Riunione Settimanale ML_INFN

Monday 15 May 2023, 16:00 → 17:00 Europe/Rome

Meeting room: https://l.infn.it/mlinfn-room

Minute: https://l.infn.it/mlinfn-minutes

1 Fast classifier-based goodness of fit test for online data quality monitoring

The Neyman–Pearson theory of hypothesis testing can be employed for goodness of fit if the alternative hypothesis H1 is generic enough not to introduce a significant bias while at the same time avoiding overfitting. A practical implementation of this idea (dubbed NPLM) has been developed in the context of high energy physics. After a general introduction to the approach,I will present an implementation based on efficient machine learning methods for the monitoring of particle detectors in real-time.

Speaker: Marco Letizia (Istituto Nazionale di Fisica Nucleare)

MarcoLetizia_MLINFN_May2023.pdf