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Context. The third Gaia data release (DR3) provides a wealth of new data products. The early part of the release, Gaia EDR3, already provided the astrometric and photometric data for nearly two billion sources. The full release now adds improved parameters compared to Gaia DR2 for radial velocities, astrophysical parameters, variability information, light curves, and orbits for Solar System objects. The improvements are in terms of the number of sources, the variety of parameter information, precision, and accuracy. For the first time, Gaia DR3 also provides a sample of spectrophotometry and spectra obtained with the Radial Velocity Spectrometer, binary star solutions, and a characterisation of extragalactic object candidates. Aims. Before the publication of the catalogue, these data have undergone a dedicated transversal validation process. The aim of this paper is to highlight limitations of the data that were found during this process and to provide recommendations for the usage of the catalogue. Methods. The validation was obtained through a statistical analysis of the data, a confirmation of the internal consistency of different products, and a comparison of the values to external data or models. Results. Gaia DR3 is a new major step forward in terms of the number, diversity, precision, and accuracy of the Gaia products. As always in such a large and complex catalogue, however, issues and limitations have also been found. Detailed examples of the scientific quality of the Gaia DR3 release can be found in the accompanying data-processing papers as well as in the performance verification papers. Here we focus only on the caveats that the user should be aware of to scientifically exploit the data.

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A few errors in Table 3, equations 14,16, 18, 25, 29, and 38, and two supplementary files (ESM4 and ESM9) were found in the above-mentioned paper. The corrected table and equations are published here. However, these errors do not compromise the discussion and analysis in the paper. The publisher and authors apologize for these errors and for inconveniences they may have caused. ].

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The detection of radioactive hot-spots and the identification of the radionuclides present have been a challenge for the security sector, especially in situations involving chemical, biological, radiological, nuclear and explosive threats, as well as naturally occurring radioactive materials. This work proposes a solution based on Machine Learning techniques, with a focus on artificial neural networks (NNs), in order to localise, quantify and identify radioactive sources. Firstly, the created RHLnet model uses observations of radiological intensity counts and corresponding localisations to estimate the number, location and activity of unknown radioactive sources present in a given scenario. Then, another model (RHIdnet) gets the gamma spectrum of the sources to perform the identification of the corresponding radionuclides. For this, a training data set composed of simulated data is used during the training process, and so, using algorithms with the models already trained, fast and accurate predictions are achieved, ensuring the reliability of such a NN-based approach. The proposed solution is tested in simulated and real scenarios, with multiple sources, providing a low number of limitations, related to possible false negatives and false positives. Besides, the results have shown that the algorithm is scalable for very large regions, as well as for very small scenarios. Single and multiple isotope identification on each sample is explored, highlighting the benefits as well as possible improvements. Thus, NNs have demonstrated the capability of being an emerging tool with the potential to make a difference in the nuclear field, by helping in the development of novel techniques and new solutions in order to safeguard human lives.

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