Henning Bahl

A bstract: Final states comprising two or more top quarks are important search channels at the Large Hadron Collider for scalar particles predicted in models of physics beyond the Standard Model. While the di-top final state profits from a higher signal cross section, it can be subject to intricate interference patterns. Besides the interference with the large QCD background, in case of the presence of more than one high-mass scalar also large signal–signal interference contributions can occur. We show that in such scenarios it is crucial to account for loop-level mixing for obtaining accurate exclusion bounds. We demonstrate how the interference patterns can obscure the interpretation of possible deviations from the Standard Model expectations. We show that the four-top final state, while giving rise to a smaller signal cross section, provides important complementary information due to its much smaller signal–background interference contributions. Thus, the results obtained from the four-top final state can be instrumental for pinpointing the underlying new physics scenario.

Neural simulation-based inference has been shown to outperform traditional, histogram-based inference in numerous phenomenological and experimental studies at the LHC. So far, these analyses have focused on individual processes. We study the combination of four different di-boson processes in terms of the Standard Model Effective Field Theory. Our results demonstrate how neural simulation-based inference also wins over traditional methods for more global LHC analyses.

A central objective of future collider experiments is to probe the structure of the Higgs potential, which requires access to trilinear scalar couplings, in particular the self-coupling of the observed Higgs boson. While this coupling is fixed in the Standard Model (SM), it can receive sizable modifications in many Beyond the SM (BSM) scenarios, often connected to solutions of open problems such as the origin of the matter–antimatter asymmetry of the Universe. In theories with extended scalar sectors, radiative corrections involving additional scalar states can significantly affect both the Higgs self-coupling and other trilinear scalar interactions, with important consequences for predictions of physical observables. Precise theoretical calculations are therefore essential for the interpretation of precision Higgs measurements and for identifying indirect signatures of new physics. This contribution presents the latest version of the public tool , which provides automated calculations of all trilinear scalar couplings at full one-loop order in arbitrary renormalisable theories, including full momentum dependence and flexible renormalisation scheme choices. In addition, the new module for di-Higgs production in gluon fusion is discussed in several exemplary BSM models, including scenarios with multiple resonances.

Higher-order theory predictions are crucial for the precision LHC program, but the time-consuming amplitude evaluation challenges the corresponding Monte-Carlo simulations. Machine-learned amplitude surrogates can resolve this problem, if we can guarantee their precision over the entire phase space. First, we show that our surrogates provide a calibrated learned uncertainty, even for non-Gaussian systematics; second, we describe how less accurate phase space regions can be identified; third, we demonstrate how the precision in these regions can be improved reliably.

Generative networks are perfect tools to enhance the speed and precision of LHC simulations. Especially when generating events beyond the size of the training dataset, it is important to understand their statistical precision. We present two complementary methods to estimate the amplification factor without large holdout datasets. Averaging amplification uses Bayesian networks or ensembling to estimate amplification from the precision of integrals over given phase-space volumes. Differential amplification uses hypothesis testing to quantify amplification without any resolution loss. Applied to state-of-the-art event generators, both methods indicate that amplification is already possible in specific regions of phase space.

Ultra-fast, precise, and controlled amplitude surrogates are essential for future LHC event generation. First, we investigate the noise reduction and biases of network ensembles and outline a new method to learn well-calibrated systematic uncertainties for them. We also establish evidential regression as a sampling-free method for uncertainty quantification. In a second part, we tackle localized disturbances for amplitude regression and demonstrate that learned uncertainties from Bayesian networks, ensembles, and evidential regression all identify numerical noise or gaps in the training data.

Final states comprising two or more top quarks are important search channels at the Large Hadron Collider for scalar particles predicted in models of physics beyond the Standard Model. While the di-top final state profits from a higher signal cross section, it can be subject to intricate interference patterns. Besides the interference with the large QCD background, in case of the presence of more than one high-mass scalar also large signal--signal interference contributions can occur. We show that in such scenarios it is crucial to account for loop-level mixing for obtaining accurate exclusion bounds. We demonstrate how the interference patterns can obscure the interpretation of possible deviations from the Standard Model expectations. We show that the four-top final state, while giving rise to a smaller signal cross section, provides important complementary information due to its much smaller signal--background interference contributions. Thus, the results obtained from the four-top final state can be instrumental for pinpointing the underlying new physics scenario.

Searching for violation in Higgs interactions at the LHC is as challenging as it is important. Although modern machine learning outperforms traditional methods, its results are difficult to control and interpret, which is especially important if an unambiguous probe of a fundamental symmetry is required. We propose solving this problem by learning analytic formulas with symbolic regression. Using the complementary PySR and SymbolNet approaches, we learn -sensitive observables at the detector level for WBF Higgs production and top-associated Higgs production. We find that they offer advantages in interpretability and performance.