V Γ proves to be perfectly reliable to any number of degrees of freedom (dof). Finally we compare this method with an other method using the Karhunen-Loève transfrom (KLT). We find an upper limit of the signal level slightly different as the one of V Γ since KLT better takes into account the available informations.

The organization of the distributed user analysis on the Worldwide LHC Computing Grid (WLCG) infrastructure is one of the most challenging tasks among the computing activities at the Large Hadron Collider. The Experiment Dashboard offers a solution that not only monitors but also manages (kill, resubmit) user tasks and jobs via a web interface. The ATLAS Dashboard Task Monitor provides analysis users with a tool that is independent of the operating system and Grid environment. This contribution describes the functionality of the application and its implementation details, in particular authentication, authorization and audit of the management operations.

Regression of data generated in simulations or experiments has important implications in sensitivity studies, uncertainty analysis, and prediction accuracy. Depending on the nature of the physical model, data points may not be evenly distributed. It is not often practical to choose all points for regression of a model because it doesn't always guarantee a better fit. Fitness of the model is highly dependent on the number of data points and the distribution of the data along the curve. In this study, the effect of the number of points selected for regression is investigated and various schemes aimed to process regression data points are explored. Time series data i.e., output varying with time, is our prime interest mainly the temperature profile from enhanced geothermal system. The objective of the research is to find a better scheme for choosing a fraction of data points from the entire set to find a better fitness of the model without losing any features or trends in the data. A workflow is provided to summarize the entire protocol of data preprocessing, regression of mathematical model using training data, model testing, and error analysis. Six different schemes are developed to process data by setting criteria such as equal spacing along axes (X and Y), equal distance between two consecutive points on the curve, constraint in the angle of curvature, etc. As an example for the application of the proposed schemes, 1 to 20% of the data generated from the temperature change of a typical geothermal system is chosen from a total of 9939 points. It is shown that the number of data points, to a degree, has negligible effect on the fitted model depending on the scheme. The proposed data processing schemes are ranked in terms of R2 and NRMSE values.

Chaos is ubiquitous in physical systems. The associated sensitivity to initial conditions is a significant obstacle in forecasting the weather and other geophysical fluid flows. Data assimilation is the process whereby the uncertainty in initial conditions is reduced by the astute combination of model predictions and real-time data. This chapter reviews recent findings from investigations on the impact of chaos on data assimilation methods: for the Kalman filter and smoother in linear systems, analytic results are derived; for their ensemble-based versions and nonlinear dynamics, numerical results provide insights. The focus is on characterising the asymptotic statistics of the Bayesian posterior in terms of the dynamical instabilities, differentiating between deterministic and stochastic dynamics. We also present two novel results. Firstly, we study the functioning of the ensemble Kalman filter in the context of a chaotic, coupled, atmosphere-ocean model with a quasi-degenerate spectrum of Lyapunov exponents, showing the importance of having sufficient ensemble members to track all of the near-null modes. Secondly, for the fully non-Gaussian method of the particle filter, numerical experiments are conducted to test whether the curse of dimensionality can be mitigated by discarding observations in the directions of little dynamical growth of uncertainty. The results refute this option, most likely because the particles already embody this information on the chaotic system. The results also suggest that it is the rank of the unstable-neutral subspace of the dynamics, and not that of the observation operator, that determines the required number of particles. We finally discuss how knowledge of the random attractor can play a role in the development of future data assimilation schemes for chaotic multiscale systems with large scale separation.