Cluster ID Plans: Algorithm and Tool G. Bower, R. Cassell, N. Graf, A. Johnson, S. Pathak

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 2 The Algorithm Previously showed cluster ID results. Goal is to identify particle type (photon, charged hadron, neutral hadron, …, fragment) that created each cal cluster. Based on a set of discriminators measured for each cluster ( shape and pointing parameters, …) Previously used a set of cuts.

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 3 The Tool Now working on a general JAS user interface to use the algorithm and much more. Key elements are a general cluster ID class, a reconstructed particle class and a neural net. Sim/real data > hits > clusters > cluster ID > recon particles > analysis

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 4 The Cluster ID Class Describes a set of one or more “primitive” clusters that an algorithm has designated to be due to a particular particle. Examples: -Photon makes isolated contiguous cluster. -Contiguous cluster is separated into two primitive clusters which ID two particles. (Maciel, Graf) -One primitive cluster is a fragment of a second and together they ID one particle.

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 5 Reconstructed Particle Physics analyses will loop over a set of reconstructed particles. They will come from tracking, vertex, cal, muon and PID systems information. May only have partial ID info, eg, it’s a charged hadron. Will include decay heirarchy, eg, pi0s from gammas, Bs and Ds, Ws, Zs, Hs…

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 6 Neural Net A critical problem is combining diverse bits of information into the judgment: “a particle of type X created this particular set of hits in various subdetectors and its 3-momentum is P.” Can use cuts but the problem is multidimensional. Want a uniform method to facilitate assessment of new techniques.

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 7 Example Cuts code if ( NE[0] < gammaNE1Cut && NE[1] < gammaNE2Cut && NE[2] < gammaNE3Cut && NE[0]/NE[1] < gammaNE0dNE1Cut && NE[1]/NE[2] > gammaNE1dNE2Cut && !(firstL == 0 && aveLE5 < gammaAveLE5Cut ) && nhits > gammaHitsMin && angsep < gammaAngSepCut && firstL < gammaFirstLCut && ) { isAnIPGamma = true; }

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 8 Neural Net Training Data Set D 11, D 12, D 13, …, D 1k, C 11, C 12, C 13, …, C 1n, D 21, D 22, D 23, …, D 2k, C 21, C 22, C 23, …, C 2n, O D N1, D N2, D N3, …, D Nk, C N1, C N2, C N3, …, C Nn, There are k discriminators, e.g., shape parameters. There are n cluster type choices, e.g., photon. One of the Cs in each (k+n)-tuple is set true, all others set false. There are N (k+n)-tuples. ***********************************************

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 9 Using a NN: a 3 step process 1 – Prepare a training data set and a test data set with the format of the previous slide. [This is pre-NN.] 2 – “Train” the net using the training data set, check it’s accuracy using the test data set, iterate until accuracy is best. [Use NN here.] 3 – Use the trained net to evaluate data expressed as a k-tuple of discriminants. Net returns an n-tuple of probabilities, one for each of the n possible (cluster type) choices. [Use NN here.]

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 10 Using the tool Benchmark measurements: -Event selection -Physics measurements Evaluation of cal designs and techniques -Compare digital and analog had cal (Steve, Vishnu) -Test clustering algorithms (Arthur, Ron, Norman) -Test ID techniques (GRB, Masako, Abe, Europeans)

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 11 General Remark on Eflow Experience is showing there is no magic bullet to solve this problem. Rather, many specially designed magic bullets are need for many special sub-problems. How many of you have worked on an cal problem only to find yourself confronted with a Gordian knot of a bunch of unexpected interrelated problems and the deeper you go the worse it gets? The Cluster ID Tool will provide a framework to focus attacking and solving each special problem and then tying together all of our solutions.

Sept 30, 2002Cluster ID: Bower, Cassell, Graf, Johnson, Pathak 12 Availability Aim to have a beta test lcd.jar file available soon (=before Nov mtg) containing a NN trained cluster ID algorithm for evaluation and applications that do not require retraining. Aim to have the full retrainable cluster ID and general NN application code in production when JAS3+LCD is ready (=before 2003).