Aug 14 – 18, 2023
Europe/Berlin timezone

Reconsidering the correlation between Type Ia Supernova Luminosity and local star formation environment inferred from global properties of host galaxies

Aug 16, 2023, 4:40 PM
30m
Orion 2

Orion 2

Speaker

Young-Lo Kim (Lancaster University)

Description

Since the discovery of the accelerating expansion of the universe [1,2], observational cosmology using Type Ia supernovae (SNe Ia), the thermonuclear explosions of white dwarfs, has entered a mature stage. However, the physics of the SNe Ia, such as a progenitor system and explosion mechanism, are still not fully understood [3]. Regarding the progenitor star, current studies use a host galaxy environment as a proxy of the progenitor star and the dependence of SN Ia luminosity, after the standard light-curve corrections, on host galaxy properties has been well established [4-6].
Recent studies focus on the local environment (e.g., local star formation rate (SFR), mass, and color) where the SN exploded, considering that this is more directly linked to the SN progenitors [7-9]. However, there is a debate about the local environmental, specifically local SFR, dependence of the SN Ia luminosity. There is a recent claim that the dependence is insignificant (0.051 ± 0.020; < 2.5σ), based on the local SFR measurement made by fitting local photometry data with the LePHARE spectral energy distribution fitting code [10]. However, we find that this photometric local SFR measurement is inaccurate. We argue this based on the theoretical background of SFR measurement and the methodology used to make that claim, especially due to a limited range of extinction parameters used when running LePHARE. Therefore, we re-analyse the same host galaxies with the same LePHARE code, but with more physically motivated extinction treatments. We estimate global stellar mass and star formation rate, and then local star formation environments are inferred from them. We show that there is significant local environmental dependence of SN Ia luminosities: SNe Ia in locally star-forming environments are 0.072 ± 0.021 mag (3.4σ) fainter than those in locally passive environments, even though SN Ia luminosities have been further corrected by the BBC method [11] that reduces the size of the dependence.

References

[1] Riess, A. G. et al. Observational Evidence from Supernovae for an Accelerating Universe and a Cosmological Constant. Astron. J. 116, 1009 (1998).
[2] Perlmutter, S. et al. Measurements of Ω and Λ from 42 High-Redshift Supernovae. Astrophys. J. 517, 565 (1999).
[3] Maoz, D. & Mannucci, F. Type-Ia Supernova Rates and the Progenitor Problem: A Review. Pub. Astron. Soc. Aus. 29, 447 (2012).
[4] Kelly, P. L., Hicken, M., Burke, D. L., Mandel, K. S. & Kirshner, R. P. Hubble Residuals of Nearby Type Ia Supernovae are Correlated with Host Galaxy Masses. Astrophys. J. 715, 743 (2010).
[5] Lampeitl, H. et al. The Effect of Host Galaxies on Type Ia Supernovae in the SDSS-II Supernova Survey. Astrophys. J. 722, 566 (2010).
[6] Sullivan, M. et al. The dependence of Type Ia Supernovae luminosities on their host galaxies. Mon. Not. R. Astron. Soc. 406, 782 (2010).
[7] Rigault, M. et al. Evidence of environmental dependencies of Type Ia supernovae from the Nearby Supernova Factory indicated by local Hα. Astron. Astrophys. 560, A66 (2013).
[8] Kim, Y.-L., Smith, M., Sullivan, M. & Lee, Y.-W. Environmental Dependence of Type Ia Supernova Luminosities from a Sample without a Local-Global Difference in Host Star Formation. Astrophys. J. 854, 24 (2018).
[9] Kelsey, L. et al. The effect of environment on Type Ia supernovae in the Dark Energy Survey three-year cosmological sample. Mon. Not. R. Astron. Soc. 501, 4861 (2021).
[10] Jones, D. O. et al. Should Type Ia Supernova Distances Be Corrected for Their Local Environments? Astrophys. J. 867, 108 (2018)
[11] Kessler, R. and Scolnic, D. Correcting Type Ia Supernova Distances for Selection Biases and Contamination in Photometrically Identified Samples. Astrophys. J. 836, 56 (2017).

Keywords supernovae: general – galaxies: fundamental parameters – methods: data analysis

Primary author

Young-Lo Kim (Lancaster University)

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