As a particle phenomenologist with a focus on computation, my research it's half way between the experimental and theoretical worlds, without fully falling in any of them. Putting it in layman terms, I use theoretical model to compute simulations that can then be tested by my experimentalist colleagues in the various experiments across the globe (most of them, actually, in Geneva (Switzerland)).

Of the wide world of particle physics, my work is mainly within the realm of Quantum Chromodynamics. My aim is to obtain more precise predictions with more faithful errors which can then be compared to the experiments.

In the computational side, my research is on Machine Learning and Monte Carlo simulations. They are all pieces of the same big puzzle: artificial intelligence is used to model the internal structure of the colliding protons, the theory of Quantum Chromodynamics allows us to model what happens after the collision. The resulting complicated integrals are then computed using Monte Carlo methods.

In this page you can read a bit more about what I do and also find an up to date list of all my papers, talks and open-sourced published software.

If you want to check my contributions in iNSPIRE, you can use the following search string: find a cruz-martinez, j

Machine Learning

A branch of Artificial Intelligence, this scientific discipline uses automated learning to perform tasks without knowing the necessary explicit instructions. We use ML algorithms in order construct mathematical models which, based on sample data (commonly known as training data), are able to make prediction on data that the model has never seen. Nowadays machine learning techniques are widespread in the world of physics (jet tagging, model building, pdf determination, ...) and beyond (email filtering, computer vision, ...). Currently my main topic of research as part of N3PDF consist on the application of ML techniques to the determination of the internal structure of the proton.

The internal structure of the proton

One very important ingredient for the high energy physics program of the Large Hadron Collider (LHC) at CERN is the determination of the parton content of the proton. The internal structure of the proton in terms of quarks and gluons is given by the Parton Distribution Functions.

In the NNPDF collaboration we use Machine Learning techniques in order to determinate the aforementioned PDFs.

Some of our work on PDF determination has been featured in the cover of the European Physical Journal C.

QCD corrections to Higgs physics

The Higgs boson is at the center of the physics program of the LHC at CERN. After its discovery in 2012, the effort of the phenomenological community (hep-ph) has been focused on the more precise determination of its properties.

I have worked on some of the most precise calculations for the determination of the corrections due to Quantum Chromodynamics (QCD) effects to Higgs boson production on the gluon fusion and Vector Boson Fusion (VBF) production channels.

Numerical Calculations, Monte Carlo methods

State-of-the-art computations in High Energy Physics (HEP) require computing very complex multi-dimensional integrals numerically, as the analytical result is often not known. Monte Carlo algorithms are generally the option of choice be it in HEP applications or elsewhere, as the error of such algorithms does not grow with the number of dimensions.

Our aim is to improve the efficiency of widely used methods in order to enable more physics in less time. Some examples are our implementation of the Vegas algorithm in tensorflow (Vegasflow), PDFFlow or the distributed computing tool pyHepGrid.

New Hardware in High Energy Physics

Very complicated calculations are also very costly in terms of computing resources. As a result physics at the frontier of knowledge require technologies at the frontier of software and hardware. I am also dedicated to adapt these very complicated calculations to hardware accelerators which have seen an amazing development during the last decades. With a focus on Graphical Processing Units (GPUs) with packages like Vegasflow or PDFFlow, we are also considering Field Programmable Gate Arrays (FPGAs) or, more recently, we are exploring the usage of quantum circuits as co-processor in order to accelerate calculation, such as the QPDF for PDF fitting using Variational Quantum Circuits.

Journal articles and proceedings

Theory pipeline for PDF fittingPoS (2022) ICHEP2022, 7842211.10447
Evidence for intrinsic charm quarks in the protonNature (2022) 608, 483--4872208.08372
Snowmass 2021 whitepaper: Proton structure at the precision frontier (2022) 2203.13923
Event Generators for High-Energy Physics Experiments2022 Snowmass Summer Study (2022) 2203.11110
A data-based parametrization of parton distribution functionsEur. Phys. J. (2022) C82, 1632111.02954
An open-source machine learning framework for global analyses of parton distributionsEur. Phys. J. C (2021) 81, 9582109.02671
The path to proton structure at 1\% accuracy: NNPDF CollaborationEur. Phys. J. C (2022) 82, 4282109.02653
MadFlow: automating Monte Carlo simulation on GPU for particle physics processesEur. Phys. J. C (2021) 81, 6562106.10279
A comparative study of Higgs boson production from vector-boson fusionJHEP (2021) 11, 1082105.11399
Towards the automation of Monte Carlo simulation on GPU for particle physics processes25th International Conference on Computing in High-Energy and Nuclear Physics (2021) 2105.10529
Compressing PDF sets using generative adversarial networksEur. Phys. J. C (2021) 81, 5302104.04535
Future tests of parton distributionsActa Phys. Polon. B (2021) 52, 2432103.08606
PDFFlow: hardware accelerating parton density access40th International Conference on High Energy Physics (2020) 2012.08221
Determining the proton content with a quantum computerPhys. Rev. D (2021) 103, 0340272011.13934
VegasFlow: accelerating Monte Carlo simulation across platforms40th International Conference on High Energy Physics (2020) 2010.09341
Constructing PineAPPL grids on hardware acceleratorsPoS (2021) LHCP2020, 0572009.11798
PDFFlow: Parton distribution functions on GPUComputer Physics Communications (2021) 264, 1079952009.06635
Les Houches 2019: Physics at TeV Colliders: Standard Model Working Group Report11th Les Houches Workshop on Physics at TeV Colliders: {PhysTeV Les Houches} (2020) 2003.01700
VegasFlow: accelerating Monte Carlo simulation across multiple hardware platformsComput. Phys. Commun. (2020) 254, 1073762002.12921
Studying the parton content of the proton with deep learning modelsArtificial Intelligence for Science, Industry and Society (2020) 2002.06587
Towards hardware acceleration for parton densities estimationFrascati Phys. Ser. (2019) 69, 1-61909.10547
Towards a new generation of parton densities with deep learning modelsEur. Phys. J. (2019) C79, 6761907.05075
Report from Working Group 1CERN Yellow Rep. Monogr. (2019) 7, 1-2201902.04070
NNLO corrections to VBF Higgs boson productionPoS (2018) LL2018, 0031807.07908
Second-order QCD effects in Higgs boson production through vector boson fusionPhys. Lett. (2018) B781, 672-6771802.02445
Jet cross sections and transverse momentum distributions with NNLOJETPoS (2018) RADCOR2017, 0741801.06415
The HiggsTools handbook: a beginners guide to decoding the Higgs sectorJ. Phys. (2018) G45, 0650041711.09875
NNLO QCD corrections to Higgs boson production at large transverse momentumJHEP (2016) 10, 0661607.08817
Higgs Production at NNLO in VBFActa Phys. Polon. Supp. (2018) 11, 277-284
Small $x$ extrapolation for parton distributionsPoS (2022) EPS-HEP2021, 371

Seminars and conferences

Theory developments in PDF determination QCD@LHC 2022IJCLab Orsay, FranceNovember 2022 slides
NNPDF4.0 and the path to reliable uncertainties in PDF determination QCD SeminarCERN, SwitzerlandNovember 2022 slides
GPU accelerated particle physics Invited seminarNikhef, Amsterdam (The Netherlands)September 2022
Status of the NNPDF fitting framework and theory pipeline NNPDF Collaboration MeetingGargnano, Lake Garda (Italy)September 2022
Facilitating GPU acceleration for Monte Carlo simulations Invited seminarFreiburg (Germany)July 2022
MadFlow: automating Monte Carlo simulation on GPU for particle physics 41th International Conference on High Energy Physics, ICHEP 2022Bologna (Italy)July 2022 slides
Machine Learning in PDF determination: NNPDF4.0 Transversity 2022Pavia (Italy)May 2022 slides
Accelerating Monte Carlo simulations across hardware platforms Invited seminarUSM/LMU Munich (Germany)May 2022
NNPDF4.0: the path to proton structure at 1\% accuracy Invited seminar, Dalitz seriesOxford (UK, Virtual)November 2021
GPU acceleration in High Energy Physics The 2021 International Workshop on the High Energy Circular Electron Positron ColliderNanjing (China, Virtual)November 2021 slides
Towards a GPU future for particle physics Monte Carlo simulations Invited Seminar (virtual)KIT Karlsruhe (Germany)June 2021
MadFlow: towards the automation of Monte Carlo simulation on GPU for particle physics 25th International Conference on Computing in High-Energy and Nuclear Physics (vCHEP)VirtualMay 2021 slides
New studies from the NNPDF group PDF4LHC 2021VirtualMarch 2021 slides
Offloading Monte Carlo simulations to hardware accelerators Milano Joint Phenomenology SeminarMilan (Italy, Virtual)February 2021 slides
PDF determination with a quantum hardware Invited Seminar (virtual)IFIC Valencia (Spain)February 2021 slides
PDF/Vegas-Flow HSF WLCG Virtual WorkshopVirtual meetingNovember 2020 slides
VegasFlow and PDFFlow: accelerating Monte Carlo simulation across multiple devices (joint talk with M. Rossi) Generator Infrastructure and Tools Subgroup MeetingCERN (Virtual meeting)October 2020 slides
VegasFlow: accelerating Monte Carlo simulation across platforms 40th International Conference on High Energy Physics, ICHEPPrague (Virtual meeting)August 2020 slides
Optimizing the hyperoptimization NNPDF Collaboration meetingAmsterdam (The Netherlands)February 2020
Studying the parton content of the proton with deep learning models Artificial Intelligence for Science, Industry and Society Symposium (AISIS 2019)Ciudad de Mexico (Mexico)October 2019 slides
Methodological improvements in PDF determination James Stirling Memorial Conference \& PDF4LHCDurham (UK)September 2019 slides
n3fit and hyperoptimization in the context of NNPDF 4.0 NNPDF Collaboration meetingVarenna (Italy)August 2019
Towards a new generation of PDFs with deep learning models QCD@LHC 2019Buffalo, New York (USA)July 2019 slides
Numerical Integration with Neural Networks NNLOJET Collaboration meetingZurich (Switzerland)May 2019
N3PDF studies of new methodologies NNPDF Collaboration meetingAmsterdam (The Netherlands)February 2019
Recent developments within NNLOJET NNPDF Collaboration \& N3PDF Kickoff MeetingGargnano, Lake Garda (Italy)September 2018 slides
NNLO corrections to VBF Higgs boson production Loops and Legs in Quantum Field Theory 2018St. Goar (Germany)May 2018
NNLO phenomenology with Antenna Subtraction HiggsTools Final MeetingDurham (UK)September 2017 slides
\phi^*_\eta observable for Higgs production Internal SeminarDurham (UK)May 2017
Higgs phenomenology with antenna subtraction Student SeminarDurham (UK)February 2017
Higgs phenomenology with antenna subtraction Invited SeminarValencia (Spain)January 2017 slides
NNLO calculations for Higgs processes HiggsTools Second Annual MeetingGranada (Spain)April 2016 slides
Renormalisation Scale Dependence as a Testing Ground for NNLO calculations Internal SeminarDurham (UK)February 2016
Building and Playing with NNLO Monte Carlos Student SeminarDurham (UK)February 2016
NNLO predictions for Higgs production at LHC HiggsTools First Annual MeetingFreiburg (Germany)April 2015 slides

Academic open-sourced Software

pyHepGridDistributed computing made easyZenodo (2019)
evolutionary kerasAn evolutionary algorithm implementation for KerasZenodo (2020)
vegasflowAccelerating Monte Carlo integrations across multiple hardware platformsZenodo (2020)
ekoSolves the DGLAP equations in Mellin space and produces evolution kernel operators (EKO)Zenodo (2020)
pdfflowFast device agnostic Parton Distribution Function interpolationZenodo (2020)
ganpdfsPDF (parton distribution functions) enhancement using Generative Adversarial NetworksZenodo (2021)
pycompressorPDF (parton distribution functions) compression frameworkZenodo (2021)
madflowAutomating event generation MC simulation on hardware accelerators.Zenodo (2021)
nnpdfAn open-source machine learning framework for global analyses of parton distributionsZenodo (2021)
pinekoZenodoZenodo (2022)
runcardsZenodoZenodo (2022)