The FAIR Universe – HiggsML Uncertainty Challenge focused on measuring the physical properties of elementary particles with imperfect simulators. Participants were required to compute and report confidence intervals for a parameter of interest regarding the Higgs boson while accounting for various systematic (epistemic) uncertainties. The dataset is a tabular dataset of 28 features and 280 million instances. Each instance represents a simulated proton-proton collision as observed at CERN’s Large Hadron Collider in Geneva, Switzerland. The features of these simulations were chosen to capture key characteristics of different types of particles. These include primary attributes, such as the energy and three-dimensional momentum of the particles, as well as derived attributes, which are calculated from the primary ones using domain-specific knowledge. Additionally, a label feature designates each instance’s type of proton-proton collision, distinguishing the Higgs boson events of interest from three background sources. As outlined in this paper, the permanent dataset release allows long-term benchmarking of new techniques. The leading submissions, including Contrastive Normalising Flows and Density Ratios estimation through classification, are described. Our challenge has brought together the physics and machine learning communities to advance our understanding and methodologies in handling systematic uncertainties within AI techniques.

As next-generation experimental and observational instruments for scientific research are being deployed with higher resolutions and faster data capture rates, the fundamental demands of producing high-quality scientific throughput require portability and performance to meet the high productivity goals. Understanding such a workflow’s end-to-end performance on HPC systems is formidable work. In this paper, we address this challenge by introducing a Workflow Roofline model, which ties a workflow’s end-to-end performance with peak node- and system-performance constraints. We analyze four workflows: LCLS, a time-sensitive workflow that is bound by system external bandwidth; BerkeleyGW, a traditional HPC workflow that is bound by node-local performance; CosmoFlow, an AI workflow that is bound by the CPU preprocessing; and GPTune, an auto tuner that is bound by the data control flow. We demonstrate the ability of our methodology to understand various aspects of performance and performance bottlenecks on workflows and systems and motivate workflow optimizations.

To attain its ultimate discovery goals, the luminosity of the Large Hadron Collider at CERN will increase so the amount of additional collisions will reach a level of 200 interaction per bunch crossing, a factor 7 w.r.t the current (2017) luminosity. This will be a challenge for the ATLAS and CMS experiments, in particular for track reconstruction algorithms. In terms of software, the increased combinatorial complexity will have to harnessed without any increase in budget. To engage the Computer Science community to contribute new ideas, we organized a Tracking Machine Learning challenge (TrackML) running on the Kaggle platform from March to June 2018, building on the experience of the successful Higgs Machine Learning challenge in 2014. The data were generated using [ACTS], an open source accurate tracking simulator, featuring a typical all silicon LHC tracking detector, with 10 layers of cylinders and disks. Simulated physics events (Pythia ttbar) overlaid with 200 additional collisions yield typically 10000 tracks (100000 hits) per event. The first lessons from the Accuracy phase of the challenge will be discussed.