text: 135,81,1439975250565816323,935991962460721153,Aishik Ghosh,"New paper! Led by @BPNachman, a Cautionary Tale of Decorrelating Theory Uncertainties: <LINK> Could making ourselves insensitive to uncertainties just be hiding the truth? 1/n What if decorrelation shrinks only our ad-hoc estimate of the uncertainty, while the actual uncertainty remains large? Intuitive illustration: 2/n <LINK> Example 1: We painstakingly sacrifice separation power for reduced difference between Pythia and Herwig. Alas, the difference to a third generator (Sherpa) remains large. 3/n <LINK> Example 2: Decorrelating scale uncertainty at LO reduces the error bands, but we’re only fooling ourselves, the difference to NLO remains large. 4/n <LINK> Message: Decorrelation only does what it is trained to do, doesn’t solve the general problem. Until we have a better description of these uncertainties, think carefully before decorrelating! 5/5 Learning twitter from the boss @DanielWhiteson :P",https://arxiv.org/abs/2109.08159,"A variety of techniques have been proposed to train machine learning classifiers that are independent of a given feature. While this can be an essential technique for enabling background estimation, it may also be useful for reducing uncertainties. We carefully examine theory uncertainties, which typically do not have a statistical origin. We will provide explicit examples of two-point (fragmentation modeling) and continuous (higher-order corrections) uncertainties where decorrelating significantly reduces the apparent uncertainty while the actual uncertainty is much larger. These results suggest that caution should be taken when using decorrelation for these types of uncertainties as long as we do not have a complete decomposition into statistically meaningful components. ",A Cautionary Tale of Decorrelating Theory Uncertainties,6,"['New paper! Led by @BPNachman, a Cautionary Tale of Decorrelating Theory Uncertainties:\n<LINK>\n\nCould making ourselves insensitive to uncertainties just be hiding the truth?\n1/n', 'What if decorrelation shrinks only our ad-hoc estimate of the uncertainty, while the actual uncertainty remains large? Intuitive illustration:\n2/n https://t.co/jrV8JCVs8h', 'Example 1: We painstakingly sacrifice separation power for reduced difference between Pythia and Herwig. Alas, the difference to a third generator (Sherpa) remains large.\n3/n https://t.co/LVdS1hCEa7', 'Example 2: Decorrelating scale uncertainty at LO reduces the error bands, but we’re only fooling ourselves, the difference to NLO remains large.\n4/n https://t.co/VxiDHwdtCJ', 'Message: Decorrelation only does what it is trained to do, doesn’t solve the general problem. Until we have a better description of these uncertainties, think carefully before decorrelating!\n\n5/5', 'Learning twitter from the boss @DanielWhiteson :P']",21,09,912