Establishing an etching procedure for monazite fission tracks (MFTs) is essential for MFT dating. In this short communication, we investigated the parameters governing the etching rate of MFTs, particularly the effect of radiation damage. Our results show an inverse relationship between MFT etching time and the degree of radiation damage. We show that existing etching recipes may not sufficiently etch MFTs in young monazite, advocating the importance of revising etching methods.

Fission tracks are damage trails from uranium fission in minerals, whose ages and thermal histories are deduced from their number and length. A mineral is etched for observing the tracks with a microscope. We show that the etching and observation conditions affect the track count and explain it in the framework of a recent etch model. We conclude that established solutions do not secure that the ages and thermal histories inferred from track counts and measurements are accurate.

Fission tracks are generated in minerals due to spontaneous fission of uranium-238. The initial track length decreases with time due to recrystallization. The age of a track can be calculated by counting the number of the shorter tracks per volume and including the decay constant. However, the theoretical order of the track length versus time is disrupted by uncertainties. We shown how the order can be re-established. The age of tectonic events such as uplift and burial can then be determined.

We introduce a new model of how etching reveals damage tracks left by fissioning atoms, which accounts for variable along-track etching rates. This complete characterization explains many observations, including community difficulty in obtaining consistent track length measurements. It also provides a quantitative basis for optimizing etching procedures, discerning more about how radiation damage anneals, and ultimately deriving more reproducible fission-track ages and thermal histories.

Artificial intelligence techniques are capable of automatically detecting fission tracks in minerals. The AI-Track-tive software presented here can be used to automatically determine fission track densities for apatite fission track dating studies. Apatite fission track dating is mainly applied to tectonic research on exhumation rates in orogens. Time-consuming manual track counting can be replaced by deep neural networks capable of automatically finding the large majority of tracks.