WIPUNet, a U-Net with residual subtraction, sigma maps, SE attention, and learned resampling, beats vanilla U-Net at high Gaussian noise by 0.3 to 1.2 dB.
Pileup mitigation at the Large Hadron Collider with Graph Neural Networks
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abstract
At the Large Hadron Collider, the high transverse-momentum events studied by experimental collaborations occur in coincidence with parasitic low transverse-momentum collisions, usually referred to as pileup. Pileup mitigation is a key ingredient of the online and offline event reconstruction as pileup affects the reconstruction accuracy of many physics observables. We present a classifier based on Graph Neural Networks, trained to retain particles coming from high-transverse-momentum collisions, while rejecting those coming from pileup collisions. This model is designed as a refinement of the PUPPI algorithm, employed in many LHC data analyses since 2015. Thanks to an extended basis of input information and the learning capabilities of the considered network architecture, we show an improvement in pileup-rejection performances with respect to state-of-the-art solutions.
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WIPUNet: A Physics-inspired Network with Weighted Inductive Biases for Image Denoising
WIPUNet, a U-Net with residual subtraction, sigma maps, SE attention, and learned resampling, beats vanilla U-Net at high Gaussian noise by 0.3 to 1.2 dB.