Publications

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Implications of exclusive J/$\psi$ photoproduction in a tamed collinear factorisation approach to NLO

Chris A. Flett, Alan D. Martin, Misha G. Ryskin, Thomas Teubner

SciPost Phys. Proc. 15, 005 (2024) · published 2 April 2024 | · pdf

We discuss exclusive J/$\psi$ photoproduction, initially in conventional collinear factorisation at NLO and then subsequently in a refined approach with a programme of low x resummation and implementation of a crucial low $Q_0$ subtraction included. We compare and contrast predictions in both frameworks and remark about the possibility to constrain and ultimately determine the low x and low scale gluon PDF, emphasising the significance of this for future global PDF analyses.
Can quantized fragmentation replace colour reconnection models?

Šárka Todorova-Nová

SciPost Phys. Proc. 15, 001 (2024) · published 2 April 2024 | · pdf

A brief overview of properties of the model of quantized fragmentation of a helical QCD string is followed by the discussion of colour reconnection models and of the ways to reproduce the evolution of the average transverse momentum in hadronic events, as function of charged particle multiplicity. Reference to recent experimental data is included.

1 citation
Probing muon g-2 at a future muon collider

Jason Arakawa, Arvind Rajaraman, Taotao Sui, Tim M.P. Tait

SciPost Phys. 16, 072 (2024) · published 12 March 2024 | · pdf

The $4.2\sigma$ discrepancy in the $(g-2)$ of the muon provides a hint that may indicate that physics beyond the standard model is at play. A multi-TeV scale muon collider provides a natural testing ground for this physics. In this paper, we discuss the potential to probe the BSM parameter space that is consistent with solving the $(g-2)_{\mu}$ discrepancy in the language of the SMEFT, utilizing the statistical power provided by fitting event rates collected running at multiple energies. Our results indicate the importance of including interference between the BSM and the SM amplitudes, and illustrates how a muon collider running at a handful of lower energies and with less total collected luminosity can better significantly constrain the space of relevant SMEFT coefficients than would be possible for a single high energy run.

1 citation
EFT matching from analyticity and unitarity

Stefano De Angelis, Gauthier Durieux

SciPost Phys. 16, 071 (2024) · published 12 March 2024 | · pdf

We present a new on-shell method for the matching of ultraviolet models featuring massive states onto their massless effective field theory. We employ a dispersion relation in the space of complex momentum dilations to capture, in a single variable, the relevant analytic structure of scattering amplitudes at any multiplicity. Multivariate complex analysis and crossing considerations are therefore avoided. Remarkably, no knowledge about the infrared effective field theory is required in dimensional regularisation. All matching information is extracted from the residues and iscontinuities of the ultraviolet scattering amplitudes, which unitarity expresses in terms of lower-point and lower-loop results, respectively. This decomposition into simpler building blocks could deliver new insight in the structure of the effective field theories obtained from classes of ultraviolet scenarios and facilitate computations at higher loop orders.

2 citations
Codebase release r1.4 for CoVVVR

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

SciPost Phys. Codebases 28-r1.4 (2024) · published 4 March 2024 | · src

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
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 | · 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
High-dimensional and permutation invariant anomaly detection

Vinicius Mikuni, Benjamin Nachman

SciPost Phys. 16, 062 (2024) · published 29 February 2024 | · pdf

Methods for anomaly detection of new physics processes are often limited to low-dimensional spaces due to the difficulty of learning high-dimensional probability densities. Particularly at the constituent level, incorporating desirable properties such as permutation invariance and variable-length inputs becomes difficult within popular density estimation methods. In this work, we introduce a permutation-invariant density estimator for particle physics data based on diffusion models, specifically designed to handle variable-length inputs. We demonstrate the efficacy of our methodology by utilizing the learned density as a permutation-invariant anomaly detection score, effectively identifying jets with low likelihood under the background-only hypothesis. To validate our density estimation method, we investigate the ratio of learned densities and compare to those obtained by a supervised classification algorithm.

6 citations
Codebase release 0.5 for mapyde

Giordon Stark, Camila Aristimuno Ots, Mike Hance

SciPost Phys. Codebases 27-r0.5 (2024) · published 28 February 2024 | · 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
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 | · 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
An unfolding method based on conditional invertible neural networks (cINN) using iterative training

Mathias Backes, Anja Butter, Monica Dunford, Bogdan Malaescu

SciPost Phys. Core 7, 007 (2024) · published 21 February 2024 | · pdf

The unfolding of detector effects is crucial for the comparison of data to theory predictions. While traditional methods are limited to representing the data in a low number of dimensions, machine learning has enabled new unfolding techniques while retaining the full dimensionality. Generative networks like invertible neural networks~(INN) enable a probabilistic unfolding, which map individual data events to their corresponding unfolded probability distribution. The accuracy of such methods is however limited by how well simulated training samples model the actual data that is unfolded. We introduce the iterative conditional INN (IcINN) for unfolding that adjusts for deviations between simulated training samples and data. The IcINN unfolding is first validated on toy data and then applied to pseudo-data for the $pp \to Z \gamma \gamma$ process.

3 citations

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