Calo-objects for the upgrade (Dec. 2017)

Transcription

1 Calo-objects for the upgrade (Dec. 2017)

2 Overview Run III: higher pile-up, no SPD/PS Description of current calo software sequences Review of previous work (Upgrade PID TDR) Alternative cluster shapes Neutral & electron PID Summary 2

3 Calo sequences Calo sequences split into reco (clusters & E,3D corrections) & PID sequence (DLLs) More elaborated neutral PID (isphoton, isnoth, isnote) calculated later in neutral protoparticle sequence FullSeq = caloseq[recoseq,pidseq] + protoseq[cprotoseq,nprotoseq] recoseq = [ digits, clusters, photons, mergedpi0s, electrons ] pidseq = [ charged, neutrals ] charged = [ inacc, match, energy, chi2, dlle, dllmu ] neutrals = [ photonid, mergedid, photonfrommergedid ] 3

4 DV output (v42r5) Re-running calo reco only TURCAL 2017 data 1000 events CaloDigits Get ADC value, suppress zeros ClusterReco Cellular Automaton + energy sharing btw. Clusters + covariance PhotonReco CaloTrackMatch χ 2 2D > 4 (E,3D) corrections MergedPi0Reco Split big clusters into 2 Subclusters corrected as photons ElectronReco like PhotonReco (χ 2 2D < 25) 4

5 DV output (v42r5) Re-running calo reco only TURCAL 2017 data 1000 events InCaloAcceptance Track extrapolation in calo CaloMatch Calculates χ 2 calo-trk match: trk before magnet brem trk after maget electron CaloEnery From SPD, PS, ECAL, HCAL CaloChi2 ECAL estimator (χ 2 2D, E/p) CaloDLLe(mu) Calculate DLL (PS, ECAL, HCAL) Neutral DLL χ 2 2D, E seed, E PS 5

6 Pile-up and cluster shape/size Due to pile-up (overlap of showers) the reconstructed energy of the clusters can be larger than the true energy. Effect is more important for low energy photons. PS: some corrections applied during reco based on global event multiplicity (room for improvement using local multiplicity around cluster) Smaller clusters seem to reduce this pile-up effect, without significantly degrading the energy resolution. Alexis Vallier 6

7 Photon ID The effect of the removal of the SPD/PS is an absolute reduction of 10 to 15% in the efficiency at a fixed background retention. Using for the test a BDT with following inputs: cluster spread in ECAL, track-cluster matching χ 2 2D and E HCAL. Comparison VS case case where also the E PS and m SPD hit are used Comparing 3 BDTs trained and tested at 3 different luminosity: the performances do not depend strongly on the luminosity. Frédéric ν = 2.0 7

8 Current input variables IsNotH/isNotE χ 2 2D, E19 ECAL, E HCAL /E ECAL, cluster spread in ECAL, m SPD, E PS, m PS, E4Max PS, E19 PS Training samples: IsNotH: all photons (K*gamma) VS hadronic (generic MC) IsNotE: photons (K*gamma) VS electrons (K*ee) Multi-Layer-Perceptron. Will be re-trained with upgrade MC samples and without the SPD/PS information. Mostafa Hoballah IsNotH on MC IsNotE on MC 8

9 IsPhoton Current input variables: ECAL cluster shape + SPD/PS info Training samples: K*gamma VS pi0 cocktail from different MC samples. Same LMP tool than the one used in the current software, but trained on MC Upgrade samples Small degradation of background rejection. For 90% efficiency on photons, around 10% more background is kept. Mainly due to pile-up. Small effect due to no SPD/PS info available. Miriam MC2012 MC2012 (no SPD/PS) MCUpgrade 9

10 Electron ID Study uses Upgrade MC samples of Vγ (upstream conversion) At low energy (p < 10 GeV/c) Degradation of the background rejection as a function of the luminosity. Sensitivity to the absence of the SPD/PS. At high energy (p > 10 GeV/c) Better stability with respect to the luminosity. Very small effect from the absence of the SPD/PS. Electrons DLL to be retuned with upgrade MC samples. Study to apply cluster-track matching dx corrections,... Dmitry Golubkov 10

11 Summary Already in place A switch in the reconstruction to ignore SPD/PS info Alternative cluster shapes (per ECAL section) to reduce effect of pile-up. IsPhoton for Upgrade To do: adapt/retune IsNotX, electronid Energy & position corrections Pile-up corrections... An optimization of Calo algorithms is mandatory/envisaged (new Gaudi framework, vectorisation, C ). 11