Publications

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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.
LHC tau-pair production constraints on $a_\tau$ and $d_\tau$

Ulrich Haisch, Luc Schnell, Joachim Weiss

SciPost Phys. 16, 048 (2024) · published 14 February 2024 | · pdf

We point out that relevant constraints on the anomalous magnetic ($a_\tau$) and electric ($d_\tau$) moment of the tau lepton can be derived from tau-pair production measurements performed at the LHC. Our conclusion is based on the observation that the leading relative deviations from the Standard Model prediction for $pp \to \tau^+ \tau^-$ due to $a_\tau$ and $d_\tau$ are enhanced at high energies. Less precise measurements at hadron colliders can therefore offer the same or better sensitivity to new physics with respect to high-precision low-energy measurements performed at lepton machines. We derive bounds on $a_\tau$ and $d_\tau$ using the full LHC Run II data set on tau-pair production and compare our findings with the current best limits on the tau anomalous moments.

1 citation
Back to the formula - LHC edition

Anja Butter, Tilman Plehn, Nathalie Soybelman, Johann Brehmer

SciPost Phys. 16, 037 (2024) · published 29 January 2024 | · pdf

While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We use symbolic regression trained on matrix-element information to extract, for instance, optimal LHC observables. This way we invert the usual simulation paradigm and extract easily interpretable formulas from complex simulated data. We introduce the method using the effect of a dimension-6 coefficient on associated ZH production. We then validate it for the known case of CP-violation in weak-boson-fusion Higgs production, including detector effects.

1 citation
To profile or to marginalize - A SMEFT case study

Ilaria Brivio, Sebastian Bruggisser, Nina Elmer, Emma Geoffray, Michel Luchmann, Tilman Plehn

SciPost Phys. 16, 035 (2024) · published 29 January 2024 | · pdf

Global SMEFT analyses have become a key interpretation framework for LHC physics, quantifying how well a large set of kinematic measurements agrees with the Standard Model. This agreement is encoded in measured Wilson coefficients and their uncertainties. A technical challenge of global analyses are correlations. We compare, for the first time, results from a profile likelihood and a Bayesian marginalization for a given data set with a comprehensive uncertainty treatment. Using the validated Bayesian framework we analyse a series of new kinematic measurements. For the updated dataset we find and explain differences between the marginalization and profile likelihood treatments.
Gravitational Regge bounds

Kelian Häring, Alexander Zhiboedov

SciPost Phys. 16, 034 (2024) · published 26 January 2024 | · pdf

We review the basic assumptions and spell out the detailed arguments that lead to the bound on the Regge growth of gravitational scattering amplitudes. The minimal extra ingredient compared to the gapped case - in addition to unitarity, analyticity, subexponentiality, and crossing - is the assumption that scattering at large impact parameters is controlled by known semi-classical physics. We bound the Regge growth of amplitudes both with the fixed transferred momentum and smeared over it. Our basic conclusion is that gravitational scattering amplitudes admit dispersion relations with two subtractions. For a sub-class of smeared amplitudes, black hole formation reduces the number of subtractions to one. Finally, using dispersion relations with two subtractions we derive bounds on the local growth of relativistic scattering amplitudes. Schematically, the local bound states that the amplitude cannot grow faster than $s^2$. The results obtained in the paper are valid for $d> 4$ for which the $2\to2$ scattering amplitude is well-defined.

4 citations
Spey: Smooth inference for reinterpretation studies

Jack Y. Araz

SciPost Phys. 16, 032 (2024) · published 26 January 2024 | · 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.
How to understand limitations of generative networks

Ranit Das, Luigi Favaro, Theo Heimel, Claudius Krause, Tilman Plehn, David Shih

SciPost Phys. 16, 031 (2024) · published 25 January 2024 | · pdf

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generative network, identify potential problems in the density estimation, relate them to the underlying physics, and tie in with a comprehensive precision and uncertainty treatment for generative networks.

2 citations
Higgs associated production with a vector decaying to two fermions in the geoSMEFT

Tyler Corbett, Adam Martin

SciPost Phys. 16, 019 (2024) · published 19 January 2024 | · pdf

We present the inclusive calculations of a Higgs boson produced in association with massive vector bosons in the Standard Model Effective Field Theory (SMEFT) to order $1/\Lambda^4$ for the 13 TeV LHC. The calculations include the decay of the vector boson into massless constituents and are done using the geometric formulation of the SMEFT supplemented by the relevant dimension eight operators not included in the geoSMEFT. We include some discussion of distributions to motivate how detailed collider and experimental searches for SMEFT signals could be improved.

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