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.
|
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.
|
Bhattacharya, S., Sil, A., Roshan, R., & Vatsyayan, D. (2022). Symmetry origin of baryon asymmetry, dark matter, and neutrino mass. Phys. Rev. D, 106(7), 075005–10pp.
Abstract: We propose a minimal model based on lepton number symmetry (and violation), to address a common origin of baryon asymmetry, dark matter and neutrino mass generation. The model consists of a vectorlike fermion to constitute the dark sector, three right-handed neutrinos (RHNs) to dictate leptogenesis and neutrino mass, while an additional complex scalar is assumed to be present in the early Universe the decay of which produces both dark matter and RHNs via lepton number violating and lepton number conserving interactions respectively. Interestingly, the presence of the same scalar helps in making the electroweak vacuum stable until the Planck scale. The unnatural largeness and smallness of the parameters required to describe correct experimental limits are attributed to lepton number violation. The allowed parameter space of the model is illustrated via a numerical scan.
|
Bas i Beneito, A., Herrero-Garcia, J., & Vatsyayan, D. (2022). Multi-component dark sectors: symmetries, asymmetries and conversions. J. High Energy Phys., 10(10), 075–31pp.
Abstract: We study the relic abundance of several stable particles from a generic dark sector, including the possible presence of dark asymmetries. After discussing the different possibilities for stabilising multi-component dark matter, we analyse the final relic abundance of the symmetric and asymmetric dark matter components, paying special attention to the role of the unavoidable conversions between dark matter states. We find an exponential dependence of the asymmetries of the heavier components on annihilations and conversions. We conclude that having similar symmetric and asymmetric components is a natural outcome in many scenarios of multi-component dark matter. This has novel phenomenological implications, which we briefly discuss.
|
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).
|
