Constraining Ultralight Axions with CSST Weak Gravitational Lensing and Galaxy Clustering Photometric Surveys
Ultralight axion (ULA) can be one of the potential candidates for dark matter. The extremely low mass of the ULA can lead to a de Broglie wavelength the size of galaxies which results in a suppression of the growth of structure on small scales. In this work, we forecast the constraint on the ULA par...
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Veröffentlicht in: | arXiv.org 2024-02 |
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Zusammenfassung: | Ultralight axion (ULA) can be one of the potential candidates for dark matter. The extremely low mass of the ULA can lead to a de Broglie wavelength the size of galaxies which results in a suppression of the growth of structure on small scales. In this work, we forecast the constraint on the ULA particle mass \(m_{\text{a}}\) and relative fraction to dark matter \(f_{\text{a}} = \Omega_{\text{a}}/\Omega_{\text{d}}\) for the forthcoming Stage IV space-based optical survey equipment \(\it{CSST}\) (China Space Station Telescope). We focus on the \(\it{CSST}\) cosmic shear and galaxy clustering photometric surveys, and forecast the measurements of shear, galaxy, and galaxy-galaxy lensing power spectra (i.e. 3\(\times\)2pt). The effects of neutrino, baryonic feedback, and uncertainties of intrinsic alignment, shear calibration, galaxy bias, and photometric redshift are also included in the analysis. After performing a joint constraint on all the cosmological and systematical parameters based on the simulated data from the theoretical prediction, we obtain a lower limit of the ULA particle mass \(\text{log}_{10}(m_{\text{a}}/\text{eV}) \geqslant -22.5\) and an upper limit of the ULA fraction \(f_{\text{a}} \leqslant 0.83\) at 95\% confidence level, and \(\text{log}_{10}(m_{\text{a}}/\text{eV}) \geqslant -21.9\) with \(f_{\text{a}} \leqslant 0.77\) when ignoring the baryonic feedback. We find that the CSST photometric surveys can improve the constraint on the ULA mass by about one order of magnitude, compared to the current constraints using the same kind of observational data. |
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ISSN: | 2331-8422 |