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We introduce a new technique called Drapes to enhance the sensitivity insearches for new physics at the LHC. By training diffusion models on side-banddata, we show how background templates for the signal region can be generatedeither directly from noise, or by partially applying the diffusion process toexisting data. In the partial diffusion case, data can be drawn from side-bandregions, with the inverse diffusion performed for new target conditionalvalues, or from the signal region, preserving the distribution over theconditional property that defines the signal region. We apply this technique tothe hunt for resonances using the LHCO di-jet dataset, and achievestate-of-the-art performance for background template generation using highlevel input features. We also show how Drapes can be applied to low levelinputs with jet constituents, reducing the model dependence on the choice ofinput observables. Using jet constituents we can further improve sensitivity tothe signal process, but observe a loss in performance where the signalsignificance before applying any selection is below 4$\sigma$.

Improving new physics searches with diffusion models for event observables and jet constituents