Update on Diffractive Dijets Hardeep Bansil University of Birmingham 12/07/2013

Overall situation 2 For combined P8 SD+DD+ND anti-k T R=0.6 jets: Out of events, pass both recon and truth cuts, pass truth only, pass recon only More reconstructed jets passing cuts than at truth level (shows up in unfolding) pT distribution of jets also exponential which can lead to big migrations Dijet cuts are |η| Jet 1, Jet 2 30 GeV, pT Jet 2 > 20 GeV Pythia 8 SD+DD+ND MC Hadron Level MC Reconstructed Data to Unfold Data Unfolded Pythia 8 SD+DD+ND smearing matrix Pythia 8 SD+DD+ND normalised matrix

Overall situation 3 For combined P8 SD+DD+ND anti-k T R=0.6 jets: Out of events, pass both recon and truth cuts, pass truth only, pass recon only More reconstructed jets passing cuts than at truth level (shows up in unfolding) Look at bin by bin acceptance corrections for different samples shows large correction factors needed after gap sizes of 2 when selecting either jets at 30, 20 GeV or 20, 20 GeV Big spikes in Pythia 8 ND distribution can be attributed to getting the gap start position wrong more often than in SD and DD samples 30, 20 GeV cuts20, 20 GeV cuts

Migrations in pT 4 From last time, looked at matching jets between recon and truth level. Occasionally, need to swap the order of the leading and sub-leading jet in reconstructed dijet pair in order to match better to truth Matching of jets done based on dR cone = 0.5×jet cone size Look at p T percentage shift (recon-truth)/truth between jet at truth level and recon level when recon cuts only are passed, as a function of η in order to determine if there is actual p T dependence across detector Blue line (best fit) shows some pT dependence for shift – relatively small across pt range shown so aim to have one consistent correction factor to start with Pythia 8 SD+DD+ND (recon pT – truth pT)/ truth pT Pythia 8 SD+DD+ND mean of gaussian fit

Migrations in pT (as function of η) 5 Look at p T percentage shift (recon-truth)/truth between jet at truth level and recon level when recon cuts only are passed, as a function of η in order to determine if there is actual dependence across detector Plot on right gives good impression of calibration applied to recon jets as function of η. Linear fit to mean consistent with previous result - mean 5.8% over- reconstruction across fit range averaged over η (c.f. 8% for Herwig++ SD+ND) Variation of only 0.02% when varying tightness of matching – p T percentage shift relatively stable Pythia 8 SD+DD+ND (recon pT – truth pT)/ truth pT Pythia 8 SD+DD+ND mean of gaussian fit

After jet pT shift correction 6 Jet correction centres pT shift roughly around 0% now Slightly reduced number of jets passing reconstructed level cuts Applied factors to MC so far, not yet applied in data – need to investigate how number of events remaining once shift applied Pythia 8 SD+DD+ND mean of gaussian fit

After jet pT shift correction 7 30, 20 GeV cuts30, 20 GeV cuts after Pt shift Bin by bin acceptance corrections in gap size become smaller but still quite large Dijet distributions have acceptance corrections that look reasonable before and after pt shift correction so migrations in gap size are key factor here 30, 20 GeV cuts 30, 20 GeV cuts after Pt shift

Migrations in gap size 8 Due to dijet requirement, gap size distribution has a exponential fall Require forward gap > 3.0 in rapidity starting at edge of detector (|η| = 4.9) For combined P8 SD+DD+ND: Out of events, pass both truth and recon, pass truth only, pass recon only Pythia 8 gap filtered samples without weights (passing anti-kt6 dijet cuts): All samples produce significant migrations from small truth to larger recon gaps but requirement of dijet system in event puts some limit on the amount of migration, ND is main contributor for migrations in reverse direction Pythia 8 SDPythia 8 DDPythia 8 ND

Matching truth particles to clusters 9 Try to understand differences between truth particles and calorimeter clusters Taking particle at truth level that caused the forward gap algorithm to terminate and match to reco EM cluster (both with pT > 200 MeV) with dR Mean shift plot gives impression of calibration applied to clusters as function of η Matching suggests

Matching truth particles to clusters 10 Try to understand differences between truth particles and calorimeter clusters Taking particle at truth level that caused the forward gap algorithm to terminate and match to reco EM cluster (both with pT > 200 MeV) Make dR cut used to match clusters to truth particles tighter Best matched  Best matched with dr < 0.1  Best matched with dr < 0.05 Overall calibration behaviour stays the same Unlike with jets, mean shift not very stable as cluster matching requirements become tighter (~10% shift each time), although the percentage shift gets closer to 0% Pythia 8 SD+DD+ND Best matched Pythia 8 SD+DD+ND Best matched dR < 0.1 Pythia 8 SD+DD+ND Best matched dR < 0.05

Cluster E v η 11 Clusters passing gap selection criteria (p T > 200 MeV, significance cuts) At |η|=4.9, particles with p T > 200 MeV correspond to particles with E > 12 GeV Very little activity after |η|=4.8 Noticeably, get small number of clusters with E < 0 passing significance cuts Big jump in activity between HEC-FCAL transition (affects recon gap distribution)

Energy Flow method 12 Look at changing to energy flow method for selecting gaps and reconstructing ξ E±pz from clusters Track cuts remain unchanged: |eta| 200 MeV & at least 4 SCT hits, Pt > 300 MeV & at least 6 SCT hits d 0 wrt PV >= 1.5, z0*sin(theta) >= 1.5 (Stable) Truth particles: |η| 200 MeV, p charged > 500 MeV to better reflect what will be observed in the calorimeter Clusters (EM calibration): |η| < 4.8 For clusters, with No cut on p (looked at cut with p cluster > 200 MeV but made negligible difference) For gap definition – not in Tile calorimeter, significance > threshold remains the same

Energy Flow method 13 Changing to use to energy flow method improves bin by bin corrections significantly for all samples Correction factor down to 2 for gaps between 3-5 Now more sensitive to all clusters, particularly in forward regions where cells are large so get more truth events at smaller gap size Pythia 8 SD+DD+ND intially Pythia 8 SD+DD+ND with Energy flow method

Energy Flow method + p T shift 14 Apply p T correction shift in addition to energy flow Controlling the excess of jets passing at recon level improves correction factor to be around 1.5 for gaps > 3 Compared to the rapidity gaps method, gap sizes are very slightly smaller Pythia 8 SD+DD+ND with Energy flow method Pythia 8 SD+DD+ND with Energy flow method + pt shift

Energy Flow method + pT shift 15 Improvement in unfolding so that ratio of data to unfolded goes from 4-5 to 1.5 for gaps > 3 Suggest that energy flow reconstruction method and pT shift correction should be used in future Pythia 8 SD+DD+ND Rapidity Gaps Method, No pt shift MC Hadron Level MC Reconstructed Data to Unfold Data Unfolded MC Hadron Level MC Reconstructed Data to Unfold Data Unfolded Pythia 8 SD+DD+ND with Energy flow method + pt shift

Comparison of Matrices 16 Rapidity Gaps Method, No Pt shift Pythia 8 SD+DD+ND with Energy flow method + pt shift Pythia 8 SD+DD+ND smearing matrix Pythia 8 SD+DD+ND normalised matrix Pythia 8 SD+DD+ND smearing matrix Pythia 8 SD+DD+ND normalised matrix

Summary Step by step process to determine appropriate correction factors Energy flow improves ability to match truth particles to measured clusters in calorimeters Look at ξ E±pz using energy flow method – determine correction factor for correcting recon back to truth in Pythia 8 and data – need to correct back to ξ=M X 2 /s Compare shapes of Pythia 8 ND and SD+DD to data Produce first differential cross sections using new method Look at systematic errors again Start investigating 20, 20 GeV cuts more seriously 17 Next steps