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 branching fraction measurement of a heavy-flavour decay. The second method employs reinforcement learning and it is applied to the determination of the
P
5
′
angular observable in
B
0
→
K
∗
0
μ
+
μ
-
decays. We find that for the former, the size of a hypothetical hidden systematic uncertainty strongly depends on the kinematic overlap between the signal and normalisation channel, while the latter is very robust against possible mismodellings of the efficiency.
We propose a new method based on machine learning to \emph{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 branching fraction measurement of a heavy-flavour decay. The second method employs reinforcement learning and it is applied to the determination of the \(P_{5}^{'}\) angular observable in \(B^0 \to K^{*0} \mu^+\mu^-\) decays. We find that for the former, the size of a hypothetical hidden systematic uncertainty strongly depends on the kinematic overlap between the signal and normalisation channel, while the latter is very robust against possible mismodellings of the efficiency.
