429 citations · 891 across the 38 of their papers we have counts for
41 papers
Unknown Unknowns: Model Misspecification in Machine Learning for Physics
Juan Cruz-Martinez, Carolina Cuesta-Lazaro, Alexander Held +1
Machine learning is now a central tool for solving inverse problems in particle physics and astronomy. Models are trained on simulation and deployed on real data, raising the quest…
Impact of Z-boson transverse-momentum resummation on PDF determination
Juan M. Cruz-Martinez, Emanuele R. Nocera, Luca Rottoli +1
We study the impact of small-transverse-momentum resummation for neutral-current Drell-Yan lepton-pair production on the determination of collinear parton distribution functions (P…
Higher-order effects in amplitude-assisted polarisation extraction with machine-learning techniques
Juan M. Cruz-Martinez, Jakob Linder, Mathieu Pellen +2
With increasing experimental precision, the prospect of extracting the polarisation of electroweak gauge bosons is becoming particularly attractive. To this end, regression and cla…
Hyperoptimisation algorithm for the next generation of PDF determinations: ensemble regression with an unbiased selection model
Juan M. Cruz-Martinez, Tommaso Giani, Tanjona R. Rabemananjara
We present a new automated procedure for selecting fitting methodologies in the determination of parton distribution functions (PDFs), based on a hyperoptimisation algorithm using…
Assessing the Impact of Fitting Methodology at aNLO with FPPDF: an Open Source Tool for Extracting Parton Distribution Functions in the Hessian Approach
J. M. Cruz-Martinez, T. Giani, L. A. Harland-Lang
We present a new public code, FPPDF, to perform global fits of parton distribution functions (PDFs). The fitting methodology follows that implemented by the MSHT collaboration, nam…
A Determination of at Accuracy from a Global PDF Analysis
The NNPDF Collaboration, Richard D. Ball, Andrea Barontini +6
We present a determination of the strong coupling from a global dataset including both fixed-target and collider data from deep-inelastic scattering and a variety of had…
NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order
Juan Cruz-Martinez, Toon Hasenack, Felix Hekhorn +6
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-…
Fast interpolation grids for the Drell-Yan process
Juan Cruz-Martinez, Alexander Huss, Christopher Schwan
Modern analyses of experimental data from hadron colliders rely on theory predictions at high orders in perturbation theory and a variety of input settings. Interpolation grids fac…
Parton distributions confront LHC Run II data: a quantitative appraisal
Amedeo Chiefa, Mark N. Costantini, Juan Cruz-Martinez +6
We present a systematic comparison of theoretical predictions and various high-precision experimental measurements, specifically of differential cross sections performed by the LHC…
Hyperparameter Optimisation in Deep Learning from Ensemble Methods: Applications to Proton Structure
Juan Cruz-Martinez, Aaron Jansen, Gijs van Oord +4
Deep learning models are defined in terms of a large number of hyperparameters, such as network architectures and optimiser settings. These hyperparameters must be determined separ…
LO, NLO, and NNLO Parton Distributions for LHC Event Generators
Juan Cruz-Martinez, Stefano Forte, Niccolo Laurenti +2
We present NNPDF4.0MC, a variant of the NNPDF4.0 set of parton distributions (PDFs) at LO, NLO and NNLO, with and without inclusion of the photon PDF, suitable for use with Monte C…
The Path to NLO Parton Distributions
The NNPDF Collaboration, Richard D. Ball, Andrea Barontini +16
We extend the existing leading (LO), next-to-leading (NLO), and next-to-next-to-leading order (NNLO) NNPDF4.0 sets of parton distribution functions (PDFs) to approximate next-to-ne…
Determination of the theory uncertainties from missing higher orders on NNLO parton distributions with percent accuracy
The NNPDF Collaboration, Richard D. Ball, Andrea Barontini +16
We include uncertainties due to missing higher order corrections to QCD computations (MHOU) used in the determination of parton distributions (PDFs) in the recent NNPDF4.0 set of P…
Photons in the proton: implications for the LHC
The NNPDF Collaboration, Richard D. Ball, Andrea Barontini +16
We construct a set of parton distribution functions (PDFs), based on the recent NNPDF4.0 PDF set, that also include a photon PDF. The photon PDF is constructed using the LuxQED for…
The intrinsic charm quark valence distribution of the proton
Richard D. Ball, Alessandro Candido, Juan Cruz-Martinez +7
We provide a first quantitative indication that the wave function of the proton contains unequal distributions of charm quarks and antiquarks, i.e. a nonvanishing intrinsic valence…
The LHC as a Neutrino-Ion Collider
Juan M. Cruz-Martinez, Max Fieg, Tommaso Giani +4
Proton-proton collisions at the LHC generate a high-intensity collimated beam of neutrinos in the forward (beam) direction, characterised by energies of up to several TeV. The rece…
Multi-variable integration with a variational quantum circuit
Juan M. Cruz-Martinez, Matteo Robbiati, Stefano Carrazza
In this work we present a novel strategy to evaluate multi-variable integrals with quantum circuits. The procedure first encodes the integration variables into a parametric circuit…
Theory prediction in PDF fitting
Andrea Barontini, Alessandro Candido, Juan M. Cruz-Martinez +2
Continuously comparing theory predictions to experimental data is a common task in analysis of particle physics such as fitting parton distribution functions (PDFs). However, typic…
Determining probability density functions with adiabatic quantum computing
Matteo Robbiati, Juan M. Cruz-Martinez, Stefano Carrazza
The two main approaches to quantum computing are gate-based computation and analog computation, which are polynomially equivalent in terms of complexity, and they are often seen as…
Pineline: Industrialization of High-Energy Theory Predictions
Andrea Barontini, Alessandro Candido, Juan M. Cruz-Martinez +2
We present a collection of tools automating the efficient computation of large sets of theory predictions for high-energy physics. Calculating predictions for different processes o…
Extending MadFlow: device-specific optimization
Stefano Carrazza, Juan M. Cruz-Martinez, Gabriele Palazzo
In this proceedings we demonstrate some advantages of a top-bottom approach in the development of hardware-accelerated code. We start with an autogenerated hardware-agnostic Monte…
Response to "Parton distributions need representative sampling"
The NNPDF Collaboration, Richard D. Ball, Juan Cruz-Martinez +7
We respond to the criticism raised by Courtoy et al., in which the faithfulness of the NNPDF4.0 sampling is questioned and an under-estimate of the NNPDF4.0 PDF uncertainties is im…
Theory pipeline for PDF fitting
Andrea Barontini, Alessandro Candido, Juan Cruz-Martinez +3
Fitting PDFs requires the integration of a broad range of datasets, both from data and theory side, into a unique framework. While for data the integration mainly consists in the s…
Evidence for intrinsic charm quarks in the proton
Richard D. Ball, Alessandro Candido, Juan Cruz-Martinez +6
The theory of the strong force, quantum chromodynamics, describes the proton in terms of quarks and gluons. The proton is a state of two up quarks and one down quark bound by gluon…