Raising the bar: Deep learning on comprehensive database sets new benchmark for automated fission-track detection

Apatite fission-track (FT) thermochronology is widely used for constraining the thermal evolution of crustal rocks. However, manual FT identification is time-intensive and subjective. Although recent AI-based approaches have shown promise, performance often declines for complex, natural samples due to limited and overly idealised training data. We introduce two open-access convolutional neural networks (CNNs) for automatic detection of surface-intersecting FTs in apatite and mica. The first, HALtracks 2D , uses paired reflected- and transmitted-light surface images, while HALtracks 3D incorporates an additional 3D stack of transmitted light images. HALtracks 2D exhibits mean accuracies (94.2–91.6 %) that are as good or better than both HALtracks 3D and all previous FT algorithms across a broad range of apatite fission track densities (up to 8.54 × 10 6 tracks/cm 2 ) on expert-curated reference data. This improvement is due to a comprehensive training dataset comprising a wider range of track densities and etch-pit morphologies. Unexpectedly, HALtracks3D performed worse (91.5–80.1 %), likely because reflected-light information—critical for recognising track openings—became underrepresented among multiple transmitted-light inputs during CNN training. At very high track densities (>8.54 × 10 6 tracks/cm 2 ) pushing the analytical boundaries of optical fission-track counting, Coincidence Mapping (Gleadow et al., 2009) remains more accurate than HALtracks 2D . Thermochronologists might therefore consider utilising a combination of automated fission-track algorithms depending on FT density. Future work could expand the open-access training dataset to include a broader range of apatite FT specimens, and increased metadata for targeted CNN training on spurious features such as surface imperfections and dislocations, which are misidentified as fission tracks by existing algorithms. The open-access testing dataset presented here provides a benchmark for evaluating future FT algorithms. Nevertheless, HALtracks 2D's enhanced accuracy brings apatite FT analysis significantly closer to full automation, with the potential to mitigate observer bias, reduce inter-laboratory variability, and broaden the accessibility of the technique to the wider geoscience community. • HALtracks 2D enables near-automated FT analysis across broad track density ranges. • It outperforms all prior FT algorithms on simple and complex real-world samples. • Detection accuracy is better than inter-lab variability in conventional FT counts. • Open-access datasets support training and benchmarking future FT detection algorithms.