arXiv subjects

Riccardo Torre

Publications and source records attributed to Riccardo Torre.

At least 19 recordsLinked to original sources

arXiv2025

Isolating chirality-breaking SMEFT operators with Drell-Yan angular analysis

We present a comprehensive strategy to isolate the effect of a class of chirality-breaking interactions in the Standard Model Effective Field Theory (SMEFT) by exploiting Drell-Yan angular analysis and the violation of the Lam-Tung relation. Unlike most SMEFT interpretation of Drell-Yan measurements, dominated by growing-with-energy effects generated by the interference of SMEFT-induced and SM amplitudes, this method isolates operators that contribute only quadratically in the Wilson coefficients, allowing for an independent probe of non-interfering operators. Denoting with $v$ the electroweak vev, with $\sqrt{s}$ the center-of-mass energy, and with $\Lambda$ the scale of new physics, the non-interfering contributions to the amplitude generated by the chirality-breaking operators can be proportional to $v\sqrt{s}/\Lambda^{2}$ or $s/\Lambda^{2}$. We argue that these two classes can be further distinguished by analyzing the angular observables of the lepton pair in the transverse momentum and in the invariant mass distribution of the lepton pair. We therefore present an analysis of the lepton-pair angular observables in both these distributions. Based on a precise estimate of the Standard Model contribution to the relevant observables for the $pp\to l^{+}l^{-}+X$ process up to $O(\alpha_{S}^{2})$, we present realistic projections for the sensitivity of the LHC with $300$ fb$^{-1}$ and for the HL-LHC with $3$ ab$^{-1}$ to chirality-breaking interactions, demonstrating that angular observables provide an independent and clean handle on SMEFT effects, especially in regions where the Standard Model contribution is naturally suppressed thanks to the Lam-Tung relation. This analysis becomes crucial to go beyond single parameter global fits, since it helps breaking degeneracies with chirality preserving operators and to disentangle overlapping directions in the EFT parameter space.

hep-ph↗

arXiv2025

Comparing Generative Models with the New Physics Learning Machine

The rise of generative models for scientific research calls for the development of new methods to evaluate their fidelity. A natural framework for addressing this problem is two-sample hypothesis testing, namely the task of determining whether two data sets are drawn from the same distribution. In large-scale and high-dimensional regimes, machine learning offers a set of tools to push beyond the limitations of standard statistical techniques. In this work, we put this claim to the test by comparing a recent proposal from the high-energy physics literature, the New Physics Learning Machine, to perform a classification-based two-sample test against a number of alternative approaches, following the framework presented in Grossi et al. (2025). We highlight the efficiency tradeoffs of the method and the computational costs that come from adopting learning-based approaches. Finally, we discuss the advantages of the different methods for different use cases.

stat.ML↗

arXiv2024

Refereeing the Referees: Evaluating Two-Sample Tests for Validating Generators in Precision Sciences

We propose a robust methodology to evaluate the performance and computational efficiency of non-parametric two-sample tests, specifically designed for high-dimensional generative models in scientific applications such as in particle physics. The study focuses on tests built from univariate integral probability measures: the sliced Wasserstein distance and the mean of the Kolmogorov-Smirnov statistics, already discussed in the literature, and the novel sliced Kolmogorov-Smirnov statistic. These metrics can be evaluated in parallel, allowing for fast and reliable estimates of their distribution under the null hypothesis. We also compare these metrics with the recently proposed unbiased Fréchet Gaussian Distance and the unbiased quadratic Maximum Mean Discrepancy, computed with a quartic polynomial kernel. We evaluate the proposed tests on various distributions, focusing on their sensitivity to deformations parameterized by a single parameter $ε$. Our experiments include correlated Gaussians and mixtures of Gaussians in 5, 20, and 100 dimensions, and a particle physics dataset of gluon jets from the JetNet dataset, considering both jet- and particle-level features. Our results demonstrate that one-dimensional-based tests provide a level of sensitivity comparable to other multivariate metrics, but with significantly lower computational cost, making them ideal for evaluating generative models in high-dimensional settings. This methodology offers an efficient, standardized tool for model comparison and can serve as a benchmark for more advanced tests, including machine-learning-based approaches.

stat.ML↗

arXiv2024

A Simplified Model of Heavy Vector Singlets for the LHC and Future Colliders

We study a simplified model of two colourless heavy vector resonances in the singlet representation of $SU(2)_{L}$, with zero and unit hypercharge. We discuss mixing with the Standard Model gauge bosons due to electroweak symmetry breaking, semi-analytic formulae for production at proton colliders, requirements to obey the narrow width approximation and selected low energy constraints. We show current LHC constraints and sensitivity projections for the HL-LHC, HE-LHC, SPPC and FCC-hh on the charged and neutral heavy vectors. The utility of the simplified model Lagrangian is demonstrated by matching these results onto three explicit models: a weakly coupled abelian extension of the Standard Model gauge group, a weakly coupled non-abelian extension and a strongly coupled minimal composite Higgs model. All our results are presented in terms of physical resonance masses, using expressions which are accurate even at vector masses near the electroweak scale due to a parameter inversion we derive. We discuss the importance of this inversion and point out that its effect, and the effects of electroweak symmetry breaking, can remain important up to resonance masses of several TeV. Finally, we clarify the relation between this simplified model and the Heavy Vector Triplet (HVT) model, a simplified model for heavy $SU(2)_{L}$ triplets with zero hypercharge, and provide exact and approximate matching relations.

hep-ph↗

arXiv2024

The NFLikelihood: an unsupervised DNNLikelihood from Normalizing Flows

We propose the NFLikelihood, an unsupervised version, based on Normalizing Flows, of the DNNLikelihood proposed in Ref.[1]. We show, through realistic examples, how Autoregressive Flows, based on affine and rational quadratic spline bijectors, are able to learn complicated high-dimensional Likelihoods arising in High Energy Physics (HEP) analyses. We focus on a toy LHC analysis example already considered in the literature and on two Effective Field Theory fits of flavor and electroweak observables, whose samples have been obtained throught the HEPFit code. We discuss advantages and disadvantages of the unsupervised approach with respect to the supervised one and discuss possible interplays of the two.

hep-ph↗

arXiv2024

More variables or more bins? Impact on the EFT interpretation of Drell-Yan measurements

We generalize previous studies on constraining operators of the Standard Model Effective Field Theory using Drell-Yan (DY) measurements to include at the same time all relevant operators and uncertainties. It has been shown that fully differential measurements (triple differential for neutral and double differential for charged) are more sensitive to EFT effects. Nevertheless, due to the finite statistics, the fully differential measurements sacrifice some statistical power on the shape (less invariant mass or transverse momentum bins) in favour of more kinematic variables. We show that when the observables are particularly sensitive to the shape of the distributions, such as the invariant mass of the two leptons in neutral DY, the single differential measurement with more bins, may be as sensitive as the fully differential one, at least for specific EFT operators. This suggests to always supplement fully differential analyses with projections into the relevant distributions evaluated with finer bins.

hep-ph↗

arXiv2023

Comparison of Affine and Rational Quadratic Spline Coupling and Autoregressive Flows through Robust Statistical Tests

Normalizing flows have emerged as a powerful brand of generative models, as they not only allow for efficient sampling of complicated target distributions but also deliver density estimation by construction. We propose here an in-depth comparison of coupling and autoregressive flows, both based on symmetric (affine) and non-symmetric (rational quadratic spline) bijectors, considering four different architectures: real-valued non-Volume preserving (RealNVP), masked autoregressive flow (MAF), coupling rational quadratic spline (C-RQS), and autoregressive rational quadratic spline (A-RQS). We focus on a set of multimodal target distributions of increasing dimensionality ranging from 4 to 400. The performances were compared by means of different test statistics for two-sample tests, built from known distance measures: the sliced Wasserstein distance, the dimension-averaged one-dimensional Kolmogorov--Smirnov test, and the Frobenius norm of the difference between correlation matrices. Furthermore, we included estimations of the variance of both the metrics and the trained models. Our results indicate that the A-RQS algorithm stands out both in terms of accuracy and training speed. Nonetheless, all the algorithms are generally able, without too much fine-tuning, to learn complicated distributions with limited training data and in a reasonable time of the order of hours on a Tesla A40 GPU. The only exception is the C-RQS, which takes significantly longer to train, does not always provide good accuracy, and becomes unstable for large dimensionalities. All algorithms were implemented using \textsc{TensorFlow2} and \textsc{TensorFlow Probability} and have been made available on \href{https://github.com/NF4HEP/NormalizingFlowsHD}{GitHub}.

stat.ML↗

arXiv2023

The Role of Vector Boson Fusion in the Production of Heavy Vector Triplets at the LHC and HL-LHC

We clarify the role of vector boson fusion (VBF) in the production of heavy vector triplets at the LHC and the HL-LHC. We point out that the presence of VBF production leads to an unavoidable rate of Drell-Yan (DY) production and highlight the subtle interplay between the falling parton luminosities and the increasing importance of VBF production as the heavy vector mass increases. We discuss current LHC searches and HL-LHC projections in di-boson and di-lepton final states and demonstrate that VBF production outperforms DY production for resonance masses above 1 TeV in certain regions of the parameter space. We define two benchmark parameter points which provide competitive production rates in vector boson fusion.

hep-ph↗

arXiv2022

Testing the boundaries: Normalizing Flows for higher dimensional data sets

Normalizing Flows (NFs) are emerging as a powerful class of generative models, as they not only allow for efficient sampling, but also deliver, by construction, density estimation. They are of great potential usage in High Energy Physics (HEP), where complex high dimensional data and probability distributions are everyday's meal. However, in order to fully leverage the potential of NFs it is crucial to explore their robustness as data dimensionality increases. Thus, in this contribution, we discuss the performances of some of the most popular types of NFs on the market, on some toy data sets with increasing number of dimensions.

stat.ML↗

arXiv2021

Publishing statistical models: Getting the most out of particle physics experiments

The statistical models used to derive the results of experimental analyses are of incredible scientific value and are essential information for analysis preservation and reuse. In this paper, we make the scientific case for systematically publishing the full statistical models and discuss the technical developments that make this practical. By means of a variety of physics cases -- including parton distribution functions, Higgs boson measurements, effective field theory interpretations, direct searches for new physics, heavy flavor physics, direct dark matter detection, world averages, and beyond the Standard Model global fits -- we illustrate how detailed information on the statistical modelling can enhance the short- and long-term impact of experimental results.

hep-ph↗

arXiv2021

Biblioranking fundamental physics

We propose measures of the impact of research that improve on existing ones such as counting of number of papers, citations and $h$-index. Since different papers and different fields have largely different average number of co-authors and of references we replace citations with individual citations, shared among co-authors. Next, we improve on citation counting applying the PageRank algorithm to citations among papers. Being time-ordered, this reduces to a weighted counting of citation descendants that we call PaperRank. Similarly, we compute an AuthorRank applying the PageRank algorithm to citations among authors. These metrics quantify the impact of an author or paper taking into account the impact of those authors that cite it. Finally, we show how self- and circular- citations can be eliminated by defining a closed market of citation-coins. We apply these metrics to the InSpire database that covers fundamental physics, ranking papers, authors, journals, institutes, towns, countries, continents, genders, for all-time and in recent time periods.

cs.DL↗

arXiv2021

On the W&Y interpretation of high-energy Drell-Yan measurements

High-energy neutral and charged Drell--Yan differential cross-section measurements are powerful probes of quark-lepton contact interactions that produce growing-with-energy effects. This paper provides theoretical predictions of the new physics effects at the Next-to-Leading order in QCD and including one-loop EW corrections at the single-logarithm accuracy. The predictions are obtained from SM Monte Carlo simulations through analytic reweighting. This eliminates the need of performing a scan on the new physics parameter space, enabling the global exploration of all the relevant interactions. Furthermore, our strategy produces consistently showered events to be employed for a direct comparison of the new physics predictions with the data, or to validate the unfolding procedure than underlies the cross-section measurements. Two particularly relevant interactions, associated with the W and Y parameters of EW precision tests, are selected for illustration. Projections are presented for the sensitivity of the LHC and of the HL-LHC measurements. The impact on the sensitivity of several sources of uncertainties is quantified.

hep-ph↗

arXiv2020

Bibliometrics for collaboration works

An important issue in bibliometrics is the weighing of co-authorship in the production of scientific collaborations, which are becoming the standard modality of research activity in many disciplines. The problem is especially relevant in the field of high-energy physics, where collaborations reach 3000 authors, but it can no longer be ignored also in other domains, like medicine or biology. We present theoretical and numerical arguments in favour of weighing the individual contributions as $1/N_{\rm aut}^α$ where $N_{\rm aut}$ is the number of co-authors. When counting citations we suggest the exponent $α\approx 1$, that corresponds to fractional counting. When counting the number of papers we suggest $α\approx 1/3 - 1/2$, with the former (latter) value more appropriate for larger (smaller) collaborations. We expect and verify that the $h$ index scales as the square root of the average number of co-authors, and define a fractionalized $h$ index that does not scale with collaboration size.

cs.DL↗

arXiv2019

The DNNLikelihood: enhancing likelihood distribution with Deep Learning

We introduce the DNNLikelihood, a novel framework to easily encode, through Deep Neural Networks (DNN), the full experimental information contained in complicated likelihood functions (LFs). We show how to efficiently parametrise the LF, treated as a multivariate function of parameters and nuisance parameters with high dimensionality, as an interpolating function in the form of a DNN predictor. We do not use any Gaussian approximation or dimensionality reduction, such as marginalisation or profiling over nuisance parameters, so that the full experimental information is retained. The procedure applies to both binned and unbinned LFs, and allows for an efficient distribution to multiple software platforms, e.g. through the framework-independent ONNX model format. The distributed DNNLikelihood can be used for different use cases, such as re-sampling through Markov Chain Monte Carlo techniques, possibly with custom priors, combination with other LFs, when the correlations among parameters are known, and re-interpretation within different statistical approaches, i.e. Bayesian vs frequentist. We discuss the accuracy of our proposal and its relations with other approximation techniques and likelihood distribution frameworks. As an example, we apply our procedure to a pseudo-experiment corresponding to a realistic LHC search for new physics already considered in the literature.

hep-ph↗

arXiv2019

Hypercharged Naturalness

We present an exceptional twin-Higgs model with the minimal symmetry structure for an exact implementation of twin parity along with custodial symmetry. Twin particles are mirrors of the Standard Model yet they carry hypercharge, while the photon is identified with its twin. We thoroughly explore the phenomenological signatures of hypercharged naturalness: long-lived charged particles, a colorless twin top with electric charge $2/3$ that once pair-produced, bounds via twin-color interactions and can annihilate to dileptons or a Higgs plus a photon or a $Z$, and glueballs produced from Higgs decays and twin-quarkonium annihilation that either decay displaced, or are stable on collider scales and eventually decay to diphotons. Prospects for detection of these signatures are also discussed.

hep-ph↗

arXiv2019

Flavour anomalies after the $R_{K^*}$ measurement

The LHCb measurement of the $μ/e$ ratio $R_{K^*}$ indicates a deficit with respect to the Standard Model prediction, supporting earlier hints of lepton universality violation observed in the $R_K$ ratio. We show that the $R_K$ and $R_{K^*}$ ratios alone constrain the chiralities of the states contributing to these anomalies, and we find deviations from the Standard Model at the $4σ$ level. This conclusion is further corroborated by hints in the theoretically challenging $b\to sμ^+μ^-$ distributions. Theoretical interpretations in terms of $Z'$, lepto-quarks, loop mediators, and composite dynamics are discussed. We highlight their distinctive features in terms of chiralities and flavour structure relevant for the observed anomalies.

hep-ph↗

arXiv2018

Clockwork/Linear Dilaton: Structure and phenomenology

I briefly discuss the main phenomenological features and constraints of the Clockwork/Linear Dilaton (CW/LD) 5D geometry. This contribution is based on the work of ref. arXiv:1711.08437, to which the reader is referred for an extensive discussion of the subject and the full list of relevant references. The only original result of this proceeding is adding the constraint arising from the CMS analysis CMS-PAS-EXO-17-017 to the summary plot.

hep-ph↗

arXiv2018

Clockwork / Linear Dilaton: Structure and Phenomenology

The linear dilaton geometry in five dimensions, rediscovered recently in the continuum limit of the clockwork model, may offer a solution to the hierarchy problem which is qualitatively different from other extra-dimensional scenarios and leads to distinctive signatures at the LHC. We discuss the structure of the theory, in particular aspects of naturalness and UV completion, and then explore its phenomenology, suggesting novel strategies for experimental searches. In particular, we propose to analyze the diphoton and dilepton invariant mass spectra in Fourier space in order to identify an approximately periodic structure of resonant peaks. Among other signals, we highlight displaced decays from resonantly-produced long-lived states and high-multiplicity final states from cascade decays of excited gravitons.

hep-ph↗