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Sborlini, G. F. R., de Florian, D., & Rodrigo, G. (2014). Triple collinear splitting functions at NLO for scattering processes with photons. J. High Energy Phys., 10(10), 161–29pp.
Abstract: We present splitting functions in the triple collinear limit at next-to-leading order. The computation was performed in the context of massless QCD+QED, considering only processes which include at least one photon. Through the comparison of the IR divergent structure of splitting amplitudes with the expected known behavior, we were able to check our results. Besides that we implemented some consistency checks based on symmetry arguments and cross-checked the results among them. Studying photon-started processes, we obtained very compact results.
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Tain, J. L. et al, Algora, A., Estevez, E., Rubio, B., Valencia, E., & Jordan, D. (2011). Beta Decay Studies of Neutron Rich Nuclei Using Total Absorption Gamma-ray Spectroscopy and Delayed Neutron Measurements. J. Korean Phys. Soc., 59(2), 1499–1502.
Abstract: A complete characterisation of the beta-decay of neutron-rich nuclei can be obtained from the measurement of beta-delayed gamma rays and, whenever the process is energetically possible, beta-delayed neutrons. The accurate determination of the beta-intensity distribution and the beta-delayed neutron emission probability is of great relevance in the fields of reactor technology and nuclear astrophysics. A programme for combined measurements using the total absorption gamma-ray spectroscopy technique and both neutron counters and neutron time-of-flight spectrometers is presented.
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Vatsyayan, D., & Goswami, S. (2023). Lowering the scale of fermion triplet leptogenesis with two Higgs doublets. Phys. Rev. D, 107(3), 035014–9pp.
Abstract: In this paper, we consider the possibility of generating the observed baryon asymmetry of the Universe via leptogenesis in the context of a triplet fermion-mediated type-III seesaw model of neutrino mass. With a hierarchical spectrum of the additional fermions, the lower bound on the lightest triplet mass is similar to 1010 GeV for successful leptogenesis, a couple of orders higher than that of the type-I case. We investigate the possibility of lowering this bound in the framework of two-Higgs-doublet models. We find that the bounds can be lowered down to 107 GeV for a hierarchical spectrum. If we include the flavor effects, then a further lowering by one order of magnitude is possible. We also discuss if such lowering can be compatible with the naturalness bounds on the triplet mass.
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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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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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