Speaker
Description
To explore what our universe is made of, scientists at CERN are colliding protons, essentially recreating mini big bangs, and meticulously observing these collisions with intricate silicon detectors.
While orchestrating the collisions and observations is already a massive scientific accomplishment, analyzing the enormous amounts of data produced from the experiments is becoming an overwhelming challenge.
Event rates have already reached hundreds of millions of collisions per second, meaning physicists must sift through tens of petabytes of data per year. And, as the resolution of detectors improve, ever better software is needed for real-time pre-processing and filtering of the most promising events, producing even more data.
To help address this problem, a team of Machine Learning experts and physics scientists working at CERN (the world largest high energy physics laboratory), has partnered with Kaggle and prestigious sponsors to answer the question: can machine learning assist high energy physics in discovering and characterizing new particles?
Specifically, in this competition, you’re challenged to build an algorithm that quickly reconstructs particle tracks from 3D points left in the silicon detectors. This challenge consists of two phases:
The Accuracy phase has run on Kaggle from May to 13th August 2018 (Winners to be announced by end September). Here we’ll be focusing on the highest score, irrespective of the evaluation time. This phase is an official IEEE WCCI competition (Rio de Janeiro, Jul 2018).
The Throughput phase will run on Codalab starting in September 2018. Participants will submit their software which is evaluated by the platform. Incentive is on the throughput (or speed) of the evaluation while reaching a good score. This phase is an official NIPS competition (Montreal, Dec 2018).
All the necessary information for the Accuracy phase is available here on Kaggle site. The overall TrackML challenge web site is there.
