RPC DPG Results 2020

RPC Threshold Scan Analysis (Walaa Elmetenawee, Roumyana)

Presented on:

Introduction

Every CMS RPC chamber is subdivided in three eta partitions (rolls) in the endcap and in two or three eta partitions in the barrel. The LV thresholds applied to the detector electronics are studied in terms of the effect on the main detector characteristics - efficiency, cluster size and detector intrinsic noise rate. The default thresholds differ for the particular roll and Front-end electronics and they are stored in the configuration database. The usual range of the threshold values is within 210-240 mV, where 1 mV corresponds to about 3.2 fC. [1]

The threshold scan is performed during normal cosmics data taking in 2018. The scan was performed in steps of 5 mV. Because of the low statistics in the endcap region, the analysis is focused mainly on the barrel rolls.

[1] M. Abbrescia et al., "New developments on front-end electronics for the CMS Resistive Plate Chambers", Nucl. Instr. and Meth. in Phys. Res., Volume 456, Issues 1-2, 21 December 2000, Pages 143-149

The Effect on the efficiency

efith1.png efith2.png
The plots show the RPC efficiency vs. the difference with the default threshold (Vthr-app-Vthr-def). One roll for the barrel (left) and one for the endcap (right) are shown. Due to the low statistics in the endcap, a detailed study was possible in the barrel region only. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

efiba.png efidif.png
The plot shows the RPC barrel efficiency distribution at the different applied thresholds of the voltage scan. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch The plot shows the efficiency variation when decreasing the threshold voltage by 5 mV, which results in an efficiency gain of about 0.9% in the barrel. The single entries with very high differences are caused by low statistics. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

efirb1.png efirb4.png
The plots show the efficiency variation when decreasing the threshold voltage by 5 mV, which results in an efficiency gain of about 0.7% and 1.6% for the rolls from the innermost (closest to the beam pipe) RPC barrel layer RB1in (left), and for the outermost one - RB4 (right) respectively. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

The Effect on the Cluster Size

clu1.png clu2.png
The plots show the RPC Cluster size vs. the difference with the default threshold(Vthr-app-Vthr-def) for one roll in the barrel (left) and one in the endcap (right). Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

cluba.png cludif.png
The plot shows the RPC Cluster size distribution at the different applied thresholds of the voltage scan. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch The plot shows the variation in the Cluster size when decreasing the threshold voltage by 5 mV, which results in a slight cluster size increase of a bout 0.068 in the barrel. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

clurb1.png clurb4.png
The plots show the Cluster size variation when decreasing the threshold voltage by 5 mV, which results in a slight cluster size increase of a bout 0.064 and 0.047 strips for the rolls from the innermost (closest to the beam pipe) RPC barrel layer RB1in (left) and for the outermost one - RB4 (right) respectively. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

The Effect on the intrinsic noise rate

noi1.png noi2.png
The plots show the intrinsic noise rate vs. the difference with the default threshold (Vthr-app-Vthr-def) for one roll in the barrel (left) and one in the endcap (right). Quadratic polynomial function is used for the fit. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

noidif.png
The plot shows the variation of the rate when decreasing the threshold voltage by 5 mV. Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

RPC Run-II Performance Efficiency 2D Plots (Heewon Lee)

Presented on:

Introduction

  • Plot approval for RPC Run-II performance paper
  • Performance results using Tag & Probe method with track extrapolation
  • Efficiency 2D plots for 2015–2018
    • Efficiency 2D plots of each rolls (Barrel roll, Endcap + and – roll)
    • 4 years * 3rolls = 12 plots for approval

2015 Efficiency 2D plot of Barrel rolls

Run2015_cRB.png

This figure shows the 2015 efficiency of barrel RPC chambers, in longitudinal direction z and azimuthal angle φ of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2015 barrel

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2016 Efficiency 2D plot of Barrel rolls

Run2016_cRB.png

This figure shows the 2016 efficiency of barrel RPC chambers, in longitudinal direction z and azimuthal angle φ of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2016 barrel

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2017 Efficiency 2D plot of Barrel rolls

Run2017_cRB.png

This figure shows the 2017 efficiency of barrel RPC chambers, in longitudinal direction z and azimuthal angle φ of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2017 barrel

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2018 Efficiency 2D plot of Barrel rolls

Run2018_cRB.png

This figure shows the 2018 efficiency of barrel RPC chambers, in longitudinal direction z and azimuthal angle φ of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2018 barrel

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2015 Efficiency 2D plot of Endcap+ rolls

Run2015_cREP.png

This figure shows the 2015 efficiency of endcap+ RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2015 endcap+

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2016 Efficiency 2D plot of Endcap+ rolls

Run2016_cREP.png

This figure shows the 2016 efficiency of endcap+ RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2016 endcap+

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2017 Efficiency 2D plot of Endcap+ rolls

Run2017_cREP.png

This figure shows the 2017 efficiency of endcap+ RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2017 endcap+

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2018 Efficiency 2D plot of Endcap+ rolls

Run2018_cREP.png

This figure shows the 2018 efficiency of endcap+ RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2018 endcap+

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2015 Efficiency 2D plot of Endcap- rolls

Run2015_cREN.png

This figure shows the 2015 efficiency of endcap- RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2015 endcap-

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2016 Efficiency 2D plot of Endcap- rolls

Run2016_cREN.png

This figure shows the 2016 efficiency of endcap- RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2016 endcap-

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2017 Efficiency 2D plot of Endcap- rolls

Run2017_cREN.png

This figure shows the 2017 efficiency of endcap- RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2017 endcap-

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

2018 Efficiency 2D plot of Endcap- rolls

Run2018_cREN.png

This figure shows the 2018 efficiency of endcap- RPC chambers, in the x-y coordinate of expected impact region of muons.

Each point correspond to roll efficiency. Data points with low statistics or temporary problems are removed.

Efficiency was obtained using the Tag-and-Probe method with single muon triggered dataset. Probe muons are reconstructed using the tracker muon algorithm which is independent to RPC system, requiring at least one segment to be matched in local x position within 3cm and pull 3. Probe muons are also required to have at least 10GeV in transverse momentum.

The regions with black color correspond to the chambers without any RPC hits, caused by the known hardware problems(ex. chambers with gas leak or LV problems). There are regions with lower efficiency due to the inactive regions induced by spacers, boundaries of chambers or masked readout strips.

pdf file Run2018 endcap-

Contact: cms-dpg-conveners-rpc@cernNOSPAMPLEASE.ch

RPC Phase-2 Expected Background RPC-BRIL (Roumyana Hadjiiska)

Presented on:

Introduction

RPC expected background has been studied using MC prediction with CMS FLUKA simulation of the detector cavern. Latest Phase-2 geometry v.3.7.20.0 has been used. The present FLUKA geometry describes third and forth RPC stations (RE3 and RE4) up to |η|<1.9 and does not include the RPC upgrade between 1.9 < |η|< 2.4. The presented results will be updated accordingly after an update of the FLUKA geometry description.

The background has been studied in terms of expected particle rates, integrated dose and fluence. Two sets of detector sensitivities [1, 2] have been applied – for iRPC and for the present RPC system. The particle fluxes, predicted by FLUKA, have been convoluted with the RPC sensitivities for a given particle type to obtain the hit rate.

The base HL-LHC (High Luminosity LHC) scenario – expected instantaneous luminosity of 5x1034 cm-2s-1 and integrated luminosity of 3000fb-1, has been compared to the ultimate scenario where the expected instantaneous and integrated luminosity are respectively 7.5x1034 cm-2s-1 and 4000fb-1.

Estimations included SF (Safety factor) of 3 have been added in the captions. However the systematic uncertainty is yet to be fully qualified.

[1] C. Uribe Estrada, S. Carpinteyro Bernardino, A. Castaneda Hernandez for CMS Collaboration, RPC radiation background simulations for the high luminosity phase in the CMS experiment, Journal of Instrumentation, Volume 14, September 2019;

[2] A. Gelmi, E.Voevodina on behalf of the CMS Collaboration, "Background rate study for the CMS improved-RPC at HL-LHC usingGEANT4" NIMA Volume 936, 21 August 2019, Pages 430-432

regions.png

Detector regions investigated:

● The third and fourth endcap stations, RE3/1 and RE4/1 – first rings of the RE3 and RE4 (Upgrade Project)

● Present chambers on RE3/23 and RE4/23- second and third ring of the RE3 and RE4

● The balcony region, where the Electronics (Link System) RPC racks are installed (in the range is 750 < R < 800).

Expected rates in RE3.1 and RE4.1 Base HL-LHC scenario: L = 5x1034 cm-2s-1

hitRate_re3_1_3000.png hitRate_re4_1_3000.png
RPC hit rates, expected at instantaneous luminosity of 5x1034cm-2s-1 in the upgrade RE3/1 (on the left) and RE4/1 (on the right) regions are shown. All values are averaged over φ. Averaged expected RE3/1 hit rate is ~660 Hz/cm2, including SF of 3, it is ~ 2000 Hz/cm2. For RE4/1 the averaged expected rate is ~500 Hz/cm2. Including SF of 3 it is ~ 1600 Hz/cm2. Per strip: The expected rate per RE3/1 strip in the range (150 cm <=R <=320) is ~93 kHz. Including SF of 3 it is about 280 kHz or 0.007 [hits/bx]. The expected rate per RE4/1 strip in the range (180 cm <=R <=320) is ~68 kHz. Including SF of 3 it is about 204 kHz or 0.005 [hits/bx]. The systematic uncertainty is yet to be fully qualified. C file RE3/1 C file RE4/1 Contact: cms-dpg-conveners-rpc@SPAMNOTcernNOSPAMPLEASE.ch

Expected rates in RE3.1 and RE4.1 Ultimate HL-LHC scenario: L = 7.5x1034 cm-2s-1

hitRate_re3_1_4000.png hitRate_re4_1_4000.png
RPC hit rates, expected at instantaneous luminosity of 7.5x1034cm-2s-1 in the upgrade RE3/1 (on the left) and RE4/1 (on the right) regions are shown. All values are averaged over φ. Averaged expected RE3/1 hit rate is ~1000 Hz/cm2, including SF of 3, it is ~ 3000 Hz/cm2. For RE4/1 the averaged expected rate is ~800 Hz/cm2. Including SF of 3 it is ~ 2400 Hz/cm2. Per strip: The expected rate per RE3/1 strip in the range (150 cm <=R <=320) is ~140 kHz. Including SF of 3 it is about 420 kHz or 0.011 [hits/bx]. The expected rate per RE4/1 strip in the range (180 cm <=R <=320) is ~102 kHz. Including SF of 3 it is about 306 kHz or 0.008 [hits/bx]. The systematic uncertainty is yet to be fully qualified. C file RE3/1 C file RE4/1 Contact: cms-dpg-conveners-rpc@SPAMNOTcernNOSPAMPLEASE.ch

Expected rates in RE3/2&3 and RE4/2&3 Base HL-LHC scenario: L = 5x1034 cm-2s-1

hitRate_re3_23_3000.png hitRate_re4_23_3000.png
RPC hit rates, expected at instantaneous luminosity of 5x1034cm-2s-1 in the present RE3/2&3 (on the left) and RE4/2&3 (on the right) regions are shown. All values are averaged over φ. Averaged expected RE3/2 and RE3/3 hit rates are ~135 Hz/cm2 and 70 Hz/cm2 respectively. Including SF of 3 they are ~ 410 Hz/cm2 and 210 Hz/cm2. Averaged expected RE4/2 and RE4/3 hit rates are ~180 Hz/cm2 and 120 Hz/cm2 respectively. Including SF of 3 they are ~ 540 Hz/cm2 and 360 Hz/cm2. The systematic uncertainty is yet to be fully qualified. C file RE3 C file RE4 Contact: cms-dpg-conveners-rpc@SPAMNOTcernNOSPAMPLEASE.ch

Expected rates in RE3/2&3 and RE4/2&3 Ultimate HL-LHC scenario: L = 7.5x1034 cm-2s-1

hitRate_re3_23_4000.png hitRate_re4_23_4000.png
RPC hit rates, expected at instantaneous luminosity of 7.5x1034cm-2s-1 in the present RE3/2&3 (on the left) and RE4/2&3 (on the right) regions are shown. All values are averaged over φ. Averaged expected RE3/2 and RE3/3 hit rates are ~200 Hz/cm2 and 100 Hz/cm2 respectively. Including SF of 3 they are ~ 600 Hz/cm2 and 300 Hz/cm2. Averaged expected RE4/2 and RE4/3 hit rates are ~260 Hz/cm2 and 180 Hz/cm2 respectively. Including SF of 3 they are ~ 780 Hz/cm2 and 540 Hz/cm2. The systematic uncertainty is yet to be fully qualified. C file RE3 C file RE4 Contact: cms-dpg-conveners-rpc@SPAMNOTcernNOSPAMPLEASE.ch

Expected absorbed dose in iRPC. Base (3000 fb/cm-1) vs Ultimate (4000 fb/cm-1) HL-LHC scenario

tid_re31_3000_4000.png tid_re41_3000_4000.png
Absorbed dose after collected 3000 fb/cm-1 (in blue) and 4000 fb/cm-1 (in red) is shown on the plots. Plot on the left represents RE3/1 region and the one on the right – RE4/1. All values are averaged over φ. The highest value at 165 cm is systematic and is caused by the geometry differences and larger Z bin (Z bin = 10 cm). Thus the value in the Z bin is averaged over RPC material and air. The average ratio between the values from the ultimate and base HL-LHC scenario is 1.33. Expected dose at R=303 cm for RE3/1 is ~10 (13.6) Gy, and at R=304 cm for RE4/1 it is about 18 (24) Gy, where R=303 (304)cm are the expected FEB positions. Safety factor of 3 is not included. The systematic uncertainty is yet to be fully qualified. C file RE3/1 C file RE4/1 Contact: cms-dpg-conveners-rpc@SPAMNOTcernNOSPAMPLEASE.ch

Expected fluence in RPC system (3000 fb/cm-1) vs Ultimate (4000 fb/cm-1) HL-LHC scenario

fluence_iRPC.png fluence_RPC_re3_re4.png
Expected fluence in terms of 1-MeV neutron equivalent in Si, after collected 3000 fb/cm-1 (in blue) and 4000 fb/cm-1 (in red) is shown on the plots. Plot on the left represents the upgrade iRPC region – RE3/1 and RE4/1 and on the right – the present RE3/2&3 and RE4/2&3. All values are averaged over φ. The average ratio between the ultimate and base HL-LHC scenario is 1.33. Expected fluence at R=303 cm for RE3/1 is ~4.3 (5.8) x1011 n/cm2, and at R=304 cm for RE4/1 it is about 6.2 (8.2) x1011 n/cm2, where R=303 (304)cm are the expected FEB positions. Safety factor of 3 is not included. The systematic uncertainty is yet to be fully qualified. C file iRPC C file RPC Contact: cms-dpg-conveners-rpc@SPAMNOTcernNOSPAMPLEASE.ch

Expected absorbed dose and fluence at HL-LHC in the Balcony region

tiDose_balcony.png neutronFluence_balcony.png
|The expected absorbed dose in the balcony region after collected 3000 (4000) fb/cm-1 is shown on the left plot. All values are averaged over φ. The average ratio between the ultimate and base HL-LHC scenario is 1.33. Maximum expected integrated dose for the barrel (Z,600 cm) is about 1.5 (2) Gy, while for the endcap (Z>600 cm) the highest expectations are below 10 Gy. Two points above this value are caused by statistical fluctuations. Plot on the right shows the expected neutron fluence after collected 3000 fb/cm-1. The highest contribution is from the neutrons with E<20 MeV. Maximum expected fluence for the barrel (Z<600 cm) is below 350x109 n/cm2, while for the endcap (Z>600 cm) the highest expectations are about 800x109 n/cm2. The values in the Z bin are averaged over detector material and air. The systematic uncertainty is yet to be fully qualified. C file Dose-Balcony C file Fluence-Balcony Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch|

RE+4 Currents in Muon Stasis Lab (Mehar Ali Shah)

Presented on General Muon Meeting (GMM) on 27 January 2020. Slides

Figures:

shut1.png Figure represents HV current vs time for one of the RE+4 gaps. HV stability was done on the surface in a newly built Muon stasis lab after chambers were dismounted from CMS. Currents were found higher than expected and significant reduction of currents observed by keeping the detector at higher voltages. This plot shows the currents at 6500 V on the left and at 9000 V for rest of the period. In around 1 month of the period currents were getting to a stable value which was determined by a dedicated current analysis during heavy ion running where an exponential decrease of currents was observed for all the chambers with high currents.

pdf file

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

shut2.png Figure represents HV current vs applied HV for one of the RE4 super modules RE+4/R2/R3.09. These measurements were done on the surface in a newly built Muon stasis lab after chambers were dismounted from CMS. Currents were found higher than expected and significant reduction of currents observed by keeping the detector at higher voltages. In around 1 month of the period currents were getting to a stable value which was determined by a dedicated current analysis during heavy ion running where an exponential decrease of currents was observed for all the chambers.

pdf file

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

A new approach for CMS RPC current monitoring using Machine Learning techniques (Peicho Petkov)

Presented on:

Introduction

Monitoring the RPC current stability proves to be a tedious work in CMS experiment where one needs to deals with more than a thousand individual high voltage (HV) channels. The current depends from several parameters (applied voltage, luminosity, environmental parameters, etc.) and sometimes it's not obvious if it changes due to variation of the external parameters or if it's due to a chamber malfunction.

We present a new Machine Learning (ML) approach to monitor and spot possible HV problems. A Generalized Linear Regression algorithm is trained to recognize the behavior of the HV current of a given chamber. Then the algorithm is used to predict the HV current at given data taking conditions and environmental parameters. The divergence between the predicted and the measured HV current is an indication for a problem.

The results for several chambers would be shown. The algorithm is trained and tested on 2017 and 2018 data. The software development is on “proof of concepts” level and the results are encouraging.

The model

To predict the RPC HV channel current taking into account Inst. Luminosity, Working Point and environmental parameters.

model.png

Determination of the constants Ci for each HV channel allows for calculating the RPC Current prediction.

  • Linst – instantaneous luminosity
  • HV – applied high voltage
  • T – environmental temperature
  • P – environmental pressure
  • RH – environmental relative humidity
  • dt - the time interval since the origin for a given year

Terms purposes

  • C1*Linst – RPC current linear w.r.t instantaneous luminosity
  • C2*HV – proportional to the ohmic current
  • C3*T – “pedestal” proportional to the temperature
  • C4*Linst*e(HV/P) – working point correction
  • C5*RH – environmental relative humidity influence
  • C6*P – environmental pressure influence
  • C7*dt – accounts for the tendency of the current to increase with time with respect to the initial conditions for a given year

UXC Environmental pressure

ml1.png A plot of the UXC pressure from May 2016 to the end of Nov 2018. The data are taken from the online condition database and exhibit seasonal behavior.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

UXC Environmental Relative Humidity

ml2.png A plot of the UXC relative humidity from May 2016 to the end of Nov 2018. The data are taken from the online condition database and exhibit seasonal behavior.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Applied HV to an RPC

ml3.png An example plot of the applied HV for a particular RPC (named W+1_S2_RB1out – outer chamber in station 1 in sector 2 of the barrel wheel +1) from May 2016 to the end of Nov 2018. The HV is corrected for pressure and temperature variations according to: Veff=V(p0/p)[1+alpha(T/T0-1)], where alpha is experimentally determined constant, p0 and T0 are the reference pressure and temperature, respectively.

The data are taken from the online condition database and exhibit seasonal behavior due to the correlation with the UXC pressure.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Input data - current (I_mon)

ml4.png An example plot of the monitored RPC current (I_mon) for a particular chamber (named W+1_S2_RB1out – outer chamber in station 1 in sector 2 of the barrel wheel +1) from May 2016 to the end of Nov 2018. The current shows year bases behaviour - increases with time, but the next period starts with lower I_mon than at the end of the previous period. The I_mon values have accuracy of 0.2 uA and are taken from the online condition database.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

2D histogram of Monitored Current vs. luminosity (example W+2_S3_RB3- for 2018)

ml5.png The monitored RPC current (Imon) vs. instantaneous luminosity (Linst) an RPC in the negative phi-part of barrel muon station 3 in sector 3 of wheel +2 (W+2_S3_RB3-).

The two group of points with different slopes corresponding to two HV working points.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Predicted Current vs. luminosity (example W+2_S3_RB3- for 2018)

ml6.png The predicted RPC current (I_pred) vs. instantaneous luminosity (L_inst) for an RPC in the negative phi-part of the barrel muon station 3 in sector 3 of wheel +2 (W+2_S3_RB3-).

The plot shows how the linear regression model reproduces the overall average dependence on instantaneous luminosity (L_inst). The spread is due to the impact of the other parameters that influence the RPC current at a given L_inst.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Predicted current follows the data points

ml7.png The plot presents an example of the agreement between predicted (red line) and measured (black points) current for an RPC in the inner layer of barrel muon station 3 in sector 3 of wheel +2 (W+2_S3_RB2in) although HV working point is changed by 200V on 19 Aug 2018.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Typical distribution of the difference between monitored and predicted current (W+1_S2_RB1out)

ml8.png Distribution of the difference between monitored and predicted current for an RPC in the outer layer in the barrel muon station 1 in sector 2 of wheel +1. One entry in the histogram is the difference between monitored and predicted current for an I_mon data point in 2018.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Distribution of the average Imon-Ipred for 446 barrel RPCs

ml9.png The plot shows the distribution of the average difference between monitored and predicted current for 446 Barrel RPCs.

One entry in the histogram is the average difference between monitored and predicted current for a Barrel RPC taken from the distribution of the difference for 2018.

All RPCs entries with an average difference greater than 2 μA are chambers with problems.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

Distribution of the average Imon-Ipred for each Barrel RPC station

rb1.png rb2.png
rb3.png rb4.png

Distribution of the average Imon-Ipred for each Wheel

w-2.png w-1.png w0.png
w+2.png w+1.png

An example of wrongly predicted current due to inconsistent training dataset

mll1.png Due to HV problems the RPC in the outer layer in barrel muon station 1 in sector 2 of wheel +2 (W+2_S2_RB1out) one of the single gap layers was disconnected at the end of July 2017. The overall current drawn by the chamber decreases stepwise as seen in the top plot (black points). This results as a model with decreasing current with time (red line in the top plot).

Using the latter model to predict the current for the next year (2018) leads to the disagreement between measured (black points in the bottom plot) and the predicted (red line in the bottom plot) current.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

mll2.png

mll3.png The plot shows the monitored and predicted current of the RPC located in the negative phi-part of the barrel muon station 4 in sector 4 of wheel -1 (W-1_S4_RB4--) as an example case of an RPC with current that increases with a steeper slope than predicted. The RPC is found to have gas leak.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

mll4.png The plot shows the monitored and predicted current of the RPC located in the positive phi-part of the barrel muon station 4 in sector 1 of wheel -1 (W-1_S4_RB4+) as an example case of RPC with current that is higher than predicted, but well below the overcurrent limit of the HV supply module. The RPC is found to have gas leak.

Contact: cms-dpg-conveners-rpc@SPAMNOTcern.ch

UPGRADE PLOTS 2020

  • Plots for the upgrade on the following twiki page

Longevity study & HF production in CMS-RPC - twiki with plots (Andrea Gelmi)

-- AndresCabrera - 2020-01-14

Topic attachments
I Attachment History Action Size Date Who Comment
PNGpng 2015n.png r1 manage 233.1 K 2020-01-15 - 12:47 AndresCabrera  
PNGpng 2015p.png r1 manage 234.9 K 2020-01-15 - 12:48 AndresCabrera  
PNGpng 2016n.png r1 manage 233.1 K 2020-01-15 - 12:47 AndresCabrera  
PNGpng 2016p.png r1 manage 237.2 K 2020-01-15 - 12:48 AndresCabrera  
PNGpng 2017n.png r1 manage 234.2 K 2020-01-15 - 12:47 AndresCabrera  
PNGpng 2017p.png r1 manage 238.1 K 2020-01-15 - 12:48 AndresCabrera  
PNGpng 2018n.png r1 manage 239.1 K 2020-01-15 - 12:47 AndresCabrera  
PNGpng 2018p.png r1 manage 240.6 K 2020-01-15 - 12:48 AndresCabrera  
PDFpdf Run2015_cRB.pdf r1 manage 46.8 K 2020-01-17 - 06:03 HeewonLee 2015 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2015_cRB.png r1 manage 24.3 K 2020-01-17 - 06:03 HeewonLee 2015 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2015_cREN.pdf r1 manage 44.8 K 2020-01-17 - 06:03 HeewonLee 2015 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2015_cREN.png r1 manage 32.7 K 2020-01-17 - 06:03 HeewonLee 2015 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2015_cREP.pdf r1 manage 44.9 K 2020-01-17 - 06:03 HeewonLee 2015 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2015_cREP.png r1 manage 33.9 K 2020-01-17 - 06:03 HeewonLee 2015 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2016_cRB.pdf r1 manage 46.8 K 2020-01-17 - 06:05 HeewonLee 2016 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2016_cRB.png r1 manage 24.2 K 2020-01-17 - 06:05 HeewonLee 2016 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2016_cREN.pdf r1 manage 44.9 K 2020-01-17 - 06:05 HeewonLee 2016 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2016_cREN.png r1 manage 33.0 K 2020-01-17 - 06:05 HeewonLee 2016 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2016_cREP.pdf r1 manage 45.0 K 2020-01-17 - 06:05 HeewonLee 2016 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2016_cREP.png r1 manage 34.8 K 2020-01-17 - 06:05 HeewonLee 2016 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2017_cRB.pdf r1 manage 46.7 K 2020-01-17 - 06:06 HeewonLee 2017 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2017_cRB.png r1 manage 24.0 K 2020-01-17 - 06:06 HeewonLee 2017 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2017_cREN.pdf r1 manage 44.8 K 2020-01-17 - 06:06 HeewonLee 2017 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2017_cREN.png r1 manage 33.3 K 2020-01-17 - 06:06 HeewonLee 2017 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2017_cREP.pdf r1 manage 45.0 K 2020-01-17 - 06:06 HeewonLee 2017 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2017_cREP.png r1 manage 34.3 K 2020-01-17 - 06:06 HeewonLee 2017 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2018_cRB.pdf r1 manage 46.9 K 2020-01-17 - 06:06 HeewonLee 2018 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2018_cRB.png r1 manage 24.2 K 2020-01-17 - 06:06 HeewonLee 2018 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2018_cREN.pdf r1 manage 45.0 K 2020-01-17 - 06:06 HeewonLee 2018 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2018_cREN.png r1 manage 34.2 K 2020-01-17 - 06:06 HeewonLee 2018 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PDFpdf Run2018_cREP.pdf r1 manage 45.1 K 2020-01-17 - 06:06 HeewonLee 2018 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng Run2018_cREP.png r1 manage 35.8 K 2020-01-17 - 06:06 HeewonLee 2018 RPC Efficiency 2D Plots by Barrel roll, Endcap+ roll and Endcap - roll
PNGpng balconies.png r1 manage 382.3 K 2020-01-15 - 13:13 AndresCabrera  
PNGpng clu1.png r1 manage 65.9 K 2020-01-14 - 23:48 AndresCabrera  
PNGpng clu2.png r1 manage 66.2 K 2020-01-14 - 23:48 AndresCabrera  
PNGpng cluba.png r1 manage 91.6 K 2020-01-14 - 23:48 AndresCabrera  
PNGpng cludif.png r1 manage 121.3 K 2020-01-14 - 23:48 AndresCabrera  
PNGpng clurb1.png r1 manage 102.5 K 2020-01-14 - 23:48 AndresCabrera  
PNGpng clurb4.png r1 manage 107.4 K 2020-01-14 - 23:48 AndresCabrera  
PNGpng dose.png r1 manage 198.5 K 2020-01-15 - 13:14 AndresCabrera  
PNGpng efi20152d.png r1 manage 161.9 K 2020-01-15 - 12:22 AndresCabrera  
PNGpng efi20162d.png r1 manage 156.6 K 2020-01-15 - 12:22 AndresCabrera  
PNGpng efi20172d.png r1 manage 155.8 K 2020-01-15 - 12:22 AndresCabrera  
PNGpng efi20182d.png r1 manage 157.4 K 2020-01-15 - 12:22 AndresCabrera  
PNGpng efiba.png r1 manage 92.1 K 2020-01-14 - 23:28 AndresCabrera  
PNGpng efibackup.png r1 manage 139.3 K 2020-01-15 - 00:06 AndresCabrera  
PNGpng efidif.png r1 manage 112.1 K 2020-01-14 - 23:28 AndresCabrera  
PNGpng efirb1.png r1 manage 97.2 K 2020-01-14 - 23:28 AndresCabrera  
PNGpng efirb4.png r1 manage 89.6 K 2020-01-14 - 23:28 AndresCabrera  
PNGpng efith1.png r1 manage 70.6 K 2020-01-14 - 23:16 AndresCabrera  
PNGpng efith2.png r1 manage 71.3 K 2020-01-14 - 23:16 AndresCabrera  
PNGpng fluence.png r1 manage 363.8 K 2020-01-15 - 13:13 AndresCabrera  
C source code filec fluence_RPC_re3_re4.C r1 manage 15.6 K 2020-01-28 - 13:04 RoumyanaHadjiiska  
PNGpng fluence_RPC_re3_re4.png r1 manage 26.6 K 2020-01-28 - 13:04 RoumyanaHadjiiska  
C source code filec fluence_iRPC.C r1 manage 13.0 K 2020-01-28 - 13:04 RoumyanaHadjiiska  
PNGpng fluence_iRPC.png r1 manage 25.9 K 2020-01-28 - 13:04 RoumyanaHadjiiska  
C source code filec hitRate_re3_1_3000.C r1 manage 12.9 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re3_1_3000.png r1 manage 25.8 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re3_1_4000.C r1 manage 12.9 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re3_1_4000.png r1 manage 24.3 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re3_23_3000.C r1 manage 15.0 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re3_23_3000.png r1 manage 27.2 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re3_23_4000.C r1 manage 15.1 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re3_23_4000.png r1 manage 28.1 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re4_1_3000.C r1 manage 12.6 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re4_1_3000.png r1 manage 25.7 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re4_1_4000.C r1 manage 12.6 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re4_1_4000.png r1 manage 24.2 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re4_23_3000.C r1 manage 15.3 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re4_23_3000.png r1 manage 27.5 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
C source code filec hitRate_re4_23_4000.C r1 manage 15.3 K 2020-01-28 - 12:39 RoumyanaHadjiiska  
PNGpng hitRate_re4_23_4000.png r1 manage 28.3 K 2020-01-28 - 12:30 RoumyanaHadjiiska  
PNGpng l5.png r1 manage 324.1 K 2020-01-15 - 13:15 AndresCabrera  
PNGpng l5r32.png r1 manage 347.6 K 2020-01-15 - 13:15 AndresCabrera  
PNGpng l7.png r1 manage 298.5 K 2020-01-15 - 13:15 AndresCabrera  
PNGpng l7r32.png r1 manage 357.8 K 2020-01-15 - 13:14 AndresCabrera  
PNGpng ml1.png r1 manage 120.4 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml2.png r1 manage 115.6 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml3.png r1 manage 98.4 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml4.png r1 manage 134.4 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml5.png r1 manage 51.0 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml6.png r1 manage 39.0 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml7.png r1 manage 181.0 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml8.png r1 manage 50.1 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng ml9.png r1 manage 39.6 K 2020-11-02 - 13:10 AndresCabrera  
PNGpng mll1.png r1 manage 147.0 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng mll2.png r1 manage 122.4 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng mll3.png r1 manage 190.3 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng mll4.png r1 manage 316.3 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng model.png r1 manage 23.0 K 2020-11-02 - 12:57 AndresCabrera  
C source code filec neutronFluence_balcony.C r1 manage 30.3 K 2020-01-28 - 13:08 RoumyanaHadjiiska  
PNGpng neutronFluence_balcony.png r1 manage 27.1 K 2020-01-28 - 13:08 RoumyanaHadjiiska  
PNGpng noi1.png r1 manage 76.8 K 2020-01-15 - 00:06 AndresCabrera  
PNGpng noi2.png r1 manage 80.1 K 2020-01-15 - 00:06 AndresCabrera  
PNGpng noidif.png r1 manage 133.5 K 2020-01-15 - 00:06 AndresCabrera  
PNGpng rb1.png r1 manage 68.8 K 2020-11-16 - 11:17 AndresCabrera  
PNGpng rb2.png r1 manage 70.9 K 2020-11-16 - 11:17 AndresCabrera  
PNGpng rb3.png r1 manage 66.4 K 2020-11-16 - 11:17 AndresCabrera  
PNGpng rb4.png r1 manage 65.6 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng regions.png r1 manage 420.6 K 2020-01-15 - 13:15 AndresCabrera  
PDFpdf shut1.pdf r1 manage 35.6 K 2020-11-01 - 21:50 AndresCabrera  
PNGpng shut1.png r1 manage 29.0 K 2020-11-01 - 21:50 AndresCabrera  
PDFpdf shut2.pdf r1 manage 57.6 K 2020-11-01 - 21:50 AndresCabrera  
PNGpng shut2.png r1 manage 65.2 K 2020-11-01 - 21:50 AndresCabrera  
C source code filec tiDose_balcony.C r1 manage 13.6 K 2020-01-28 - 13:08 RoumyanaHadjiiska  
PNGpng tiDose_balcony.png r1 manage 22.9 K 2020-01-28 - 13:08 RoumyanaHadjiiska  
C source code filec tid_re31_3000_4000.C r1 manage 7.6 K 2020-01-28 - 13:03 RoumyanaHadjiiska  
PNGpng tid_re31_3000_4000.png r1 manage 18.5 K 2020-01-28 - 13:03 RoumyanaHadjiiska  
C source code filec tid_re41_3000_4000.C r1 manage 7.4 K 2020-01-28 - 13:03 RoumyanaHadjiiska  
PNGpng tid_re41_3000_4000.png r1 manage 18.7 K 2020-01-28 - 13:03 RoumyanaHadjiiska  
PNGpng w+1.png r1 manage 92.9 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng w+2.png r1 manage 90.3 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng w-1.png r1 manage 180.1 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng w-2.png r1 manage 87.1 K 2020-11-16 - 11:16 AndresCabrera  
PNGpng w0.png r1 manage 85.8 K 2020-11-16 - 11:16 AndresCabrera  
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Topic revision: r13 - 2020-11-16 - AndresCabrera
 
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