--- UserCode/Friis/TancNote/note/abstract.tex	2010/03/17 20:01:25	1.1
+++ UserCode/Friis/TancNote/note/abstract.tex	2010/03/31 01:22:23	1.2
@@ -1,11 +1,23 @@
-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. 
+%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.  A new algorithm for identifying hadronic tau decays, the Tau Neural
+%Classifier (TaNC) is presented in this paper.  Using the reconstructed objects
+%from particle flow, the algorithm reconstructs the hadronic decay of the tau
+%lepton and uses an ensemble of neural nets to discriminate against common
+%background.  This strategy 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.  A technical
+%description of the algorithm and measurements of performance are included.
+
+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.  
