DeepAI AI Chat
Log In Sign Up

Handling missing data in a neural network approach for the identification of charged particles in a multilayer detector

by   S. Riggi, et al.

Identification of charged particles in a multilayer detector by the energy loss technique may also be achieved by the use of a neural network. The performance of the network becomes worse when a large fraction of information is missing, for instance due to detector inefficiencies. Algorithms which provide a way to impute missing information have been developed over the past years. Among the various approaches, we focused on normal mixtures models in comparison with standard mean imputation and multiple imputation methods. Further, to account for the intrinsic asymmetry of the energy loss data, we considered skew-normal mixture models and provided a closed form implementation in the Expectation-Maximization (EM) algorithm framework to handle missing patterns. The method has been applied to a test case where the energy losses of pions, kaons and protons in a six-layers Silicon detector are considered as input neurons to a neural network. Results are given in terms of reconstruction efficiency and purity of the various species in different momentum bins.


page 2

page 7


A Robust and Flexible EM Algorithm for Mixtures of Elliptical Distributions with Missing Data

This paper tackles the problem of missing data imputation for noisy and ...

Handling missing data in model-based clustering

Gaussian Mixture models (GMMs) are a powerful tool for clustering, class...

Semiparametric fractional imputation using Gaussian mixture models for handling multivariate missing data

Item nonresponse is frequently encountered in practice. Ignoring missing...

Envelope Methods with Ignorable Missing Data

Envelope method was recently proposed as a method to reduce the dimensio...

Finite mixture modeling of censored and missing data using the multivariate skew-normal distribution

Finite mixture models have been widely used to model and analyze data fr...

SSIM - A Deep Learning Approach for Recovering Missing Time Series Sensor Data

Missing data are unavoidable in wireless sensor networks, due to issues ...