-- AminaZghiche - 2019-10-29

CMS-DP-2019/040

CMS ECAL Trigger

Abstract: CMS ECAL Trigger plots.

CDS entry

iCMS entry


Figure Caption

pdf version

DATA bias etbin strip 8b4e 26 28.png

pdf version

DATA spread etbin strip 8b4e 26 28.png

●The plots show the amplitude bias and resolution for different sets of amplitude weights versus signal ET by strip

○The fractional bias is defined as: ( online/offline - 1)

○The fractional spread is defined as: 𝜎(online/offline - 1) / (online/offline)

○Error on the points: error on the mean of the bias and error on the spread of the bias

●Dataset: Full-Readout ZeroBias data from November 2017 with PU=40 with the 8b4e filling scheme (8 colliding bunch trains separated by 4 empty bunches) for TPs with abs(η) > 2.3

●There is a strong ET dependence to the amplitude bias and resolution. It is more striking for low energy (< 2 GeV) TPs (by strip)

●There are significant improvements observed when updating from the current weights, especially at the lowest ET values.

●PU optimised weights perform the best in term of resolution for all energies.

pdf version

DATA bias train 8b4e 26 28.png

pdf version

DATA spread train 8b4e 26 28.png

●The plots show the amplitude bias and resolution for different sets of amplitude weights versus bunch crossing (BX) position in LHC train.

○The fractional bias is defined as: ( online/offline - 1)

○The fractional spread is defined as: 𝜎(online/offline - 1) / (online/offline)

○Error on the points: error on the mean of the bias and error on the spread of the bias

●Dataset: Full-Readout ZeroBias data from November 2017 with PU=40 with 8b4e filling scheme (8 colliding bunch trains separated by 4 empty bunches) for TPs with abs(η)> 2.3

●There is a strong bunch position dependence to the amplitude bias

●There are significant improvements observed when updating from the current weights, especially in terms of flatness in the response along the LHC train

PU optimised weights perform the best in term of resolution.

pdf version

DATA bias train 8b4e 26 28.png

pdf version

MC bias train 8b4e 26 28.png

●Fractional TP Amplitude bias versus position in LHC bunch train

○for 2017 data and simulation, with 8b4e filling scheme (8 colliding bunch trains separated by 4 empty bunches) for TPs with abs(η) > 2.3

■Data: Full-Readout ZeroBias data from November 2017 with PU=40

■Standalone MC: Simulated signals with same TP ET spectrum and BX distribution as data, with PU=40

■Mean TP energy by strip : 1 GeV

●The plots show the amplitude bias for different sets of amplitude weights:

○The fractional bias is defined as:

■data: ( online/offline - 1)

■Standalone MC: ( reconstructed/true -1 )

○There is a strong bunch position dependence to the amplitude bias, seen in both data and simulation with the same trend

○There are significant improvements observed when updating from the current weights

pdf version

DATA spread train 8b4e 26 28.png

pdf version

MC spread train 8b4e 26 28.png

●Spread in TP Amplitude spread versus position in LHC bunch train

○for 2017 data and simulation, with 8b4e filling scheme (8 colliding bunch trains separated by 4 empty bunches) for TPs with abs(η) > 2.3

■Data: Full-Readout ZeroBias data from November 2017 with PU=40

■Standalone MC: Simulated signals with same TP ET spectrum and BX distribution as data, with PU=40

■Mean TP energy by strip : 1 GeV

●The fractional spread is defined as:

■data: 𝜎(online/offline - 1) / (online/offline)

■Standalone MC: 𝜎(reconstructed/true -1) / (reconstructed/true)

○There is a strong bunch position dependence to the spread, seen in both data and simulation with the same trend.

○The spread, or resolution of the TPs, improves when the weights are updated

○PU-optimised weights perform best, removing the bunch crossing dependence, for both data and standalone MC

Topic attachments
I Attachment History Action Size Date Who Comment
PDFpdf DATA_bias_etbin_strip_8b4e_26_28.pdf r1 manage 16.2 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PNGpng DATA_bias_etbin_strip_8b4e_26_28.png r1 manage 26.4 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PDFpdf DATA_bias_train_8b4e_26_28.pdf r1 manage 15.9 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PNGpng DATA_bias_train_8b4e_26_28.png r1 manage 25.1 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PDFpdf DATA_spread_etbin_strip_8b4e_26_28.pdf r1 manage 15.9 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PNGpng DATA_spread_etbin_strip_8b4e_26_28.png r1 manage 26.8 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PDFpdf DATA_spread_train_8b4e_26_28.pdf r1 manage 16.1 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PNGpng DATA_spread_train_8b4e_26_28.png r1 manage 24.5 K 2019-10-29 - 15:15 AminaZghiche Trigger Primitives plots
PDFpdf MC_bias_train_8b4e_26_28.pdf r1 manage 15.8 K 2019-10-29 - 15:17 AminaZghiche MC tp plots
PNGpng MC_bias_train_8b4e_26_28.png r1 manage 26.5 K 2019-10-29 - 15:17 AminaZghiche MC tp plots
PDFpdf MC_spread_train_8b4e_26_28.pdf r1 manage 15.9 K 2019-10-29 - 15:17 AminaZghiche MC tp plots
PNGpng MC_spread_train_8b4e_26_28.png r1 manage 24.3 K 2019-10-29 - 15:17 AminaZghiche MC tp plots
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Topic revision: r3 - 2019-11-13 - AminaZghiche
 
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