TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.18.4.117
TI  - Full event particle-level unfolding with variable-length latent variational diffusion
PY  - 2025/04/01
UR  - https://scipost.org/SciPostPhys.18.4.117
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 18
IS  - 4
SP  - 117
A1  - Shmakov, Alexander
AU  - Greif, Kevin Thomas
AU  - Fenton, Michael James
AU  - Ghosh, Aishik
AU  - Baldi, Pierre
AU  - Whiteson, Daniel
AB  - The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently, generative machine learning models have shown promise for performing unbinned unfolding in a high number of dimensions. However, all current generative approaches are limited to unfolding a fixed set of observables, making them unable to perform full-event unfolding in the variable dimensional environment of collider data. A novel modification to the variational latent diffusion model (VLD) approach to generative unfolding is presented, which allows for unfolding of high- and variable-dimensional feature spaces. The performance of this method is evaluated in the context of semi-leptonic $t\bar{t}$ production at the Large Hadron Collider.
ER  -

@Article{10.21468/SciPostPhys.18.4.117,
	title={{Full event particle-level unfolding with variable-length latent variational diffusion}},
	author={Alexander Shmakov and Kevin Greif and Michael James Fenton and Aishik Ghosh and Pierre Baldi and Daniel Whiteson},
	journal={SciPost Phys.},
	volume={18},
	pages={117},
	year={2025},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.18.4.117},
	url={https://scipost.org/10.21468/SciPostPhys.18.4.117},
}