Publication: The DL Advocate: playing the devil’s advocate with hidden systematic uncertainties
The DL Advocate: playing the devil’s advocate with hidden systematic uncertainties
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Golutvin, A., Iniukhin, A., Mauri, A., Owen, P., Serra, N., & Ustyuzhanin, A. (2023). The DL Advocate: playing the devil’s advocate with hidden systematic uncertainties. European Physical Journal C : Particles and Fields, 83(9), 779. https://doi.org/10.1140/epjc/s10052-023-11925-w
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We propose a new method based on machine learning to play the devil’s advocate and investigate the impact of unknown systematic effects in a quantitative way. This method proceeds by reversing the measurement process and using the physics results to interpret systematic effects under the Standard Model hypothesis. We explore this idea with two alternative approaches: the first one relies on a combination of gradient descent and optimisation techniques, its application and potentiality is illustrated with an example that studies the br
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- Golutvin, Andrei
- Iniukhin, Aleksandr
- Mauri, Andrea
- Owen, Patrick
- Serra, Nicola
- Ustyuzhanin, Andrey
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Golutvin, A., Iniukhin, A., Mauri, A., Owen, P., Serra, N., & Ustyuzhanin, A. (2023). The DL Advocate: playing the devil’s advocate with hidden systematic uncertainties. European Physical Journal C : Particles and Fields, 83(9), 779. https://doi.org/10.1140/epjc/s10052-023-11925-w
