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Villanueva-Domingo, P., Villaescusa-Navarro, F., Genel, S., Angles-Alcazar, D., Hernquist, L., Marinacci, F., et al. (2023). Weighing the Milky Way and Andromeda galaxies with artificial intelligence. Phys. Rev. D, 107(10), 103003–8pp.
Abstract: We present new constraints on the masses of the halos hosting the Milky Way and Andromeda galaxies derived using graph neural networks. Our models, trained on 2,000 state-of-the-art hydrodynamic simulations of the CAMELS project, only make use of the positions, velocities and stellar masses of the galaxies belonging to the halos, and are able to perform likelihood-free inference on halo masses while accounting for both cosmological and astrophysical uncertainties. Our constraints are in agreement with estimates from other traditional methods, within our derived posterior standard deviation.
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Villanueva-Domingo, P., Villaescusa-Navarro, F., Angles-Alcazar, D., Genel, S., Marinacci, F., Spergel, D. N., et al. (2022). Inferring Halo Masses with Graph Neural Networks. Astrophys. J., 935(1), 30–15pp.
Abstract: Understanding the halo-galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a similar to 0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub (https://github.com/PabloVD/HaloGraphNet).
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Yoshida, T., Hagura, N., Umezu, R., Algora, A., Tain, J. L., Jordan, D., et al. (2011). Impact of TAGS Measurement on FP Decay Data and Decay Heat Calculations. J. Korean Phys. Soc., 59(2), 1543–1546.
Abstract: Nuclear level schemes are usually constructed from the high-resolution data of the gamma-ray transitions which immediately follow the beta-decay of their parents. It is recognized that this procedure may lead to the “pandemonium problem”. If we use the decay data suffering from the pandemonium problem for the decay heat calculations the beta-ray component will be overestimated and the gamma-ray component underestimated. The beta-feeding data obtained by the total absorption gamma-ray spectroscopy (TAGS) is proved to be free from this problem. In the case of the Japanese data base for the FP decay heat calculation, the theoretical values based on the gross theory of beta-decay are widely introduced to circumvent the pandemonium problem. The gross theory, however, is not good at describing any beta-transition exclusively concentrating to a single level in the daughter nucleus. The TAGS method is also proved to be able to save this situation. Further, we have to change our comprehension over the currently published decay schemes.
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