A Physics Analysis of a Particle Flow Algorithm For Use with a Silicon Detector Keith Fratus Stanford Linear Accelerator Center August 14th, 2008

The Large Hadron Collider: Good for the time being Collides protons at a center of mass energy of 14 TeV Hope to see Higgs, along with other physics effects Precision measurements limited by composite nature of hadrons Less chaotic “events” are desirable Photo Source: New York Times The solution is...

The International Linear Collider Proposed Linear Collider Will collide opposing beams of electrons and positrons 500 GeV initially, with eventual upgrade to 1 TeV Considerably less energy than LHC, but events are much “cleaner” Must be linear, because of increased losses to synchrotron radiation due to light electron mass Photo Source: lbl.gov

What type of detector? Several Choices Linear Collider Detector (LCD) team at SLAC investigates this issue SLAC, among other institutions, advocates a silicon based detector, referred to as “SiD” Si/W based calorimeter, with a completely Si based tracker Past experience suggests issues with other detector designs Photo Source: linearcollider.org Competing designs include ILD, with a gaseous tracker

How do we pick a detector? Simulations! Want to prove that certain benchmark physics properties are attainable to within some desired level Design that produces best physics results with lowest cost will be chosen Simulation issue becomes very non-trivial when the volume of data approaches that encountered at a particle accelerator Photo Source: JAS3

How it works... 1.) Event Generation 2.) Simulated Detector Response 3.) Reconstruction 4.) Physics Analysis Photo Source: Wikimedia Commons

My Work Verifying that step three provides results usable in step four Lots of software, no hardware Some software written on my own, other software already developed by others Complexity of problems found dictates my ultimate involvement with them Does not provide the sort of results that a “typical” research project would Years of software development can ultimately lead to no actual physical realization Photo Source: JAS3 Trade physical complications for software complications -> more annoying!

The Software To Be Tested Three Packages for event generation and reconstruction Using 500 GeV ZHH decays, 500 event samples Only concerned with output of these packages Most basic is “Fast Monte Carlo” simulation and reconstruction Ron Cassell's PFA Matt Charles' PFA PFA is “Particle Flow Algorithm” Photo Source: JAS3 JAS3

The First Step: LCFI Vertexing Package “Linear Collider Flavor Identification” is a collaboration of researchers from several British universities, primarily Oxford Identifies hadronic “jets” based on list of reconstructed particles Hadronic jets help identify the quark structure of an event Set up and monitor jobs on a remote Unix machine Generates a data file with jet collections appended, for use with the next step Photo Source: JAS3

The Next Step: The SiD ZHH Analysis Tool Tool developed by mentor, Tim Barklow Reads in jet information, and attempts to identify quark flavor by a “neural net analysis” Focuses on events where the Z Boson decays to a quark/anti-quark pair, and both Higgs Bosons decay to bottom/anti-bottom pairs Batch jobs submitted on a remote Unix machine Generates data that can be used in a histogram to show quark content Also reveals confidence in quark content Photo Source: fnal.gov

The Results FastMC ran with no complications, as expected Sample Histogram Ron's PFA ran with no complications, and provided reasonably good physics information (not necessarily expected)‏ uds Matt's PFA died! c Issue discovered to be bad collection pointers Issue was partly resolved with code fix, partly by ignoring certain unnecessary data collections b Neural Net Result With above fix, reasonable data could once again be obtained Photo Source: PAW When viewed in PAW, the data looks like...

FastMC reconstruction, with cheating Ron's PFA, with cheating Matt's PFA, with cheating FastMC reconstruction, without cheating Ron's PFA, without cheating Matt's PFA, with cheating

Some Sample Data... Still making extensive tabulations Can give percentage of quarks definitively tagged as either 0 or 1 in b category, for case of cheating Matt's PFA: 54.59% Ron's PFA: 54.51% FastMC: 55.92% Verifies that PFA results are reasonable Photo Source: EMACS text editor

For The Future... Members of LCD are investigating why Matt's PFA has so many pointer issues Other physics analyses must be deemed compatible with PFA Further refining and development of PFA software

Further Reading: http://en.wikipedia.org/wiki/International_Linear_Collider http://www.linearcollider.org/cms/ http://silicondetector.org/display/SiD/home Photo Source: art.com And....

Thanks to Tim Barklow and everyone involved with the SULI program this summer!