MLHEP 2020 Calorimeter regression
This challenge is aimed to recover the particle initial position and momentum in LHCb calorimeter.
The calorimeter system is designed to stop particles as they pass through the detector, measuring the amount of energy lost as each one grinds to a halt. Two types of calorimeter are used at LHCb.
The electromagnetic calorimeter is responsible for measuring the energy of lighter particles, such as electrons and photons, while the experiment's hadron calorimeter samples the energy of protons, neutrons and other particles containing quarks.
In order to submit your solution please follow the instructions below:
1. Register in CodaLab, with the same email you have on github.
2. Fork baseline repository and follow this instruction or/and guideline to integrate your repository into the pipeline.
3. If you want to see solutions of others checkout leaderboard of a competition to find a submission which might inspire you. Good luck!
MLHEP 2020 Calorimeter regression: Evaluation
The submission will be evaluated using scoring function from this code gist.
MLHEP 2020 Calorimeter regression: Rules
Zipped results must be submitted before the 2020-07-24 23:00:00+00:00.
Zipped file should contain a single npz-file named data_test_prediction.npz. This npz-file should contain two numpy arrays in it: ParticlePoint and ParticleMomentum. You may submit 15 submissions every day and 200 in total.
Also, please, if you are submitting not code solution, but raw predictions, please, add a link to where we can find your code in a comment to submission.
MLHEP 2020 Calorimeter regression: Data
Dataset is in npz-format and could be opened with `np.load`. You are provided with two files: data_train.npz and data_test.npz. data_train.npz consists of four numpy-arrays: EnergyDeposit, ParticlePoint, ParticleMomentum, ParticlePDG. data_test.npz contain only one numpy-array: EnergyDeposit. For an example of how to work with it take a look on the starter-kit.