Heteroscedasticity and angle resolution in high-energy particle tracking: revisiting "Beyond the N limit of the least squares resolution and the lucky model", by G. Landi and G. E. LandiI re-examine a recent work by G. Landi and G. E. Landi. [arXiv:1808.06708 [physics.ins-det]], in which the authors claim that the resolution of a tracker ca vary linearly with the number of detection…Denis Bernard·Oct 7, 2020SaveLearn
Parameter free determination of optimum time delayWe show that the same maximum entropy principle applied to recurrence microstates configures a new way to properly compute the time delay necessary to correctly sample a data set. The new method…Thiago Lima Prado, Vandertone Santos Machado, Gilberto Corso et al.·Oct 6, 2020SaveLearn
Practical Guide of Using Kendall's τ in the Context of Forecasting Critical TransitionsRecent studies demonstrate that trends in indicators extracted from measured time series can indicate approaching to an impending transition. Kendall's τ coefficient is often used to study the…Shiyang Chen, Amin Ghadami, Bogdan I. Epureanu·Oct 6, 2020SaveLearn
What is the likelihood function, and how is it used in particle physics?Likelihood functions are ubiquitous in data analyses at the LHC and elsewhere in particle physics. Partly because "probability" and "likelihood" are virtual synonyms in everyday English, but…Robert D. Cousins·Oct 1, 2020SaveLearn
Unsupervised clustering for collider physicsWe propose a new method for Unsupervised clustering in particle physics named UCluster, where information in the embedding space created by a neural network is used to categorise collision events…Vinicius Mikuni, Florencia Canelli·Sep 28, 2020SaveLearn
SARM: Sparse Autoregressive Model for Scalable Generation of Sparse Images in Particle PhysicsGeneration of simulated data is essential for data analysis in particle physics, but current Monte Carlo methods are very computationally expensive. Deep-learning-based generative models have…Yadong Lu, Julian Collado, Daniel Whiteson et al.·Sep 23, 2020SaveLearn
Breaking Symmetries of the Reservoir Equations in Echo State NetworksReservoir computing has repeatedly been shown to be extremely successful in the prediction of nonlinear time-series. However, there is no complete understanding of the proper design of a reservoir…Joschka Herteux, Christoph Räth·Sep 21, 2020SaveLearn
Extracting the oscillatory component and defining a mean amplitude of thermokinetic oscillations in the H/Pd systemThe mean value theorem for integrals has been applied in constructing a base curve for non-equilibrium thermokinetic oscillations, q(t), recorded in oscillatory sorptions of H2(D2) in Pd. The mean…Erwin Lalik·Sep 19, 2020SaveLearn
Criteria for projected discovery and exclusion sensitivities of counting experimentsThe projected discovery and exclusion capabilities of particle physics and astrophysics/cosmology experiments are often quantified using the median expected p-value or its corresponding…Prudhvi N. Bhattiprolu, Stephen P. Martin, James D. Wells·Sep 15, 2020SaveLearn
Reproducibility and Replication of Experimental Particle Physics ResultsRecently, much attention has been focused on the replicability of scientific results, causing scientists, statisticians, and journal editors to examine closely their methodologies and publishing…Thomas R. Junk, Louis Lyons·Sep 15, 2020SaveLearn
Measuring topological descriptors of complex networks under uncertaintyRevealing the structural features of a complex system from the observed collective dynamics is a fundamental problem in network science. In order to compute the various topological descriptors…Sebastian Raimondo, Manlio De Domenico·Sep 14, 2020SaveLearn
Resolution Enhancement in Protein NMR Spectra by Deconvolution with Compressed Sensing ReconstructionMultidimensional NMR spectroscopy is one of the basic tools for determining the structure of biomolecules. Unfortunately, the resolution of the spectra is often limited by inter-nuclear couplings.…Krzysztof Kazimierczuk, Paweł Kasprzak, Panagiota S. Georgoulia et al.·Sep 11, 2020SaveLearn
Long-timescale predictions from short-trajectory data: A benchmark analysis of the trp-cage miniproteinElucidating physical mechanisms with statistical confidence from molecular dynamics simulations can be challenging owing to the many degrees of freedom that contribute to collective motions. To…John Strahan, Adam Antoszewski, Chatipat Lorpaiboon et al.·Sep 8, 2020SaveLearn
Ordinal spectrum: a frequency domain characterization of complex time seriesAlthough classical spectral analysis is a natural approach to characterise linear systems, it cannot describe a chaotic dynamics. Here, we propose the ordinal spectrum, a method based on a spectral…Mario Chavez, Johann H. Martinez·Sep 5, 2020SaveLearn
Graph neural network for 3D classification of ambiguities and optical crosstalk in scintillator-based neutrino detectorsDeep learning tools are being used extensively in high energy physics and are becoming central in the reconstruction of neutrino interactions in particle detectors. In this work, we report on the…Saúl Alonso-Monsalve, Dana Douqa, César Jesús-Valls et al.·Sep 1, 2020SaveLearn
Conception and software implementation of a nuclear data evaluation pipelineWe discuss the design and software implementation of a nuclear data evaluation pipeline applied for a fully reproducible evaluation of neutron-induced cross sections of 56Fe above the resolved…Georg Schnabel, Henrik Sjöstrand, Joachim Hansson et al.·Sep 1, 2020SaveLearn
Predictive Capability Maturity Quantification using Bayesian NetworkIn nuclear engineering, modeling and simulations (M&Ss) are widely applied to support risk-informed safety analysis. Since nuclear safety analysis has important implications, a convincing validation…Linyu Lin, Nam Dinh·Aug 31, 2020SaveLearn
Dynamic State Analysis of a Driven Magnetic Pendulum using Ordinal Partition Networks and Topological Data AnalysisThe use of complex networks for time series analysis has recently shown to be useful as a tool for detecting dynamic state changes for a wide variety of applications. In this work, we implement the…Audun Myers, Firas Khasawneh·Aug 31, 2020SaveLearn
Reliable Detection of Causal Asymmetries in Dynamical SystemsKnowledge about existence, strength, and dominant direction of causal influences is of paramount importance for understanding complex systems. With limited amounts of realistic data, however, current…Erik Laminski, Klaus R. Pawelzik·Aug 21, 2020SaveLearn
Navigating differential structures in complex networksStructural changes in a network representation of a system (e.g.,different experimental conditions, time evolution), can provide insight on its organization, function and on how it responds to…Leonardo L. Portes, Michael Small·Aug 21, 2020SaveLearn
On Explaining the Surprising Success of Reservoir Computing Forecaster of Chaos? The Universal Machine Learning Dynamical System with Contrasts to VAR and DMDMachine learning has become a widely popular and successful paradigm, including in data-driven science and engineering. A major application problem is data-driven forecasting of future states from a…Erik Bollt·Aug 14, 2020SaveLearn
The Definition and Long-term Variations of Beijing Blue DaysThe phenomenon of Beijing Blue days first occurs on June 11, 2015, and become a hot society topic in a short time. Thus, Beijing's blue day is not only what ordinary people desire but national needs.…Ruomu Gao, Yichuan Huang, Su Wang·Aug 13, 2020SaveLearn
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and GeneralizationDeep neural networks, when optimized with sufficient data, provide accurate representations of high-dimensional functions; in contrast, function approximation techniques that have predominated in…Grant M. Rotskoff, Andrew R. Mitchell, Eric Vanden-Eijnden·Aug 11, 2020SaveLearn
Methods to Quantify Dislocation Behavior with Dark-field X-ray Microscopy Timescans of Single-Crystal AluminumCrystal defects play a large role in how materials respond to their surroundings, yet there are many uncertainties in how extended defects form, move, and interact deep beneath a material's surface.…Arnulfo Gonzalez, Marylesa Howard, Sean Breckling et al.·Aug 11, 2020SaveLearn
A GPU based multidimensional amplitude analysis to search for tetraquark candidatesThe demand for computational resources is steadily increasing in experimental high energy physics as the current collider experiments continue to accumulate huge amounts of data and physicists…Nairit Sur, Leonardo Cristella, Adriano Di Florio et al.·Jul 29, 2020SaveLearn