The competition runs in several steps on CodaLab platform: https://competitions.codalab.org/competitions/19818
In the first step of this challenge, we ask you to classify non-zero pixels into two basic categories of particles: energy deposited by electron/positron, referred to as EM-particle, vs. all other particles. An accurate identification of EM-particle pixels is a crucial task to identify electron neutrino interaction for neutrino oscillation experiments using LArTPC detectors. In a traditional data reconstruction process of LArTPC experiments, this distinction is made after pixels are clustered into individual particles and analyzing the topological feature of clustered pixels. However, this is proven to be difficult. Instead, having a pixel-level distinction of EM-particles beforehand can improve the performance of clustering and simplify the rest of the data reconstruction chain.
At the second step of the challenge, we will add another distinct label to those pixels that contain energy deposited by protons. Two most basic yet important neutrino interaction final states contain electron+proton from electron neutrino interaction or muon+proton from muon neutrino interaction. Adding the proton label, therefore, improve the separation power between two interaction channels.
Finally, at the third step of the challenge, we will make the simulation sample more realistic by introducing gaps in the data sample which represents an unresponsive part of the detector. Your algorithm needs to overcome this lack of information in order to be proven useful for an application to real data.
There is going to be a competition organised within the school in order to practice your skills. More details will follow as the school starts.
To help you started, please follow this starterkit repository: https://github.com/yandexdataschool/mlhep2018-starterkit
