arXiv subjects

Nikita Schmal

Publications and source records attributed to Nikita Schmal.

5 recordsLinked to original sources

arXiv2026

MadAgents

We uncover an effective and communicative set of agents working with MadGraph. Agentic installation, learning-by-doing training, and user support provide easy access to state-of-the-art simulations and accelerate LHC research. We show in detail how MadAgents interact with inexperienced and advanced users, support a range of simulation tasks, and analyze the results. In a second step, we illustrate how MadAgents automatize event generation and run an autonomous simulation campaign, starting from a pdf file of a paper. We also present successive MadAgents updates, including a Claude Code implementation with a self-improvement loop.

hep-ph↗

arXiv2026

Unbinning global LHC analyses

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.

hep-ph↗

arXiv2026

Agentic Re-Casting using Agentic Re-Simulations

Analysis re-casting at the LHC is highly standardized and nevertheless requires resources, time, and physics input. Building on the new MadAgents.v3, we show how a global SFitter analysis can be updated by an agentic system with a physicist in the loop. The agentic interface allows us to make the advanced SFitter methodology available to a wider audience. All physical and technical aspects of this agentic re-casting study can be trivially generalized beyond SFitter.

hep-ph↗

arXiv2025

Profile Likelihoods on ML-Steroids

Profile likelihoods, for instance, describing global SMEFT analyses at the LHC are numerically expensive to construct and evaluate. Especially profiled likelihoods are notoriously unstable and noisy. We show how modern numerical tools, similar to neural importance sampling, lead to a huge numerical improvement and allow us to evaluate the complete SFitter SMEFT likelihood in five hours on a single GPU.

hep-ph↗

arXiv2025

Staying on Top of SMEFT-Likelihood Analyses

We present a new global SMEFT analysis of LHC data in the top sector. After updating our set of measurements, we show how public ATLAS likelihoods can be incorporated into an external global analysis and how our analysis benefits from the additional information. We find that, unlike for the Higgs and electroweak sector, the SMEFT analysis of the top sector is mostly limited by the theory uncertainties. Finally, we present the first global SFitter analysis combining the top and electroweak-Higgs sectors.

hep-ph↗