Speaker
Description
We present the first full release of ColliderML, a large-scale, fully simulated benchmark dataset for algorithm R&D and development, as well as machine-learning applications.
It is built on top of the OpenDataDetector (ODD) under high-luminosity collider conditions (ColliderML). ODD comprises a set of subsystems that are representative of future collider experiments like at the High-Luminosity Large Hadron Collider (HL-LHC) and the Future Circular Collider (FCC).
The dataset contains O(1 million) high-pileup proton-proton collisions, realistically simulated and digitized, distributed across O(10) important Standard Model (SM) and Beyond Standard Model (BSM) physics channels. Single-particle samples are also included, targeting track fitting and calorimeter response studies. The object content includes detailed energy depositions from tracker and calorimeter sensors, as well as reconstructed physics objects such as particle tracks and jets.
The simulation pipeline leverages tooling from the ACTS and Key4HEP projects, with digitization procedures adopting best-practices from experimental collaborations. Baseline reconstruction performance using ACTS track finding, fitting and vertexing establishes benchmarks for comparing alternative approaches. Beyond conventional reconstruction, the dataset enables diverse machine learning studies, including distinguishing physics channels from multi-scale detector information, harnessing multiple detector regions for integrated reconstruction tasks like particle flow, and assessing generalizability to unseen SM and BSM conditions. The realistic detector effects from full simulation provide crucial tests for symmetry-preserving architectures, while the multi-scale data structure supports exploration of particle physics foundation models that consume low-level readout and adapt to various downstream tasks. An intuitive software library accompanies the dataset to facilitate widespread adoption and efficient data processing.