The objective of high-energy particle physics (HEP) is to study the basic constituents of matter, largely within the theory called the Standard Model and its possible extensions. The main experimental tools are particle accelerators and colliders in which beams of particles are accelerated to very high kinetic energy and collided into other particles. The particles resulting from the collision are then detected in particle detectors consisting mainly of track detectors (high-resolution devices in which the paths of individual charged particles can be separated) and calorimeters (measuring the energy of particles or groups of particles). From these raw measurements, different events (mainly particle decays and collisions) are reconstructed, the whole “picture” is compared to model predictions, and model parameters (for example, the existence and the mass of new particles) are inferred from comparing a large statistics of collision events to simulated events.
Particle physics is one of the posterboys of today’s data science revolution. Indeed, large-scale HEP experiments assimilated computational paradigms a long time ago: both simulators and semi-automatic data analysis techniques have been applied widely for decades. In particular, nonparametric classification and regression are now routinely used as parts of the reconstruction (inference) chain. More recently, state-of- the-art budgeted learning techniques have also started to be used for real-time event selection, and this year we have also seen the first use of deep learning techniques in particle searches. Nevertheless, most of these applications went largely unnoticed by the machine learning (ML) community. The goal of this workshop is to contribute to improving the dialog between these two communities. The expected outcome is twofold: we hope to introduce a set of exciting scientific questions and open problems in computational statistics and machine learning to the NIPS community, and, at the same time, to transmit some of the state-of-the-art techniques developed in ML to the HEP community.