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SwdFold:A Reweighting and Unfolding method based on Optimal Transport Theory

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arxiv 2406.01635 v1 pith:NVUERNRQ submitted 2024-06-02 physics.data-an hep-ex

classification physics.data-anhep-ex
keywords unfoldingdatadistributionshigh-energymethodoptimalphysicsreweighting
verification ladder T0 review T1 audit T2 compute T3 formal
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High-energy physics experiments rely heavily on precise measurements of energy and momentum, yet face significant challenges due to detector limitations, calibration errors, and the intrinsic nature of particle interactions. Traditional unfolding techniques have been employed to correct for these distortions, yet they often suffer from model dependency and stability issues. We present a novel method, SwdFold, which utilizes the principles of optimal transport to provide a robust, model-independent framework to estimate the probability density ratio for data unfolding. It not only unfold the toy experimental event by reweighted simulated data distributions closely with true distributions but also maintains the integrity of physical features across various observables. We can expect it can enable more reliable predictions and comprehensive analyses as a high precision reweighting and unfolding tool in high-energy physics.

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    LOITS is a differentiable sampling method, demonstrated in a GAN closure test, that maps sampled events back to the parameters of a target density for event-level inference.

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