Speakers
Description
As the performance of the Large Hadron Collider (LHC) continues to improve in terms of energy reach and instantaneously luminosity, ATLAS faces an increasingly challenging environment. High energy proton-proton (
In order to address this, Deep Generative models for fast and precise physics Simulations (DeGeSim) endeavours to utilise deep generative image synthesis techniques to emulate calorimeter images of soft quantum chromodynamic (QCD) pile-up data collected by ATLAS at the LHC. The project ultimately uses Denoising Diffusion Probabilistic Models (DDPMs) to synthesize calorimeter images based on instances of real (observed) pile-up data collected by the ATLAS detector. However, instead of seeding the generation from gaussian noise, MC simulated images of pile-up are used. This is achieved by harnessing the intrinsic markov chain process of diffusion models to map MC images to data images, allowing for semantic based image alteration. The intention is to replace MC generated calorimeter images with data informed edited versions of the image within the ATLAS simulation chain, thereby yielding images that better resemble data.
The work that will be presented is a sub-component of the aforementioned model, which addresses a key problem in probability density mapping techniques, such as density ratio estimation, of disjoint probability density functions in which the state spaces lack support. Specifically, we demonstrate that a conditional denoising diffusion probabilistic model (DDPM) augmented with self-conditioning can be used to map between otherwise disjoint pdfs. This is achieved by utilising the conditional behaviour of DDPMs to solve a pseudo-inverse problem of generating a pdf with parameter set
