Research

My research lies at the intersection of Quantum Chromodynamics, effective field theories, high-energy neutrino scattering, and machine learning, with the overarching goal of unravelling the structure of matter and searching for physics beyond the Standard Model, from the internal dynamics of the proton to the highest-energy neutrinos. It is organised around four main themes, summarised below together with a selection of representative publications. A complete and up-to-date list of my publications is available on INSPIRE-HEP

With the discovery of the Higgs boson at the LHC, awarded with the Nobel prize in 2013, particle physics has entered a new era. The Higgs boson, responsible for giving their mass to all known particles, is the last missing element of the extremely successful Standard Model (SM), but at the same time its discovery raises a number of new fascinating questions that need to be addressed. The challenge for particle physics in the coming years is to understand in detail the properties of this new particle, the first ever fundamental scalar: any deviation with respect to the SM predictions would be a smoking gun for New Physics beyond the SM (BSM). In addition, the LHC will continue the exploration of the high energy frontier with the search for exotic heavy particles, new forces or additional space-time dimensions. The LHC program has also profound implications for exciting open problems in astronomy and cosmology, for example, many scenarios predict the production of dark matter particles at the LHC. For background information about particle physics and LHC phenomenology, you can also watch here my lecture at the Oxford’s Saturday Mornings of Theoretical Physics.

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Proton Structure and Quantum Chromodynamics

Quantum Chromodynamics (QCD) governs the strong interaction that binds quarks and gluons into protons and other hadrons. Translating LHC measurements into discoveries demands precise knowledge of how the proton’s momentum is shared among its constituents, encoded in the Parton Distribution Functions (PDFs). Because PDFs are intrinsically non-perturbative, they cannot be computed from first principles and must instead be extracted from experimental data through a global QCD analysis. A central part of my research, carried out within the NNPDF Collaboration, is the determination of the proton PDFs at the highest possible precision, now reaching the percent level and next-to-next-to-next-to-leading order (N³LO) accuracy, together with their implications for precision Standard Model measurements, Higgs physics, and searches for new phenomena. This program has also led to landmark results such as the first evidence for an intrinsic charm component in the proton wave-function.

Selected references

• R. D. Ball, …, J. Rojo et al., The path to N³LO parton distributions, Eur. Phys. J. C 84 (2024) 659. arXiv:2402.18635
• R. D. Ball, …, J. Rojo et al., Evidence for intrinsic charm quarks in the proton, Nature 608 (2022) 483. arXiv:2208.08372
• R. D. Ball, …, J. Rojo et al., The path to proton structure at 1% accuracy (NNPDF4.0), Eur. Phys. J. C 82 (2022) 428. arXiv:2109.02653
• J. Gao, L. Harland-Lang, J. Rojo, The Structure of the Proton in the LHC Precision Era, Phys. Rept. 742 (2018) 1. arXiv:1709.04922
• R. Gauld, J. Rojo, Precision determination of the small-x gluon from charm production at LHCb, Phys. Rev. Lett. 118 (2017) 072001. arXiv:1610.09373

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Collider Neutrinos

The LHC is not only the world’s most powerful proton collider but also its most intense source of high-energy neutrinos, produced copiously in the forward direction along the beamline. With the first detection of collider neutrinos by the FASER and SND@LHC experiments, this previously inaccessible particle beam is now open to systematic investigation. My group develops the theoretical framework needed to exploit these TeV-energy neutrinos: predicting the forward neutrino fluxes, computing neutrino deep-inelastic structure functions from GeV to EeV energies, and using LHC neutrino measurements to constrain the partonic structure of protons and nuclei in a kinematic regime beyond the reach of existing experiments. These efforts connect directly to the proposed Forward Physics Facility at the High-Luminosity LHC, and to the interpretation of astrophysical neutrinos at observatories such as IceCube and KM3NeT.

Selected references

• J. John, F. Kling, J. Koorn, P. Krack, J. Rojo, A First Determination of the LHC Neutrino Fluxes from FASER Data. arXiv:2507.06022
• L. A. Anchordoqui, …, J. Rojo et al., The Forward Physics Facility at the Large Hadron Collider. arXiv:2503.19010
• J. M. Cruz-Martinez, …, J. Rojo et al., The LHC as a Neutrino-Ion Collider, Eur. Phys. J. C 84 (2024) 369. arXiv:2309.09581
• A. Candido, …, J. Rojo et al., Neutrino Structure Functions from GeV to EeV Energies, JHEP 05 (2023) 149. arXiv:2302.08527
• A. Garcia, R. Gauld, A. Heijboer, J. Rojo, Complete predictions for high-energy neutrino propagation in matter, JCAP 09 (2020) 025. arXiv:2004.04756

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Effective Field Theories

If new particles are too heavy to be produced directly at the LHC, their existence can still reveal itself through subtle deviations in precision measurements. The Standard Model Effective Field Theory (SMEFT) offers a model-independent and systematically improvable framework to parametrise such deviations and to connect processes taking place at widely separated energy scales. Within the SMEFiT Collaboration I help develop global SMEFT analyses that interpret Higgs, top-quark, diboson, and electroweak data from LEP and the LHC in terms of the SMEFT, and that map these constraints onto ultraviolet-complete models of physics beyond the Standard Model. A major current focus is quantifying the new-physics reach of future colliders, in particular the FCC-ee, and deploying state-of-the-art theory ingredients such as renormalisation-group evolution (RGE) and next-to-leading-order EFT corrections, which generate a rich and often counter-intuitive pattern of correlations across the Wilson coefficient and UV physics parameter spaces.

Selected references

• T. Armadillo, …, J. Rojo et al., New Physics Reach through Precision at Future Colliders: a Multi-Pronged Approach. arXiv:2604.16596
• E. Celada, …, J. Rojo et al., Mapping the SMEFT at high-energy colliders: from LEP and the (HL-)LHC to the FCC-ee (SMEFiT3.0), JHEP 09 (2024) 091. arXiv:2404.12809
• T. Giani, G. Magni, J. Rojo, SMEFiT: a flexible toolbox for global interpretations of particle physics data. arXiv:2302.06660
• J. J. Ethier, …, J. Rojo et al., Combined SMEFT interpretation of Higgs, diboson, and top quark data from the LHC, JHEP 11 (2021) 089. arXiv:2105.00006
• N. P. Hartland, …, J. Rojo et al., A Monte Carlo global analysis of the SMEFT: the top quark sector, JHEP 04 (2019) 100. arXiv:1901.05965

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Artificial Intelligence for Particle Physics

Machine learning and artificial intelligence methods represent a core methodology of my research. The NNPDF approach pioneered the use of neural networks to represent parton distributions without restrictive functional assumptions, with uncertainties faithfully propagated through the Monte Carlo replica method. Building on this foundation, my group develops artificial-intelligence techniques across the full HEP phenomenology pipeline: from automated hyperparameter optimisation and stochastic-gradient training in PDF fits, to machine-learned unbinned multivariate observables that maximise the sensitivity of SMEFT analyses, to neural simulation-based inference (NSBI) that extracts proton structure directly from high-dimensional, unbinned collider data and bypassing the information loss inherent to traditional binned measurements. These developments aim to fully exploit the statistical power of the (High-Luminosity) LHC dataset and to establish AI as a rigorous, uncertainty-aware engine for discovery in fundamental physics.

Selected references

• R. Barrué, …, J. Rojo et al., Proton Structure from Neural Simulation-Based Inference at the LHC. arXiv:2604.13157
• J. Cruz-Martinez, …, J. Rojo et al., NNPDFpol2.0: a global determination of polarised PDFs and their uncertainties at next-to-next-to-leading order, JHEP 07 (2025) 168. arXiv:2503.11814
• J. Cruz-Martinez, …, J. Rojo et al., Hyperparameter Optimisation in Deep Learning from Ensemble Methods: Applications to Proton Structure. arXiv:2410.16248
• R. Gomez Ambrosio, J. ter Hoeve, M. Madigan, J. Rojo, V. Sanz, Unbinned multivariate observables for global SMEFT analyses from machine learning (ML4EFT), JHEP 03 (2023) 033. arXiv:2211.02058
• R. D. Ball, …, J. Rojo et al., An open-source machine learning framework for global analyses of parton distributions, Eur. Phys. J. C 81 (2021) 958. arXiv:2109.02671