相关论文: Kicking it Off(-shell) with Direct Diffusion
To meet the precision targets of upcoming LHC runs in the simulation of top pair production events it is essential to also consider off-shell effects. Due to their great computational cost I propose to encode them in neural networks. For…
Off-shell propagation of nucleons is neglected in one-body transport models of heavy-ion collisions, but it could be significant in processes that are limited by phase space, such as the threshold production of heavy particles. We estimate…
The diffusion model has demonstrated promising results in image generation, recently becoming mainstream and representing a notable advancement for many generative modeling tasks. Prior applications of the diffusion model for both fast…
We present a method to compute off-shell effects for processes involving resonant particles at hadron colliders with the possibility to include realistic cuts on the decay products. The method is based on an effective theory approach to…
The application of the diffusion in many computer vision and artificial intelligence projects has been shown to give excellent improvements in performance. One of the main bottlenecks of this technique is the quadratic growth of the kNN…
We present state-of-the-art predictions for off-shell $t\bar{t}b\bar{b}$ production with di-lepton decays at the LHC with $\sqrt{s}=13$ TeV. Results are accurate at NLO in QCD and include all resonant and non-resonant diagrams,…
We address the problem of fine-tuning diffusion models for reward-guided generation in biomolecular design. While diffusion models have proven highly effective in modeling complex, high-dimensional data distributions, real-world…
The importance of off-shell contributions is discussed for $H\to VV^{(*)}$ with $V\in\{Z,W\}$ for large invariant masses $m_{VV}$ involving a standard model (SM)-like Higgs boson with $m_H=125$GeV at a linear collider (LC). Both dominant…
We demonstrate that the use of on-shell methods, involving calculation of the discontinuity across the t-channel cut associated with the exchange of a pair of massless particles, can be used to evaluate loop contributions to both the…
Diffusion models have gained significant attention for high-fidelity image generation. Our work investigates the potential of exploiting diffusion models for adversarial robustness in image classification and object detection. Adversarial…
We consider the task of generating realistic 3D shapes, which is useful for a variety of applications such as automatic scene generation and physical simulation. Compared to other 3D representations like voxels and point clouds, meshes are…
We investigate the observable effects of off-shell propagation of nucleons in heavy-ion collisions at SIS energies. Within a semi-classical BUU transport model we find a strong enhancement of subthreshold particle production when off-shell…
In physics, density $\rho(\cdot)$ is a fundamentally important scalar function to model, since it describes a scalar field or a probability density function that governs a physical process. Modeling $\rho(\cdot)$ typically scales poorly…
The fashion industry is increasingly leveraging computer vision and deep learning technologies to enhance online shopping experiences and operational efficiencies. In this paper, we address the challenge of generating high-fidelity tiled…
Our predictions for particle physics processes are realized in a chain of complex simulators. They allow us to generate high-fidelity simulated data, but they are not well-suited for inference on the theory parameters with observed data. We…
Generative AI has redefined artificial intelligence, enabling the creation of innovative content and customized solutions that drive business practices into a new era of efficiency and creativity. In this paper, we focus on diffusion…
We study the benefit of modern simulation-based inference to constrain particle interactions at the LHC. We explore ways to incorporate known physics structures into likelihood estimation, specifically morphing-aware estimation and…
We present an improved method for handling off-shell effects in deep inelastic nuclear scattering. With a firm understanding of the effects of the nuclear wave function, including these off-shell corrections as well as binding and…
Based on recent advanced diffusion models, Text-to-image (T2I) generation models have demonstrated their capabilities to generate diverse and high-quality images. However, leveraging their potential for real-world content creation,…
We present a fast simulation application based on a Deep Neural Network, designed to create large analysis-specific datasets. Taking as an example the generation of W+jet events produced in sqrt(s)= 13 TeV proton-proton collisions, we train…