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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.

 

Sulfuryl fluoride (SO₂F₂) is a synthetic pesticide and a potent greenhouse gas that is accumulating in the global atmosphere. Rising emissions are a concern since SO₂F₂has a relatively long atmospheric lifetime and a high global warming potential. The U.S. is thought to contribute substantially to global SO₂F₂emissions, but there is a paucity of information on how emissions of SO₂F₂are distributed across the U.S., and there is currently no inventory of SO₂F₂emissions for the U.S. or individual states. Here we provide an atmospheric measurement-based estimate of U.S. SO₂F₂emissions using high-precision SO₂F₂measurements from the NOAA Global Greenhouse Gas Reference Network (GGGRN) and a geostatistical inverse model. We find that California has the largest SO₂F₂emissions among all U.S. states, with the highest emissions from southern coastal California (Los Angeles, Orange, and San Diego counties). Outside of California, only very small and infrequent SO₂F₂emissions are detected by our analysis of GGGRN data. We find that California emits 60-85% of U.S. SO₂F₂emissions, at a rate of 0.26 ( ± 0.10) Gg yr⁻¹. We estimate that emissions of SO₂F₂from California are equal to 5.5–12% of global SO₂F₂emissions.

 

In this study, we analyzed the optical observations of a subluminous Type Ia supernova (SN Ia) 2017fzw, which exhibited high photospheric velocity (HV) at B-band maximum light. The absolute B-band peak magnitude was determined to be MmaxB=−18.65±0.13 mag, similar to 91bg-like SNe Ia. An estimation of the rate of decline for the B-band light curve was determined to be Δm15(B)=1.60±0.06 mag. The spectra of SN 2017fzw were similar to those of 91bg-like SNe Ia, with prominent Ti ii and Si ii λ5972 features at early phases, gradually transitioning to spectra resembling normal (mainly HV subclass) SNe Ia at later phases, with a stronger Ca ii NIR feature. Notably, throughout all phases of observation, SN 2017fzw displayed spectral evolution characteristics that were comparable to those of HV SNe Ia, and at peak brightness, the Si ii λ6355 velocity was determined to be 13,800 ± 415 km s−1 and a more pronounced Ca ii NIR feature was also detected. Based on these findings, we classify SN 2017fzw as a transitional object with properties of both normal and 91bg-like SNe Ia, providing support for the hypothesis of a continuous distribution of supernovae between these two groups.

 

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.

 

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. 

AST3-3 is the third robotic facility of the Antarctic Survey Telescopes (AST3) for transient surveys to be deployed at Dome A, Antarctica. Due to the current pandemic, the telescope has been currently deployed at the Yaoan Observation Station in China, starting the commissioning observation and a transient survey. This article presented a fully automatic data processing system for AST3-3 observations. The transient detection pipeline uses state-of-the-art image subtraction techniques optimized for GPU devices. Image reduction and transient photometry are accelerated by concurrent task methods. Our Python-based system allows for transient detection from wide-field data in a real-time and accurate way. A ResNet-based rotational-invariant neural network was employed to classify the transient candidates. As a result, the system enables the auto-generation of transients and their light curves.

 

Abstract We present the photometry of 16 91T/99aa-like Type Ia Supernovae (SNe Ia) observed by the Las Cumbres Observatory. We also use an additional set of 21 91T/99aa-like SNe Ia and 87 normal SNe Ia from the literature for an analysis of the standardizability of the luminosity of 91T/99aa-like SNe. We find that 91T/99aa-like SNe are 0.2 mag brighter than normal SNe Ia, even when fully corrected by the light-curve shapes and colors. The weighted rms of the 91T/99aa-like SNe (with z CMB > 0.01) Hubble residuals is 0.25 ± 0.03 mag, suggesting that 91T/99aa-like SNe are also excellent relative distance indicators to ±12%. We compare the Hubble residuals with the pseudo-equivalent width (pEW) of Si ii λλ 6355 around the date of maximum brightness. We find that there is a broken linear correlation between those two measurements for our sample including both 91T/99aa-like and normal SNe Ia. As the pEW max (Si ii λλ 6355) increases, the Hubble residual increases when pEW max (Si ii λλ 6355) < 55.6 Å. However, the Hubble residual stays constant beyond this. Given that 91T/99aa-like SNe possess shallower Si ii lines than normal SNe Ia, the linear correlation at pEW max (Si ii λλ 6355) < 55.6 Å can account for the overall discrepancy of Hubble residuals derived from the two subgroups. Such a systematic effect needs to be taken into account when using SNe Ia to measure luminosity distances. 

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. 

The third Antarctic Survey Telescope array instrument at Dome A in Antarctica, the AST3-3 telescope, has been in commissioning from March 2021. We deployed AST3-3 at the Yaoan astronomical station in Yunnan Province for an automatic time-domain survey and follow-up observations with an optimised observation and protection system. The telescope system of AST3-3 is similar to that of AST3-1 and AST3-2, except that it is equipped with a 14 K × 10 K QHY411 CMOS camera. AST3-3 has a field of view of 1.65∘×1.23∘ and is currently using the g band filter. During commissioning at Yaoan, AST3-3 aims to conduct an extragalactic transient survey, coupled with prompt follow-ups of opportunity targets. In this paper, we present the architecture of the AST3-3 automatic observation system. We demonstrate the data processing of observations by representatives SN 2022eyw and GRB 210420B.