Speaker
Description
In high energy physics, the detection of rare events and the computation of their properties require precise and reliable statistical methods, with uncertainty quantification playing a crucial role. Nowadays, most research relies on machine learning methods, where the calibration of output probabilities is not always straightforward. How can we then draw conclusions with the required five sigma statistical significance, which serves as the essential threshold for validating new findings?
Conformal prediction methods are becoming one of the main approaches in both academia and industry to quantify uncertainty, calculate confidence intervals in regression tasks, and calibrate probabilities in classification tasks. This presentation will introduce the basic principles underlying conformal prediction and discuss data exchangeability and conformity scores. We will demonstrate the entire approach by applying conformal prediction to a classification task using a Monte Carlo sample from the DUNE experiment.
| Type of contribution | Talk: 30 minutes. |
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