Meneghetti et al. (2020) recently reported an excess of galaxy-galaxy strong lensing (GGSL) in galaxy clusters compared to expectations from the LCDM cosmological model. Theoretical estimates of the GGSL probability are based on the analysis of numerical hydrodynamical simulations in the LCDM cosmology. We quantify the impact of the numerical resolution and AGN feedback scheme adopted in cosmological simulations on the predicted GGSL probability and determine if varying these simulation properties can alleviate the gap with observations. We repeat the analysis of Meneghetti et al. (2020) on cluster-size halos simulated with different mass and force resolutions and implementing several independent AGN feedback schemes. We find that improving the mass resolution by a factor of ten and twenty-five, while using the same galaxy formation model that includes AGN feedback, does not affect the GGSL probability. We find similar results regarding the choice of gravitational softening. On the contrary, adopting an AGN feedback scheme that is less efficient at suppressing gas cooling and star formation leads to an increase in the GGSL probability by a factor between three and six. However, we notice that such simulations form overly massive subhalos whose contribution to the lensing cross-section would be significant while their Einstein radii are too large to be consistent with the observations. The primary contributors to the observed GGSL cross-sections are subhalos with smaller masses, that are compact enough to become critical for lensing. The population with these required characteristics appears to be absent in simulations.
The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters / M. Meneghetti, A. Ragagnin, S. Borgani, F. Calura, G. Despali, C. Giocoli, G.L. Granato, C. Grillo, L. Moscardini, E. Rasia, P. Rosati, G. Angora, L. Bassini, P. Bergamini, G.B. Caminha, G. Granata, A. Mercurio, R.B. Metcalf, P. Natarajan, M. Nonino, G.V. Pignataro, C. Ragone-Figueroa, E. Vanzella, A. Acebron Munoz, K. Dolag, G. Murante, G. Taffoni, L. Tornatore, L. Tortorelli, M. Valentini. - In: ASTRONOMY & ASTROPHYSICS. - ISSN 1432-0746. - 668:(2022 Dec 20), pp. A188.1-A188.14. [10.1051/0004-6361/202243779]
The probability of galaxy-galaxy strong lensing events in hydrodynamical simulations of galaxy clusters
C. Grillo;P. Bergamini;G. Granata;A. Acebron Munoz;
2022
Abstract
Meneghetti et al. (2020) recently reported an excess of galaxy-galaxy strong lensing (GGSL) in galaxy clusters compared to expectations from the LCDM cosmological model. Theoretical estimates of the GGSL probability are based on the analysis of numerical hydrodynamical simulations in the LCDM cosmology. We quantify the impact of the numerical resolution and AGN feedback scheme adopted in cosmological simulations on the predicted GGSL probability and determine if varying these simulation properties can alleviate the gap with observations. We repeat the analysis of Meneghetti et al. (2020) on cluster-size halos simulated with different mass and force resolutions and implementing several independent AGN feedback schemes. We find that improving the mass resolution by a factor of ten and twenty-five, while using the same galaxy formation model that includes AGN feedback, does not affect the GGSL probability. We find similar results regarding the choice of gravitational softening. On the contrary, adopting an AGN feedback scheme that is less efficient at suppressing gas cooling and star formation leads to an increase in the GGSL probability by a factor between three and six. However, we notice that such simulations form overly massive subhalos whose contribution to the lensing cross-section would be significant while their Einstein radii are too large to be consistent with the observations. The primary contributors to the observed GGSL cross-sections are subhalos with smaller masses, that are compact enough to become critical for lensing. The population with these required characteristics appears to be absent in simulations.File | Dimensione | Formato | |
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