21st Century Colliding Beam Detector for Particle Identification and Imaging Calorimetry

1. Sergei Chekanov ManojJadhav Sylvester Joosten Jessica Metcalfe Paul Reimer José Repond Argonne National Laboratory ← I wish I was here too EIC Users Group Meeting Paris, France July 22 – 26, 2019

2. 21st Century Colliding Beam Detector MC Hadron level DIS event Detector output Goal of : provide same type of information (not just the usual tracker hits and calorimeter clusters)

3. : The 5D Concept Measure (E,x,y,z,t) for (every) hit in tracker + calorimeter Silicon pixel vertex + strip tracker Imaging em calorimeter Imaging hadron calorimeter Superconducting solenoid (3T) Forward gaseous RICH Forward dipole + cloak or toroid w/out cloak Forward silicon disks Forward calorimetry Backward silicon disks Backward crystal calorimeter Particle identification ( pion – kaon– proton separation) Particle momenta < 10 GeV/c for most of the solid angle from tracker + calorimeter Requires silicon sensors with time resolution of about 10 ps Eliminates The need for preshower counters, TRDs, TOF or Čerenkov (in front of the calorimeter), muon chambers (in back of calorimeter) Particle identifying imaging calorimeter

4. Imaging Calorimetry The idea Replace the traditional tower structure with very fine granularity (lateral and longitudinally) Few 1,000 channels → few 10,000,000 channels Option to reduce resolution on single channels to 1 – 2 bits (digital readout) Technologies developed in past decade Silicon sensors with 1 x 1 cm2, 0.25 x 0.25 cm2 and 0.16 cm2 pixels Scintillator strips (4.5 x 0.5 cm2) or scintillator pads (3 x 3 cm2) Resistive Plate chambers with 1 x 1 cm2 pads Micromegas and GEMs with 1 x 1 cm2 pads Review article Experimental Tests of Particle Flow Calorimetry F. Sefkow et al. arXiv:1507.05893 [physics.ins-det] Review of Modern Physics 88, 15003 (2016)

5. Advantages of Imaging Calorimetry I Particle ID Electrons, muons, hadrons → (almost) trivial Muon system redundant Software compensation Typical calorimeters have e/h ≠ 1 Weighting of individual sub-showers possible → significant improvement in σEhad Leakage corrections Use longitudinal shower information to compensate for leakage → significant improvement in σEhad Measure momentum of charged particles exiting calorimeter J. Repond: TOPSiDE

6. Advantages of Imaging Calorimetry II Gain monitoring Reconstruct track segments within hadronic showers Utilize MIP signal to monitor gain Assess local radiation damage Identify underlying events Multiparton interactions generate background of low energy particles This background can be identified and subtracted (LHC) Application of Particle Flow Algorithms (PFAs) Use PFAs to reconstruct the energy of hadronic jets J. Repond: TOPSiDE

7. Particle Flow Algorithms KL Attempt to measure the energy/momentum of each particle in a hadronic jet with the detector subsystem providing the best resolution HCAL ECAL π+ γ 18%/√E PANDORA PFA based on ILD detector concept Factor ~2 better jet energy resolution than previously achieved EIC environment: particularly suited for PFAs, due to low particle multiplicity and low momenta J.Repond: Calorimetry reinvented

8. Advantages of Silicon is robust (no gas, relatively low voltages) Minimal number of different detector technologies (Silicon, UFSD, Photo sensors for RICH, Xtal calorimeter + forward/backward systems) No need for muon system Minimal amount of dead material in front of the calorimeter (no coil, TOF counters , Čerenkov counters or Transition Radiation Detectors) → Better photon detection/measurement (DVCS) Measurement and identification of each particle individually → e.g. Reconstruction of hadronic quarkonium decays Optimized for Particle Flow Algorithms → Improved jet reconstruction (gluon density, photon structure function…) → Better kinematic reconstruction (double angle, Jacquet-Blondel)

9. Simulating Benefits of the simulation • Estimate the performance of various detector concepts/designs • Estimate the measurement precision to be achieved • Optimize the detector concept/design • All tools assembled • Unified approach to geometry: DD4Hep • Some tools are not maintainable and are beingreplaced (Currently use ‘cheater’ (true information) to associate hits with tracks) • Unified approach to tracking/particle reconstruction: machine learning Assembly of a simulation toolkit

10. Data Model →ProIO Published in Computer Physics Communications: D. Blyth et al., Volume 241, p. 98 ProIO Event-oriented streaming data model Uses Google’s Protocol buffers (protobufs) Highly language neutral Performs well in comparison with Root/IO → (Being) adopted by the EIC SW community Was developed by D. Blyth at Argonne Re-encode event rate

11. Exclusive Quarkonium production Process Measurement of –t Momentum transfer to the proton Fourier transform of –t distribution → spatial (transverse) distribution of gluons Photoproduction Deep inelastic scattering with k … 4-momentum of the beam electron k’ … 4-momentum of scattered electron PVM … 4-momentum of Quarkonium

12. Exclusive Upsilon production Events with exactly 2 electrons Collisions 100 GeV protons on 10 GeV electrons Center-of-mass = 200 GeV Cross section 16.33 pb Truth – level kinematics

13. -t distributions Reconstructed from true scattered electron and decay leptons Reconstructed from true scattered proton Slopes Consistent with input to event generator b = -4.5 Depletion of events at –t < 0.3 GeV2 due to large Upsilon mass

14. Reconstructed –t distribution Photoproduction Nice agreement between reconstructed and generated values

15. Reconstructed –t distribution Deep Inelastic Scattering Q2 > 1 GeV2 Wrong slope Due to poorly reconstructed scattered electron (energy) → Poor momentum resolution (low pTbut relatively high p) → Calorimeter information not used (yet)

16. Tracking efficiency with Muons Forward tracking Extended to η~ 4 Needs tuning (we can to better) Overall Efficiency close to 100% Tracking efficiency with Electrons Overall Efficiency slightly reduced compared to muons Can be improved with use of calorimeter information

17. Track finding: further developments Exclusive J/ψ→μ-μ+ → Finding all hits belonging to a charged track → Needed as input to track fitting (GenFit) Currently ‘Cheater’ using true information Tracking extended to far forward Next step Replace with conventional track finder: e.g. Hough transform taking into account B-field Interface to GenFit → Use as benchmark 2nd step Develop track finder based on Machine Learning techniques Several freeware packages available (e.g. ) Expected to outperform ‘conventional’ approach Avoids retuning (by hand) of track finders during detector optimization Independence of geometrical layout → applicable for all detector concepts

18. Particle Flow and Identification Algorithms → PFA: reconstruct all particle types (photons, electrons, muons, charged hadrons, neutral hadrons) → PIA: identify charged hadrons as pions, kaons, protons Currently Pandora PFA (hard wired) No PIA implemented 3st step Replace Pandora with new PFA based on ML Main challenge is identification of calorimeter clusters as belonging to charged/neutral particles Expected to be easier to implement once track finding with ML successful 4th step Develop PIA based on ML Expected to be easier after implementation of 2nd and 3rd steps Once completed Tracking Particle Flow, and Particle Identification tools Ready for systematic detector optimization

19. Conclusions EIC environment/physics poses specific challenges to the detector design Detection of forward proton/neutron/ions Measurement of scattered electron at low angles Measurement of hadronic final state Particle identification (pion – kaon – proton) over large solid angle Kinematic reconstruction of charged current events (no scattered electron)… Challenges being addressed by various concepts being developed BEAST, sPHENIX, JLEIC, Novel Ideas for colliding beam detectors Forward magnetic fields (dipole) → Cloaking of magnetic field Forward RICH detector 5D concept → Imaging calorimetry → Ultra-fast silicon Machine learning techniques Streaming readout

20. Backup slides