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With increasing deployment of machine learning systems in various real-world tasks, there is a greater need for accurate quantification of predictive uncertainty. While the common goal in uncertainty quantification (UQ) in machine learning…

Machine Learning · Computer Science 2021-09-22 Youngseog Chung , Ian Char , Han Guo , Jeff Schneider , Willie Neiswanger

We discuss the uncertainty on processes computed using next-to-next-to leading (NNLO) parton distributions (PDFs) due to the neglect of higher order perturbative corrections in the PDF determination, in the specific case of Higgs production…

High Energy Physics - Phenomenology · Physics 2015-06-18 Stefano Forte , Andrea Isgro' , Gherardo Vita

We investigate the Monte Carlo approach to propagation of experimental uncertainties within the context of the established "MSTW 2008" global analysis of parton distribution functions (PDFs) of the proton at next-to-leading order in the…

High Energy Physics - Phenomenology · Physics 2012-08-13 G. Watt , R. S. Thorne

The high-performance computing resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted, bring a new compulsory step to strengthen the credence given to…

Computation · Statistics 2019-03-01 J-B. Blanchard , G. Damblin , J-M. Martinez , G. Arnaud , F. Gaudier

The parton distribution functions (PDFs) which characterize the structure of the proton are currently one of the dominant sources of uncertainty in the predictions for most processes measured at the Large Hadron Collider (LHC). Here we…

We present a new procedure to determine Parton Distribution Functions (PDFs), based on Markov Chain Monte Carlo (MCMC) methods. The aim of this paper is to show that we can replace the standard $\chi^2$ minimization by procedures grounded…

High Energy Physics - Phenomenology · Physics 2017-11-07 Yémalin Gabin Gbedo , Mariane Mangin-Brinet

In the first three years of running, the LHC has delivered a wealth of new data that is now being analysed. With over 20 fb$^{-1}$ of integrated luminosity, both ATLAS and CMS have performed many searches for new physics that theorists are…

High Energy Physics - Phenomenology · Physics 2014-01-29 Manuel Drees , Herbi Dreiner , Jong Soo Kim , Daniel Schmeier , Jamie Tattersall

Many uncertainty propagation software exist, written in different programming languages, but not all of them are able to handle functional correlation between quantities. In this paper we review one strategy to deal with uncertainty…

Data Analysis, Statistics and Probability · Physics 2016-10-28 Mosè Giordano

Recently a new set of Parton Distribution Functions (NNPDF1.2) has been produced and released by the NNPDF Collaboration. The inclusion of dimuon data in the analysis allows a determination of the strange content of the proton with faithful…

High Energy Physics - Phenomenology · Physics 2009-07-28 Alberto Guffanti , Juan Rojo , Maria Ubiali

The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for…

Instrumentation and Detectors · Physics 2026-03-12 Julia Gonski , Jenni Ott , Shiva Abbaszadeh , Sagar Addepalli , Matteo Cremonesi , Jennet Dickinson , Giuseppe Di Guglielmo , Erdem Yigit Ertorer , Lindsey Gray , Ryan Herbst , Christian Herwig , Tae Min Hong , Benedikt Maier , Maryam Bayat Makou , David Miller , Mark S. Neubauer , Cristián Peña , Dylan Rankin , Seon-Hee , Seo , Giordon Stark , Alexander Tapper , Audrey Corbeil Therrien , Ioannis Xiotidis , Keisuke Yoshihara , G Abarajithan , Sagar Addepalli , Nural Akchurin , Carlos Argüelles , Saptaparna Bhattacharya , Lorenzo Borella , Christian Boutan , Tom Braine , James Brau , Martin Breidenbach , Antonio Chahine , Talal Ahmed Chowdhury , Yuan-Tang Chou , Seokju Chung , Alberto Coppi , Mariarosaria D'Alfonso , Abhilasha Dave , Chance Desmet , Angela Di Fulvio , Karri DiPetrillo , Javier Duarte , Auralee Edelen , Jan Eysermans , Yongbin Feng , Emmett Forrestel , Dolores Garcia , Loredana Gastaldo , Julián García Pardiñas , Lino Gerlach , Loukas Gouskos , Katya Govorkova , Carl Grace , Christopher Grant , Philip Harris , Ciaran Hasnip , Timon Heim , Abraham Holtermann , Tae Min Hong , Gian Michele Innocenti , Koji Ishidoshiro , Miaochen Jin , Jyothisraj Johnson , Stephen Jones , Andreas Jung , Georgia Karagiorgi , Ryan Kastner , Nicholas Kamp , Doojin Kim , Kyoungchul Kong , Katie Kudela , Jelena Lalic , Bo-Cheng Lai , Yun-Tsung Lai , Tommy Lam , Jeffrey Lazar , Aobo Li , Zepeng Li , Haoyun Liu , Vladimir Lončar , Luca Macchiarulo , Christopher Madrid , Benedikt Maier , Zhenghua Ma , Prashansa Mukim , Mark S. Neubauer , Victoria Nguyen , Sungbin Oh , Isobel Ojalvo , Hideyoshi Ozaki , Simone Pagan Griso , Myeonghun Park , Christoph Paus , Santosh Parajuli , Benjamin Parpillon , Sara Pozzi , Ema Puljak , Benjamin Ramhorst , Amy Roberts , Larry Ruckman , Kate Scholberg , Sebastian Schmitt , Noah Singer , Eluned Anne Smith , Alexandre Sousa , Michael Spannowsky , Sioni Summers , Yanwen Sun , Daniel Tapia Takaki , Antonino Tumeo , Caterina Vernieri , Belina von Krosigk , Yash Vora , Linyan Wan , Michael H. L. S. Wang , Amanda Weinstein , Andy White , Simon Williams , Felix Yu

Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated…

Materials Science · Physics 2022-06-20 Ling Lan , Chia-Hao Liu , Qiang Du , Simon J. L. Billinge

A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z's). A wide plethora of methods have been developed, based either on template models fitting or on empirical…

Instrumentation and Methods for Astrophysics · Physics 2016-12-13 Stefano Cavuoti , Valeria Amaro , Massimo Brescia , Civita Vellucci , Crescenzo Tortora , Giuseppe Longo

After the LHC Run 1, the standard model (SM) of particle physics has been completed. Yet, despite its successes, the SM has shortcomings vis-\`{a}-vis cosmological and other observations. At the same time, while the LHC restarts for Run 2…

High Energy Physics - Phenomenology · Physics 2017-03-09 André David , Giampiero Passarino

A thorough understanding of the issues surrounding the determination of parton distributions is crucial due to their importance to calculations of LHC observables. However, it is still not fully understood how much of an impact…

High Energy Physics - Phenomenology · Physics 2014-09-16 Christopher S. Deans

Studying the impact of new-physics models on low-energy observables necessitates matching to effective field theories at the relevant mass thresholds. We introduce the first public version of Matchete, a computer tool for matching…

High Energy Physics - Phenomenology · Physics 2023-07-31 Javier Fuentes-Martín , Matthias König , Julie Pagès , Anders Eller Thomsen , Felix Wilsch

One major challenge for the legacy measurements at the LHC is that the likelihood function is not tractable when the collected data is high-dimensional and the detector response has to be modeled. We review how different analysis strategies…

High Energy Physics - Phenomenology · Physics 2020-08-20 Johann Brehmer , Kyle Cranmer , Irina Espejo , Felix Kling , Gilles Louppe , Juan Pavez

We present results from a detailed assessment of the ultimate constraining power of LHC data on the PDFs that can be expected from the complete dataset, in particular after the High-Luminosity (HL) phase. To achieve this, HL-LHC pseudo-data…

High Energy Physics - Phenomenology · Physics 2019-07-11 Shaun Bailey

We present NNPDFpol2.0, a new set of collinear helicity parton distribution functions (PDFs) of the proton based on legacy measurements of structure functions in inclusive neutral-current longitudinally polarised deep-inelastic scattering…

We present version 2.3 of SModelS, a public tool for the fast reinterpretation of LHC searches for new physics on the basis of simplified-model results. The main new features are a database update with the latest available experimental…

High Energy Physics - Phenomenology · Physics 2023-11-08 Mohammad Mahdi Altakach , Sabine Kraml , Andre Lessa , Sahana Narasimha , Timothée Pascal , Wolfgang Waltenberger
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