Performance of the ATLAS Tile Calorimeter · 2019. 12. 1. · To maximise the use of radial space,...
Transcript of Performance of the ATLAS Tile Calorimeter · 2019. 12. 1. · To maximise the use of radial space,...
Performance of the ATLAS Tile Calorimeter
Calorimetry for High Energy Frontier (CHEF 2019)
Pawel Klimek
on behalf of the ATLAS Collaboration
Northern Illinois University
November 27, 2019
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 1 / 21
The Tile Calorimeter
Central hadronic calorimeter(|η| < 1.7) in ATLAS detector
Used to measure the 4-vectors ofthe jets and the missing transverseenergy and in the ATLAS Level-1trigger
Sampling calorimeter:steel and scintillating plastic tiles
Double photomultiplier readoutusing wave length shifting fibers
9892 PMTs
2008 JINST 3 S08003
supplies which power the readout are mounted in an external steel box, which has the cross-sectionof the support girder and which also contains the external connections for power and other servicesfor the electronics (see section 5.6.3.1). Finally, the calorimeter is equipped with three calibrationsystems: charge injection, laser and a 137Cs radioactive source. These systems test the opticaland digitised signals at various stages and are used to set the PMT gains to a uniformity of ±3%(see section 5.6.2).
5.3.1.2 Mechanical structurePhotomultiplier
Wavelength-shifting fibre
Scintillator Steel
Source
tubes
Figure 5.9: Schematic showing how the mechan-ical assembly and the optical readout of the tilecalorimeter are integrated together. The vari-ous components of the optical readout, namelythe tiles, the fibres and the photomultipliers, areshown.
The mechanical structure of the tile calorime-ter is designed as a self-supporting, segmentedstructure comprising 64 modules, each sub-tending 5.625 degrees in azimuth, for each ofthe three sections of the calorimeter [112]. Themodule sub-assembly is shown in figure 5.10.Each module contains a precision-machinedstrong-back steel girder, the edges of whichare used to establish a module-to-module gapof 1.5 mm at the inner radius. To maximisethe use of radial space, the girder provides boththe volume in which the tile calorimeter read-out electronics are contained and the flux returnfor the solenoid field. The readout fibres, suit-ably bundled, penetrate the edges of the gird-ers through machined holes, into which plas-tic rings have been precisely mounted. Theserings are matched to the position of photomul-tipliers. The fundamental element of the ab-sorber structure consists of a 5 mm thick mas-ter plate, onto which 4 mm thick spacer platesare glued in a staggered fashion to form thepockets in which the scintillator tiles are lo-cated [113]. The master plate was fabricatedby high-precision die stamping to obtain the dimensional tolerances required to meet the specifica-tion for the module-to-module gap. At the module edges, the spacer plates are aligned into recessedslots, in which the readout fibres run. Holes in the master and spacer plates allow the insertion ofstainless-steel tubes for the radioactive source calibration system.
Each module is constructed by gluing the structures described above into sub-modules on acustom stacking fixture. These are then bolted onto the girder to form modules, with care beingtaken to ensure that the azimuthal alignment meets the specifications. The calorimeter is assembledby mounting and bolting modules to each other in sequence. Shims are inserted at the inner andouter radius load-bearing surfaces to control the overall geometry and yield a nominal module-to-module azimuthal gap of 1.5 mm and a radial envelope which is generally within 5 mm of thenominal one [112, 114].
– 122 –
48 Chapter 5: The Tile Calorimeter
to be close to zero. Nevertheless, this setting is accurate to 3 ns [55]. Iter-ative OFL takes the time of the maximum sample as an initial value of thephase. In next iterations, the phase is taken to be equal to tOFL calculated inprevious iteration. The algorithm converges to the actual phase value withaccuracy better than 0.5 ns in absence of pile-up pulses. At the end of re-construction procedure, so called quality factor QFOFL is calculated in orderto verify the quality of the estimation:
QFOFL =
vuut7X
i=1
(Si � AOFL · gi � POFL)2 (5.8)
where the gi are the values of the normalised pulse shape function computedat the time of the 7 samples Si. When the deviation between the true shapeand pulse shape function used in reconstruction is large, then QFOFL alsotakes large value. Therefore, quality factor can be used to detect problemsin the reconstruction procedure as developed in Chapter 6.
Non-iterative Optimal Filtering method
In the Non-iterative Optimal Filtering method the phase is taken to be equalto the time of the maximum sample and no further iterations are performed.Due to insu�cient processing time in the DSP the OFL reconstruction mustbe performed without iterations if the trigger rate is above 50 Hz. Therefore,the non-iterative Optimal Filtering method is now used online by the RODs.
In Paper IV comparison of iterative and non-iterative Optimal Filteringphase reconstruction with presence of out-of-time pile-up is presented. Non-iterative method shows better performance. This is due to the fact thatthe phase needed to compute coe�cients is adjusted to be approximatelyzero. The out-of-time pile up can lead to reconstructed phase values far fromnominal biasing the energy reconstruction when iterative method is used.The non-iterative Optimal Filtering method reconstructs better the phase ofin-time-pulse in presence of out-of-time pile-up. This method is also morerobust against the electronic noise for very low signals.
x
y
z
48 Chapter 5: The Tile Calorimeter
to be close to zero. Nevertheless, this setting is accurate to 3 ns [55]. Iter-ative OFL takes the time of the maximum sample as an initial value of thephase. In next iterations, the phase is taken to be equal to tOFL calculated inprevious iteration. The algorithm converges to the actual phase value withaccuracy better than 0.5 ns in absence of pile-up pulses. At the end of re-construction procedure, so called quality factor QFOFL is calculated in orderto verify the quality of the estimation:
QFOFL =
vuut7X
i=1
(Si � AOFL · gi � POFL)2 (5.8)
where the gi are the values of the normalised pulse shape function computedat the time of the 7 samples Si. When the deviation between the true shapeand pulse shape function used in reconstruction is large, then QFOFL alsotakes large value. Therefore, quality factor can be used to detect problemsin the reconstruction procedure as developed in Chapter 6.
Non-iterative Optimal Filtering method
In the Non-iterative Optimal Filtering method the phase is taken to be equalto the time of the maximum sample and no further iterations are performed.Due to insu�cient processing time in the DSP the OFL reconstruction mustbe performed without iterations if the trigger rate is above 50 Hz. Therefore,the non-iterative Optimal Filtering method is now used online by the RODs.
In Paper IV comparison of iterative and non-iterative Optimal Filteringphase reconstruction with presence of out-of-time pile-up is presented. Non-iterative method shows better performance. This is due to the fact thatthe phase needed to compute coe�cients is adjusted to be approximatelyzero. The out-of-time pile up can lead to reconstructed phase values far fromnominal biasing the energy reconstruction when iterative method is used.The non-iterative Optimal Filtering method reconstructs better the phase ofin-time-pulse in presence of out-of-time pile-up. This method is also morerobust against the electronic noise for very low signals.
x
y
z
48 Chapter 5: The Tile Calorimeter
to be close to zero. Nevertheless, this setting is accurate to 3 ns [55]. Iter-ative OFL takes the time of the maximum sample as an initial value of thephase. In next iterations, the phase is taken to be equal to tOFL calculated inprevious iteration. The algorithm converges to the actual phase value withaccuracy better than 0.5 ns in absence of pile-up pulses. At the end of re-construction procedure, so called quality factor QFOFL is calculated in orderto verify the quality of the estimation:
QFOFL =
vuut7X
i=1
(Si � AOFL · gi � POFL)2 (5.8)
where the gi are the values of the normalised pulse shape function computedat the time of the 7 samples Si. When the deviation between the true shapeand pulse shape function used in reconstruction is large, then QFOFL alsotakes large value. Therefore, quality factor can be used to detect problemsin the reconstruction procedure as developed in Chapter 6.
Non-iterative Optimal Filtering method
In the Non-iterative Optimal Filtering method the phase is taken to be equalto the time of the maximum sample and no further iterations are performed.Due to insu�cient processing time in the DSP the OFL reconstruction mustbe performed without iterations if the trigger rate is above 50 Hz. Therefore,the non-iterative Optimal Filtering method is now used online by the RODs.
In Paper IV comparison of iterative and non-iterative Optimal Filteringphase reconstruction with presence of out-of-time pile-up is presented. Non-iterative method shows better performance. This is due to the fact thatthe phase needed to compute coe�cients is adjusted to be approximatelyzero. The out-of-time pile up can lead to reconstructed phase values far fromnominal biasing the energy reconstruction when iterative method is used.The non-iterative Optimal Filtering method reconstructs better the phase ofin-time-pulse in presence of out-of-time pile-up. This method is also morerobust against the electronic noise for very low signals.
x
y
z
500 1000 1500 mm0
A3 A4 A5 A6 A7 A8 A9 A10A1 A2
BC1 BC2 BC3 BC5 BC6 BC7 BC8BC4
D0 D1 D2 D3
A13 A14 A15 A16
B9
B12 B14 B15
D5 D6
D4
C10
0,7 1,0 1,1
1,3
1,4
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B11 B13
A12
E4
E3
E2
E1
beam axis
0,1 0,2 0,3 0,4 0,5 0,6 0,8 0,9 1,2
2280 mm
3865 mm=0,0η
~~Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 2 / 21
Calibration Systems
E [GeV] = A [ADC] · CADC→pC · CpC→GeV · CTileSize · CCs · CLas
Systems used for calibration in Tile calorimeterCharge Injection System (CIS): Calibrates the response of ADCs: CADC→pC
Cesium system: Calibrates optical components and PMT gains: CCs
Laser System: Calibrates variations due to electronics and PMTs: CLas
Minimum Bias System (MB): Calibrates optical components and PMT gains
CpC→GeV EM scale constant measured during test beam campaignsCTileSize correction addressing the different size of tiles in different layers
Cell response is not constant in time due to the PMT gain variation andscintillator degradation due to the exposure to beam
137Cs source
CalorimeterTiles
PhotomultiplierTubes
Integrator Readout(Cs & Particles)
Charge injection (CIS)
Digital Readout(Laser & Particles)
Particles
Laser light
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 3 / 21
Cesium Calibration
A moveable radioactive source 137Cs passes throughthe calorimeter body, 2-3 times per year in Run-2
The source emits γ-rays with well known energy662 keV
It uses the independent integrator readout system(τ = 10 ms) during source movement
Calibration of the complete optical chain(scintillators, fibres, PMTs) and monitoring of thedetector response over time: CCs
Between Run I and Run II: Improvement of stabilityand safety of Cesium system and procedure (newwater storage system, lower pressure, precise waterlevel metering)
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 4 / 21
Cesium CalibrationPrecision of the system in a singletypical cell is approximately 0.3%
Deviation of the cell response in timeis caused by the PMT gain variationand scintillator degradation due to theexposure to beam
Maximal drift is observed for layer A,that is closest to the collision point
It allows to adjust PMT gain (changinghigh voltage) to restore EM scale andcalorimeter response uniformity
500 1000 1500 mm0
A3 A4 A5 A6 A7 A8 A9 A10A1 A2
BC1 BC2 BC3 BC5 BC6 BC7 BC8BC4
D0 D1 D2 D3
A13 A14 A15 A16
B9
B12 B14 B15
D5 D6
D4
C10
0,7 1,0 1,1
1,3
1,4
1,5
1,6
B11 B13
A12
E4
E3
E2
E1
beam axis
0,1 0,2 0,3 0,4 0,5 0,6 0,8 0,9 1,2
2280 mm
3865 mm=0,0η
~~
Date [month and year]
Jul 15 Jan 16 Jul 16 Dec 16 Jul 17 Dec 17 Jul 18 Dec 18
De
via
tio
n f
rom
exp
ecte
d C
s r
esp
on
se
[%
]
15−
10−
5−
0
5
10Layer A
Layer BC
Layer D
ATLAS PreliminaryTile CalorimeterRun 2
η
1.5− 1− 0.5− 0 0.5 1 1.5
drift [%
]
15−
10−
5−
0
5
10Layer A
Layer B
Layer D
ATLAS PreliminaryTile CalorimeterFeb 2015 Oct 2018
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 5 / 21
Laser Calibration
PMT gain drift affects the detector responseand calibration thus it is measured regularly
A controlled amount of light with wavelengthclose to the one of physical signals (532 nmlight) creating a pulse with similar shape andduration sent into each PMT
The gain variation is measured between twoCesium scans: Laser measures the drift seenin PMTs w.r.t the last Cesium scan. Also, itallows to detect the HV changes
Performed during dedicated calibration runs.Laser pulses also sent during collision runs(empty bunches), used to calibrate timing.
1"
BEAM EXPANDER
Figure 5: Scheme of the LaserII system
the photodiode box, and a charge injection splitter (CIS), a module dispatching a chargethat aims at monitoring the stability of the electronics,
• PHOCAL is an internal calibration system setup to monitor the stability of the ten pho-todiodes located in the photodiode box. It is composed of a LED emitting a signal in alight mixer that transmit the light to a set of eleven optics fibers, with ten connected tothe photodiode box, and one coupled to a reference photodiode. The stability of the tenphotodiodes is performed by monitoring the ratio of the LED signal seen by these photodi-odes to the magnitude measured by the reference photodiode. The stability of the referencephotodiode is estimated with a static radioactive source,
• PMT box: two PhotoMultiplier Tubes (PMTs) are used to trigger the LaserII acquisitionwhen the laser is flashing. The PMT box also contains two electronic cards to drive thePMT box (LicPMT) and the filter wheel (LicMot),
• Optical filters are used to attenuate the laser signal transmitted to the photodiodes andthe PMTs,
• VME crate: two boards located in the VME crate are used to drive the LaserII system:a VME Single Board Computer (SBC); LASCAR, that incorporates on one mezzanine acharge analog-to-digital converter (qADC), as well as LILAS, and HOLA card on anothermezzanine. LASCAR also includes the TTCrx chip,
10
Mean 0.01814< RMS 0.2101
Channel gain variation [%]10< 5< 0 5 10
Cha
nnel
s / b
in1
10
210
310
410
Mean 0.01814< RMS 0.2101
ATLAS PreliminaryTile Calorimeter July-August 2015
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 6 / 21
Laser Calibration
Precision better than 0.5%
Since 2016, updates of calibrationconstants are done weekly in orderto track changes in PMT responses
The maximal drift is observed in A-and E-cells which are the cells withhighest energy deposits
Deviations of any channel responsewith respect to nominal is translatedinto a calibration constant: CLas
Between Run I and Run II: upgradedelectronics and optical components,better control of the emitted light
0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1
1.2
1.3
1.4
1.6
eta
ATLAS PreliminaryTile Calorimeter
-3.24A1
-3.34A2
-3.28A3
-3.39A4
-3.27A5
-3.82A6
-3.74A7
-3.80A8
-4.05A9
-4.06A10
-1.70B1
-1.73B2
-1.66B3
-1.81B4
-1.82B5
-1.72B6
-1.89B7
-2.52B8
-2.31B9
-1.70C1
-1.73C2
-1.66C3
-1.81C4
-1.82C5
-1.72C6
-1.89C7
-2.52C8
-.34D0
-.31D1
-.26D2
-1.26D3
-3.55A12
-4.40A13
-3.99A14
-2.80A15
-1.89A16
-2.87B11
-2.70B12
-1.97B13
-1.31B14
-.94B15
-2.54C10
-2.16D4
-2.00D5
-.78D6
-4.61E1
-5.93E2
-4.62E3
-3.92E4
<-5.0% -2.5% 0% 2.5% >5.0%
run 364147, 2018-10-22.
]-1
Inte
gra
ted
Del
iver
ed L
um
ino
sity
[fb
0
10
20
30
40
50
60
70
Time [dd/mm and year]201820/03
201819/04
201819/05
201818/06
201818/07
201817/08
201816/09
201816/10
Ave
rag
e L
aser
Dri
ft [
%]
4−
3−
2−
1−
0
1
2
3Layer ALayer BCLayer DLuminosity
PreliminaryATLASTile Calorimeter
= 13 TeVs2018 Data
Start of pp collisions
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 7 / 21
Charge Injection Calibration
Calibrates the response of ADCs (electronics)digital gains and linearities
Calibration performed weekly
The system injects a signal of known chargeand measures the electronic response
Spanning the full ADC range (0-800 pC)and saturate both LG and HG for all channels
Also used to calibrate analog L1 calo trigger
Extract the conversion factors from ADCcounts to pC: CADC→pC
Precision of 0.7%, stability over time of 0.03%Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 8 / 21
Minimum Bias SystemHigh energy proton-proton collisions aredominated by soft parton interactions:Minimum Bias (MB) events
The same integrator readout as in Cs systemmeasures integrated PMT signals over alarge time (∼10 ms)
As the Cesium system, the Minimum Biassystem monitors the full optical chain. Alsocalibrates E-cells and MBTS.
Measured currents are linearly dependent onthe instantaneous luminosity
Monitors the instantaneous luminosity andprovides an independent measurement givenan initial calibration (luminosity coefficient)
cellη
1.5− 1− 0.5− 0 0.5 1 1.5
Cur
rent
[nA
]
1
10
210
310
410 A cells
BC cells
D cells
E cells
ATLAS PreliminaryTile CalorimeterData 2018
-1s-2 cm3310× = 13 TeV, L = 1.7s
EB
C,m
od
22
,ch
17
cu
rre
nt
[nA
]
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30
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44
=13 TeVsData 2015,
Pol1 fit: f(x)= 13.455x+1.032
Preliminary
Tile Calorimeter
ATLAS
]-1s-2cm33
Inst. Luminosity [10
2.2 2.4 2.6 2.8 3C
urr
en
t/f(
x)
0.99
1
1.01
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 9 / 21
Combined CalibrationComparison of cell response variation betweenCesium/MB and Laser measurements
Cesium and Minimum Bias access PMT gaindrift and scintillator agingLaser only monitor PMT gain drifts
Down drifts observed during collisions. Updrifts during maintenance periods
Differences between Cesium/MB and Lasermeasurements interpreted as a scintillatoraging due to irradiation
In 2015, a good agreement observed
In 2016 - 2018 this effect was clearly observedfor some of the most irradiated cells in the A-and E-cells
Laser drift [%]6− 4− 2− 0 2 4 6
Ces
ium
drif
t [%
]
6−
4−
2−
0
2
4
6
Layer ALayer BCLayer D
ATLAS PreliminaryTile Calorimeter17/07/2015 - 03/11/2015
-1Delivered luminosity: 3.9 fb
Time [dd/mm and year]201629/04
201629/05
201628/06
201628/07
201627/08
201626/09
201626/10
A13/D
6 A
vera
ge r
esponse [%
]
10−
8−
6−
4−
2−
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]-1
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tal
Inte
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ted
De
liv
ere
d L
um
ino
sit
y [
pb
0
5000
10000
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20000
25000
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Laser
Minimum Bias
Cesium Scan
ATLAS Preliminary
Tile Calorimeter
= 13 TeVs2016 Data -1
Total Delivered: 39 fbA13 | < 1.3η1.2 < |
inner layer
D6 | < 1.3η1.1 < |outer layer
]-1
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al In
tegr
ated
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iver
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umin
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0
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Time [dd/mm and year]201723/06
201723/07
201722/08
201721/09
201721/10
A13
/D6
Ave
rage
res
pons
e [%
]
6−
5−
4−
3−
2−
1−
0
1
A13
D6inner layer
< 1.3η1.2 <
outer layer < 1.3η1.1 <
Minimum Bias
Laser
ATLAS Preliminary
Tile Calorimeter = 13 TeVs2017 Data
-1Total Delivered: 46.5 fb
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 10 / 21
Time Calibration
A precise time calibration is important for thecell energy reconstruction
It adjusts a digitizer sampling clock to the peakof signal produced by the particle travelingfrom the interaction point at the speed of lightthrough the cell. Large phases result inunderestimated reconstructed amplitude.
Can also be exploited in TOF measurements,e.g. in search for heavy R-hadrons
Time calibration calculated using jets andmonitored during physics data taking with laser
Resolution is better than 1 ns forEcell > 4 GeV
Cell energy [GeV]
0 20 40 60 80 100
Cell
tim
e r
esolu
tion [ns]
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0.0004±p2 1.0380
0.0003±p1 1.6252
0.0003±p0 0.2715
high-gain
0.18±p2 13.32
0.04±p1 2.62
0.003±p0 0.316
low-gain
2
E2
p+
2
E
1p
+ 2
0p = σ
PreliminaryATLAS
Tile Calorimeter
= 13 TeV, 25 nss
Cell energy [GeV]
0 20 40 60 80 100
Mean c
ell
tim
e [ns]
0.5−
0
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1
1.5
PreliminaryATLAS
Tile Calorimeter
= 13 TeV, 25 nss
Cell energy [GeV]
0 20 40 60 80 100M
ean c
ell
tim
e [ns]
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0
0.5
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1.5
PreliminaryATLAS
Tile Calorimeter
= 13 TeV, 25 nss
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 11 / 21
Noise
The total noise per cell in the calorimetercomes from two sources:
Electronic noise - measured in dedicatedruns with no signal in the detectorPile-up contribution - originates frommultiple interactions occurring at thesame bunch crossing or from the eventsfrom previous/following bunch crossings
Electronics noise stays at the level below20 MeV for most of the cells. Noise ismeasured regularly with calibration runs.
Total noise is increasing with pile-up
The largest noise values are in the regionswith the highest exposure (A-cells, E-cells)
φcell 0 1 2 3 4 5 6
elec
tron
ic n
oise
[MeV
]
16
18
20
22
24
26
28
30
PreliminaryATLASTile Calorimeter
(September 2014)
HighGain-HighGainA cellsBC cellsD cells
>µ<
0 10 20 30 40 50
Noi
se [M
eV]
20
40
60
80
100
120
140
160
180ATLAS PreliminaryTile Calorimeter
=13 TeVsData MC
LBA
A5
BC5
D2
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 12 / 21
Detector Status and Data Quality
TileCal monitoring includes identifyingand masking problematic channelscorrecting for miscalibrations,monitoring data corruption or otherhardware issues
During maintenance periods there is acampaign to fix all issues, allowing fora good recovery of the system
Redundancy of cell readout systemreduces the impact of masked channels
Tile had 99.7% DQ efficiency in Run-2
2015: 100%, 2016: 99.3%,2017: 99.4%, 2018: 100%
TileCal status at the end of Run-2
month-yearDec-10 Dec-11 Dec-12 Dec-13 Dec-14 Dec-15 Dec-16 Dec-17 Dec-18
Evo
lutio
n of
Mas
ked
Cha
nnel
s an
d C
ells
[%]
0
1
2
3
4
5
6(1.07%)Masked Channels
(0.48%)Masked Cells
Maintenance
ATLAS PreliminaryTile Calorimeter
Evolution of Masked Channels and Cells: 2018-12-03
η1.5− 1− 0.5− 0 0.5 1 1.5
φ
2−
0
2
0
0.5
1
1.5
2
2.5
3
ATLAS PreliminaryTile Calorimeter
Amount of Tile Masked Cells 2018-12-03
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 13 / 21
Single Particle Response
The ratio of the calorimeter energyat EM scale to the trackmomentum 〈E/p〉 of single hadronsis used to evaluate uniformity andlinearity during data taking
Measured in Minimum Bias events
Expect 〈E/p〉 < 1 due to thesampling non-compensatingcalorimeter
Data and Monte Carlo simulation(Pythia8) agree within 5%
Jets are further calibrated to jetenergy scale
Nor
mal
ized
Dis
trib
utio
n
6−10
5−10
4−10
3−10
2−10
1−10
1
= 0.67⟩pE/⟨
pE/0 1 2 3 4 50.5
1.01.52.0
⟩µ⟨Data 2015, Low-Pythia8 MinBias
Dat
a/M
C
⟩µ⟨Data 2015, Low-Pythia8 MinBias
ATLAS PreliminaryTile Calorimeter
-1 = 13 TeV, 1.6 nbs
⟩p
E/
⟨
0.4
0.5
0.6
0.7
0.8
0.9
1.0
[GeV]p0 5 10 15 20 25 30
0.91.01.1
⟩µ⟨Data 2015, Low-Pythia8 MinBias
Dat
a/M
C
⟩µ⟨Data 2015, Low-Pythia8 MinBias
ATLAS PreliminaryTile Calorimeter
-1 = 13 TeV, 1.6 nbs
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 14 / 21
Muons
Muons from cosmic rays are used tostudy in situ the electromagneticenergy scale and intercalibration ofTile cells
A good energy response uniformitybetween calorimeter cells
< 5% response non-uniformity in ηwith cosmic muons
[rad]φTrack
1.9− 1.8− 1.7− 1.6− 1.5− 1.4− 1.3− 1.2−
Cel
l Res
pons
e [M
eV]
0
200
400
600
800
1000
1200
1400
1600 Tile Calorimeter
Cosmic data 2015
ATLAS Preliminary
η
1.5− 1− 0.5− 0 0.5 1 1.5
>dxdE
/<dxdE
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6Tile Calorimeter
Cosmic data 2015
All layersATLAS Preliminary
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 15 / 21
Jet Performance
A good description of the cell energydistribution and of the noise in thecalorimeter is crucial for the buildingof topoclusters which are used for jetand missing transverse energyreconstruction
Good agreement in Tile cell energydistribution
Aim for jet energy resolution:σE
E = 0.5√E⊕ 0.03
Jet energy resolution is better than 10%at pT > 100 GeV
Constant term is within expected 3%
Jetperformance
• GoodagreementinTilecellenergydistribuGon• Consistentoveralljetenergyscale• JetenergyresoluGonisbelow10%atpT>100GeV• Constanttermiswithinexpected3%02.07.17 O.Solovyanov-QFTHEP17 15
Tile Cell Energy [GeV]
Cel
ls /
0.06
GeV
/ Ev
ent
5−10
4−10
3−10
2−10
1−101
10210
310
410
510
Tile Cell Energy [GeV]1− 0 1 2 3 4 5
Dat
a / M
C
0.00.51.01.5
ATLAS Preliminary = 0.9 TeVsData,
= 13 TeVsData,
Random Filled Bunch Crossing
Random Empty Bunch Crossing
= 13 TeVsMinimum Bias MC,
[GeV]jet
Tp
20 30 210 210×2 310 310×2
Tp
) /
Tp(
σJe
t ene
rgy
reso
lutio
n,
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
in situDijet
Total uncertaintyMC prediction
= 0.4, EM+JESR tkanti-| < 0.7η |≤0.2
ATLAS Preliminary-1 = 13 TeV, 43.6 fbs
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 16 / 21
Conclusions
Tile Calorimeter is an important part of ATLAS detector at LHC
It is a key detector to measure the 4-vectors of the jets and missing energy
A set of calibration systems is used to calibrate and monitor the calorimeterresponse
Intercalibration and uniformity are monitored with isolated charged hadronsand cosmic muons
The stability of the absolute energy scale at the cell level was maintainedto be better than 1% during Run 2 data taking
In Run-3: New crack scintillators (E3-4), covering a larger |η| < 1.72 range,will result in a significant improvement in the energy resolution for electrons,photons and jets in this region
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 17 / 21
The Di-jet Event Produced in 2017Two jest with pT = 2.9 TeV and mjj = 9.3 TeV
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 18 / 21
The Di-jet Event Produced in 2017Two jest with pT = 2.9 TeV and mjj = 9.3 TeV
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 19 / 21
Back-up
Back-up
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 20 / 21
Back-up
Calibration System
Pawel Klimek (Northern Illinois University) CHEF 2019 November 27, 2019 21 / 21