Philip Harris

Uncontrolled systematic errors have long barred neural representations from rigorous scientific inference—now, coupling contrastive learning with downstream nuisance parameterization forces latent manifolds to covary predictably under continuous physical distortions.

Automated symbolic regression can rediscover known HEP functions while achieving high-quality fits, streamlining data analysis in particle physics.