Publications

Order by: publication date number of citations

• Spey: Smooth inference for reinterpretation studies

  Jack Y. Araz

  SciPost Phys. 16, 032 (2024) · published 26 January 2024 | Toggle abstract · pdf

  Statistical models serve as the cornerstone for hypothesis testing in empirical studies. This paper introduces a new cross-platform Python-based package designed to utilize different likelihood prescriptions via a flexible plug-in system. This framework empowers users to propose, examine, and publish new likelihood prescriptions without developing software infrastructure, ultimately unifying and generalising different ways of constructing likelihoods and employing them for hypothesis testing within a unified platform. We propose a new simplified likelihood prescription, surpassing previous approximation accuracies by incorporating asymmetric uncertainties. Moreover, our package facilitates the integration of various likelihood combination routines, thereby broadening the scope of independent studies through a meta-analysis. By remaining agnostic to the source of the likelihood prescription and the signal hypothesis generator, our platform allows for the seamless implementation of packages with different likelihood prescriptions, fostering compatibility and interoperability.

  1 citation

• Entanglement, partial set of measurements, and diagonality of the density matrix in the parton model

  Haowu Duan

  SciPost Phys. Proc. 8, 160 (2022) · published 14 July 2022 | Toggle abstract · pdf

  To study quantum properties of the hadron wavefunction at small x, we derived the reduced density matrix for soft gluons in the CGC framework. We explicitly showed that the reduced density matrix is not diagonal in the particle number basis. The off-diagonal components are usually ignored in the conventional parton model. We thus defined the density matrix of ignorance by keeping only the part of the reduced density matrix which can be probed in a limited set of experimental measurements. We calculated Von Neumann entropy for both the reduced and ignorance density matrices. The entropy of ignorance is always greater than the entanglement entropy (computed drom the reduced density matrix) of gluons. Finally, we showed that the CGC reduced density matrix for soft gluons can be diagonalized in a thermal basis with Boltzmann weights suggesting thermalization of new quasi-particle states which we dubbed entangolons.

  1 citation

• Gluon pseudo-distributions at short distances

  I. Balitsky, W. Morris, A. Radyushkin

  SciPost Phys. Proc. 8, 161 (2022) · published 14 July 2022 | Toggle abstract · pdf

  We present the results that are necessary in the ongoing lattice calculations of the gluon parton distribution functions (PDFs) within the pseudo-PDF approach. We identify the two-gluon correlator functions that contain the invariant amplitude determining the gluon PDF in the light-cone $z^2 \to 0$ limit, and perform one-loop calculations in the coordinate representation in an explicitly gauge-invariant form. Ultraviolet (UV) terms, which contain $\ln (-z^2)$-dependence cancel in the reduced Ioffe-time distribution (ITD), and we obtain the matching relation between the reduced ITD and the light-cone ITD. Using a kernel form, we get a direct connection between lattice data for the reduced ITD and the normalized gluon PDF.

  1 citation

• Towards a data-driven model of hadronization using normalizing flows

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

  SciPost Phys. 17, 045 (2024) · published 12 August 2024 | Toggle abstract · pdf

  We introduce a model of hadronization based on invertible neural networks that faithfully reproduces a simplified version of the Lund string model for meson hadronization. Additionally, we introduce a new training method for normalizing flows, termed MAGIC, that improves the agreement between simulated and experimental distributions of high-level (macroscopic) observables by adjusting single-emission (microscopic) dynamics. Our results constitute an important step toward realizing a machine-learning based model of hadronization that utilizes experimental data during training. Finally, we demonstrate how a Bayesian extension to this normalizing-flow architecture can be used to provide analysis of statistical and modeling uncertainties on the generated observable distributions.

  1 citation

• Renormalization-group improvement in hadronic $τ$ decays in 2018

  Diogo Boito, Pere Masjuan, Fabio Oliani

  SciPost Phys. Proc. 1, 049 (2019) · published 22 February 2019 | Toggle abstract · pdf

  One of the main sources of theoretical uncertainty in the extraction of the strong coupling from hadronic tau decays stems from the renormalization group improvement of the series. Perturbative series in QCD are divergent but are (most likely) asymptotic expansions. One needs knowledge about higher orders to be able to choose the optimal renormalization-scale setting procedure. Here, we discuss the use of Pad\'e approximants as a model-independent and robust method to extract information about the higher-order terms. We show that in hadronic \boldmath $\tau$ decays the fixed-order expansion, known as fixed-order perturbation theory (FOPT), is the most reliable mainstream method to set the scale. This fully corroborates previous conclusions based on the available knowledge about the leading renormalon singularities of the perturbative series.

  1 citation

• Theoretical study of deeply virtual Compton scattering off $^4$He

  Sara Fucini, Sergio Scopetta, Michele Viviani

  SciPost Phys. Proc. 3, 012 (2020) · published 24 February 2020 | Toggle abstract · pdf

  An interesting breakthrough in understanding the elusive inner content of nuclear systems in terms of partonic degrees of freedom is represented by deeply virtual Compton scattering processes. In such a way, tomographic view of nuclei and bound nucleons in coordinate space could be achieved for the first time. Moreover, nowadays experimental results for such a process considering $^4$He targets recently released at Jefferson Lab are available. In this talk, the recent results of our rigorous Impulse Approximation for DVCS off $^4$He, in terms of state-of-the-art models of the nuclear spectral function and of the parton structure of the bound proton, able to explain present data, has been shown.

  1 citation

• Reduce, reuse, reinterpret: An end-to-end pipeline for recycling particle physics results

  Giordon Stark, Camila Aristimuno Ots, Mike Hance

  SciPost Phys. Codebases 27 (2024) · published 28 February 2024 | Toggle abstract · pdf

  Searches for new physics at the Large Hadron Collider have constrained many models of physics beyond the Standard Model. Many searches also provide resources that allow them to be reinterpreted in the context of other models. We describe a reinterpretation pipeline that examines previously untested models of new physics using supplementary information from ATLAS Supersymmetry (SUSY) searches in a way that provides accurate constraints even for models that differ meaningfully from the benchmark models of the original analysis. The public analysis information, such as public analysis routines and serialized probability models, is combined with common event generation and simulation toolkits MADGRAPH, PYTHIA8, and DELPHES into workflows steered by TOML configuration files, and bundled into the mapyde Python package. The use of mapyde is demonstrated by constraining previously untested SUSY models with compressed sleptons and electroweakinos using ATLAS results.

  1 citation

• Codebase release 0.5 for mapyde

  Giordon Stark, Camila Aristimuno Ots, Mike Hance

  SciPost Phys. Codebases 27-r0.5 (2024) · published 28 February 2024 | Toggle abstract · src

  Searches for new physics at the Large Hadron Collider have constrained many models of physics beyond the Standard Model. Many searches also provide resources that allow them to be reinterpreted in the context of other models. We describe a reinterpretation pipeline that examines previously untested models of new physics using supplementary information from ATLAS Supersymmetry (SUSY) searches in a way that provides accurate constraints even for models that differ meaningfully from the benchmark models of the original analysis. The public analysis information, such as public analysis routines and serialized probability models, is combined with common event generation and simulation toolkits MADGRAPH, PYTHIA8, and DELPHES into workflows steered by TOML configuration files, and bundled into the mapyde Python package. The use of mapyde is demonstrated by constraining previously untested SUSY models with compressed sleptons and electroweakinos using ATLAS results.

  1 citation

• The charged $Z_c$ and $Z_b$ structures in a constituent quark model approach

  Pablo Garcia Ortega, Jorge Segovia, David Rodriguez Entem and Francisco Fernández

  SciPost Phys. Proc. 3, 013 (2020) · published 25 February 2020 | Toggle abstract · pdf

  The nature of the recently discovered $Z_c$ and $Z_b$ structures is intriguing. Their charge forces its minimal quark content to be $Q\bar Q q\bar q$ (where $Q=\{c,b\}$ and $q=\{u,d\}$). In this work we perform a molecular coupled-channels calculation of the $I^G(J^{PC})=1^+(1^{+-})$ charm and bottom sectors in the framework of a constituent quark model which satisfactorily describes a wide range of properties of (non-)conventional hadrons containing heavy quarks. All the relevant channels are included for each sector, i.e.: The $D^{(\ast)}\bar D^{\ast}+h.c.$, $\pi J/\psi$ and $\rho\eta_c$ channels for the $Z_c$ and $B^{(\ast)}B^{\ast}$ and $\Upsilon(nS)\pi$ ($n=1,2,3$) channels for the $Z_b$ analysis. Possible structures of these resonances will be discussed

  1 citation

• Variance reduction via simultaneous importance sampling and control variates techniques using vegas

  Prasanth Shyamsundar, Jacob L. Scott, Stephen Mrenna, Konstantin T. Matchev, Kyoungchul Kong

  SciPost Phys. Codebases 28 (2024) · published 4 March 2024 | Toggle abstract · pdf

  Monte Carlo (MC) integration is an important calculational technique in the physical sciences. Practical considerations require that the calculations are performed as accurately as possible for a given set of computational resources. To improve the accuracy of MC integration, a number of useful variance reduction algorithms have been developed, including importance sampling and control variates. In this work, we demonstrate how these two methods can be applied simultaneously, thus combining their benefits. We provide a python wrapper, named CoVVVR, which implements our approach in the VEGAS program. The improvements are quantified with several benchmark examples from the literature.

  1 citation