Application of new computational technologies to age calculation

There is a need for exploiting the effective implementation of new technologies within the archaeological investigation, alongside with the improvement of scientific measurement techniques. In general, application of new technologies to age calculation and provenance methodologies can be divided into two major branches; instrumentation technologies as well as information technology and computer science.

The first branch includes applications of, and research in, modern cutting-edge technologies and trends in the state of the art in instrumentation internationally for all techniques in the field of scientific dating. For stimulated luminescence dating, the use of instrumentation with technological features such as spatially resolved measurements, allows measuring alternative as well as innovative luminescence signals, such as TA – OSL and IRPL, in excellent alignment with the current need for luminescence age limit’s extension effort internationally. Thanks to this new instrumentation advances, luminescence measurements become both more complicated as well as more numerous, making thus the role of analysis decisive. Towards this direction, RCDOT Group is fully aligned to the needs of the modern cutting-edge research of 21st century, trying to implement more robust either signal or statistical analysis routines, by applying the latest trends in information technology and computer science. Nevertheless, new computational technologies were introduced due to another prominent reason, namely the need for acquainting, improving, and storing large volumes of data quickly and cheaply, in order to be subsequently reused in diverse ways. Due to the big existing repository in experimental data on Radiocarbon Dating, Applications of Stimulated Luminescence and Marble Provenance methodologies, incorporation of new information and computing technologies in all aforementioned research fields has become mandatory.

During the past 5 years there has been a concerted effort in the research community to develop open-access extensive software packages for both analysis of experiment data and modeling. Commonly used computing environments such as MATLAB, R and Excel, can all be compared when it comes to automating and indicating deficiencies in terms of significance. In contrast to commercially available software, both R and Python are capable of processing big data quantities, make quick calculations, and its formulae can be more sophisticated and practical (Pagonis, 2021; 2022). The use of Python opens up the possibility of further applications of these computational technologies in Luminescence dating and Luminescence Dosimetry applications. Python's libraries are important for data analysis since they include numerous capabilities and allow users to develop and construct their own analytical models. 

Building up on the knowledge, experience and precision that has developed over the last years, the scope, research and services of the RCDOT Group have broadened to a much wider range of fields, implementing the Python computing language to various tasks, such as:

  1. Modelling of physical properties using numerical and Monte Carlo Simulations (Tsoutsoumanos et al., 2022);
  2. Statistical based machine learning methodologies;
  3. Signal analysis tools (for example deconvolution and fitting algorithms, Konstantinidis et al., 2021; Prevezanou et al., 2022);
  4. Development of new analysis software tools (Pagonis and Kitis, 2022).

Statistical analysis, in geo-archaeo-chronology, generally aims to improve the precision, accuracy, and range of dating methods. Archaeologists usually rely on a range of data sources to reach their conclusions. The application of Bayesian statistics plays a pivotal role in the development of age assessment techniques and dating approaches. Bayesian modeling and the construction of robust probabilistic frameworks address questions that presently are not possible to answer due to dating imprecision. In a formal statistical method, Bayesian modeling permits data to be examined with relative archaeological knowledge such as "prior information," strati-graphic, contextual features, and material-related uncertainty. This can result in more precise formal date calculations, as well as resilient probability distributions for certain boundary events inside the developed models. The estimated probabilities in the boundaries are critical in inter-site and inter-regional comparisons because they usually correlate to the beginning and end of archaeological stages. Furthermore, using Bayesian approaches allows to incorporate dates from several techniques into a single chronometric model. For routine C-14 dating, the IntCal20 internationally-agreed radiocarbon calibration curve combined with open access analysis tool, are widely applied during the last decade. The application also of Bayesian modelling in Stimulated Luminescence is a hot research topic.

For cases where exploratory analysis hasn’t enabled proper determination of the shape of the underlying model, various machine learning techniques based on statistics, and statistical indicators such as Pearson’s correlation, Principal component analysis, Cluster analysis etc., can be applied in the data sets in order to identify correlations, classify sensible data and throw off assumptions attached to the statistical methodology. Once a model has been built and applied to a single data set, the statistical framework provides a mechanism for learning by experience. When more data of the same type become available, the posterior becomes the prior. Instead of viewing each inquiry as a unique case, a model might be created based on previous data gathered from earlier study.


Konstantinidis, P.G., Kioumourtzoglou, S., Polymeris, G.S., Kitis, G., 2022. Applied Radiation and Isotopes 176, 109870.

Pagonis, V., 2021. Luminescence: Data Analysis and Modeling Using R. Springer.

Pagonis, V., 2022. Luminescence Signal Analysis Using Python. Springer.

Pagonis, V., Kitis, G., 2022. Radiation Measurements 153, 106730.

Prevezanou, K., Kioselaki, G., Tsoutsoumanos, E., Konstantinidis, P.G., Polymeris, G.S., Pagonis, V., Kitis, G., 2022. Radiation Measurements 154, 106772.

Tsoutsoumanos, E., Konstantinidis, P.G., Polymeris, G.S., Karakasidis, T., Kitis, G., 2022. Radiation Measurements 153, 106735.

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