Deep image reconstruction for background subtraction in heavy-ion collisions

Jet reconstruction in an ultrarelativistic heavy-ion collision suffers from a notoriously large, fluctuating thermal background. Traditional background‐subtraction methods struggle to remove this soft background while preserving the jet's hard substructure. In this Letter, we present , a machine-learning-based approach for full-event background‐subtraction. utilizes a model based on Swin Transformer layers to denoise jet images and disentangle hard jets from the heavy-ion background. significantly outperforms existing subtraction techniques by reproducing key jet observables such as jet <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"> <a:msub> <a:mi>p</a:mi> <a:mi mathvariant="normal">T</a:mi> </a:msub> </a:math> and mass, and substructure observables such as girth and the energy correlation function, at the subpercent to percent level. As such, paves the way for precision heavy-ion measurements in hitherto inaccessible kinematic regimes.