Speaker
Description
Any physics experiment consists, in essence, of collecting times, locations and charges. The quantity of interest that the experiment measures must be derived from these three basic ones, for example via a neural network that takes these data as input and produces an estimate of the value in concern as output. However, the true distribution of the quantity in interest is convolved (or folded) with the response of the detector and the calculation method used in the experiment. Unfolding refers to the technique of recovering the true distribution of a quantity from the smeared distribution of quantities computed directly from the detector response. In machine learning, the same technique is known as quantification learning. In this talk, the concept and mathematical basis of unfolding is presented with examples solidly rooted both in the worlds of physics and machine learning.