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The purpose of this paper is to leverage the growth of AI-enabled tools to support the democratization of mine observation (MO) research. Mining is essential to meet projected demand for renewable energy technologies crucial to global climate mitigation objectives, but all mining activities pose local and regional challenges to environmental sustainability. Such challenges can be mitigated by good governance, but unequal access among stakeholders to accurately interpreted satellite imagery can weaken good governance. Using readily available software—QGIS, and Segment Anything Model (SAM)—this paper develops and tests the reliability of MO-SAM, a new method to identify and delineate features within the spatially-explicit mine extent at a high level of detail. It focuses on dry tailings, waste dumps, and stockpiles in above-ground mining areas. While we intend for MO-SAM to be used generally, this study tested it on mining areas for energy-critical materials: lithium (Li), cobalt (Co), rare earth elements (REE), and platinum group elements (PGE), selected for their importance to the global transition to renewable energy. MO-SAM demonstrates generalizability through prompt engineering, but performance limitations were observed in imagery with complex mining landscape scenarios, including spatial variations in image morphology and boundary sharpness. Our analysis provides data-driven insights to support advances in the use of MO-SAM for analyzing and monitoring large-scale mining activities with greater speed than methods that rely on manual delineation, and with greater precision than practices that focus primarily on changes in the spatially-explicit mine extent. It also provides insights into the importance of multidisciplinary human expertise in designing processes for and assessing the accuracy of AI-assisted remote sensing image segmentation as well as in evaluating the significance of the land use and land cover changes identified. This has widespread potential to advance the multidisciplinary application of AI for scientific and public interest, particularly in research on global scale human-environment interactions such as industrial mining activities. This is methodologically significant because the potential and limitations of using large pre-trained image segmentation models such as SAM for analyzing remote sensing data is an emergent and underexplored issue. The results can help advance the utilization of large pre-trained segmentation models for remote sensing imagery analysis to support sustainability research and policy. 

Abstract While the spectroscopic classification scheme for stripped-envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all of their envelopes of hydrogen and helium, the photometric evolution of classes within this family is not fully characterized. Photometric surveys, like the Vera C. Rubin Legacy Survey of Space and Time, will discover tens of thousands of transients each night, and spectroscopic follow-up will be limited, prompting the need for photometric classification and inference based solely on photometry. We have generated 54 data-driven photometric templates for SESNe of subtypes IIb, Ib, Ic, Ic-bl, and Ibn inU/u,B,g,V,R/r,I/i,J,H,K_s, and Swiftw2,m2,w1 bands using Gaussian processes and a multisurvey data set composed of all well-sampled open-access light curves (165 SESNe, 29,531 data points) from the Open Supernova Catalog. We use our new templates to assess the photometric diversity of SESNe by comparing final per-band subtype templates with each other and with individual, unusual and prototypical SESNe. We find that SNe Ibn and SNe Ic-bl exhibit a distinctly faster rise and decline compared to other subtypes. We also evaluate the behavior of SESNe in the PLAsTiCC and ELAsTiCC simulations of LSST light curves, highlighting differences that can bias photometric classification models trained on the simulated light curves. Finally, we investigate in detail the behavior of fast-evolving SESNe (including SNe Ibn) and the implications of the frequently observed presence of two peaks in their light curves. 

We introduce the rapidly emerging field of multi-messenger gravitational lensing—the discovery and science of gravitationally lensed phenomena in the distant universe through the combination of multiple messengers. This is framed by gravitational lensing phenomenology that has grown since the first discoveries in the twentieth century, messengers that span 30 orders of magnitude in energy from high-energy neutrinos to gravitational waves, and powerful ‘survey facilities’ that are capable of continually scanning the sky for transient and variable sources. Within this context, the main focus is on discoveries and science that are feasible in the next 5–10 years with current and imminent technology including the LIGO–Virgo–KAGRA network of gravitational wave detectors, the Vera C. Rubin Observatory and contemporaneous gamma/X-ray satellites and radio surveys. The scientific impact of even one multi-messenger gravitational lensing discovery will be transformational and reach across fundamental physics, cosmology and astrophysics. We describe these scientific opportunities and the key challenges along the path to achieving them. This article therefore describes the consensus that emerged at the eponymous Theo Murphy meeting in March 2024, and also serves as an introduction to this Theo Murphy meeting issue. This article is part of the Theo Murphy meeting issue ‘Multi-messenger gravitational lensing (Part 2)’. 

Mining for rare earth elements is rapidly increasing, driven by current and projected demands for information and energy technologies. Following China’s Central Government’s 2012 strategy to shift away from mining in favor of value-added processing, primary extraction has increased outside of China. Accordingly, changes in mineral exploitation in China and Myanmar have garnered considerable attention in the past decade. The prevailing assumption is that mining in China has decreased while mining in Myanmar has increased, but the dynamic in border regions is more complex. Our empirical study used Google Earth Engine (GEE) to characterize changes in mining surface footprints between 2005 and 2020 in two rare earth mines located on either side of the Myanmar–China border, within Kachin State in northern Myanmar and Nujiang Prefecture in Yunnan Province in China. Our results show that the extent of the mining activities increased by 130% on China’s side and 327% on Myanmar’s side during the study period. We extracted surface reflectance images from 2005 and 2010 from Landsat 5 TM and 2015 and 2020 images from Landsat 8 OLI. The Normalized Vegetation Index (NDVI) was applied to dense time-series imagery to enhance landcover categories. Random Forest was used to categorize landcover into mine and non-mine classes with an overall accuracy of 98% and a Kappa Coefficient of 0.98, revealing an increase in mining extent of 2.56 km2, covering the spatial mining footprint from 1.22 km2 to 3.78 km2 in 2005 and 2020, respectively, within the study area. We found a continuous decrease in non-mine cover, including vegetation. Both mines are located in areas important to ethnic minority groups, agrarian livelihoods, biodiversity conservation, and regional watersheds. The finding that mining surface areas increased on both sides of the border is significant because it shows that national-level generalizations do not align with local realities, particularly in socially and environmentally sensitive border regions. The quantification of such changes over time can help researchers and policymakers to better understand the shifting geographies and geopolitics of rare earth mining, the environmental dynamics in mining areas, and the particularities of mineral extraction in border regions. 

Abstract Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys. 

Abstract Current and future optical and near-infrared wide-field surveys have the potential to find kilonovae, the optical and infrared counterparts to neutron star mergers, independently of gravitational-wave or high-energy gamma-ray burst triggers. The ability to discover fast and faint transients such as kilonovae largely depends on the area observed, the depth of those observations, the number of revisits per field in a given time frame, and the filters adopted by the survey; it also depends on the ability to perform rapid follow-up observations to confirm the nature of the transients. In this work, we assess kilonova detectability in existing simulations of the Legacy Survey of Space and Time strategy for the Vera C. Rubin Wide Fast Deep survey, with focus on comparing rolling to baseline cadences. Although currently available cadences can enable the detection of >300 kilonovae out to ∼1400 Mpc over the 10 year survey, we can expect only 3–32 kilonovae similar to GW170817 to be recognizable as fast-evolving transients. We also explore the detectability of kilonovae over the plausible parameter space, focusing on viewing angle and ejecta masses. We find that observations in redderizybands are crucial for identification of nearby (within 300 Mpc) kilonovae that could be spectroscopically classified more easily than more distant sources. Rubin’s potential for serendipitous kilonova discovery could be increased by gain of efficiency with the employment of individual 30 s exposures (as opposed to 2 × 15 s snap pairs), with the addition of red-band observations coupled with same-night observations ingorrbands, and possibly with further development of a new rolling-cadence strategy. 

Unlike the vast majority of astrophysical plasmas, the solar wind is accessible to spacecraft, which for decades have carried in-situ instruments for directly measuring its particles and fields. Though such measurements provide precise and detailed information, a single spacecraft on its own cannot disentangle spatial and temporal fluctuations. Even a modest constellation of in-situ spacecraft, though capable of characterizing fluctuations at one or more scales, cannot fully determine the plasma’s 3-D structure. We describe here a concept for a new mission, the Magnetic Topology Reconstruction Explorer (MagneToRE), that would comprise a large constellation of in-situ spacecraft and would, for the first time, enable 3-D maps to be reconstructed of the solar wind’s dynamic magnetic structure. Each of these nanosatellites would be based on the CubeSat form-factor and carry a compact fluxgate magnetometer. A larger spacecraft would deploy these smaller ones and also serve as their telemetry link to the ground and as a host for ancillary scientific instruments. Such an ambitious mission would be feasible under typical funding constraints thanks to advances in the miniaturization of spacecraft and instruments and breakthroughs in data science and machine learning. 

ABSTRACT This paper presents a new optical imaging survey of four deep drilling fields (DDFs), two Galactic and two extragalactic, with the Dark Energy Camera (DECam) on the 4-m Blanco telescope at the Cerro Tololo Inter-American Observatory (CTIO). During the first year of observations in 2021, >4000 images covering 21 deg2 (seven DECam pointings), with ∼40 epochs (nights) per field and 5 to 6 images per night per filter in g, r, i, and/or z have become publicly available (the proprietary period for this program is waived). We describe the real-time difference-image pipeline and how alerts are distributed to brokers via the same distribution system as the Zwicky Transient Facility (ZTF). In this paper, we focus on the two extragalactic deep fields (COSMOS and ELAIS-S1) characterizing the detected sources, and demonstrating that the survey design is effective for probing the discovery space of faint and fast variable and transient sources. We describe and make publicly available 4413 calibrated light curves based on difference-image detection photometry of transients and variables in the extragalactic fields. We also present preliminary scientific analysis regarding the Solar system small bodies, stellar flares and variables, Galactic anomaly detection, fast-rising transients and variables, supernovae, and active Galactic nuclei. 

Abstract The Vera C. Rubin Legacy Survey of Space and Time (LSST) holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey’s first light.