The Si D Particle Flow Algorithm List of

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The Si. D Particle Flow Algorithm

The Si. D Particle Flow Algorithm

List of Contents • • Assume Particle Flow needs no introduction The Si. D

List of Contents • • Assume Particle Flow needs no introduction The Si. D 02 used in the PFA An Overview of the algorithm Status at the time of LOI Introspection of the Si. D-PFA The fix-ups and improvements Where we are Where to next (short and longer term)

The Detector (Si. D 02) ECAL: 30+1 layers of (320μm Si + 2. 5/5.

The Detector (Si. D 02) ECAL: 30+1 layers of (320μm Si + 2. 5/5. 0 mm W), 3. 5 x 3. 5 mm cells. HCAL: 40 layers of (1. 2 mm RPC + 2 cm steel), 1 cm x 1 cm cells. 3

Basic Building Blocks of the (Iowa) PFA • • • MC hits within 100

Basic Building Blocks of the (Iowa) PFA • • • MC hits within 100 ns from IP are digitized Photon, Muon and Electron ID Track and Seed Cluster (Directed Tree) Building Charged Hadron Shower Reconstructing Particles (four-vectors) Hits belonging to Photon, Muon and Electron are removed from the hit list for clustering algorithm Next Use Directed. Tree Clustering for classifying the remaining hits into sub-cluster types like MIPs, Clumps, Blocks and `leftover’s Finally, start building Hadron Showers for one charged track at a time 4

Cluster Building • Extrapolate (each) track to the ECAL surface • Find Seed: sub-cluster

Cluster Building • Extrapolate (each) track to the ECAL surface • Find Seed: sub-cluster directly connected to extrapolated track • Each track typically has one seed Special cases: track without seed, or does not reach calorimeter • Now start connecting other sub-clusters to the seed of each track • Start with lowest and then progressively higher momentum tracks • Up to ten iterations until all track-cluster match satisfy (E – p) within tolerance Connecting Clusters Scoring : (a poor man’s) Probability of a link Based on the sub-cluster type and geometric proximity a score between 0 and 1 is assigned between any two sub-clusters starting with the cluster in consideration The higher the score the higher the probability of a link A cut-off threshold is obtained for an energy by tuning with events 5

Energy dependence Performance at LOI Study just how much is contributed because of leakage

Energy dependence Performance at LOI Study just how much is contributed because of leakage 6

Leakage study at 500 Ge. V and 1 Te. V Produce data sets a

Leakage study at 500 Ge. V and 1 Te. V Produce data sets a Si. D 02 -like detector MC with 6 HCAL for 1 Te. V, 500 Ge. V, 200 Ge. V • Change Steel for Cu for absorber • Increase to 54 layers from 40 layers in HCAL • 1. 7 more material in HCAL • No gap between HCAL and Muon endcap (instead of 10 cm) Compare sid 02 with sid 02 -Cu at various energies Check leakage by observing # hits in Muon detector : punch thru; a measure of leakage Simultaneously study the corresponding change in Energy resolution The relative measure from the two gives an approximate semi-quantitative measure of leakage vs performance Although substantial leakage is present at 500 Ge. V confusion is clearly important 7

Punch-through muon hits Si. D 02 -Cu Si. D 02 8

Punch-through muon hits Si. D 02 -Cu Si. D 02 8

Resolution study (Si. D 02 -Cu comparison) real tracking Si. D 02 -Cu Si.

Resolution study (Si. D 02 -Cu comparison) real tracking Si. D 02 -Cu Si. D 02 9

Conclusions from Leakage study • At 1 Te. V leakage comparison shows large difference

Conclusions from Leakage study • At 1 Te. V leakage comparison shows large difference in performance between Si. D-nominal (dashed) and Si. D-Cu detectors (solid) • At 500 Ge. V leakage comparison shows significant difference in performance between Si. D-nominal (dashed) and Si. D-Cu detectors (solid) • Performance of 1 Te. V Si. D-Cu is similar to 500 Ge. V Si. D-nominal in leakage • At 1 Te. V performance in resolution is worse with Si. D-nominal (dashed) and Si. D-Cu detectors (solid) • At 500 Ge. V performance in resolution is worse with Si. D-nominal (dashed) and Si. D-Cu detectors (solid) • However : The difference of performance in resolution between 1 Te. V Si. D-Cu and 500 Ge. V Si. D-nominal is not similar to that in leakage Although substantial leakage is present at 500 Ge. V, algorithm (confusion) has an important part 10

A 500 Ge. V qqbar event from one side jet Raw MC hits are

A 500 Ge. V qqbar event from one side jet Raw MC hits are displayed, each color shows an individual shower Contains a low energy 12 Ge. V neutral hadron and several photons in the ECAL; charged hadrons interacts

reconstructed The same as before shown without Now shown without the isolated hits the

reconstructed The same as before shown without Now shown without the isolated hits the isolated and unmatched hits : but after reconstruction, alogorithm still no PFA reconstruction, only of charged hadron track-cluster with knowledge of MC match (cone algorithm) p (orange) = 119 Ge. V, E/p match, enough hits (green) = 17 Ge. V , algorithm introduced a cone-like path in the reclustering to pick up secondary neutrals; but ended up being too aggressive in stealing pieces from

has a low energy 12 Ge. V neutral hadron and several photons present in

has a low energy 12 Ge. V neutral hadron and several photons present in the ECAL; interaction of charged hadron p (orange) = 119 Ge. V, E/p match, enough hits (green) = 17 Ge. V reconstructed Refined. Cheat. Cluster Diagnosis of `A’ problem: an example Refined. Cluster - sharedhits Had introduced a cone-like path in the reclustering to pick up secondary neutrals; but ends up being too aggressive 13

The `Cone’ Algorithm Cluster Dir. Angle DCA IP Seed Pos. Angle Interaction point

The `Cone’ Algorithm Cluster Dir. Angle DCA IP Seed Pos. Angle Interaction point

A detailed Study

A detailed Study

All plots show variables defined for links between a seed and a cluster. If

All plots show variables defined for links between a seed and a cluster. If the seed and the cluster belong to the same truth particle, the link is quoted as “Signal” otherwise it is quoted as “Background” Top-Left Plot: Scores just before the First cone algorithm runs. Top-Middle Plot: Scores just after the First cone algorithm runs. Top-Right Plot: Impact Parameter (IP): Distance between the center of the seed and the straight line from the center of the cluster extrapolated along the cluster’s direction Bottom-Left Plot: Distance of closest approach (DCA) between two straight lines taken respectively from the center of the seed and the center of the cluster and along the respective directions. Bottom-Middle Plot: Angle at the interaction point formed by the positions of the seed and the cluster. Bottom-Right Plot: Angular difference between the direction of the seed and the direction of the cluster.

Score disteribution for links when the first cone algorithm modifies the score. Left plot:

Score disteribution for links when the first cone algorithm modifies the score. Left plot: Scores before the first cone algorithm. Right plot: Scores after the cone algorithm. While Signal/Background discrimination is better after the first cone algorithm, backgrounds now peak in the Signal region.

Correlated Variables now zoom on signal region: look at links when the first cone

Correlated Variables now zoom on signal region: look at links when the first cone algorithm gives a high score (>0. 8).

Sharing of hits: Breaking up into smaller clusters Extending to smaller pieces

Sharing of hits: Breaking up into smaller clusters Extending to smaller pieces

Next Steps Allow flexibility in assignment of hits in clusters from tracks in the

Next Steps Allow flexibility in assignment of hits in clusters from tracks in the vicinity; Allocate after arbitration Check where exactly the `cone’ is needed, modify this, dump the rest Wait for results from ongoing study here…. Faster turn around time Improved resolution Next major step : Incorporate the PFA with realistic Si. D (Si. D 03) geometry Now progressing in parallel Expect to take a step backward: non-trivial Improve sophisticated modifications for special types of clusters, like backscattering, complex rare occurrences

Particle Flow • Energy of a hadronic jet in a calorimeter Ejet = E

Particle Flow • Energy of a hadronic jet in a calorimeter Ejet = E ( + 0) + Ehadronic (neglecting ’s and leakage etc) Electromagnetic and hadronic components have different responses Solutions: compensating calorimetry, measure hadronic and EM separately…. . • However: Ejet = Ephotons + Eneutral-hadrons + Echarged-hadrons Obtain Charged hadron energy ( 60 ) from tracking Obtain photon/EM energy ( 30 ) from ECAL with 19 / E resolution Get neutral hadron energy ( 10 ) from E/HCAL with 67 / E resolution • Therefore the jet energy resolution is Ejet Echarged Ephotons 0 19 / (0. 3 x Ejet) 20 / E fantastic ! Eneutral hadrons 67 / (0. 1 x Ejet)

Particle Flow contd • The concept depends on ability to measure particles independently •

Particle Flow contd • The concept depends on ability to measure particles independently • Charged and neutral particle confusion degrades resolution Ejet Ephotons Eneutral hadrons confusion The confusion should be minimized in a good PFA Need excellent pattern recognition (also high granularity and low occupancy) • sid 02 : ECAL: 30+1 layers of (320μm Si + 2. 5/5. 0 mm W), 3. 5 x 3. 5 mm cells. HCAL: 40 layers of (1. 2 mm RPC + 2 cm steel), 1 cm x 1 cm cells.

Categorizing: Directed. Tree Clustering 23

Categorizing: Directed. Tree Clustering 23

Final Clustering : a flow for each track 24

Final Clustering : a flow for each track 24