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Abstract We present an algorithm to derive difference images for data taken with JWST with matched point-spread functions (PSFs). It is based on the saccadic fast Fourier transform method but with revisions to accommodate the rotations and spatial variations of the PSFs. It allows for spatially varying kernels in B-spline form with separately controlled photometric scaling and Tikhonov kernel regularization for harnessing the ultimate fitting flexibility. We present this method using the JWST/NIRCam images of galaxy cluster Abell 2744 acquired in JWST Cycle 1 as the test data. The algorithm can be useful for time-domain source detection and differential photometry with JWST. It can also coadd images of multiple exposures taken at different field orientations. The coadded images preserve the sharpness of the central cores of the PSFs, and the positions and shapes of the objects are matched precisely with B-splines across the field. 

Abstract SN 2023ehl, a normal Type Ia supernova with a typical decline rate, was discovered in the galaxy UGC 11555 and offers valuable insights into the explosion mechanisms of white dwarfs. We present a detailed analysis of SN 2023ehl, including spectroscopic and photometric observations. The supernova exhibits high-velocity features in its ejecta, which are crucial for understanding the physical processes during the explosion. We compared the light curves of SN 2023ehl with other well-observed Type Ia supernovae, finding similarities in their evolution. The line strength ratioR(Siii) was calculated to be 0.17 ± 0.04, indicating a higher photospheric temperature compared to other supernovae. The maximum quasi-bolometric luminosity was determined to be 1.52 × 10⁴³erg s⁻¹, and the synthesized⁵⁶Ni mass was estimated at 0.77 ± 0.05M_⊙. The photospheric velocity atB-band maximum light was measured as 10,150 ± 240 km s⁻¹, classifying SN 2023ehl as a normal velocity Type Ia supernova. Our analysis suggests that SN 2023ehl aligns more with both the gravitationally confined detonation, providing a comprehensive view of the diversity and complexity of Type Ia supernovae. 

Abstract 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. 

Abstract 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.