Publications

Order by: publication date number of citations

• Attenuation of cosmic-ray up-scattered dark matter

  Helena Kolesova

  SciPost Phys. Proc. 12, 055 (2023) · published 5 July 2023 | Toggle abstract · pdf

  GeV-scale dark matter particles with strong coupling to baryons evade the standard direct detection limits as they are efficiently stopped in the overburden and, consequently, are not able to reach the underground detectors. On the other hand, it has been shown that it is possible to probe this parameter space taking into account the flux of dark matter particles boosted by interactions with cosmic rays. We revisit these bounds paying particular attention to interactions of the relativistic dark matter particles in the Earth's crust. The effects of nuclear form factors, inelastic scattering and extra dependence of the cross section on transferred momentum (e.g. due to presence of light mediators) are studied and are found to be crucial for answering the question as to whether the window for GeV-scale strongly interacting dark matter is closed or not.

  2 citations

• Describing hadronization via histories and observables for Monte-Carlo event reweighting

  Christian Bierlich, Phil Ilten, Tony Menzo, Stephen Mrenna, Manuel Szewc, Michael K. Wilkinson, Ahmed Youssef, Jure Zupan

  SciPost Phys. 18, 054 (2025) · published 17 February 2025 | Toggle abstract · pdf

  We introduce a novel method for extracting a fragmentation model directly from experimental data without requiring an explicit parametric form, called Histories and Observables for Monte-Carlo Event Reweighting (HOMER), consisting of three steps: the training of a classifier between simulation and data, the inference of single fragmentation weights, and the calculation of the weight for the full hadronization chain. We illustrate the use of HOMER on a simplified hadronization problem, a $q\bar{q}$ string fragmenting into pions, and extract a modified Lund string fragmentation function $f(z)$. We then demonstrate the use of HOMER on three types of experimental data: (i) binned distributions of high-level observables, (ii) unbinned event-by-event distributions of these observables, and (iii) full particle cloud information. After demonstrating that $f(z)$ can be extracted from data (the inverse of hadronization), we also show that, at least in this limited setup, the fidelity of the extracted $f(z)$ suffers only limited loss when moving from (i) to (ii) to (iii). Public code is available at https://gitlab.com/uchep/mlhad.

  2 citations

• A simple guide from machine learning outputs to statistical criteria in particle physics

  Charanjit Kaur Khosa, Veronica Sanz, Michael Soughton

  SciPost Phys. Core 5, 050 (2022) · published 2 November 2022 | Toggle abstract · pdf

  In this paper we propose ways to incorporate Machine Learning training outputs into a study of statistical significance. We describe these methods in supervised classification tasks using a CNN and a DNN output, and unsupervised learning based on a VAE. As use cases, we consider two physical situations where Machine Learning are often used: high-$p_T$ hadronic activity, and boosted Higgs in association with a massive vector boson.

  2 citations

• Lectures in quantum gravity

  Ivano Basile, Luca Buoninfante, Francesco Di Filippo, Benjamin Knorr, Alessia Platania, Anna Tokareva

  SciPost Phys. Lect. Notes 98 (2025) · published 7 August 2025 | Toggle abstract · pdf

  Formulating a quantum theory of gravity lies at the heart of fundamental theoretical physics. This collection of lecture notes encompasses a selection of topics that were covered in six mini-courses at the Nordita PhD school "Towards Quantum Gravity". The scope was to provide a coherent picture, from its foundation to forefront research, emphasizing connections between different areas. The lectures begin with perturbative quantum gravity and effective field theory. Subsequently, two ultraviolet-complete approaches are presented: Asymptotically safe gravity and string theory. Finally, elements of quantum effects in black hole spacetimes are discussed.

  2 citations

• Codebase release 2.0 for YODA

  Andy Buckley, Louie Corpe, Matthew Filipovich, Christian Gutschow, Nick Rozinsky, Simon Thor, Yoran Yeh, Jamie Yellen

  SciPost Phys. Codebases 45-r2.0 (2025) · published 16 January 2025 | Toggle abstract · src

  Histogramming is often taken for granted, but the power and compactness of partially aggregated, multidimensional summary statistics, and their fundamental connection to differential and integral calculus make them formidable statistical objects, especially when very large data volumes are involved. But expressing these concepts robustly and efficiently in high-dimensional parameter spaces and for large data samples is a highly non-trivial challenge – doubly so if the resulting library is to remain usable by scientists as opposed to software engineers. In this paper we summarise the core principles required for consistent generalised histogramming, and use them to motivate the design principles and implementation mechanics of the re-engineered YODA histogramming library, a key component of physics data–model comparison and statistical interpretation in collider physics.

  2 citations

• New machine learning techniques for simulation-based inference: InferoStatic nets, kernel score estimation, and kernel likelihood ratio estimation

  Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

  SciPost Phys. Codebases 14 (2023) · published 7 July 2023 | Toggle abstract · pdf

  We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

  2 citations

• Intermittency Analysis of Toy Monte Carlo Events

  Sheetal Sharma, Ramni Gupta

  SciPost Phys. Proc. 10, 024 (2022) · published 11 August 2022 | Toggle abstract · pdf

  Event-by-event intermittency analysis of Toy Monte Carlo events is performed in the scenario of high multiplicity events as is the case at recent colliders RHIC and LHC for AA collisions. A power law behaviour of Normalized Factorial Moments (NFM), $F_{q}$ as function of number of bins ($M$) known as intermittency, is a signature of self-similar fluctuations. Dependence of NFM on the detector efficiencies and on the presence of fluctuations have been studied. Results presented here provide a baseline to the experimental results and clarity on the application of efficiency corrections to the experimental data.

  2 citations

• Codebase release 0.1 for infstat

  Kyoungchul Kong, Konstantin T. Matchev, Stephen Mrenna, Prasanth Shyamsundar

  SciPost Phys. Codebases 14-r0.1 (2023) · published 7 July 2023 | Toggle abstract · src

  We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential $\varphi$. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.

  2 citations

• LHC EFT WG note: SMEFT predictions, event reweighting, and simulation

  Alberto Belvedere, Saptaparna Bhattacharya, Giacomo Boldrini, Suman Chatterjee, Alessandro Calandri, Sergio Sánchez Cruz, Jennet Dickinson, Franz J. Glessgen, Reza Goldouzian, Alexander Grohsjean, Laurids Jeppe, Charlotte Knight, Olivier Mattelaer, Kelci Mohrman, Hannah Nelson, Vasilije Perovic, Matteo Presilla, Robert Schoefbeck, Nick Smith

  SciPost Phys. Comm. Rep. 4 (2024) · published 29 November 2024 | Toggle abstract · pdf

  This note provides a comprehensive overview of tools for predicting observables in the Standard Model effective field theory (SMEFT) at both tree level and one loop using event generators. We evaluate three primary methodologies–event reweighting, separate simulation of squared matrix elements, and full SMEFT process simulation–focusing on their statistical performance, computational efficiency, and potential biases. Each approach is assessed in terms of its accuracy, highlighting trade-offs between precision and resource demands. Practical insights into their applicability for high-energy physics analyses are offered, with particular attention to processes where SMEFT effects are significant. Additionally, we discuss the role of helicity in reweighting strategies and its impact on the quality of predictions. By comparing the methods across various LHC processes, this note provides guidance for selecting the most effective strategy for various SMEFT studies, ensuring robust predictions while optimizing computational resources.

  2 citations

• The photon content of the proton in the CT18 global analysis

  Keping Xie, T. J. Hobbs, Tie-Jiun Hou, Carl Schmidt, Mengshi Yan, C. -P. Yuan

  SciPost Phys. Proc. 8, 074 (2022) · published 12 July 2022 | Toggle abstract · pdf

  Recently, two photon PDF sets based on implementations of the LUX ansatz into the CT18 global analysis were released. In CT18lux, the photon PDF is calculated directly using the LUX master formula for all scales, $\mu$. In an alternative realization, CT18qed, the photon PDF is initialized at the starting scale, $\mu_0$, using the LUX formulation and evolved to higher scales $\mu(>\mu_0)$ with a combined QED+QCD kernel at $\mathcal{O}(\alpha),~\mathcal{O}(\alpha\alpha_s)$ and $\mathcal{O}(\alpha^2)$. In the small-$x$ region, the photon PDF uncertainty is mainly induced by the quark and gluon PDFs, through the perturbative DIS structure functions. In comparison, the large-$x$ photon uncertainty comes from various low-energy, nonperturbative contributions, including variations of the inelastic structure functions in the resonance and continuum regions, higher-twist and target-mass corrections, and elastic electromagnetic form factors of the proton. We take the production of doubly-charged Higgs pairs, $(H^{++}H^{--})$, as an example of scenarios beyond the Standard Model to illustrate the phenomenological implications of these photon PDFs at the LHC.

  2 citations