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This paper describes the treatment of systematic uncertainties in a Likelihood formalism. RooUnfold, which includes most of the unfolding methods that are commonly used in particle physics, is used to compare a newly implemented method…

High Energy Physics - Experiment · Physics 2025-10-20 Lydia Brenner , Carsten Burgard , Vincent Alexander Croft

We introduce QSTToolkit, a Python library for performing quantum state tomography (QST) on optical quantum state measurement data. The toolkit integrates traditional Maximum Likelihood Estimation (MLE) with deep learning-based techniques to…

Quantum Physics · Physics 2025-03-19 George FitzGerald , Will Yeadon

Recent statistical evaluations for High-Energy Physics measurements, in particular those at the Large Hadron Collider, require careful evaluation of many sources of systematic uncertainties at the same time. While the fundamental aspects of…

Data Analysis, Statistics and Probability · Physics 2018-10-29 Luca Lista , Agostino De Iorio , Alberto Orso Maria Iorio

$\texttt{HEPfit}$ is a flexible open-source tool which, given the Standard Model or any of its extensions, allows to $\textit{i)}$ fit the model parameters to a given set of experimental observables; $\textit{ii)}$ obtain predictions for…

We present TurbuStat (v1.0): a Python package for computing turbulence statistics in spectral-line data cubes. TurbuStat includes implementations of fourteen methods for recovering turbulent properties from observational data. Additional…

Instrumentation and Methods for Astrophysics · Physics 2019-06-12 Eric W. Koch , Erik W. Rosolowsky , Ryan D. Boyden , Blakesley Burkhart , Adam Ginsburg , Jason L. Loeppky , Stella S. R. Offner

Energy statistics ($\mathcal{\varepsilon}$--statistics) enable powerful non-linear dependence measures such as distance correlation, but their computational burden has limited application to large datasets. We present memory-efficient…

Computation · Statistics 2025-11-06 Michail Tsagris , Manos Papadakis

ROOT is a data analysis framework broadly used in and outside of High Energy Physics (HEP). Since HEP software frameworks always strive for performance improvements, ROOT was extended with experimental support of runtime C++ Modules. C++…

Programming Languages · Computer Science 2019-10-02 Yuka Takahashi , Vassil Vassilev , Oksana Shadura , Raphael Isemann

The REST-for-Physics (Rare Event Searches Toolkit for Physics) framework is a ROOT-based solution providing the means to process and analyze experimental or Monte Carlo event data. Special care has been taken on the traceability of the code…

The Locust simulation package is a new C++ software tool developed to simulate the measurement of time-varying electromagnetic fields using RF detection techniques. Modularity and flexibility allow for arbitrary input signals, while…

This software performs the combination of $m$ correlated estimates of $n$ physics observables ($m\ge n$) using the Best Linear Unbiased Estimate (BLUE) method. It is implemented as a C++ class, to be used within the ROOT analysis package.…

Data Analysis, Statistics and Probability · Physics 2020-04-14 Richard Nisius

In the era of gravitational-wave astronomy, general-relativistic simulations of compact objects play a role of paramount importance. These calculations can be performed with the Einstein Toolkit, an open-source and community-supported…

General Relativity and Quantum Cosmology · Physics 2021-04-20 Gabriele Bozzola

VOStat is a Web service providing interactive statistical analysis of astronomical tabular datasets. It is integrated into the suite of analysis and visualization tools associated with the international Virtual Observatory (VO) through the…

Instrumentation and Methods for Astrophysics · Physics 2015-06-12 Arnab Chakraborty , Eric D. Feigelson , G. Jogesh Babu

We describe the Einstein Toolkit, a community-driven, freely accessible computational infrastructure intended for use in numerical relativity, relativistic astrophysics, and other applications. The Toolkit, developed by a collaboration…

The ROOT I/O (RIO) subsystem is foundational to most HEP experiments - it provides a file format, a set of APIs/semantics, and a reference implementation in C++. It is often found at the base of an experiment's framework and is used to…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-11-08 Brian Bockelman , Zhe Zhang , Jim Pivarski

In this paper we present the development of a modulated web based statistical system, hereafter MWStat, which shifts the statistical paradigm of analyzing data into a real time structure. The MWStat system is useful for both online storage…

Applications · Statistics 2016-05-03 Francisco Louzada , Anderson Ara

Computational tools for data analysis are being released daily on repositories such as the Comprehensive R Archive Network. How we integrate these tools to solve a problem in research is increasingly complex and requiring frequent updates.…

Other Statistics · Statistics 2019-10-17 Charles T. Gray

In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets. However, this approach may fail to…

`scores` is a Python package containing mathematical functions for the verification, evaluation and optimisation of forecasts, predictions or models. It supports labelled n-dimensional (multidimensional) data, which is used in many…

Robust estimation provides essential tools for analyzing data that contain outliers, ensuring that statistical models remain reliable even in the presence of some anomalous data. While robust methods have long been available in R, users of…

Computation · Statistics 2024-11-05 Sarah Leyder , Jakob Raymaekers , Peter J. Rousseeuw , Thomas Servotte , Tim Verdonck

QPot is an R package for analyzing two-dimensional systems of stochastic differential equations. It provides users with a wide range of tools to simulate, analyze, and visualize the dynamics of these systems. One of QPot's key features is…

Quantitative Methods · Quantitative Biology 2015-10-28 Christopher M. Moore , Christopher R. Stieha , Ben C. Nolting , Maria K. Cameron , Karen C. Abbott