An open benchmark for deep learning–based event reconstruction + in neutrino telescopes — seven large-scale simulated datasets across six + detector geometries, with baseline results for four reconstruction models + on five core tasks.
+ +Baseline performance of four reconstruction algorithms on the NuBench test + partitions, exactly as reported in the paper. Pick a task, compare models + across geometries, then drill into per-dataset metrics.
+Simulated νμ charged- and neutral-current interactions produced with + Prometheus, with a simplified detector response. Geometries are inspired by — + but not identical to — existing and proposed telescopes in water and ice. + Each dataset ships in SQLite and Parquet, with train/test selections, + baseline predictions, and trained model artifacts.
+Two architectures in active use within existing collaborations, one + graph transformer, and one Kaggle-winning transformer — all implemented and + trained in the open-source GraphNeT framework.
+Grab the format you prefer from the dataset cards above — SQLite and + Parquet contain identical data.
+wget -P data/ \
+ <download-link>
+ All files end in .tar.gz, but only some are actually
+ compressed.
# SQLite & model artifacts +tar -xzf file.tar.gz +# Parquet & predictions +tar -xf file.tar.gz+
Both formats are compatible with the + GraphNeT + Dataset classes (SQLite recommended). Train/test partitions are defined + by the included selection files.
+from graphnet.data.dataset import ( + SQLiteDataset, +)+
If you use the NuBench datasets, predictions, or model artifacts in your + work, please cite:
+@article{orsoe2025nubench,
+ title = {NuBench: An Open Benchmark for Deep Learning--Based Event
+ Reconstruction in Neutrino Telescopes},
+ author = {{\O}rs{\o}e, Rasmus F. and Meighen-Berger, Stephan and Lazar,
+ Jeffrey and Prado, Jorge and Moz{\'u}n-Mateo, Iv{\'a}n and
+ Rosted, Aske and Weigel, Philip and Llorente Anaya, Arturo},
+ journal = {arXiv preprint arXiv:2511.13111},
+ year = {2025}
+}
+