In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

In modern collider experiments, the quest to explore fundamental interactions between
elementary particles has reached unparalleled levels of precision. Signatures from particle …

We introduce caloflow, a fast detector simulation framework based on normalizing flows. For
the first time, we demonstrate that normalizing flows can reproduce many-channel …

We explore the use of autoregressive flows, a type of generative model with tractable
likelihood, as a means of efficient generation of physical particle collider events. The usual …

Monte Carlo event generators are an essential tool for data analysis in collider physics. To
include subleading quantum corrections, these generators often need to produce negative …

Generation of simulated data is essential for data analysis in particle physics, but current
Monte Carlo methods are very computationally expensive. Deep-learning-based generative …

We introduce a novel variational autoencoder (VAE) architecture that can generate realistic
and diverse high energy physics events. The model we propose utilizes several techniques …

Frontier AI systems, including large language models (LLMs), hold increasing influence over
the epistemology of human users. Such influence can reinforce prevailing societal values …

Molecular optimization, which transforms a given input molecule into another with desired
properties, is essential in molecular drug discovery. The traditional approaches either suffer …

Aiming at the problem of low prediction accuracy of traditional prediction models due to the
limited labeled sample data and the imbalance of multitimescale sample data in the cement …