Francesco Prino INFN – Sezione di Torino

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Presentation transcript:

Francesco Prino INFN – Sezione di Torino Operation of the ALICE Silicon Drift Detectors during cosmic data taking Francesco Prino INFN – Sezione di Torino RD09, Florence, September 30th 2009

Electron potential in SDD SDD sensor 291 Drift cathodes Voltage divider Central Cathode at -HV 120 mm pitch 256x2 Anodes Edrift vd (e-) HV, MV supply 70.2 mm 294 mm pitch vd (e-) Edrift Electron potential in SDD LV supply Commands Trigger Data  Hybrid with front-end electronics (4 pairs of ASICs)

SDD Front-end electronics PASCAL Reads signals from 64 anodes Preamplifier Analog Storage (256 cells/anode) Analog Memory sampled at 40 MHz (default) or 20 MHz Conversion Analog->digitaL 10-bit linear successive-approx. ADC AMBRA Digital four-event buffer Anode-by-anode baseline equalization Non-linear data compression From 10 to 8 bit per cell <Power dissipation> ≈ 6 mW / channel

Material Budget (% of X0) SDD ladders and layers Layer # ladders Modules /ladder # modules Material Budget (% of X0) 3 14 6 84 1.13 4 22 8 176 1.26 CARLOS board 8 inputs fed in parallel by 8 AMBRAs 1 Carlos per SDD module 2D 2-threshold zero suppression Formats data and sends to DAQ

Cooling the SDD Cooling agent: water Pressure inside barrel <1 bar Leak safe Flow adjusted to stay in intermediate state between laminar and turbulent regime Maximum heat exchange 13 (sectors) * 2 (A/C side) *2 (Ladder/End-ladder)circuits with independent flow and pressure control Safe operation controlled by dedicated Programmable Logic Controller: Overpressure control to guarantee leak safety Flow control to give the clearance for FEE operation.

SDD Detector Control System Systems controlled by DCS: LV power for Front-End Electronics HV and MV supplies for SDD detectors Cooling plant State of readout cards Finite States Machine (FSM) provides simple control of the whole system by well defined sequence of states FSM communicates to DAQ and gives permission to start data-taking when detector is in ready state Alarms and warnings logging system Archiving of all system parameters HV, MV, temperature recorded by thermistors …

Readout and DAQ Each CARLOSrx board: Online Monitoring Layer 4/C Global Data Concentrator (event building) Layer 4/A Layer 3 SDD: 260 modules DAQ control in Counting Room 24 CARLOSrx VME boards 6 Local Data Concentrators (PCs) Each CARLOSrx board: Uploads configuration to CARLOS/AMBRA/PASCAL (JTAG) Sends trigger to CARLOS and busy to CTP Reads data from up to 12 modules, apply data format compression and sends to DAQ Mass storage (CASTOR)

Running conditions Detector characteristics: Cathode 291 Detector characteristics: Readout and DAQ parameters 245 (out of 260) modules stably in DAQ Edrift Parameter Value HV (central cathode) 1800 V MV (cathode 291) 40 V Drift field 500 V/cm Resolution (z x r) 25x35 mm Parameter 20 MHz AM sampling 40 MHz Time bin size 50 ns 25 ns Number of time samples 128 256 Readout dead time 1 ms 2 ms Max. event rate 1 kHz 500 Hz Event size (cosmics) 12 kB 25 kB

Calibrating the SDD Three SDD specific calibration runs collected every ≈24h to measure calibration parameters Run Type: PEDESTAL Analyzes special SDD calibration runs taken without zero suppression Provides: Baselines, Noise, Common Mode Corrected Noise, Noisy anodes Run Type: PULSER Analyzes special SDD calibration runs taken with Test Pulse signal to front-end electronics Provides: Anode gain, Dead anodes Run Type: INJECTOR Analyzes injector events collected every ≈ 10 min. during physics runs Provides Drift speed (anode dependent) A detector algorithm (DA) runs automatically at the end of each calibration run Analyzes raw data and extracts relevant calibration quantities Files with calibration parameters are stored in the Offline Condition DataBase and used in the offline reconstruction

Design value for noise = 2 ADC units Pedestal runs Measure for each anode: Baselines Extract baseline equalization constants applied anode-by-anode in AMBRAs Noise (raw and common mode corrected) Tag noisy anodes (masked out in physics runs) Design value for noise = 2 ADC units

Pedestal results vs. time (2008) Oct 16th 2008 Jul 23rd 2008 Signal from MIP close to anodes ≈ 100 ADC counts

Pulser runs in 2008 Measure gain for each anode sending Test Pulse to the input of front-end electronics Used for anode-by-anode gain equalization in cluster finder AM sampling at 40 MHz AM sampling at 20 MHz Jul 23rd 2008 Oct 16th 2008 Test Pulse Signal

Drift speed from injectors Display of 1 injector event on 1 drift side of 1 module From INJECTOR runs (acquired every 24 hours) 33x3 MOS charge injectors implemented in known positions on each drift side (half-module) 33 determinations of drift speed along anode coordinate for each drift side Drift speed extremely sensitive to temperature (T-2.4) 0.8%/K variation at room temperature Need 0.1% accuracy on drift speed to reach the design resolution of 35 mm along rf Max. drift time ≈ 3.5 cm / 6.5 mm/ns ≈ 5.4 ms Drift speed on 1 drift side from fit to 3 injector points vdrift = mE T-2.4 Lower e- mobility / higher temperature on the edges

Drift speed vs. module Drift speed (electron mobility) depends on: Temperature Higher drift speed on average on Layer 4  Lower temperature on layer 4 Estimated temperatures ≈ 24C on Layer 3 and ≈ 21C on Layer 4 Doping concentration (to a much lesser extent) Resistivity: 3000-4000 cm  Doping concentration ≈1.2-1.5 1012 cm-3 Layer 3 Layer 4

Drift speed stability – first test Short term stability Check stability during ≈ 1 hour, rate ≈ 2 triggers/minute Fluctuations: r.m.s./mean ≈ 0.07% Mon, 03 Mar 2008 23:51:32 GMT Tue, 04 Mar 2008 01:05:26 GMT

Drift speed stability in 2008 Long term stability 3 months of data taking during summer 2008 Drift speed stable within 0.2% Layer 3 off less heating Layer 3, Ladder 6, Mod 275, Anode 200 July 11th 2008 October 14th 2008 Layer 4, Ladder 14, Mod 428, Anode 200

SDD timing Start-Of-Payload delay (= the number of clock-cycles after which the AM is frozen and read-out) Tuned to accommodate the 5.4 ms of max. drift time into the 6.4 ms time window defined by the memory depth Done with SOP delay scans during LHC injection tests SPS proton beam dumped close to ALICE Showers of secondary particles illuminate the ALICE detectors

Analyses on cosmic data sample Statistics collected with SPD FastOr trigger: 2008: ≈ 105 good cosmic tracks with full ITS, without B-field Goal for 2009: ≈ 105 tracks with B-on + 105 tracks with B-off Goals from analysis of cosmic data: Align the 2198 ITS sensors with precision < than their resolution Specific for SDD: calibrate the Time Zero Time Zero = time after the trigger for a particle with zero drift distance dE/dx calibration in SDD and SSD Talk by G. Aglieri Statistics collected on Layer 4

Time Zero – method 1 Cosmic track fitted in SPD and SSD layer Track-to-point residuals along drift coordinate in the two drift regions of each SDD module Show opposite signed peaks in case of mis-calibrated time zero TimeZero = DeltaXlocal / 2. / vdrift = 434±6 ns NOTE: preliminary value for 2009 data, averaged over all modules of layer 4 side A ALICE Preliminary ALICE Preliminary Xlocal residuals (cm) Xlocal residuals (cm)

Time Zero – method 2 Extract TimeZero from the minimum measured drift time for SDD reconstructed points associated to tracks PROS: no dependence on SDD calibration parameters (drift speed) CONS: requires more statistics First results from 2009 data analysis look promising TimeZero from fit with error function of the rising edge = 434 ns (Layer 4 side A), BUT errors very large ALICE Preliminary

Geometrical alignment of SDD Interplay between alignment and calibration TimeZero for each module included as free parameter in Millepede-based alignment together with geometrical rotation and translation Drift speed added as fit parameter for modules with mal-functioning injectors Resolution along drift direction affected by the jitter of the SPD FastOR trigger (at 10 MHz  4 SDD time bins) with respect to the time when the muon crosses the SDD sensor Geometry only Geometry + calibration

Charge calibration (I) Cluster charge distribution fitted with Landau+Gauss convolutions Most Probable Value of collected charge depends on drift distance/time Larger drift distance  larger charge diffusion  wider cluster tails cut by the zero suppression

Charge calibration (II) Cross check with cosmics collected on few SDDs in test station with and without zero suppression Data without zero suppression show no dependence of Landau MPV on drift time/distance Apply linear correction of charge vs. drift time to cosmic data collected in 2008 by ALICE Correction extracted from detailed MC simulations of SDD response

Conclusions SDD successfully operated during ALICE cosmic data takings in 2008 and 2009 245 SDD modules (out of 260) stably in acquisition during both the data takings, fraction of good anodes > 97.5% Calibration strategy deeply tested and tuned Good knowledge of the detector reached Noise level within the design goal (S/N for MIP close to anodes ≈ 50) Drift speed stable with 0.2% during stable operation of ITS detectors Results from cosmic data sampled allowed for Develop tools for attacking the SDD alignment which is correlated with calibration quantities Tuning of TimeZero module-by-module using Millepede Recovering drift speed for modules with mal-functioning injectors Calibrate the charge collection efficiency as a function of drift time Good results by applying the correction based on MC simulation of Zero Suppression algorithm SDD ready for the forthcoming p-p collisions at LHC

Backup

Inner Tracking System Design goals Optimal resolution for primary vertex and track impact parameter Minimize distance of innermost layer from beam axis (<r>≈ 3.9 cm) and material budget Maximum occupancy (central PbPb) < few % 2D devices in all the layers dE/dx information in the 4 outermost layers for particle ID in 1/b2 region Layer Det. Type Radius (cm) Length (cm) Resolution (mm) PbPb dN/dy=6000 rf Z Part./cm2 Occupancy (%) 1 SPD 3.9 28.2 12 100 35 2.1 2 7.6 0.6 3 SDD 15.0 44.4 25 2.5 4 23.9 59.4 1.5 1.0 5 SSD 38.0 86.2 20 830 4.0 6 43.0 97.8 0.45 3.3

ITS Commissioning data taking Detector installation completed in June 2007 Run 1 : December 2007 First acquisition tests on a fraction of modules Run 2 : Feb/Mar 2008 ≈ 1/2 of the modules in acquisition due to cooling and power supply limitations Calibration tests + first atmospheric muons seen in ITS Installation of services completed in May 2008 Run 3 : June/October 2008 Subdetector specific calibration runs Frequent monitoring of dead channels, noise, gain, drift speed … Cosmic runs with SPD FastOR trigger First alignment of the ITS modules + test TPC/ITS track matching Absolute calibration of the charge signal in SDD and SSD Run4 : started on mid August 2009 TIME

SDD correction maps All the 260 SDD modules have undergone a complete characterization (map) before assembling in ladders Charge injected with an infrared laser in > 100,000 known positions on the surface of the detector For each laser shot, calculate residual between the reconstructed coordinate and the laser position along the drift direction Systematic deviations due to: Non-constant drift field due to non-linear voltage divider Parasitic electric fields due to inhomogeneities in dopant concentration Ideal Module Non-linear volt. divider Dopant inhomogeneties

Drift speed vs. time Layer 3, Ladder 6, Mod 275, Anode 200 July 11th 2008 October 14th 2008

SDD during 2009 cosmic run SDD constantly took cosmic data from Aug 17 to Sep 13 93% of modules included in DAQ Fraction of good anodes in the good modules = 97% <Event size> ≈ 10 kB / event (@ 20 MHZ AM sampling) Factor 50 improvement w.r.t. 2008 Total number of anodes = 133120

ITS Alignment Two track-based methods to extract the alignment objects (translations and rotations) for the 2198 ITS modules: Millepede (default method, as for all LHC experiments) Determine alignment parameters of “all” modules in one go, by minimizing the global c2 of track-to-points residuals for a large set of tracks Iterative approach Align one module at a time by fitting tracks in the other modules and minimizing the residuals in the module under study Plus and hardware (based on collimated laser beams, mirrors and CCD cameras) alignment monitoring system Monitor physical movements of ITS with respect to TPC Strategy for the track-based alignment: Use geometrical survey data as a starting point Measurements of sensor positions on ladders during SDD and SSD construction Hierarchical approach: Start from SPD sectors (10) , then SPD half staves (120), then SPD sensors (240) After fixing SPD, align SSD barrel (w.r.t. SPD barrel), then SSD ladders (72) … After fixing SPD and SSD, move to SDD (which need longer time for calibration) Include SDD calibration parameters: Time Zero = time after the trigger for a particle with zero drift distance Drift speed for modules with mal-functioning injectors