the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Nov 2025
OSLThermo and ESRThermo: Libraries of codes for trapped-charge thermochronometry
Abstract. Over the past fifteen years, trapped-charge (T-C) thermochronometry has been established as an ultra-low temperature (<80 °C) thermochronometric system. Its novelty is its ability to resolve rock cooling within the final few km of Earth's surface, as well as rock-surface temperature changes since the Last Glacial Maximum to the present day. Deriving temperature histories from the luminescence signals of feldspar minerals, or the electron spin resonance signals of quartz minerals, requires the modelling of both signal accumulation and signal loss in response to mineral exposure to ionizing radiation and temperature, as well as athermal signal losses for feldspar minerals. Two open-source libraries have been developed in MATLAB that allow different numerical models to be used for this purpose; the first is applicable to the infra-red stimulated luminescence (IRSL) of feldspar minerals (OSLThermo) and the second to the electron spin resonance (ESR) signal of quartz minerals (ESRThermo). These libraries have been made available in GITHUB and this contribution describes their broad structure, the T-C models that have been implemented and their practical use.
Codes are available for download on GitHub: https://github.com/GeorginaKing/OSLThermo for luminescence thermochronometry & https://github.com/GeorginaKing/ESRThermo for ESR thermochronometry.
Competing interests: One of the co-author is part of GChron editorial board.
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Status: open (until 03 Jan 2026)
- RC1: 'Comment on egusphere-2025-5474', Jintang Qin, 17 Dec 2025 reply
Model code and software
OSLThermo library Chloé Bouscary and Georgina E. King https://github.com/GeorginaKing/OSLThermo
ESRThermo library Chloé Bouscary and Georgina E. King https://github.com/GeorginaKing/ESRThermo
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- 1
The potential of trapped-charge systems, e.g., luminescence and ESR, to serve as wide spectrum and highly adaptive thermometer, has been realized by some experimental studies. However, these kind of attempts are far more less than those were expected at the emergence of this promising and unique technique. One barrier leading to this underperformance is the lack of an automatic or semi-automatic program to bridging the experimental data to the thermal information registered. This technique note, as well as the two programs posted, is a timely contribution to the T-C community and the users from a broader field, which I believe will intrigue iterative optimizations to make it be more powerful and robust. The note is well written and easy to follow by readers. I tentatively ran both two programs on my laptop with Matlab 2023b, and they work well in general. Therefore, I'd like to recommend a quick publication of this note and the related programs for more extended application, tests and optimizations, subjecting to minor revisions suggested as follows:
1) I may suggest the authors emphasize the multi-thermometer feature of the T-C system in the introduction, e.g., Qin et al. (2015) Radiation Measurement; King et al. (2016) Quaternary Geochronology, since it is one of the most distinct pros of the T-C system. It is easy to measure in practice and the multi-thermometer strategy would reduce a lot the uncertainty and illness of the inversion problem. Indeed, both two programs take this benefits.
2) The script names and outputs are listed in Table 1, which helps on understanding what we do step by step. To have a pilot view of the whole work flow, I may suggest the authors include a figure to show the logic and data flows of the programs to make readers follow easily, as that done for most of the programs and software.
3) For section 2.4, I think it is indeed the rate equations for the T-C system under different kinetics rather than anything related to data inversion, which are suggested to move to previous sections. Instead, for the "Data inversion" section here, a general description of the sampling methods, acceptance and convergence criteria as well as framework of uncertainty quantification, is encouraged to be included here.
4) I ran the ESRThermo demo program on my laptop with Matlab 2023b. The program went well; however, the color plot of Fig. 2 and Fig. 3 does not show as that expected. Please have a check.
5) Closure temperature is the key parameter for evaluating the sensitivity of T-C system to surface process; meanwhile, it is also crucial for referring the T-C system to other thermo-chronometers. Therefore, if the closure temperature could be evaluated/calculated and shown, it would be of great benefits to colleagues from both the T-C community and broader communities.
6) line 21, suggest change ‘final’ to ‘uppermost’; line 313, suggest change ‘fit to the natural measured data’ to ‘fitting to the natural data’.