A data driven method to measure electron charge mis-identification rate

Electron charge mis-measurement is an important challenge in analyses which depend on the charge of electron. To estimate the probability of {\it electron charge mis-measurement} a data driven method is introduced and a good agreement with MC based methods is achieved.\\ The third moment of $\phi$ distribution of hits in electron SuperCluster is studied. The correlation between this variable and the electron charge is also investigated. Using this `new' variable and some other variables the electron charge measurement is improved by two different approaches.