1. lcsim Status for Muon Collider Physics and Detector Studies Norman Graf (SLAC) MAP 2012 Winter Meeting SLAC, March 8, 2012

2. slic & lcsim The detector response program slic and the event reconstruction package org.lcsim have been described a number of times at various Muon Collider meetings. This presentation builds on that knowledge and presents a path forward for Muon Collider Detector (MCD) studies. Expect this to culminate in detector designs which can be used to study the physics scenarios in the presence of the collider backgrounds. 2

3. Detector Designs Detector designs for the ILC, and to a somewhat lesser extent CLiC, are quite mature.  Based on a decade of studies starting from Snowmass 2001 to the ILC LOI  Continuing on to the CliC CDR and ILC DBD  Emphasis has been on precision physics in the presence of low and well-understood backgrounds. Muon Collider Detector design essentially just starting.  Major difference is the enormous machine backgrounds  Need to start from the basics. 3

4. Detectors in slic All details of the detectors are specified in a compact xml file format, allowing many designs to be studied without having to write or compile any C++ code or know anything about the details of Geant4. Collections of MCParticle and generic Calorimeter and Tracker Hits are written out.  Charge evolution and digitization deferred to reconstruction phase.  Very detailed studies of readout technologies supported out-of-the-box. Others can implement API. 4

5. 5 Detector Variants Runtime XML format allows variations in detector geometries to be easily set up and studied:  Sampling calorimeters: Absorber materials, dimensions Readout technologies, e.g. RPC, GEM, scintillator Layering (radii, number, composition) Readout segmentation (size, projective vs. nonprojective)  Total absorption crystal calorimeters Optical properties  Tracking detector technologies & topologies TPC, silicon microstrip, silicon pixels

6. ILC Full Detector Concepts SiD GLD LDC Same executable, different runtime input files

7. Level of Detail in Silicon Tracker 7

8. Design Strategy I Both SiD and ILD designs are very mature and simulations reflect a high level of detail.  based on years of simulations, hardware R&D and engineering CLiC detectors for the CDR are variations on the ILC design.  Environments are not too different  “Adopt, adapt, improve” workable paradigm 8

9. Design Strategy II Muon Collider environment, however, is quite different. Simply plugging in an ILC-like detector not appropriate for the extremely high backgrounds.  Radiation-hard trackers introduce significantly more material.  Higher occupancies from backgrounds require better segmentation (in both space and time) than current detector designs.  Precision “imaging” calorimetry may not be possible in MuC environment 9

10. Design Strategy III Instead of trying to adopt a detailed ILC/CLiC detector design for the MuC studies, suggest working from the ground up. Study simple detector designs in the presence of full machine backgrounds. Use information gained on particle fluxes and detector occupancies at each level of detail to inform detector technology choices for next level of realism. 10

11. Shielding 11 “Nose” fully defined and configurable by specifying zplanes (z,r min, r max ) for the surface of revolution about the z axis. Trivial to study multiple setups.

12. Endcaps 12 Define layering, r max z min, r min, opening angle Layering engine does the rest.

13. mcd00 Calorimetry 13

14. Muon Collider Detector Less than 200 lines of text (including comments!) fully define this detector. Trivial to script a study of a whole series of detectors, varying  Calorimeter materials, layering, readout  Overall detector dimensions, aspect ratio  Shielding nose configuration  Tracker dimensions, layout, number of layers  Magnetic field strength  Any other detector parameters 14

15. Event Generation Concentrate on backgrounds  Signal events insignificant Input particles from muon bunches available from MARS simulations ASCII text files converted into stdhep files  Separate into showers from individual muon interactions. Machinery to add and overlay events exists. 15

16. Overlay Driver for ILC/CLiC Merging of LCIO files after full simulation and before digitization  Merge collections  Displace hits/entries in time  Variable number of beam crossings  Multiple background files (different kinds of background)  Not all subdetectors are read out at the same time  Readout times are shifted by time of flight (to the actual hit)  The actual readout window is selected by the signal event 16

17. Overlay Driver Timing Time window determined by subdetector t 0 set by time-of-flight to origin 17

18. 18 Reconstruction/Analysis Overview Java based reconstruction and analysis package  Runs standalone or inside Java Analysis Studio (JAS)  Fast MC  Smeared tracks and calorimetry clusters  Full Event Reconstruction Beam background overlays at detector hit level, including time offsets. detector readout digitization (CCD pixels, Si  -strips, TPC pad hits) ab initio track finding and fitting for ~arbitrary geometries multiple calorimeter clustering algorithms Individual Particle reconstruction (cluster-track association)  Analysis Tools (including WIRED event display)  Physics Tools (Jet Finding, Vertex Finding, Flavor Tagging) Write once run, run anywhere  Exact same libraries run on all platforms (Windows, Mac, Linux(es), Grid) using the Java Virtual Machine.

19. Tracking Analytic covariance matrices available for fast MC smearing for each detector. Track “cheater” available for studies of full detector simulation events. Assigns hits on basis of MC parentage. Ab initio track finding packages. Fitting code incorporating multiple scattering and energy loss via weight matrix available. 19

20. 20 Tracking Detector Readout Hits in Trackers record full MC information. Module tiling and signal digitization is deferred to analysis stage.  Used to rapidly study many possible solutions. Fully-featured package to convert MC hits in silicon to pixel hits. Fully configurable at runtime. MC Hits  Pixel ID & ADC  Clusters  Hits (x ±  x) Can correctly study occupancies, overlaps, ghost hits, etc.

21. Track Finding Standalone pattern recognition code for 1D (e.g. Si  strip) and 2D (e.g. Si pixel) hits. – High efficiency, even in presence of backgrounds. – Efficient at low momentum. Conformal-mapping pattern recognition also available, applicable also to 3D pixel tracker. 21

22. Validated This suite of software tools provides:  Physics event generation & bindings to most legacy generators through the stdhep format.  Full detector response simulation using precompiled binaries & runtime geometry definition (no coding!).  Full detector digitization (x-talk, noise, diffusion, etc.)  Hit-level overlay of arbitrary background events.  Access to other LCIO-compliant software frameworks.  Full ab-initio event reconstruction and analysis suites.  Tested on hundreds of millions of events. 22

23. Short-term support for MCD Incorporate optical properties into compact.xml description. Implement sensitive detector that counts the Cerenkov photons. Implement sensitive detector for the tungsten cone to record and “kill” particles entering. Customize Overlay Driver for Muon Collider backgrounds. 23

24. Getting Started Created Confluence page: https://confluence.slac.stanford.edu/display/MCPDS/Home Currently: – Overview – Event Generation – Timing studies – Detector Models – Available Datasets You can sign up here:sign https://jira.slac.stanford.edu/signup/ 24 Hans Wenzel

25. Plan Of Action Need a working detector model for the Muon Collider.  Challenge is to deal with backgrounds while maintaining high precision (can it be done?). Calorimeter:  Total absorption crystal calorimeter (need to study how timing will affect the resolution after dual readout correction is applied)  Digital sampling calorimeter with traveling time gate, software compensation Tracker:  More like LHC than ILC, double or triple layers might be needed to help with pattern recognition. Need fast timing to reject background --> this will all come at a price (material budget) Once we have it: debug, biggest challenge will be to deal with the huge backgrounds and getting them into the simulation. (much more challenging than pile up at LHC and that was already difficult) 25 Hans Wenzel

26. Summary Tools are in place and available to initiate both detector studies and physics analyses. Documentation and tutorials available Small group of developers available to implement feature requests. Just add manpower! 26

27. Additional Information Wiki - http://confluence.slac.stanford.edu/display/ilc/ Home http://confluence.slac.stanford.edu/display/ilc/ Home lcsim.org - http://www.lcsim.orghttp://www.lcsim.org ILC Forum - http://forum.linearcollider.orghttp://forum.linearcollider.org LCIO - http://lcio.desy.dehttp://lcio.desy.de SLIC - http://www.lcsim.org/software/slichttp://www.lcsim.org/software/slic LCDD - http://www.lcsim.org/software/lcddhttp://www.lcsim.org/software/lcdd JAS3 - http://jas.freehep.org/jas3http://jas.freehep.org/jas3 AIDA - http://aida.freehep.orghttp://aida.freehep.org WIRED - http://wired.freehep.orghttp://wired.freehep.org 27