B. Maier

collaborators

3 papers

hep-ph2026

D $e^{+} e^{-}$ ffusion: Capturing the Beam-Beam Physics of $e^{+} e^{-}$ Collisions with Diffusion Models

Antonio Chahine, Mariarosaria D'Alfonso, Jan Eysermans +10

Beam-induced backgrounds at high-luminosity $e^{+} e^{-}$ colliders, such as the FCC-ee, are dominated by incoherent pair creation (IPC), and require computationally expensive simulatio…

hep-ex2026

Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors

Max Marriott-Clarke, Lazar Novakovic, Elizabeth Ratzer +3

We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignments or object-cent…

hep-ph2025

QINNs: Quantum-Informed Neural Networks

Aritra Bal, Markus Klute, Benedikt Maier +3

Classical deep neural networks can learn rich multi-particle correlations in collider data, but their inductive biases are rarely anchored in physics structure. We propose quantum-…

De+e−ffusion: Capturing the Beam-Beam Physics of e+e− Collisions with Diffusion Models

Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors

QINNs: Quantum-Informed Neural Networks

D $e^{+} e^{-}$ ffusion: Capturing the Beam-Beam Physics of $e^{+} e^{-}$ Collisions with Diffusion Models