We investigate the potential of using a sample of very high-redshift (2 ≲ z ≲ 6) (VHZ) Type Ia supernovae (SNe Ia) attainable by JWST on constraining cosmological parameters. At such high redshifts, the age of the universe is young enough that the VHZ SN Ia sample comprises the very first SNe Ia of the universe, with progenitors among the very first generation of low-mass stars that the universe has made. We show that the VHZ SNe Ia can be used to disentangle systematic effects due to the luminosity distance evolution with redshifts intrinsic to SN Ia standardization. Assuming that the systematic evolution can be described by a linear or logarithmic formula, we found that the coefficients of this dependence can be determined accurately and decoupled from cosmological models. Systematic evolution as large as 0.15 mag and 0.45 mag out to z = 5 can be robustly separated from popular cosmological models for linear and logarithmic evolution, respectively. The VHZ SNe Ia will lay the foundation for quantifying the systematic redshift evolution of SN Ia luminosity distance scales. When combined with SN Ia surveys at comparatively lower redshifts, the VHZ SNe Ia allow for the precise measurement of the history of the expansion of the universe from z ∼ 0 to the epoch approaching reionization.
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 mover’s distances (EMDs), we separated the host galaxy stellar population age distributions (SPADs) into spatially distinct regions and used maximum likelihood method to constrain the DTD of SN Ia progenitors. When a power-law model of the form DTD(t) ∝ t s (t > τ) is used, we find an SN rate decay slope s = − 1.41 − 0.33 + 0.32 and a delay time τ = 120 − 83 + 142 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 for these alternative models.
Image subtraction is essential for transient detection in time-domain astronomy. The point-spread function (PSF), photometric scaling, and sky background generally vary with time and across the field of view for imaging data taken with ground-based optical telescopes. Image subtraction algorithms need to match these variations for the detection of flux variability. An algorithm that can be fully parallelized is highly desirable for future time-domain surveys. Here we introduce the saccadic fast Fourier transform (SFFT) algorithm we developed for image differencing. SFFT uses a δ-function basis for kernel decomposition, and the image subtraction is performed in Fourier space. This brings about a remarkable improvement in computational performance of about an order of magnitude compared to other published image subtraction codes. SFFT can accommodate the spatial variations in wide-field imaging data, including PSF, photometric scaling, and sky background. However, the flexibility of the δ-function basis may also make it more prone to overfitting. The algorithm has been tested extensively on real astronomical data taken by a variety of telescopes. Moreover, the SFFT code allows for the spatial variations of the PSF and sky background to be fitted by spline functions.
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