arXiv subjects

Juan M. Cruz-Martinez

Publications and source records attributed to Juan M. Cruz-Martinez.

17 recordsLinked to original sources

arXiv2026

Impact of Z-boson transverse-momentum resummation on PDF determination

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 (PDFs). We focus on measurements of the Z-boson transverse-momentum spectrum performed by ATLAS and CMS at the LHC at centre-of-mass energies of 8 and 13 TeV, and include them in PDF fits based on the NNPDF methodology. Theoretical predictions are computed at next-to-next-to-leading order (NNLO) in perturbative QCD and are supplemented with small-transverse-momentum resummation corrections at next-to-next-to-next-to-leading logarithmic accuracy obtained with RadISH. Missing higher-order uncertainties are accounted for through a theory covariance matrix constructed from renormalisation- and factorisation-scale variations. We first revisit the treatment of the 8 TeV data, replacing the fixed-order predictions used in previous analyses with numerically stable NNLO calculations, and removing the additional numerical uncertainties introduced in earlier fits. We then assess the impact of the 13 TeV measurements, of resummation corrections, and of progressively lowering the minimum cut on the dilepton transverse momentum. We find that resummation improves the description of the Z boson transverse-momentum data and is essential for ensuring the overall consistency of the PDF fits. Nevertheless, it does not conclusively support extending the fitted kinematic region to transverse momenta below a few tens of GeV. The impact of resummation on PDFs is moderate, leading primarily to a stabilisation of the gluon PDF in a kinematic region of relevance for LHC phenomenology. Finally, we comment on the fact that the treatment of correlations among theoretical uncertainties may play a central role in PDF fits to measurements with percent- and sub-percent-level precision.

hep-ph↗

arXiv2026

Higher-order effects in amplitude-assisted polarisation extraction with machine-learning techniques

With increasing experimental precision, the prospect of extracting the polarisation of electroweak gauge bosons is becoming particularly attractive. To this end, regression and classification procedures based on precise and accurate theoretical predictions are becoming increasingly important. In this work, we present the first amplitude-assisted regression procedure at next-to-leading-order accuracy in QCD, supplemented with parton-shower effects, using machine-learning techniques to extract the rate of longitudinal-boson production in high-energy collisions. Several neural-network architectures are presented and benchmarked against a standard random-forest regressor, demonstrating the robustness of the results for di-boson production at the LHC.

hep-ph↗

arXiv2026

Hyperoptimisation algorithm for the next generation of PDF determinations: ensemble regression with an unbiased selection model

We present a new automated procedure for selecting fitting methodologies in the determination of parton distribution functions (PDFs), based on a hyperoptimisation algorithm using ensemble regression. Building on the k-folding approach previously employed by the NNPDF collaboration, we introduce a systematic strategy to generate ensembles of hyperparameter configurations and define a new k-folding metric that consistently incorporates full PDF uncertainties. The algorithm outputs an ensemble of statistically equivalent fitting methodologies, which are combined to produce a single PDF set whose uncertainties consistently account for hyperparameter variation. We assess the impact of this approach by comparing results obtained with identical data and theoretical inputs but different fitting methodologies, determined using the previous and the new hyperoptimisation procedures. The new method broadly confirms earlier NNPDF results, while yielding moderately larger uncertainties in regions where data provide limited constraints.

hep-ph↗

arXiv2025

Accelerating Berends-Giele recursion for gluons in arbitrary dimensions over finite fields

This work provides a proof of concept for the computation of pure gluonic amplitudes in quantum chromodynamics (QCD) on graphics processing units (GPUs). The implementation relies on the Berends-Giele recursion algorithm and, for the first time on a GPU, enables the numerical computation of amplitudes in an arbitrary number of space-time dimensions and over finite fields. This demonstrates the advantages of hardware acceleration, not only for the computation of tree-level amplitudes for real-radiation processes in four dimensions over complex numbers but also for generating loop integrands for virtual corrections in $d$ dimensions over finite fields. The associated computer program is publicly available.

hep-ph↗

arXiv2025

Determining probability density functions with adiabatic quantum computing

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 alternatives to each other. In this work, we present a method for fitting one-dimensional probability distributions as a practical example of how analog and gate-based computation can be used together to perform different tasks within a single algorithm. In particular, we propose a strategy for encoding data within an adiabatic evolution model, which accomodates the fitting of strictly monotonic functions, as it is the cumulative distribution function of a dataset. Subsequently, we use a Trotter-bounded procedure to translate the adiabatic evolution into a quantum circuit in which the evolution time t is identified with the parameters of the circuit. This facilitates computing the probability density as derivative of the cumulative function using parameter shift rules.

quant-ph↗

arXiv2024

Multi-variable integration with a variational quantum circuit

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. The obtained circuit is then derived with respect to the integration variables using the parameter shift rule technique. The observable representing the derivative is then used as the predictor of the target integrand function following a quantum machine learning approach. The integral is then estimated using the fundamental theorem of integral calculus by evaluating the original circuit. Embedding data according to a reuploading strategy, multi-dimensional variables can be easily encoded into the circuit's gates and then individually taken as targets while deriving the circuit. These techniques can be exploited to partially integrate a function or to quickly compute parametric integrands within the training hyperspace.

quant-ph↗

arXiv2024

The LHC as a Neutrino-Ion Collider

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 recent observation of LHC neutrinos by FASER$ν$ and SND@LHC signals that this hitherto ignored particle beam is now available for scientific inquiry. Here we quantify the impact that neutrino deep-inelastic scattering (DIS) measurements at the LHC would have on the parton distributions (PDFs) of protons and heavy nuclei. We generate projections for DIS structure functions for FASER$ν$ and SND@LHC at Run III, as well as for the FASER$ν$2, AdvSND, and FLArE experiments to be hosted at the proposed Forward Physics Facility (FPF) operating concurrently with the High-Luminosity LHC (HL-LHC). We determine that up to one million electron- and muon-neutrino DIS interactions within detector acceptance can be expected by the end of the HL-LHC, covering a kinematic region in $x$ and $Q^2$ overlapping with that of the Electron-Ion Collider. Including these DIS projections into global (n)PDF analyses, specifically PDF4LHC21, NNPDF4.0, and EPPS21, reveals a significant reduction of PDF uncertainties, in particular for strangeness and the up and down valence PDFs. We show that LHC neutrino data enables improved theoretical predictions for core processes at the HL-LHC, such as Higgs and weak gauge boson production. Our analysis demonstrates that exploiting the LHC neutrino beam effectively provides CERN with a "Neutrino-Ion Collider" without requiring modifications in its accelerator infrastructure.

hep-ph↗

arXiv2024

Pineline: Industrialization of High-Energy Theory Predictions

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 often require dedicated programs. These programs, however, accept inputs and produce outputs that are usually very different from each other. The industrialization of theory predictions is achieved by a framework which harmonizes inputs (runcard, parameter settings), standardizes outputs (in the form of grids), produces reusable intermediate objects, and carefully tracks all meta data required to reproduce the computation. Parameter searches and fitting of non-perturbative objects are exemplary use cases that require a full or partial re-computation of theory predictions and will thus benefit of such a toolset. As an example application we present a study of the impact of replacing NNLO QCD K-factors in a PDF fit with the exact NNLO predictions.

hep-ph↗

arXiv2023

Theory prediction in PDF fitting

Continuously comparing theory predictions to experimental data is a common task in analysis of particle physics such as fitting parton distribution functions (PDFs). However, typically, both the computation of scattering amplitudes and the evolution of candidate PDFs from the fitting scale to the process scale are non-trivial, computing intesive tasks. We develop a new stack of software tools that aim to facilitate the theory predictions by computing FastKernel (FK) tables that reduce the theory computation to a linear algebra operation. Specifically, I present PineAPPL, our workhorse for grid operations, EKO, a new DGLAP solver, and yadism, a new DIS library. Alongside, I review several projects that become available with the new tools.

hep-ph↗

arXiv2022

Extending MadFlow: device-specific optimization

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 Carlo generator, which is parallelized in the event axis. This allow us to take advantage of the parallelizable nature of Monte Carlo integrals even if we don't have control of the hardware in which the computation will run (i.e., an external cluster). The generic nature of such an implementation can introduce spurious bottlenecks or overheads. Fortunately, said bottlenecks are usually restricted to a subset of operations and not to the whole vectorized program. By identifying the more critical parts of the calculation one can get very efficient code and at the same time minimize the amount of hardware-specific code that needs to be written. We show benchmarks demonstrating how simply reducing the memory footprint of the calculation can increase the performance of a $2 \to 4$ process.

physics.comp-ph↗

arXiv2022

A data-based parametrization of parton distribution functions

Since the first determination of a structure function many decades ago, all methodologies used to determine structure functions or parton distribution functions (PDFs) have employed a common prefactor as part of the parametrization. The NNPDF collaboration pioneered the use of neural networks to overcome the inherent bias of constraining the space of solution with a fixed functional form while still keeping the same common prefactor as a preprocessing. Over the years various, increasingly sophisticated, techniques have been introduced to counter the effect of the prefactor on the PDF determination. In this paper we present a methodology to remove the prefactor entirely, thereby significantly simplifying the methodology, without a loss of efficiency and finding good agreement with previous results.

hep-ph↗

arXiv2021

Compressing PDF sets using generative adversarial networks

We present a compression algorithm for parton densities using synthetic replicas generated from the training of a Generative Adversarial Network (GAN). The generated replicas are used to further enhance the statistics of a given Monte Carlo PDF set prior to compression. This results in a compression methodology that is able to provide a compressed set with smaller number of replicas and a more adequate representation of the original probability distribution. We also address the question of whether the GAN could be used as an alternative mechanism to avoid the fitting of large number of replicas.

hep-ph↗

arXiv2021

VegasFlow: accelerating Monte Carlo simulation across platforms

In this work we demonstrate the usage of the VegasFlow library on multidevice situations: multi-GPU in one single node and multi-node in a cluster. VegasFlow is a new software for fast evaluation of highly parallelizable integrals based on Monte Carlo integration. It is inspired by the Vegas algorithm, very often used as the driver of cross section integrations and based on Google's powerful TensorFlow library. In this proceedings we consider a typical multi-GPU configuration to benchmark how different batch sizes can increase (or decrease) the performance on a Leading Order example integration.

physics.comp-ph↗

arXiv2020

PDFFlow: hardware accelerating parton density access

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluons at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the possibility to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community.

hep-ph↗

arXiv2020

Constructing PineAPPL grids on hardware accelerators

In this proceedings we demonstrate how to implement and construct the PineAPPL grids, designed for fast-interpolation of Monte Carlo simulation with electroweak and QCD corrections, using the VegasFlow framework for Monte Carlo simulation on hardware accelerators. We provide an example of synchronous and asynchronous filling operations of PineAPPL grids from Monte Carlo events generated by VegasFlow. We compare the performance of this procedure on multithreading CPU and GPU.

hep-ph↗

arXiv2020

PDFFlow: parton distribution functions on GPU

We present PDFFlow, a new software for fast evaluation of parton distribution functions (PDFs) designed for platforms with hardware accelerators. PDFs are essential for the calculation of particle physics observables through Monte Carlo simulation techniques. The evaluation of a generic set of PDFs for quarks and gluon at a given momentum fraction and energy scale requires the implementation of interpolation algorithms as introduced for the first time by the LHAPDF project. PDFFlow extends and implements these interpolation algorithms using Google's TensorFlow library providing the capabilities to perform PDF evaluations taking fully advantage of multi-threading CPU and GPU setups. We benchmark the performance of this library on multiple scenarios relevant for the particle physics community.

hep-ph↗

arXiv2020

VegasFlow: accelerating Monte Carlo simulation across multiple hardware platforms

We present VegasFlow, a new software for fast evaluation of high dimensional integrals based on Monte Carlo integration techniques designed for platforms with hardware accelerators. The growing complexity of calculations and simulations in many areas of science have been accompanied by advances in the computational tools which have helped their developments. VegasFlow enables developers to delegate all complicated aspects of hardware or platform implementation to the library so they can focus on the problem at hand. This software is inspired on the Vegas algorithm, ubiquitous in the particle physics community as the driver of cross section integration, and based on Google's powerful TensorFlow library. We benchmark the performance of this library on many different consumer and professional grade GPUs and CPUs.

physics.comp-ph↗