相关论文: Back to the Formula -- LHC Edition

The Higgs potential is vital to understand the electroweak symmetry breaking mechanism, and probing the Higgs self-interaction is arguably one of the most important physics targets at current and upcoming collider experiments. In…

We confront the most common CP-conserving 2HDM with the LHC data analysed so far while taking into account all previously available experimental data. A special allowed corner of the parameter space is analysed - the so-called wrong-sign…

A simulation of the search for the Standard Model Higgs boson at the LHC, in the channel gg -> H -> WW -> lnu lnu, is described. Higher-order QCD corrections are taken into account by using a reweighting procedure, which allows us to…

Logic Tensor Networks (LTN) is a Neuro-Symbolic framework that effectively incorporates deep learning and logical reasoning. In particular, LTN allows defining a logical knowledge base and using it as the objective of a neural model. This…

Symbolic regression is the task of identifying a mathematical expression that best fits a provided dataset of input and output values. Due to the richness of the space of mathematical expressions, symbolic regression is generally a…

We show that a class of parity based solutions to the strong CP problem predicts new colored particles with mass at the TeV scale, due to constraints from Planck suppressed operators. The new particles are copies of the Standard Model…

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with detectors is both time consuming and computationally expensive. With the proton-proton…

The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture multiscale physics. Here, we demonstrate such an approach using…

Learning to hash is an efficient paradigm for exact and approximate nearest neighbor search from massive databases. Binary hash codes are typically extracted from an image by rounding output features from a CNN, which is trained on a…

When the Standard Model is interpreted as the renormalizable sector of a low-energy effective theory, the effects of new physics are encoded into a set of higher dimensional operators. These operators potentially deform the shapes of…

The answers to many unsolved problems lie in the intractable chemical space of molecules and materials. Machine learning techniques are rapidly growing in popularity as a way to compress and explore chemical space efficiently. One of the…

We perform a search for beyond the standard model dimension-six operators relevant to the Higgs boson at the Large Hadron Electron Collider (LHeC) and the Future Circular Hadron Electron Collider (FCC-he). With a large amount of data (few…

Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about the unknown expression not only provide more information…

We discuss the constraints on new physics from Higgs production through vector boson fusion in the context of an effective field theory that preserves Standard Model gauge symmetries. We find that the constraints on dimension-6 operators…

Recent years have witnessed the great success of convolutional neural network (CNN) based models in the field of computer vision. CNN is able to learn hierarchically abstracted features from images in an end-to-end training manner. However,…

Holonomic functions play an essential role in Computer Algebra since they allow the application of many symbolic algorithms. Among all algorithmic attempts to find formulas for power series, the holonomic property remains the most important…

We show how the LHC potential to detect a rather light CP-even Higgs boson of the NMSSM, H1 or H2, decaying into CP-odd Higgs states, A1A1, can be improved if Higgs-strahlung off W bosons and (more marginally) off top-antitop pairs are…

We present a comprehensive study of the electroweak interactions using the available Higgs and electroweak diboson production results from LHC Runs 1 and 2 as well as the electroweak precision data, in terms of the dimension-six operators.…

Large Language Models (LLMs) are transformer-based machine learning models that have shown remarkable performance in tasks for which they were not explicitly trained. Here, we explore the potential of LLMs to perform symbolic regression --…

Effective Lagrangians are a useful tool for a data-driven approach to physics beyond the Standard Model at the LHC. However, for the new physics scales accessible at the LHC, the effective operator expansion is only relatively slowly…