Application of Artificial Neural Networks on improving predictions of nuclear radii Vasilis Skarlis1, Emmanuel Bratsolis1, Eleni Charou2, Theo J.Mertzimekis1 1Department of Physics, University of Athens, Zografou Campus, GR-15784, Athens, Greece 2IIT, NCSR “Demokritos”, GR-15310, Ag. Paraskevi, Greece Abstract Artificial Neural Networks (ANN) [1] are mathematical computing paradigms imitating the operations of biological neural systems. Their nonlinear nature and ability to learn from the environment make them highly suited to solve real-world problems from those that are still under development. In the field of Physics there are many problems that cannot be adequately solved with the physics-based methods and the use of ANN may yield better results. In the present work ANNs have been tested in predicting nuclear charge radii considering as input the atomic and mass numbers, exclusively. The performance of different supervised ANNs is evaluated. The dataset used for the training and testing was based on evaluated data of nuclear radii available in IAEA tables . Experimental Method In the first experiment, a training set was constructed from 300 randomly selected elements of the dataset and the ability to predict the nuclear radius was tested on the remaining 609 elements. In order to test the capability of networks to predict unknown nuclear radii a second experiment was performed using a subset of 400 elements randomly selected from the first 600 elements of our dataset. The networks were tested in the remaining “unknown” 309 elements. The results of those experiments are shown below. Results & Discussion Conclusion and future work The Feed Forward Neural Network (FFNN) has shown promising results and can be proven useful in predicting unknown radii in exotic regions of the isotopic chart. Work in the near future will pursue radii predictions in the superheavy region and compare the FFNN results with existing theoretical models. References [1] C.M. Bishop, Pattern Recognition and Machine Learning, Springer, Singapore (2006) [2] I. Angeli and K.P. Marinova, At. Data Nucl. Data Tables 99, 69 (2013) Figure 1: Results of FFNN by using 300 randomly nucleus as training set. With blue are the experimental errors and with orange the network errors Figure 2: Results of RBF by using 300 randomly nucleus as training set. With blue are the experimental errors and with orange the network errors Figure 3: Results of our two networks with 400 randomly selected nucleus form the first 600. With blue are the experimental errors, with orange the FFNN and with yellow the RBF errors. • After some experimentation the appropriate FFNN was the one with two hidden layers, 10 and 5 nodes respectively. • From figures 1,2 we can clearly see that FFNN has smaller errors than RBF. For this reason we can say with certainty that it is suitable for predicting an unknown nucleus radius in the known regions of the isotopic map. • If we look at the errors of the smaller nucleus we can see that our networks have relatively big errors and this is due to their incapability to understand the deeper nuclear phenomena that are present. Those phenomena have small contributions for big nuclei, therefore we can have better predictions as we go near the region of super heavy. Introduction & Motivation The information about size and nuclear density distributions in unstable (radioactive) nuclei is usually extracted from the data on interaction of radioactive nuclear beams with a nuclear target thus doing the experiments and analyzing the data is a difficult and time consuming task. Assuming the nucleus is described by the liquid-drop its radius can be described/predicted by the relation R=r0 A1/3, which is in general a rough estimate. The purpose of this work is to assess whether ANNs are capable of predicting nuclear charge radii given the atomic and mass numbers for a given nucleus. This is particularly useful for exotic species, where experimental data are very scarce