Improved particle-flow event reconstruction with scalable neural networks for current and future particle detectorsEfficient and accurate algorithms are necessary to reconstruct particles in the highly granular detectors anticipated at the High-Luminosity Large Hadron Collider and the Future Circular Collider. We…Joosep Pata, Eric Wulff, Farouk Mokhtar et al.·Sep 13, 2023SaveLearn
The dotTHz Project: A Standard Data Format for Terahertz Time-Domain Data and Elementary Data Processing ToolsFrom investigating molecular vibrations to observing galaxies, terahertz technology has found extensive applications in research and development over the past three decades. Terahertz time-domain…Jongmin Lee, Chi Ki Leung, Mingrui Ma et al.·Sep 11, 2023SaveLearn
An adaptive Bayesian approach to gradient-free global optimizationMany problems in science and technology require finding global minima or maxima of various objective functions. The functions are typically high-dimensional; each function evaluation may entail a…Jianneng Yu, Alexandre V. Morozov·Sep 8, 2023SaveLearn
Bayesian questions with frequentist answersThe two statistical methods, namely the frequentist and the Bayesian methods, are both commonly used for probabilistic inference in many scientific situations. However, it is not straightforward to…Alan H. Guth, Mohammad Hossein Namjoo·Aug 30, 2023SaveLearn
Convexity constraints on linear background models for electron energy-loss spectraIn this paper convexity constraints are derived for a background model of electron energy loss spectra (EELS) that is linear in the fitting parameters. The model outperforms a power-law both on…Wouter Van den Broek, Daen Jannis, Jo Verbeeck·Aug 29, 2023SaveLearn
A method of maximum likelihood fit to data with non-uniform efficienciesEstimations of physical parameters using data usually involve non-uniform experimental efficiencies. In this article, a method of maximum likelihood fit is introduced using the efficiency as a…Chenxu Yu, Yanxi Zhang·Aug 25, 2023SaveLearn
Probabilistic Mixture Model-Based Spectral UnmixingIdentifying pure components in mixtures is a common yet challenging problem. The associated unmixing process requires the pure components, also known as endmembers, to be sufficiently spectrally…Oliver Hoidn, Aashwin Mishra, Apurva Mehta·Aug 24, 2023SaveLearn
Efficient set-theoretic algorithms for computing high-order Forman-Ricci curvature on abstract simplicial complexesForman-Ricci curvature (FRC) is a potent and powerful tool for analysing empirical networks, as the distribution of the curvature values can identify structural information that is not readily…Danillo Barros de Souza, Jonatas T. S. da Cunha, Fernando A. N. Santos et al.·Aug 22, 2023SaveLearn
Self-consistent autocorrelation for finite-area bias correction in roughness measurementScan line levelling, a ubiquitous and often necessary step in AFM data processing, can cause a severe bias on measured roughness parameters such as mean square roughness or correlation length.…David Nečas·Aug 22, 2023SaveLearn
KinFit -- A Kinematic Fitting Package for Hadron Physics ExperimentsA kinematic fitting package, KinFit, based on the Lagrange multiplier technique has been implemented for generic hadron physics experiments. It is particularly suitable for experiments where the…Waleed Esmail, Jana Rieger, Jenny Taylor et al.·Aug 18, 2023SaveLearn
Robust reconstruction of sparse network dynamicsReconstruction of the network interaction structure from multivariate time series is an important problem in multiple fields of science. This problem is ill-posed for large networks leading to the…Tiago Pereira, Edmilson Roque dos Santos, Sebastian van Strien·Aug 12, 2023SaveLearn
Bounds on the rates of statistical divergences and mutual information via stochastic thermodynamicsStatistical divergences are important tools in data analysis, information theory, and statistical physics, and there exist well known inequalities on their bounds. However, in many circumstances…Jan Karbowski·Aug 10, 2023SaveLearn
Unsupervised Learning of Part Similarity for Goal-Guided Accelerated Experiment Design in Metal Additive ManufacturingMetal additive manufacturing is gaining broad interest and increased use in the industrial and academic fields. However, the quantification and commercialization of standard parts usually require…Rui Liu, Sen Liu, Xiaoli Zhang·Aug 3, 2023SaveLearn
Beam Detection Based on Machine Learning AlgorithmsThe positions of free electron laser beams on screens are precisely determined by a sequence of machine learning models. Transfer training is conducted in a self-constructed convolutional neural…Haoyuan Li, Qing Yin·Aug 1, 2023SaveLearn
Sorting ECGs by lag irreversibilityIn this work we introduce the lag irreversibility function as a method to assess time-irreversibility in discrete time series. It quantifies the degree of time-asymmetry for the joint probability…Nazul Merino Negrete, Cesar Maldonado, Raúl Salgado-García·Jul 29, 2023SaveLearn
Information temperature as a parameter of random sequence complexityIn this study, we continue our exploration of the concept of information temperature as a characteristic of random sequences. We describe methods for introducing the information temperature in the…O. V. Usatenko, G. M. Pritula·Jul 24, 2023SaveLearn
Statistical characterization of residual noise in the low-rank approximation filter framework, general theory and application to hyperpolarized tracer spectroscopyThe use of low-rank approximation filters in the field of NMR is increasing due to their flexibility and effectiveness. Despite their ability to reduce the Mean Square Error between the processed…R. Francischello, M. F. Santarelli, A. Flori et al.·Jul 23, 2023SaveLearn
A statistical learning framework for mapping indirect measurements of ergodic systems to emergent propertiesThe discovery of novel experimental techniques often lags behind contemporary theoretical understanding. In particular, it can be difficult to establish appropriate measurement protocols without…Nicholas Hindley, Stephen J. DeVience, Ella Zhang et al.·Jul 19, 2023SaveLearn
Renormalization Group-Motivated LearningWe introduce an RG-inspired coarse-graining for extracting the collective features of data. The key to successful coarse-graining lies in finding appropriate pairs of data sets. We coarse-grain the…Jonathan Landy, Tsvi Tlusty, YeongKyu Lee et al.·Jul 18, 2023SaveLearn
Exploring Model Misspecification in Statistical Finite Elements via Shallow Water EquationsThe abundance of observed data in recent years has increased the number of statistical augmentations to complex models across science and engineering. By augmentation we mean coherent statistical…Connor Duffin, Paul Branson, Matt Rayson et al.·Jul 11, 2023SaveLearn
A new Machine Learning-based method for identification of time-correlated events at tagged photon facilitiesWe present a new Machine Learning-based multivariate analysis method for the selection of time-correlated hits in the tagging system and devices used to detect particles in the final state at the…V. Sokhoyan, E. Mornacchi·Jul 9, 2023SaveLearn
Uncertainty components in profile likelihood fitsWhen a measurement of a physical quantity is reported, the total uncertainty is usually decomposed into statistical and systematic uncertainties. This decomposition is not only useful to understand…Andrés Pinto, Zhibo Wu, Fabrice Balli et al.·Jul 8, 2023SaveLearn
Linear approximation to the statistical significance autocovariance matrix in the asymptotic regimeApproximating significance scans of searches for new particles in high-energy physics experiments as Gaussian fields is a well-established way to estimate the trials factors required to quantify…V. Ananiev, A. L. Read·Jul 8, 2023SaveLearn
Projected Data Assimilation using Sliding Window Proper Orthogonal DecompositionPrediction of the state evolution of complex high-dimensional nonlinear systems is challenging due to the nonlinear sensitivity of the evolution to small inaccuracies in the model. Data Assimilation…Aishah Albarakati, Marko Budisic, Erik Van Vleck·Jul 6, 2023SaveLearn
Extended Dynamical Causal Modelling for Phase Coupling (eDCM PC)We present a software tool -- extended Dynamic Causal Modelling for Phase Coupling (eDCM PC) -- that is able to estimate effective connectivity between any kind of oscillating systems, e.g. distant…Azamat Yeldesbay, Silvia Daun·Jul 4, 2023SaveLearn