Learning to Identify ElectronsWe investigate whether state-of-the-art classification features commonly used to distinguish electrons from jet backgrounds in collider experiments are overlooking valuable information. A deep…Julian Collado, Jessica N. Howard, Taylor Faucett et al.·Nov 3, 2020SaveLearn
Applying clock comparison methods to pulsar timing observationsFrequency metrology outperforms any other branch of metrology in accuracy (parts in 10-16) and small fluctuations (<10-17). In turn, among celestial bodies, the rotation speed of…Siyuan Chen, Francois Vernotte, Enrico Rubiola·Nov 3, 2020SaveLearn
Accuracy and precision of the estimation of the number of missing levels in chaotic spectra using long-range correlationsWe study the accuracy and precision for estimating the fraction of observed levels φ in quantum chaotic spectra through long-range correlations. We focus on the main statistics where theoretical…I. Casal, L. Muñoz, R. A. Molina·Nov 3, 2020SaveLearn
Cluster-based network modeling -- automated robust modeling of complex dynamical systemsWe propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science…Daniel Fernex, Bernd R. Noack, Richard Semaan·Oct 30, 2020SaveLearn
Denoising scheme based on singular-value decomposition for one-dimensional spectra and its application in precision storage-ring mass spectrometryThis work concerns noise reduction for one-dimensional spectra in the case that the signal is corrupted by an additive white noise. The proposed method starts with mapping the noisy spectrum to a…X. C. Chen, Yu. A. Litvinov, M. Wang et al.·Oct 27, 2020SaveLearn
Using Deep Learning Techniques to Search for the MiniBooNE Low Energy Excess in MicroBooNE with > 3σ SensitivityThis thesis describes an analysis developed for the MicroBooNE experiment to investigate an anomalous excess of electron-like events observed in the MiniBooNE detector. The hypothesis investigated…Jarrett Moon·Oct 26, 2020SaveLearn
A Brief Note of Analyzing and Plotting νμ Disappearance in SBN Detector under ROOT FrameworkThis is a brief technical note of analyzing the νμ disappearance in SBN detector. We here provide a kind of method of plotting the histograms and the heat map plot. We will explore the properties…Castaly Fan·Oct 26, 2020SaveLearn
Independent Normalization for γ-ray Strength Functions: The Shape MethodThe Shape method, a novel approach to obtain the functional form of the γ-ray strength function (γSF) in the absence of neutron resonance spacing data, is introduced. When used in connection with…M. Wiedeking, M. Guttormsen, A. C. Larsen et al.·Oct 24, 2020SaveLearn
On the Feynman-alpha Method for Reflected Fissile AssembliesThe Feynman-alpha method is a neutron noise technique that is used to estimate the prompt neutron period of fissile assemblies. The method and quantity are of widespread interest including in…Michael Y. Hua, Jesson D. Hutchinson, George E. McKenzie et al.·Oct 22, 2020SaveLearn
Towards Bayesian Data CompressionIn order to handle large data sets omnipresent in modern science, efficient compression algorithms are necessary. Here, a Bayesian data compression (BDC) algorithm that adapts to the specific…Johannes Harth-Kitzerow, Reimar Leike, Philipp Arras et al.·Oct 20, 2020SaveLearn
Asymptotics for the fastest among n stochastics particles: role of an extended initial distribution and an additional drift componentWe derive asymptotic formulas for the mean exit time τN of the fastest among N identical independently distributed Brownian particles to an absorbing boundary for various initial…Suney Toste, David Holcman·Oct 16, 2020SaveLearn
Rossi-alpha Uncertainty Quantification by Analytic, Bootstrap, and Sample Methods to Inform Fitting Best PracticesThe prompt neutron period (the negative reciprocal of the prompt neutron decay constant) can be estimated using the Rossi-alpha technique that is predicated on fitting Rossi-alpha histograms and of…M. Y. Hua, C. A. Bravo, R. M. Marchie et al.·Oct 13, 2020SaveLearn
Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimationTraditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the…David A Benson, Diogo Bolster, Stephen Pankavich et al.·Oct 13, 2020SaveLearn
A Comparative Analysis on LaueUtil and PRECOGNITION Software Packages as Tools in Treating the Small Molecule Time-Resolved Laue Diffraction Measurements at High Flux X-ray facilitiesInvestigating metal organic systems with time-resolved photocrystallography poses a unique challenge while interpreting the time dependent photodifference maps. In these difference Fourier maps, the…J. J. Velazquez-Garcia, J. Wong, K. Basuroy et al.·Oct 12, 2020SaveLearn
Full Automation for Rapid Modulator Characterization and Accurate Analysis Using SciPyModulator testing involved complex biasing conditions, hardware connections and data analysis. Also, any optical signal distortion due to the grating coupler effect could potentially induce…T. L. Yap, A. Sasidhara, N. X. Ang et al.·Oct 11, 2020SaveLearn
Data assimilation for chaotic dynamicsChaos 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…Alberto Carrassi, Marc Bocquet, Jonathan Demaeyer et al.·Oct 10, 2020SaveLearn
A surrogate-based optimal likelihood function for the Bayesian calibration of catalytic recombination in atmospheric entry protection materialsThis work deals with the inference of catalytic recombination parameters from plasma wind tunnel experiments for reusable thermal protection materials. One of the critical factors affecting the…Anabel del Val, Olivier P. Le Maître, Olivier Chazot et al.·Oct 9, 2020SaveLearn
The spectrum decorrelation assumption for the cross-spectrum methodThis paper presents a very simple method ensuring the independence of consecutive spectra of the phase or frequency noise of an oscillator. This condition is essential for using cross-spectrum…François Vernotte, Antoine Baudiquez, Enrico Rubiola·Oct 9, 2020SaveLearn
Pulse Shape Discrimination in CUPID-Mo using Principal Component AnalysisCUPID-Mo is a cryogenic detector array designed to search for neutrinoless double-beta decay (0νββ) of 100Mo. It uses 20 scintillating 100Mo-enriched Li2MoO4 bolometers instrumented…R. Huang, E. Armengaud, C. Augier et al.·Oct 8, 2020SaveLearn
MatDRAM: A pure-MATLAB Delayed-Rejection Adaptive Metropolis-Hastings Markov Chain Monte Carlo SamplerMarkov Chain Monte Carlo (MCMC) algorithms are widely used for stochastic optimization, sampling, and integration of mathematical objective functions, in particular, in the context of Bayesian…Shashank Kumbhare, Amir Shahmoradi·Oct 8, 2020SaveLearn
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…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