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10-15 March 2019
Steinmatte conference center
Europe/Zurich timezone

Machine Learning on sWeighted data

13 Mar 2019, 15:50
20m
Steinmatte Room A

Steinmatte Room A

Oral Track 2: Data Analysis - Algorithms and Tools Track 2: Data Analysis - Algorithms and Tools

Speaker

Mr Nikita Kazeev (Yandex School of Data Analysis (RU))

Description

Analysis in high-energy physics usually deals with data samples populated from different sources. One of the most widely used ways to handle this is the sPlot technique. In this technique the results of a maximum likelihood fit are used to assign weights that can be used to disentangle signal from background. Some events are assigned negative weights, which makes it difficult to apply machine learning methods. Loss function becomes unbounded and the underlying optimization problem non-convex. In this contribution, we propose a mathematically rigorous way to apply Machine Learning methods on data with weights obtained by the sPlot. Examples of applications are also shown.

Primary authors

Mr Nikita Kazeev (Yandex School of Data Analysis (RU)) Maxim Borisyak (Yandex School of Data Analysis (RU))

Presentation materials

Peer reviewing

Paper