Flat central density profiles from scalar field dark matter halos

By A. Bernal, Tonatiuh Matos, Darío Núñez. 2003

645 citations recorded by the source corpus.

Scalar elds endowed with a cosh potential behave in the linear regime, exactly as the cold dark matter (CDM) model. Thus, the scalar eld dark matter (SFDM) hypothesis predicts the same structure formation as the CDM model. This means that CDM and SFDM are equivalent from the cosmological point of view. The free parameters of the SFDM model are determined by cosmological observations. In previous work we showed that if we use such parameters, the scalar eld collapses forming stable objects with a mass around 10 12 M . In the present work we use analytical solutions of the at and weak eld limit of the Einstein-Klein-Gordon equations and show that the SFDM density prole corresponds to a halo with an almost at central density and that it coincides with the CDM model in a broad outer region. This result could solve the problem of the density cusp DM halo in galaxies without any additional hypothesis, thus supporting the viability of the SFDM model.

Subfields: Astronomy and Astrophysics, Nuclear and High Energy Physics

View Paper: https://www.semanticscholar.org/paper/1b01aabaf57886a1041e265cea4a95cd639ed555

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Astronomy and Astrophysics

1. Anne Auger
2. Nikolaus Hansen
3. Kalyanmoy Deb
4. Alexandre Adrien Chotard

In Nuclear and High Energy Physics

1. Anne Auger
2. Nikolaus Hansen
3. Alexandre Adrien Chotard

Citations in the corpus

• Economic and Environmental Generation and Voyage Scheduling of All-Electric Ships (2016, background): “employed in optimisation problems [29], [30], especially those with constraints ones [31]–[33], and are well suited for the proposed problem.”
• Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons (2019, background): “pose a great challenge to evolutionary algorithms (EAs) due to the fact that they require EAs to offer tradeoffs among the conflicting objectives subject to all constraints [4], [5].”
• Markov Chain Analysis of Cumulative Step-Size Adaptation on a Linear Constrained Problem (2015, methodology): “Different techniques for handling constraints in randomised algorithms have been proposed, see (Mezura-Montes and Coello, 2011) for a survey.”
• Push and Pull Search for Solving Constrained Multi-objective Optimization Problems (2017, background): “In order to balance the constraints and the objectives, some researchers adopt multi-objective evolutionary algorithms (MOEAs) or MaOEAs (in the case of the number of objectives is greater than three) to deal with constraints [15].”