ACAT 2025: ColliderML Appendix

Conference Contribution

If you visited our poster during ACAT 2025, thank you for your interest! If you missed it, you can find the poster here.

Getting the Data

The ColliderML dataset is available through a lightweight library, accessing a NERSC Public Portal. For instructions on downloading and using the data, please visit the ColliderML homepage.

Timeline

Data is progressively being migrated to the NERSC Public Portal, so if you don't find the data you need, please check again in the coming days. We expect the Pilot of Release 1 to be available by Friday, September 12th 2025. This includes approx. 1TB of data, across 6 Standard Model channels each of 10k events. We expect the full Release 1 to be available by the end of September 2025. This includes approx. 10TB of data, across 6 SM and 4 BSM channels, each of 100k events.

We are striving to provide sample data (a "taster") ahead of each full release, to allow for early testing and development.

Acknowledgments

This work is made possible by a generous NERSC computing allocation: This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0034031.

DM is supported by Danish Data Science Academy, which is funded by the Novo Nordisk Foundation (NNF21SA0069429)

Bugs and Feedback

If you encounter any bugs or have any feedback, please open an issue on the GitHub repository. You can also contact daniel.thomas.murnane@cern.ch.

References

The below references are cited in the ColliderML ACAT 2025 contribution.

[1] S. Amrouche et al., “The Tracking Machine Learning Challenge: Accuracy Phase,” arXiv preprint, arXiv:1904.06778 (2019).
[2] J. Alwall et al., “The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations,” JHEP 07, 079 (2014).
[3] T. Sjöstrand et al., “An Introduction to PYTHIA 8.2,” Comput. Phys. Commun. 191, 159–177 (2015).
[4] S. Höche et al., “Vector-boson fusion at next-to-leading order QCD with parton showers,” SciPost Phys. 12, 091 (2022).
[5] ATLAS Collaboration, “ATLAS ITk Track Reconstruction with a GNN-based pipeline,” ATL-ITK-PROC-2022-006 (2022).
[6] P. Gessinger-Befurt et al., “The Open Data Detector Tracking System,” presented at Instrumentation Days (IN2P3) 2023.
[7] M. Bacchetta et al., “CLD --- A Detector Concept for the FCC-ee,” arXiv:1911.12230 (2019).
[8] C. Adloff et al. (CALICE Collaboration), “Construction and commissioning of the CALICE analog hadron calorimeter prototype,” JINST 5, P05004 (2010).
[9] H. Aihara, P. Burrows, M. Oreglia et al. (SiD Collaboration), “SiD Letter of Intent,” arXiv:0911.0006 (2009).
[10] CMS Collaboration, “The Phase-2 Upgrade of the CMS Endcap Calorimeter,” CERN-LHCC-2017-023, CMS-TDR-019 (2017).
[11] F. Gaede et al., “EDM4hep: A common event data model for HEP,” EPJ Web Conf. 251, 03026 (2021).
[12] F. Gaede et al., “The DD4hep detector description toolkit,” EPJ Web Conf. 245, 02004 (2020).
[13] S. Agostinelli et al. (GEANT4 Collaboration), “GEANT4—A Simulation Toolkit,” Nucl. Instrum. Meth. A 506, 250–303 (2003).
[14] ACTS Collaboration, “A Common Tracking Software (ACTS),” EPJ Web Conf. 245, 02028 (2020).
[15] M. Brondolin et al., “The Key4HEP Software Stack: Recent Progress,” EPJ Web Conf. 295, 05010 (2024).
[16] Key4HEP Collaboration, “k4DetPerformance: CLD/Key4HEP Reconstruction and Digitisation Examples,” (2023), https://github.com/key4hep/k4DetPerformance.
[17] J. Gao et al., “Track Reconstruction with the ACTS Combinatorial Kalman Filter and Seeding,” arXiv:2311.00241 (2023).
[18] ATLAS Collaboration, “Topological cell clustering in the ATLAS calorimeters and its performance in LHC Run 1,” Eur. Phys. J. C 77, 490 (2017).
[19] X. Ju et al., “Performance of a geometric deep learning pipeline for HL-LHC particle tracking,” Eur. Phys. J. C 81, 876 (2021).
[20] J. Duarte et al. (Exa.TrkX Collaboration), “End-to-End Particle Tracking and Reconstruction with GNNs at the HL-LHC,” arXiv preprint, arXiv:2203.08800 (2022).
[21] ATLAS Collaboration, “ATLAS ITk Track Reconstruction with a GNN-based pipeline,” ATL-ITK-PROC-2022-006 (2022).
[22] S. Caillou et al., “Physics Performance of the ATLAS GNN4ITk Track Reconstruction Chain,” EPJ Web of Conf. 295, 03030 (2024).
[23] ATLAS Collaboration, “Technical Design Report for the ATLAS High-Granularity Timing Detector (HGTD),” CERN-LHCC-2020-007, ATLAS-TDR-031 (2020).
[24] CERN LCG, “LCG Views and Releases (documentation page),” (2025), https://lcginfo.cern.ch/.

Citation

If you use the ColliderML dataset in your research, please cite:

@conference{colliderml,
  title={ColliderML: A Machine Learning Library for High-Energy Physics},
  author={Murnane, Daniel and Gessinger, Paul and Saltzburger, Andreas and Zaborowska, Anna and Stefl, Andreas and Skov, Stine Kofoed and Raaholt, Marcus},
  booktitle={ACAT 2025},
  year={2025}
}