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Louis-Gregory Strolger
Publication date
2020
fields
Physics
and research's language is
English
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We present analytical reconstructions of type Ia supernova (SN Ia) delay time distributions (DTDs) by way of two independent methods: by a Markov chain Monte Carlo best-fit technique comparing the volumetric SN Ia rate history to todays compendium cosmic star-formation history, and secondly through a maximum likelihood analysis of the star formation rate histories of individual galaxies in the GOODS/CANDELS field, in comparison to their resultant SN Ia yields. We adopt a flexible skew-normal DTD model, which could match a wide range of physically motivated DTD forms. We find a family of solutions that are essentially exponential DTDs, similar in shape to the $betaapprox-1$ power-law DTDs, but with more delayed events (>1 Gyr in age) than prompt events (<1 Gyr). Comparing these solutions to delay time measures separately derived from field galaxies and galaxy clusters, we find the skew-normal solutions can accommodate both without requiring a different DTD form in different environments. These model fits are generally inconsistent with results from single-degenerate binary population synthesis models, and are seemingly supportive of double-degenerate progenitors for most SN Ia events.
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We present the delay time distribution (DTD) estimates of Type Ia supernovae (SNe~Ia) using spatially resolved SN~Ia host galaxy spectra from MUSE and MaNGA. By employing a grouping algorithm based on k-means and earth movers distances (EMD), we separated the host galaxy star formation histories (SFHs) into spatially distinct regions and used maximum likelihood method to constrain the DTD of SNe Ia progenitors. When a power-law model of the form $DTD(t)propto t^{s} (t>tau)$ is used, we found an SN rate decay slope $s=-1.41^{+0.32}_{-0.33}$ and a delay time $tau=120^{+142}_{-83} Myr$ . Moreover, we tested other DTD models such as a broken power law model and a two-component power law model, and found no statistically significant support to these alternative models.
We use a sample of 809 photometrically classified type Ia supernovae (SNe Ia) discovered by the Dark Energy Survey (DES) along with 40415 field galaxies to calculate the rate of SNe Ia per galaxy in the redshift range $0.2 < z <0.6$. We recover the known correlation between SN Ia rate and galaxy stellar mass across a broad range of scales $8.5 leq log(M_*/mathrm{M}_{odot}) leq 11.25$. We find that the SN Ia rate increases with stellar mass as a power-law with index $0.63 pm 0.02$, which is consistent with previous work. We use an empirical model of stellar mass assembly to estimate the average star-formation histories (SFHs) of galaxies across the stellar mass range of our measurement. Combining the modelled SFHs with the SN Ia rates to estimate constraints on the SN Ia delay time distribution (DTD), we find the data are fit well by a power-law DTD with slope index $beta = -1.13 pm 0.05$ and normalisation $A = 2.11 pm0.05 times 10^{-13}~mathrm{SNe}~{mathrm{M}_{odot}}^{-1}~mathrm{yr}^{-1}$, which corresponds to an overall SN Ia production efficiency $N_{mathrm{Ia}}/M_* = 0.9~_{-0.7}^{+4.0} times 10^{-3}~mathrm{SNe}~mathrm{M}_{odot}^{-1}$. Upon splitting the SN sample by properties of the light curves, we find a strong dependence on DTD slope with the SN decline rate, with slower-declining SNe exhibiting a steeper DTD slope. We interpret this as a result of a relationship between intrinsic luminosity and progenitor age, and explore the implications of the result in the context of SN Ia progenitors.
We study the implementation of mechanical feedback from supernovae (SNe) and stellar mass loss in galaxy simulations, within the Feedback In Realistic Environments (FIRE) project. We present the FIRE-2 algorithm for coupling mechanical feedback, which can be applied to any hydrodynamics method (e.g. fixed-grid, moving-mesh, and mesh-less methods), and black hole as well as stellar feedback. This algorithm ensures manifest conservation of mass, energy, and momentum, and avoids imprinting preferred directions on the ejecta. We show that it is critical to incorporate both momentum and thermal energy of mechanical ejecta in a self-consistent manner, accounting for SNe cooling radii when they are not resolved. Using idealized simulations of single SN explosions, we show that the FIRE-2 algorithm, independent of resolution, reproduces converged solutions in both energy and momentum. In contrast, common fully-thermal (energy-dump) or fully-kinetic (particle-kicking) schemes in the literature depend strongly on resolution: when applied at mass resolution >100 solar masses, they diverge by orders-of-magnitude from the converged solution. In galaxy-formation simulations, this divergence leads to orders-of-magnitude differences in galaxy properties, unless those models are adjusted in a resolution-dependent way. We show that all models that individually time-resolve SNe converge to the FIRE-2 solution at sufficiently high resolution. However, in both idealized single-SN simulations and cosmological galaxy-formation simulations, the FIRE-2 algorithm converges much faster than other sub-grid models without re-tuning parameters.
We introduce ProSpect, a generative galaxy spectral energy distribution (SED) package that encapsulates the best practices for SED methodologies in a number of astrophysical domains. ProSpect comes with two popular families of stellar population libraries (BC03 and EMILES), and a large variety of methods to construct star formation and metallicity histories. It models dust through the use of a Charlot & Fall attenuation model, with re-emission using Dale far-infrared templates. It also has the ability to model AGN through the inclusion of a simple AGN and hot torus model. Finally, it makes use of MAPPINGS-III photoionisation tables to produce line emission features. We test the generative and inversion utility of ProSpect through application to the Shark galaxy formation semi-analytic code, and informed by these results produce fits to the final ultraviolet to far-infrared photometric catalogues produces by the Galaxy and Mass Assembly Survey (GAMA). As part of the testing of ProSpect, we also produce a range of simple photometric stellar mass approximations covering a range of filters for both observed frame and rest frame photometry.
We derive the delay-time distribution (DTD) of type-Ia supernovae (SNe Ia) using a sample of 132 SNe Ia, discovered by the Sloan Digital Sky Survey II (SDSS2) among 66,000 galaxies with spectral-based star-formation histories (SFHs). To recover the best-fit DTD, the SFH of every individual galaxy is compared, using Poisson statistics, to the number of SNe that it hosted (zero or one), based on the method introduced in Maoz et al. (2011). This SN sample differs from the SDSS2 SN Ia sample analyzed by Brandt et al. (2010), using a related, but different, DTD recovery method. Furthermore, we use a simulation-based SN detection-efficiency function, and we apply a number of important corrections to the galaxy SFHs and SN Ia visibility times. The DTD that we find has 4-sigma detections in all three of its time bins: prompt (t < 420 Myr), intermediate (0.4 < t < 2.4 Gyr), and delayed (t > 2.4 Gyr), indicating a continuous DTD, and it is among the most accurate and precise among recent DTD reconstructions. The best-fit power-law form to the recovered DTD is t^(-1.12+/-0.08), consistent with generic ~t^-1 predictions of SN Ia progenitor models based on the gravitational-wave induced mergers of binary white dwarfs. The time integrated number of SNe Ia per formed stellar mass is N_SN/M = 0.00130 +/- 0.00015 Msun^-1, or about 4% of the stars formed with initial masses in the 3-8 Msun range. This is lower than, but largely consistent with, several recent DTD estimates based on SN rates in galaxy clusters and in local-volume galaxies, and is higher than, but consistent with N_SN/M estimated by comparing volumetric SN Ia rates to cosmic SFH.
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