1. Systematic errors on optical-SED stellar mass estimates for galaxies across cosmic time and their impact on cosmology
- Author
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Ana Paulino-Afonso, Santiago González-Gaitán, Lluís Galbany, Ana Maria Mourão, Charlotte R. Angus, Mathew Smith, Joseph P. Anderson, Joseph D. Lyman, Hanindyo Kuncarayakti, Myriam Rodrigues, Fundação para a Ciência e a Tecnologia (Portugal), Agencia Estatal de Investigación (España), Ministerio de Ciencia, Innovación y Universidades (España), Villum Fonden, European Commission, European Research Council, and Centre National de la Recherche Scientifique (France)
- Subjects
Galaxies: fundamental parameters ,Cosmology and Nongalactic Astrophysics (astro-ph.CO) ,Cosmological parameters ,Supernovae: general ,FOS: Physical sciences ,DUST ,Astrophysics::Cosmology and Extragalactic Astrophysics ,DEPENDENCE ,STAR-FORMING GALAXIES ,fundamental parameters [galaxies] ,SUPERNOVA HOST GALAXIES ,SIMPLE-MODEL ,Astrophysics::Solar and Stellar Astrophysics ,cosmological parameters ,Astrophysics::Galaxy Astrophysics ,QB ,LEGACY SURVEY ,IA SUPERNOVAE ,METALLICITY RELATION ,Cosmology: observations ,LUMINOSITIES ,Astronomy and Astrophysics ,HUBBLE-SPACE-TELESCOPE ,Astrophysics - Astrophysics of Galaxies ,observations [cosmology] ,Space and Planetary Science ,Astrophysics of Galaxies (astro-ph.GA) ,Astrophysics::Earth and Planetary Astrophysics ,general [supernovae] ,Astrophysics - Cosmology and Nongalactic Astrophysics - Abstract
Studying galaxies at different cosmic epochs entails several observational effects that need to be taken into account to compare populations across a large time-span in a consistent manner. We use a sample of 166 nearby galaxies that hosted type Ia supernovae (SNe Ia) and have been observed with the integral field spectrograph MUSE as part of the AMUSING survey. Here, we present a study of the systematic errors and bias on the host stellar mass with increasing redshift, which are generally overlooked in SNe Ia cosmological analyses. We simulate observations at different redshifts (0.1 < z < 2.0) using four photometric bands (griz, similar to the Dark Energy Survey-SN program) to then estimate the host galaxy properties across cosmic time. We find that stellar masses are systematically underestimated as we move towards higher redshifts, due mostly to different rest-frame wavelength coverage, with differences reaching 0.3 dex at z ∼ 1. We used the newly derived corrections as a function of redshift to correct the stellar masses of a known sample of SN Ia hosts and derive cosmological parameters. We show that these corrections have a small impact on the derived cosmological parameters. The most affected is the value of the mass step ΔM, which is reduced by ∼0.004 (6% lower). The dark energy equation of state parameter w changes by Δw∼ 0.006 (0.6% higher) and the value of Ωm increases at most by 0.001 (∼0.3%), all within the derived uncertainties of the model. While the systematic error found in the estimate of the host stellar mass does not significantly affect the derived cosmological parameters, it is an important source of systematic error that needs to be corrected for as we enter a new era of precision cosmology., This work was supported by Fundação para a Ciência e a Tecnologia (FCT) through the research grant UIDB/00099/2020 and the CRISP project PTDC/FIS-AST-31546/2017. L.G. acknowledges financial support from the Spanish Ministry of Science, Innovation and Universities (MICIU) under the 2019 Ramón y Cajal program RYC2019-027683 and from the Spanish MICIU project HOSTFLOWS PID2020-115253GA-I00. CRA was supported by grants from VILLUM FONDEN (project numbers 16599 and 25501). J.D.L. acknowledges support from a UK Research and Innovation Future Leaders Fellowship (MR/T020784/1). Computations were performed at the cluster “Baltasar-Sete-Sóis”, supported by the H2020 ERC Grant “Matter and strong field gravity: New frontiers in Einstein’s theory” grant agreement no. MaGRaTh-646597, and at COIN, the CosmoStatistics Initiative, whose purchase was made possible due to a CNRS MOMENTUM 2018–2020 under the project “Active Learning for large scale sky surveys”. This work was only possible by the use of the following PYTHON packages: NumPy & SciPy (Walt et al. 2011; Jones et al. 2001), Matplotlib (Hunter 2007), and Astropy (Astropy Collaboration et al. 2013).
- Published
- 2022
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