arXiv subjects

Mark N. Costantini

Publications and source records attributed to Mark N. Costantini.

9 recordsLinked to original sources

arXiv2026

Tailored PDFs for New Physics searches

Given the non-negligible interplay between parton distribution functions (PDFs) at large x and potential New Physics (NP) effects in the high-energy tails of hadron collider observables, a central question is which PDFs can be reliably employed in beyond-the-Standard-Model (BSM) analyses. In this work, we examine the fine balance between using PDF sets with small uncertainties in the large-x region -- crucial for maximising BSM sensitivity -- and adopting conservative PDF fits that exclude high-energy data potentially contaminated by unaccounted NP contributions. We systematically assess a range of conservative PDF fitting strategies designed to mitigate such biases and provide a recommendation for the class of PDFs best suited for robust BSM searches. In addition, we investigate the alternative approach of performing simultaneous fits of Standard Model Effective Field Theory (SMEFT) Wilson coefficients and PDFs, thereby consistently accounting for their mutual correlations. Starting from a toy model to illustrate the underlying mechanisms, we then analyse two realistic NP scenarios: one modifying high-mass Drell-Yan production and another affecting the high-invariant-mass tail of top-quark pair production. Both cases are representative of measurements that will be probed with high precision during the High-Luminosity phase of the LHC.

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arXiv2026

Propagating data noise through the fit: the Monte Carlo replica distribution

The Monte Carlo (MC) replica method quantifies parameter uncertainties in global fits of parton distribution functions (PDFs) and Standard Model Effective Field Theory (SMEFT) Wilson coefficients by fitting a model to many noise-perturbed copies of the data and taking the empirical distribution of the best-fit parameters as the uncertainty. The method reproduces the Bayesian posterior exactly only when the model is linear in its parameters, and departs from it in the nonlinear case. We derive the leading-order distribution the method produces and compare it with the Laplace approximation of the Bayesian posterior: the two differ by a single computable matrix, the residual-weighted Hessian of the model at the best fit, whose sign and magnitude set the over- or under-estimation of the parameter uncertainties. This closed-form expression quantifies when and by how much the MC method departs from Bayesian inference. We illustrate it on two single-parameter examples solvable in closed form and point to its evaluation in full PDF and SMEFT fits as a natural next step.

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arXiv2026

A linear PDF model for Bayesian inference

A robust uncertainty estimate in global analyses of Parton Distribution Functions (PDFs) is essential at the Large Hadron Collider (LHC), especially in view of the high-precision data anticipated by experimentalists in the High-Luminosity phase of the LHC. A Bayesian framework to determine PDFs provides a rigorous treatment of uncertainties and full control on the prior, though its practical implementation can be computationally demanding. To address these challenges, we introduce a novel approach to PDF determination tailored for Bayesian inference, based on the use of linear models. Unlike traditional parametrisations, our method represents PDFs as vectors in a functional space spanned by specially chosen bases, derived from the dimensional reduction of a neural network functional space, providing a compact yet versatile representation of PDFs. The low-dimensionality of the preferred models allows for particularly fast inference. The size of the bases can be systematically adjusted, allowing for transparent control over underfitting and overfitting, and facilitating principled model selection through Bayesian workflows. In this work, the methodology is applied to a fit of Deep Inelastic Scattering synthetic data, and thoroughly tested via multi-closure tests, thus paving the way to its application to global PDF fits.

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arXiv2025

Colibri: A new tool for fast-flying PDF fits

We present Colibri, an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use the built-in functionalities of Colibri for a fast computation of observables. It grants easy access to experimental data, several error propagation methodologies, including the Hessian method, the Monte Carlo replica method, and an efficient numerical Bayesian sampling algorithm. To demonstrate the capabilities of Colibri, we consider its simplest application: a polynomial PDF parametrisation. We perform closure tests using a full set of DIS data and compare the results of Hessian and Monte Carlo fits with those from a Bayesian fit. We further discuss how the functionalities illustrated in this example can be extended to more complex PDF parametrisations. In particular, the Bayesian framework in Colibri provides a principled approach to model selection and model averaging, making it a valuable tool for benchmarking and combining different PDF parametrisations on solid statistical grounds.

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arXiv2025

Parton distributions confront LHC Run II data: a quantitative appraisal

We present a systematic comparison of theoretical predictions and various high-precision experimental measurements, specifically of differential cross sections performed by the LHC run II for Drell-Yan gauge boson, top-quark pair, single-inclusive jet and di-jet production, and by HERA for single-inclusive jet and di-jet production. Theoretical predictions are computed at next-to-next-to-leading order (NNLO) accuracy in perturbative Quantum Chromodynamics. The most widely employed sets of Parton Distribution Functions (PDFs) are used, and PDF, strong coupling, and missing higher order uncertainties are taken into account. We quantitatively assess the predictive power of each PDF set and the contribution of the different sources of experimental and theoretical uncertainty to the agreement between data and predictions. We show that control over all of these aspects is crucial to precision physics studies, such as the determination of Standard Model parameters at the LHC.

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arXiv2025

Evaluating the faithfulness of PDF uncertainties in the presence of inconsistent data

We critically assess the robustness of uncertainties on parton distribution functions (PDFs) determined using neural networks from global sets of experimental data collected from multiple experiments. We view the determination of PDFs as an inverse problem, and we study the way the neural network model tackles it when inconsistencies between input datasets are present. We use a closure test approach, in which the regression model is applied to artificial data produced from a known underlying truth, to which the output of the model can be compared and its accuracy can be assessed in a statistically reliable way. We explore various phenomenologically relevant scenarios in which inconsistencies arise due to incorrect estimation of correlated systematic uncertainties. We show that the neural network generally corrects for the inconsistency except in cases of extreme uncertainty underestimation. When the inconsistency is not corrected, we propose and validate a procedure to detect inconsistencies.

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arXiv2024

The Monte Carlo replica method: investigating the effects of non-linearity

This paper presents an in-depth mathematical analysis of the Monte Carlo replica method, commonly used in global fitting studies within the high-energy physics theory field. For the first time, we offer a rigorous derivation of the parameter distributions resulting from this method, demonstrating that, while they align with Bayesian posteriors in linear models, they deviate in non-linear cases. We then numerically assess this discrepancy in a phenomenologically important context: fitting SMEFT Wilson coefficients. Our findings reveal that when non-linearity plays a significant role, the uncertainty estimates for key quantities differ between the two approaches.

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arXiv2024

A critical study of the Monte Carlo replica method

We present a detailed mathematical study of the Monte Carlo replica method as applied in the global fitting literature from the high-energy physics theory community. For the first time, we provide a rigorous derivation of the parameter distributions implied by the method, and show that, whilst they agree with Bayesian posteriors for linear models, they disagree otherwise. We proceed to numerically quantify the disagreement between the Monte Carlo replica method and the Bayesian method in the context of two phenomenologically relevant scenarios: fits of the SMEFT Wilson coefficients, and fits of PDFs (albeit in a toy scenario). In both scenarios, we find that uncertainty estimates of the quantities of interest are discrepant between the two approaches when non-linearity is relevant. Our findings motivate future investigation of Bayesian methodologies for global PDF fits, especially in the context of simultaneous determination of PDFs and SMEFT Wilson coefficients.

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arXiv2024

SIMUnet: an open-source tool for simultaneous global fits of EFT Wilson coefficients and PDFs

We present the open-source SIMUnet code, designed to fit Standard Model Effective Field Theory (SMEFT) Wilson coefficient alongside Parton Distribution Functions (PDFs) of the proton. SIMUnet can perform SMEFT global fits, as well as simultaneous fits of the PDFs and of an arbitrarily large number of SMEFT degrees of freedom, by including both PDF-dependent and PDF-independent observables. SIMUnet can also be used to determine whether the effects of any New Physics models can be fitted away in a global fit of PDFs. SIMUnet is built upon the open-source NNPDF code and is released together with documentation, and tutorials. To illustrate the functionalities of the new tool, we present a new global analysis of the SMEFT Wilson coefficients accounting for their interplay with the PDFs. We increment our previous analysis of the LHC Run II top quark data with both (i) the Higgs production and decay rates data from the LHC, and (ii) the precision electroweak and diboson measurements from LEP and the LHC.

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