Scalable hierarchical BayeSN inference: Investigating dependence of SN Ia host galaxy dust properties on stellar mass and redshift
Monthly Notices of the Royal Astronomical Society(2024)
摘要
We apply the hierarchical probabilistic SED model BayeSN to analyse a sample
of 475 SNe Ia (0.015 < z < 0.4) from Foundation, DES3YR and PS1MD to
investigate the properties of dust in their host galaxies. We jointly infer the
dust law R_V population distributions at the SED level in high- and low-mass
galaxies simultaneously with dust-independent, intrinsic differences. We find
an intrinsic mass step of -0.049±0.016 mag, at a significance of
3.1σ, when allowing for a constant intrinsic, achromatic magnitude
offset. We additionally apply a model allowing for time- and
wavelength-dependent intrinsic differences between SNe Ia in different mass
bins, finding ∼2σ differences in magnitude and colour around peak
and 4.5σ differences at later times. These intrinsic differences are
inferred simultaneously with a difference in population mean R_V of
∼2σ significance, demonstrating that both intrinsic and extrinsic
differences may play a role in causing the host galaxy mass step. We also
consider a model which allows the mean of the R_V distribution to linearly
evolve with redshift but find no evidence for any evolution - we infer the
gradient of this relation η_R = -0.38±0.70. In addition, we discuss in
brief a new, GPU-accelerated Python implementation of BayeSN suitable for
application to large surveys which is publicly available and can be used for
future cosmological analyses; this code can be found here:
https://github.com/bayesn/bayesn.
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