2 of 7

Timelines and Budgets

HEP.TrkX

Oct 2106 to March 2018 (12+6 months)� Six months no-cost extension

Budget:� Caltech $200K� FNAL $275K� LBL $275K

12 Authors�2 Papers�11 Conference talks

Exa.TrkX

Jun 2019 to Nov 2021 (30 months)

Budget:� Caltech $525K� FNAL $525K� LBL $525K� SLAC $525K

3 of 7

Goals and Objectives

Provide a framework to develop and evaluate new algorithms for track finding and classification. The framework will be demonstrated by applying advanced pattern recognition techniques to track candidate formation

Develop Tracking algorithms that can run efficiently and robustly in production on the massively parallel architectures (GPUs, TPUs, etc) which will be used by Exascale-class HPCs, and by the online Trigger and Data Acquisition (TDAQ) systems of HEP experiments.

4 of 7

HEP.TrkX Milestones

Group	Deliverable	Due	Institutions	Status	Notes
Simulation	1: Realistic full simulation samples of generic HL-LHC tracking detector.	Q4 16	LBL, FNAL	✅	aCTS, TrackML
Algorithm	2: Reference solution for seeding and track candidate formation using state of the art algorithms.	Q2 17	FNAL, Caltech	❌	Not defined, nor run.
	3: Pattern recognition algorithm to build track candidates from seeds and space-points.	Q3 17	Caltech, LBL	✅	Seeding much better than track following
	4: Adapt track finding algorithm and evaluate in LArTPC environment	Q4 17	FNAL, Caltech	❌	Never started
Milestone: Demonstrate full simulation and reconstruction chain for generic HL-LHC tracking detector and produce plan for full development and implementation		Q4 17	ALL	❌	Doable, not done

5 of 7

Exa.TrkX Milestones Year 1 (Jun 19-Jun 20)

	Description	Due	Institutes	Who
	Paper submission for IPDPS	Oct 19	LBL	Steve
	Classical tracking algorithms running on trackML data, possibly in aCTS	Oct 19	Caltech	JR+Joosep
D2a	First implementation 3D point making GNN (Proto-DUNE)	Dec 19	Fermi	Giuseppe
D2b	First implementation 3D clustering pixel detector DUNE ND	Dec 19	SLAC	Kazu
D1	End-to-end reference solution based on Geometric Deep Learning, initially targeting TrackML dataset.	Dec 19	Caltech, LBNL	?
D3	Sensitivity studies of LHC and LArTPC tracking models, identifying most important nuisance parameters.	Mar 20	Caltech, FNAL	FNAL postdoc?
D4	Strategy to increase tracking model robustness against nuisance parameters.	Mar 20	LBNL, SLAC	LHC Exalearn postdoc
D5	Identify best approaches (Bayesian, Evolutionary, autoML) to optimize tracking model architecture and hyperparameters.	Jun 20	Caltech, FNAL	Y2 Q2
M1	Demonstrate distributed training of HEP tracking models.	Jun 20	All	Y2 Q2

6 of 7

Exa.TrkX Milestones Year 2

	Description	Due	Institutes
D6	Optimize tracking model resource usage with HEP Trigger applications in mind.	Sep 20	FNAL, SLAC
D7	Optimize distributed data delivery, parallelize input data preparation.	Dec 20	Caltech, LBNL
D8	Demonstration of HPC-scale distributed optimization for HEP tracking models.	Mar 21	Caltech, LBNL
D9	Optimize training and parameters of LArTPC network; evaluate resulting performance on different experiments.	Mar 21	FNAL, SLAC
M2	Run HEP Tracking Grand Challenge in collaboration with NERSC Big Data Center.	Jun 21	All

7 of 7

Exa.TrkX Milestones Year 3