The Santa Cruz Linear Collider Group Simulation Effort SiLC Workshop Prague, Czech Republic April 26, 2007 Bruce Schumm

What are we working on? Tracker/Reconstruction/Fitter Validation Package  Working on making it platform-independent Pulse Development Simulation  Resolution, Efficiency vs.   Strip pitch / Readout pitch Tracking with an All-Silicon Tracker  Non-prompt tracks  Performance vs. z segmentation

People Myself, plus a number of very dedicated undergraduate physics majors: Tyler Rice Lori Stevens (summer: SLAC Pope Fellow) Chris Meyer Luke Kelley We’re working on results for LCWS07; not too much news for this meeting

Tracking Efficiency and Momentum Resolution Analysis Chris Meyer UCSC ILC Simulation Reconstruction Meeting March 13, 2007

SODTrack Analyzer SODTrack reconstruction written by Fred Blanc; extends VXD stubs into central tracker. For now uses cheating for VXD stubs. Fitter: simple helix fit. Geared towards SiD detector concept Original tracking efficiency code written by Eric Wallace (UCSC Undergraduate, now at U of W) was implemented to create root output files The SID-01 files were used for ZPole bbbar events, and SID-AUG-05 for 500 GeV uds events.

Definition of findable track (filters) Cos(  ) < 0.5 rOrg < 400 mm Pathlength < 500mm Final or Intermediate State Particle Purity of hits on track > 0.79 from same MC Particle Also: Events only accepted if cos(  T ) 0.94 Criteria developed by Bruce Schumm, Lori Stevens, and Tyler Rice after some study

ZPole bbbar All tracks originating outside 1.4 cm aren’t found…

Total Efficiency for Prompt Tracks 500 GeV uds Making a pT > 5 GeV cut increases efficiency about 0.3% 98.46% efficiency for pT >.75 GeV/c 98.77% efficiency for pT > 5 GeV/c

Efficiency vs pT GeV uds At higher pT -1 (lower pT) there is some inefficiency

Fake Rates vs. Momentum Calculated from all SODTrack hits; “fake” is < 80% of hits from same MC Particle. Range (GeV)Fake TracksTotal Tracks% Fake 0.5 – – – – – – – –

Efficiency vs.  500 GeV uds Let  be the angle between a track and the thrust axis No obvious effect as you go into the jet core (small  ) pT > 0.75 GeV pT > 5.0 GeV

“Tri-plots” Residuals Error Matrix Expectation Billior Calculation Expectation

Tracking Validation Package Chris Meyer working on making package platform- independent – will run on ROOT output from any code framework. User would need to Apply selection algorithm to filter “findable” tracks (framework dependent, but our criteria are good guide) Do hit-by-hit MC Truth association Output track-by-track information in specified ROOT format Provide look-up table (in p and cos  ) ofexpected momentum resolution (can be gotten by running LCDTRK, or your favorite routine)

Pulse Development Studies Have been exploring angle effects and strip/readout pitch for long (60 cm) ladders Starting to get results now; expecting to have organized body of results for discussion in Hamburg Preliminary (very) sense: Efficiency vs. entrance angle not a problem for L=60cm. Was a problem for L=167cm; exploring transition now

Resolution vs. Incidence Angle  for 25  m Pitch Sensors Read Out with a 50  m Pitch (60 cm Ladder) Track through read- out channel Track through un- read-out channel Average

Resolution vs. Incidence Angle  for 37.5  m Pitch Sensors Read Out with a 75  m Pitch (60 cm Ladder) By way of comparison, for sensors w/ equal sense and readout pitch d d = 50  m  = 4.6  m d = 75  m  = 7.1  m

RECONSTRUCTING NON-PROMPT TRACKS Snowmass ‘05: Tim Nelson wrote axial-only algorithm to reconstruct tracks in absence of Vertex Detector UCSC idea: use this to “clean up” after vertex- stub based reconstruction (VXDBasedReco) About 5% of tracks originate beyond the VXD inner layers For now: study Z-pole qq events Work done with Tyler Rice, Lori Stevens

Cheater VXDBasedReco had not yet been ported to org.lcsim framework, so… Wrote “cheater” to emulate perfectly efficient VXDBasedReco; assume anything that can be found by VXDBasedReco is found and the hits flagged as used Loops over TkrBarrHits and MCParticles, finds particles with rOrigin < 20mm and hits from those particles, removes them from collections rOrigin defined as sqrt(particle.getOriginX()^2 + particle.getOriginY()^2)

AxialBarrelTrackFinder (Tim Nelson, SLAC) Loops over all hits in each layer, from the outside in, and finds 3 “seed” hits, one per layer Performs CircleFit (alogrithm provided by Norman Graf) to seed hits If successful, looks for hits on the remaining layers that can be added to seed fit, refitting after each hit added. If at least 4 hits on track, and Chi^2 of fit reasonable, creates track object and adds to collection Only two (half-barrel) segments in z for now

AxialBarrelTrackFinder Performance Define “findable” particle as P t > 0.75 Radius of origin < 400 mm (require four layers) Path Length > 500 mm |cos  | < 0.8 Calculate efficiency for finding such non- prompt tracks

Particle is “found” if it is associated with a track with four or more hits, with at most one hits coming from a different track. All non-associated tracks with p t >0.75 and DCA < 100mm are labeled “fake”. ParticlesFakes Found 5 Hits131(43%) 1 Found 4 Hits100(33%) 305 Not Found 73(26%) Total304(100%)

Further Exploration of AxialBarrelTracker (new) Four-hit tracks very impure… Restrict to particles that have exactly one hit on each of the five layers  five-hit track efficiency increases to 68% Additionally require that all hits on three-hit “seeds” come from same particle  five-hit track efficiency increases to 87%

AxialBarrelTracker Modifications Under Development Require that  of hits on track be at least within  /2 of one another  may reduce 4-hit fakes by up to 50% Project underway (Lori Stevens) to introduce z segmentation into pattern recognition (have only been requiring all hits have same sign in z up until now)  may clean up three-hit seeds if segmentation is fine enough (?) Multiple passes through AxialBarrelTracker with initially pass having very stringent requirement on seed quality The challenge now is to try to reconstruct particles with fewer than five hits!!

Some Other Studies (if Time Permits)? Chris Meyer is beginning to look into jet energy resolution assuming perfect measurement of neutrals How low do we need to go in p t ? What tracking efficiency is needed? Wrap-up UCSC undergrads working on basic but important studies – mostly, but not all, SiD-related. Tracker validation and pulse simulation more generic, but five-layer tracker studies clearly geared towards SiD. More results at Hamburg, and over summer.

RANDOM BACK-UP SLIDES

Number of hits on track Track Momentum What’s Left after “Cheating”? (258 events, no backgrounds) “Good” “Other” “Knock-on” (less than 10 MeV) “Looper” Total hits: % Good hits: % Looper hits: % Knock-on hits: % Other hits: % Total tracks: % Good tracks:4456.6% Looper tracks:4596.8% Knock-on tracks: % Other tracks: %

Radius of Origin (mm) Of Tracks And where do these tracks originate? “Good” “Looper” “Knock-on” “Other”

Particles can be found more than once… (but there’s only one entry per particle in the previous table) Number of times each found particle is found

Motivation To explore the effects of limited detector resolution on our ability to measure SUSY parameters in the forward (|cos(  )| >.8) region.