Open Dataset Challenge

IceCube Neutrino Energy Regression

Estimate incoming neutrino energy from sparse optical-module observations and reconstructed event-level features.

Muon particle tracks in a detector event
CategoryEnergy reconstruction
Detector typeIceCube
Technical methodStatistics
TimeframeDataset definition pending
Dataset typeIceCube optical-module event records
Challenge typeOpen dataset benchmark
Technical problemNeutrino energy regression

Protocol

Scope

This planned challenge will evaluate energy regression from IceCube-style sparse detector observations and event summaries. The detector context follows the optical-module geometry and timing structure described for the IceCube Neutrino Observatory (Aartsen et al. 2017).

Protocol needs

The final protocol should define target energy, preprocessing, event containment assumptions, split strategy, and uncertainty reporting. The proposed headline score is median absolute log error,

\[ \mathrm{MALE} = \mathrm{median}_i \left| \log E_i - \log \hat{E}_i \right|. \]

Leaderboard

The leaderboard is not open until the dataset record and baseline regressor are ready. The challenge should separate point-estimate quality from calibrated uncertainty so probabilistic submissions can be compared fairly.

References

Aartsen, M. G. et al. 2017. “The IceCube Neutrino Observatory: Instrumentation and Online Systems.” Journal of Instrumentation 12 (03): P03012. https://doi.org/10.1088/1748-0221/12/03/P03012.

Baseline

Baseline solution

Repository location for the minimal reproducible script (MRS) used to reproduce the baseline score for this task.

View baseline source

Validation

Validation

Fetch the evaluation bundle for this dataset, then send an HDF5 submission shaped according to the dataset schema.

Submission schema
opendc eval get icecube-energy-regression-v0
opendc eval send <submission.h5>