Speaker
Description
We present the first release of a large-scale, fully simulated benchmark dataset using the OpenDataDetector (ODD) under high-luminosity collider conditions (aka ColliderML). The ODD integrates several advanced next-generation detector technologies to realistically capture the complexity of collisions expected at future collider experiments, notably at the High-Luminosity Large Hadron Collider (HL-LHC) and the Future Circular Collider (FCC). This dataset comprises O(1 million) high-pileup collision events, realistically simulated and digitized, covering O(10) important Standard Model (SM) and Beyond Standard Model (BSM) physics channels. Additionally, a similar dataset of single-particle samples are included for 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.
In this talk, we present the development of the simulation pipeline, using tooling from the ACTS and Key4HEP projects, and the digitization procedure, which uses best-practices borrowed from experimental collaborations. We present studies of reconstruction performance, in particular using ACTS track finding, fitting and vertexing, which forms a baseline and benchmark to compare alternative reconstruction approaches against. To facilitate widespread adoption, the ColliderML dataset is accompanied by an intuitive software library designed for efficient data access and processing.