JetsHF
EIC Project Detector Jets and Heavy Flavor Working Group
Meeting Coordinates and Summary
We meet on Wednesdays at 22:00 EDT and Thursdays at 13:00 EDT alternating weeks. Please see the indico page for details and zoom links.
A summary of meeting dates and topics discussed can be found below:
- Discussion of reconstruction priorities (All)
- Update on jet capability in epic-analysis framework (Kevin)
- Update on EICrecon jet finder (Derek)
- Discussion on tasks and priorities going forward and potential benchmarks (Olga, Brian, All)
- Initial discussion of new Jet Finding Taskforce - Including jet capability in EICrecon (Derek)
- Discussion on including jet capability in epic-analysis framework (Kevin)
- Discussion on Proposed Agenda for Collaboration Meeting (All)
- First Look at Jets from Full Simulation (Brian)
- Impact of Low-p PID on HF Reconstruction - Wenqing
- D0 and Jet Reconstruction - Summary of Interest from Indian Colleagues (Siddharth S., Mihir P., Siddharth J.)
- Impact of Tracking Efficiency on Jet Reconstruction (Did not have time to present) (Brian Page)
- Heavy Flavor Hadron Modification in eA with BeAGLE (Wenqing Fan)
- Analysis Interests
- Xuan Li: Heavy flavor reconstruction, HF tagged jets
- Brian Page: JES/JER, Substructure, Low-x / Photoproduction, Negative Endcap
- Stony Brook: Flavor-tagged jets, diffractive dijets
- SIDIS-EIC Software Framework (Chris Dilks)
- Simulation Needs Request (Brian Page)
- Introduction and Discussion
- User Interest Survey
- Previous Work
- Discussion of Charge
- Interaction with Other Groups
Simulation Requests
May 2022 Request: Our response to the initial request for information from the Simulation and QA group can be found here
Jets and Heavy Flavor Task List
| 1. Jet energy scale and resolutions | |
| Analyzer | Brain Page |
| Subsystem focus | Tracking and calorimetry |
| Goal | Hermicity, backward calorimetry, low energy neutrons |
| Analysis needs | calorimeter clustering, track/cluster matching |
| Presentations | |
| 2. Jet substracture | |
| Analyzer | Brain Page |
| Subsystem focus | Tracking and forward calorimetry |
| Goal | Calorimetry granularity |
| Analysis needs | calorimeter clustering, track/cluster matching |
| Presentations | |
| 3. Forward jets | |
| Analyzer | Miguel |
| Subsystem focus | Tracking and forward calorimetry |
| Goal | Forward calorimetry acceptance |
| Analysis needs | calorimeter clustering, track/cluster matching |
| Presentations | |
| 4. TMD via Centauro jets | |
| Analyzer | John Lajoie |
| Subsystem focus | Tracking and forward calorimetry |
| Goal | neutral tracks |
| Analysis needs | calorimeter clustering, track/cluster matching |
| Presentations | |
| 5. D/B tagged jets | |
| Analyzer | Xuan Li |
| Subsystem focus | Tracking, vertex, and PID |
| Goal | Tracking performance, vertex measurement, PID |
| Analysis needs | Tracking, vertex reconstruction, PID, calorimeter clustering, track/cluster matching |
| Presentations | |
| 5. λc | |
| Analyzer | Wenqing Fan |
| Subsystem focus | Tracking, vertex, and PID |
| Goal | Tracking performance, vertex measurement, PID |
| Analysis needs | Tracking, vertex reconstruction, PID |
| Presentations | |
| 6. Trace anomaly via charmonium | |
| Analyzer | Xinbai Li |
| Subsystem focus | electromagnetic calorimeter, PID |
| Goal | Tracking performance, vertex measurement, ePID |
| Analysis needs | Tracking, vertex reconstruction, PID, calorimeter clustering, track/cluster matching |
| Presentations | |
Reconstruction Needs
(What functionality / algorithms do we need from the main reconstruction)
Output File Information
(What information do we need in the output files to allow for tuning and development of algorithms downstream)
Jet Reconstruction Task Force
In February 2023, a task force was set up with the goal of implementing a jet finder in the EICrecon framework to allow simple jet studies using the flat tree output and to facilitate benchmarks.
Meeting Coordinates
The task force meets on Tuesdays at 3 pm Eastern
Jet Finder Documentation
One will find two jet collections in the default output of EICrecon, GeneratedJets and ReconstructedJets. In both cases, the jets are formed via FastJet3 according the parameters listed in the table below. The former collections corresponds to jets created from the truth particles (i.e. particles from the MCParticles collection with getGeneratorStatus() == 1, and the latter corresponds to jets created from charged reconstructed particles (i.e. particles from the ReconstructedParticles collection).
| Parameter | Name | Value |
|---|---|---|
| Jet algorithm | m_jetAlgo | anti-kT |
| Jet recombination scheme | m_recombScheme | E-scheme |
| Jet resolution parameter | m_rJet | 1 |
| Minimum constituent | m_minCstPt | 0.2 GeV/c |
| Maximum constituent | m_maxCstPt | 100 GeV/c |
| Minimum jet | m_minJetPt | 1 GeV/c |
| Area type | m_areaType | active |
| Maximum ghost rapidity | m_ghostMaxRap | 3.5 |
| No. of repeated ghosts | m_numGhostRepeat | 1 |
| Area per ghost | m_ghostArea | 0.001 |
Note that the jet-finding parameters can not be set at runtime by the user currently. However, the ability to do so will be implemented in the near future. Furthermore, it should be emphasized that these default jet collections are primarily for benchmarking and simple jet studies. More sophisticated analyses are better handled via user-created plugins. One should consult the documentation and tutorials for more information on the subject.
The jets themselves are stored as edm4eic::ReconstructedParticle objects. Due to PODIO-limitations, both the jets and their constituents are saved in the same output collections: the jets themselves are flagged with getType() == 0, while the constituents are flagged with getType() == 1 with the index of the corresponding jet stored in the PDG ID field (getPDG() == <jet index>). Additional jet information, such as the area, are currently not saved in the EICrecon output, but options for doing so are being explored.