Particle identification at DO/ using neural networks

We have investigated the possibility of employing neural networks for identification of electrons and τ leptons with the DO/ detector in the upcoming run of the Tevatron collider at Fermilab. Preliminary results based on Monte Carlo simulations indicate that for any acceptable level of signal efficiency, neural networks consistently outperform covariance matrices so far employed for the same purpose. Using a subset of variables used by a covariance matrix, a properly trained neural network offers 2 times better background rejection for taus, and 10 times for electrons, at 90% signal efficiency. Similar enhancements can be expected for identification of other objects (such as muons, b or c jets, quark vs gluon jets, neutrinos, etc).