Using data from the Complete Nearby (redshift z (host) < 0.02) sample of Type Ia Supernovae (CNIa0.02), we find a linear relation between two parameters derived from the B - V color curves of Type Ia supernovae: the color stretch s ( BV ) and the rising color slope s(0)*(B-V) BV . The s ( BV ) parameter is known to be tightly correlated with the peak luminosity, especially for fast decliners (dim Type Ia supernovae), and the luminosity correlation with s ( BV ) is markedly better than with the classic light-curve width parameters such as ?m (15)(B). Thus, our new linear relation can be used to infer peak luminosity from s(0)* s ( BV ) (or ?m (15)(B)), the measurement of s(0)*(B-V)

In this work, we present a new catalogue of >40 000 ionised nebulae distributed across the 19 galaxies observed by the PHANGS-MUSE survey. The nebulae have been classified using a new model-comparison-based algorithm that exploits the odds ratio principle to assign a probabilistic classification to each nebula in the sample. The resulting catalogue is the largest catalogue containing complete spectral and spatial information for a variety of ionised nebulae available so far in the literature. We developed this new algorithm to address some of the main limitations of the traditional classification criteria, such as their binarity, the sharpness of the involved limits, and the limited amount of data they rely on for the classification. The analysis of the catalogue shows that the algorithm performs well when selecting H II regions. In fact, we can recover their luminosity function, and its properties are in line with what is available in the literature. We also identify a rather significant population of shock-ionised regions (mostly composed of supernova remnants), which is an order of magnitude larger than any other homogeneous catalogue of supernova remnants currently available in the literature. The number of supernova remnants we identify per galaxy is in line with results in our Galaxy and in other very nearby sources. However, limitations in the source detection algorithm result in an incomplete sample of planetary nebulae, even though their classification seems robust. Finally, we demonstrate how applying a correction for the contribution of the diffuse ionised gas to the nebulae's spectra is essential to obtain a robust classification of the objects and how a correct measurement of the extinction using diffuse-ionised-gas-corrected line fluxes prompts the use of a higher theoretical H alpha/H beta ratio (3.03) than what is commonly used when recovering the E(B - V) via the Balmer decrement technique in massive star-forming galaxies.

The locations of minerals and mineral-forming environments, despite being of great scientific importance and economic interest, are often difficult to predict due to the complex nature of natural systems. In this work, we embrace the complexity and inherent "messiness" of our planet's intertwined geological, chemical, and biological systems by employing machine learning to characterize patterns embedded in the multidimensionality of mineral occurrence and associations. These patterns are a product of, and therefore offer insight into, the Earth's dynamic evolutionary history. Mineral association analysis quantifies high-dimensional multicorrelations in mineral localities across the globe, enabling the identification of previously unknown mineral occurrences, as well as mineral assemblages and their associated paragenetic modes. In this study, we have predicted (i) the previously unknown mineral inventory of the Mars analogue site, Tecopa Basin, (ii) new locations of uranium minerals, particularly those important to understanding the oxidation-hydration history of uraninite, (iii) new deposits of critical minerals, specifically rare earth element (REE)- and Li-bearing phases, and (iv) changes in mineralization and mineral associations through deep time, including a discussion of possible biases in mineralogical data and sampling; furthermore, we have (v) tested and confirmed several of these mineral occurrence predictions in nature, thereby providing ground truth of the predictive method. Mineral association analysis is a predictive method that will enhance our understanding of mineralization and mineralizing environments on Earth, across our solar system, and through deep time.

Breyite is the second most abundant mineral inclusion in super-deep diamonds after ferropericlase. Though breyite stability extends to 300 km along typical mantle geotherm, this phase is often assumed to be the product of retrograde transformation of CaSiO3-perovskite, and thus has the potential to retain informa-tion from as deep as 800-1000 km. In this study, we determined the depth of formation of a breyite inclusion still enclosed in its host diamond from Juina, Brazil, by X-ray diffraction. The measured >5 % smaller unit cell for breyite indicates a stored residual pressure showing that the breyite was entrapped between about 9(1) and 10(1) GPa. These are the highest estimates of formation pressure ever determined for a breyite inclusion. For ambient mantle temperatures higher than 1400-1500 degrees C, these pressures would exceed the maximum P of the breyite stability field. Breyite in this diamond cannot be primary but is rather a back -transformation product from CaSiO3-perovskite formed in the transition zone or the lower mantle. The co-existence magnesite in diamond JU55 and the slab -association of sublithospheric diamonds is evidence of carbon transport to lower mantle depths.