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Following our previous study of Artificial Intelligence Assisted Inversion (AIAI) of supernova analyses, we train a set of deep neural networks based on the 1D radiative transfer code TARDIS to simulate the optical spectra of Type Ia supernovae (SNe Ia) between 10 and 40 days after the explosion. The neural networks are applied to derive the mass of⁵⁶Ni in velocity ranges above the photosphere for a sample of 124 well-observed SNe Ia in the TARDIS model context. A subset of the SNe have multi-epoch observations for which the decay of the radioactive⁵⁶Ni can be used to test the AIAI quantitatively. The⁵⁶Ni mass derived from AIAI using the observed spectra as inputs for this subset agrees with the radioactive decay rate of⁵⁶Ni. AIAI reveals that a spectral signature near 3890 Å is related to the Niii4067Å line, and the⁵⁶Ni mass deduced from AIAI is found to be correlated with the light-curve shapes of SNe Ia, with SNe Ia with broader light curves showing larger⁵⁶Ni mass in the envelope above the photosphere. AIAI enables spectral data of SNe to be quantitatively analyzed under theoretical frameworks based on well-defined physical assumptions.

 

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 aδ-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 theδ-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.

 

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 toz= 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 fromz∼ 0 to the epoch approaching reionization.

 

Abstract We present a data-driven method based on long short-term memory (LSTM) neural networks to analyze spectral time series of Type Ia supernovae (SNe Ia). The data set includes 3091 spectra from 361 individual SNe Ia. The method allows for accurate reconstruction of the spectral sequence of an SN Ia based on a single observed spectrum around maximum light. The precision of the spectral reconstruction increases with more spectral time coverages, but the significant benefit of multiple epoch data at around optical maximum is only evident for observations separated by more than a week. The method shows great power in extracting the spectral information of SNe Ia and suggests that the most critical information of an SN Ia can be derived from a single spectrum around the optical maximum. The algorithm we have developed is important for the planning of spectroscopic follow-up observations of future SN surveys with the LSST/Rubin and WFIRST/Roman telescopes. 

Abstract 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.