We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better. In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.
Scientific results produced by LLM generated analysis code must be understandable and reproducible. However, uncertainty can arise at different stages of the process, both in the original natural language specification and in the generated implementation. As a result, even executable code may not provide a clear understanding of which quantities are being computed or which assumptions determine the final results. To address this challenge, we introduce quantity grounded semantic differencing, a multi-agent framework for analyzing and comparing scientific programs generated by LLMs. The framework assigns code generation, execution, tracing, and validation to separate agents, allowing it to reconstruct how key output quantities are produced and to identify differences between the intended analysis and the implemented code. We also introduce a module that inspects ambiguities in the initial user instruction and suggests alternative rewrites before code generation. Its modular design enables application to different scientific domains by replacing domain specific resources while preserving the same workflow. We validate the framework on representative collider physics analyses. The results demonstrate that the modular task decomposition enhances both transparency and reliability relative to the previous single prompt approach, while enabling substantially smaller models to execute the complete workflow.
In this paper, we introduce a scanner package enhanced by deep learning (DL) techniques. The proposed package addresses two significant challenges associated with previously developed DL-based methods: slow convergence in high-dimensional scans and the limited generalization of the DL network when mapping random points to the target space. To tackle the first issue, we utilize a similarity learning network that maps sampled points into a representation space. In this space, in-target points are grouped together while out-target points are effectively pushed apart. This approach enhances the scan convergence by refining the representation of sampled points. The second challenge is mitigated by integrating a dynamic sampling strategy. Specifically, we employ a VEGAS mapping to adaptively suggest new points for the DL network while also improving the mapping when more points are collected. Our proposed framework demonstrates substantial gains in both performance and efficiency compared to other scanning methods.