--- UserCode/Friis/TancNote/note/abstract.tex	2010/03/17 20:01:25	1.1
+++ UserCode/Friis/TancNote/note/abstract.tex	2010/04/29 23:23:12	1.3
@@ -1,11 +1,9 @@
-Due to the low signal rates for many new physics scenarios and large QCD
-backgrounds, efficiently identifying tau leptons while maintaining an extremely
-low mis-tag rate will be an important part of the CMS physics program.  The CMS
-particle flow algorithm combines all sub-detectors to provide a global
-reconstruction of a collision event and potentially improves spatial and energy
-resolution.  Using the reconstructed objects from particle flow, an algorithm
-using an ensemble of neural nets corresponding to different tau decay
-resonances has been developed.  This algorithm provides a large performance
-improvement with respect to previous CMS tau identification strategies and can
-potentially increase the reach of many CMS searches for physics beyond the
-Standard Model.  Preliminary results are presented in this summary. 
+The Tau Neural Classifier (TaNC) is a novel algorithm for identification of
+hadronic tau decays.  The algorithm includes two components, the reconstruction
+of tau lepton hadronic decay modes and discrimination of tau lepton hadronic
+decays from quark and gluon jets.  The reconstruction of decay modes is based
+on the reconstruction of individual charged hadrons and photons by the
+particle--flow algorithm and is utilized in the discrimination to train a set
+of neural networks using input variables that are sensitive to particular decay
+modes.  We observe a significant improvement in identification performance in
+comparison to previous algorithms.