NOνA (E929)
NuMI Off-Axis νe Appearance Experiment

NOvA Home       Fermilab at Work

NOVA Document 44809-v2

[NOvA DocDB Home]

Data Driven cross checks for the nue selection efficiency - APS Practice Talk

Document #:
Document type:
Conference talk
Submitted by:
Anna Maureen Hall
Updated by:
Maury Goodman
Document Created:
13 Apr 2020, 11:31
Contents Revised:
14 Apr 2020, 12:04
Metadata Revised:
11 May 2020, 18:18
Viewable by:
  • Public document
Modifiable by:

Quick Links:
Latest Version

Other Versions:
NOvA is a long-baseline neutrino oscillation experiment, designed to make precision neutrino oscillation measurements using νµ disappearance and νe appearance. It consists of two functionally equivalent detectors and utilizes the Fermilab NuMI neutrino beam. NOvA uses a convolutional neural network for particle identification of νe events in each detector. As part of the validation process of this classifier’s performance, we apply a data-driven technique called Muon Removal. In a Muon-Removed Electron-Added study we select νµ charged-current candidates from both data and simulation in our Near Detector and then replace the muon candidate with a simulated electron of the same energy. In a Muon-Removed Decay-In-Flight study we identify muons that have decayed in flight in either detector and remove the muon, resulting in a sample of just electromagnetic showers. Each sample is then evaluated by our classifier to obtain selection efficiencies. Our last analysis found agreement between the selection efficiencies of data and simulation, showing that our classifier selection is generally robust in νe charged-current signal selection.
Notes and Changes:
Updated after notes taken during my practice talk
Associated with Events:
held on 13 Apr 2020 in Remote only:
DocDB Home ]  [ Search ] [ Last 20 Days ] [ List Authors ] [ List Topics ]

Security, Privacy, LegalFermi National Accelerator Laboratory

DocDB Version 8.8.9, contact Document Database Administrators