SCIPP R&D on Time-Over-Threshold Electronics and Long-Ladder Readout Beijing Linear Collider Workshop Beijing, China February Bruce Schumm

BRIEF SUMMARY OF STATUS Testing of 8-channel (LSTFE-1) prototype fairly advanced: Reproducible operation (4 operating boards) Most features working, with needed refinements understood A number of “subtleties” (e.g. channel matching, environmental sensitivity) under control Starting to make progress on fundamental issues confronting long-ladder/high-resolution limit. Design of 128-channel prototype (LSTFE-2) well underway (April submission) Now for the details…

Noise vs. Capacitance (at  shape = 1.2  s) Measured dependence is roughly (noise in equivalent electrons)  noise = *C with C in pF. Experience at 0.5  m had suggested that model noise parameters needed to be boosted by 20% or so; these results suggest 0.25  m model parameters are accurate  Noise performance somewhat better than anticipated. Observed Expected 1 meter EQUIVALENT CAPACITANCE STUDY

Channel-to-Channel Matching Offset: 10 mV rms Gain: 150 mV/fC <1% rms Occupancy threshold of 1.2 fC (1875 e - )  180 mV ± 2 mV (20 e - ) from gain variation ± 10 mV (100 e - ) from offset variation

Power Cycling Idea: Latch operating bias points and isolate chip from outside world. Per-channel power consumption reduces from ~1 mW to ~1  W. Restoration to operating point should take ~ 1 msec. Current status: Internal leakage (protection diodes + ?)degrades latched operating point Restoration takes ~40 msec (x5 power savings) Injection of small current (< 1 nA) to counter leakage allows for 1 msec restoration. Future (LSTFE-2) Low-current feedback will maintain bias points; solution already incorporated in LSTFE-2 design

Preamp Response Power Control Shaper Response Power Cycling with Small Injected Current Solution in hand to maintain bias levels in “off” state with low-power feedback; will eliminate need for external trickle current

Measured Noise vs. Sum of Estimated Contributions Estimated strip noise actual 60  m strip (part of estimate) Projected strip noise for 20  m strip (not part of estimate) Measured noise Sum of estimates

Simulation Results from the Santa Cruz Linear Collider Group Beijing Linear Collider Workshop Beijing, China February Bruce Schumm

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

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)

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”

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 Number of “findable” particles per event

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 Not Found175(46.4%) Found 4 Hits 88(23.3%) 270 Found 5 Hits114(30.2%) 1 Total377(100%)

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

But there’s really no reason why the algorithm should be this inefficient for these non-prompt particles Radius of origin of missed particles We have a few ideas as to why these are being missed, and are looking into it.