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ABSTRACT Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval‐reranking framework leveraging large language models to enhance the spatiotemporal and semantic associated mining and recommendation of relevant, unusual climate and environmental events described in news articles and web posts. This framework uses advanced natural language processing techniques to address the limitations of traditional manual curation methods in terms of high labor costs and lack of scalability. Specifically, we explore an optimized solution to employ cutting‐edge embedding models for semantically analyzing spatiotemporal events (news) and propose a Geo‐Time Re‐ranking strategy that integrates multi‐faceted criteria including spatial proximity, temporal association, semantic similarity, and category‐instructed similarity to rank and identify similar spatiotemporal events. We apply the proposed framework to a dataset of four thousand local environmental observer network events, achieving top performance on recommending similar events among multiple cutting‐edge dense retrieval models. The search and recommendation pipeline can be applied to a wide range of similar data search tasks dealing with geospatial and temporal data. We hope that by linking relevant events, we can better aid the general public to gain enhanced understanding on climate change and its impact on different communities. 

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. 

We conducted an exhaustive analysis combining optical photometry and spectroscopy of the type Ia supernova designated SN 2023xqm. Our observational period spanned from the two weeks preceding to 88 days after theB-band peak luminosity time. We determined the peak brightness in theB-band to be −18.90 ± 0.50 mag, and it is accompanied by a moderately slow decay rate of 0.90 ± 0.07 mag. The maximum quasi-bolometric luminosity was estimated to be 1.52 × 10⁴³erg s⁻¹and correlated with a calculated⁵⁶Ni mass of 0.74 ± 0.05M_⊙, aligning with the modestly reduced rate of light curve decay. A plateau that can be observed in ther − icolor curve might correlate with the minor elevation noted between the principal and secondary peaks of thei-band light curve. An initial spectral analysis of SN 2023xqm revealed distinct high-velocity features (HVFs) in Ca IIthat contrast with the subdued HVFs observed in Si II. Such attributes may stem from variations in ionization or temperature or from scenarios involving enhanced element abundance, suggesting a naturally lower photospheric temperature for SN 2023xqm, which could be indicative of incomplete burning during the white dwarf’s detonation. The observed traits in the light curve and the spectral features offer significant insights into the variability among type Ia supernovae and their explosion dynamics. 

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 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.