Conveners
Generative: Partons and Phase Space
- Ramon Winterhalder (UCLouvain)
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Dr Alexander Mück (RWTH Aachen University)07/11/2023, 14:00
Transformers have become the primary architecture for natural language processing. In this study, we explore their use for auto-regressive density estimation in high-energy jet physics. We draw an analogy between sentences and words in natural language and jets and their constituents. Specifically, we investigate density estimation for light QCD jets and hadronically decaying boosted top jets....
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Mathias Kuschick07/11/2023, 14:15
For Monte Carlo event generators simulating events with full inclusion of off-shell effects is a computationally very costly task. In the talk, a method making use of modern machine learning techniques is presented that enables the modelling of full off-shell effects. Using this method as a surrogate for simulations, we expect significant improvements in the feasibility of high-precision event...
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Jonas Spinner07/11/2023, 14:30
Generative networks are promising tools in fast event generation for the LHC, yet struggle to meet the required precision when scaling up to large multiplicities. We employ the flexibility of autoregressive transformers to tackle this challenge, focusing on Z and top quark pair production with additional jets. In order to further increase precision, we use classifiers to reweight the generated...
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Davide Valsecchi (ETH Zurich (CH))07/11/2023, 14:45
In High Energy Physics, generating physically meaningful parton configurations from a collision reconstructed within a detector is a critical step for many complex analysis tasks such as the Matrix Element Method computation and Bayesian inference on parameters of interest. This contribution introduces a novel approach that employs generative machine learning architectures, Transformers...
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Nathan Huetsch (Institut für Theoretische Physik, Universität Heidelberg)07/11/2023, 15:00
The matrix element method remains a crucial tool for LHC inference in scenarios with limited event data. We enhance our neural network-based framework, now dubbed MEMeNNto, by optimizing phase-space integration techniques and introducing an acceptance function. Additionally, employing new architectures, like transformer and diffusion models, allows us to better handle complex jet combinatorics...
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Theo Heimel (Heidelberg University)07/11/2023, 15:15
Theory predictions for the LHC require precise numerical phase-space integration and generation of unweighted events. We combine machine-learned multi-channel weights with a normalizing flow for importance sampling to improve classical methods for numerical integration. By integrating buffered training for potentially expensive integrands, VEGAS initialization, symmetry-aware channels, and...
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