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Abstract Large scientific institutions, such as the Space Telescope Science Institute, track the usage of their facilities to understand the needs of the research community. Astrophysicists incorporate facility usage data into their scientific publications, embedding this information in plain text. Traditional automatic search queries prove unreliable for accurate tracking due to the misidentification of facility names in plain text. As automatic search queries fail, researchers are required to manually classify publications for facility usage, which consumes valuable research time. In this work, we introduce a machine learning classification framework for the automatic identification of facility usage of observation sections in astrophysics publications. Our framework identifies sentences containing telescope mission keywords (e.g., Kepler and TESS) in each publication. Subsequently, the identified sentences are transformed using term frequency–inverse document frequency and classified with a support vector machine. The classification framework leverages the context surrounding the identified telescope mission keywords to provide relevant information to the classifier. The framework successfully classifies the usage of MAST-hosted missions with a 92.9% accuracy. Furthermore, our framework demonstrates robustness when compared to other approaches, considering common metrics and computational complexity. The framework’s interpretability makes it adaptable for use across observatories and other scientific facilities worldwide. 

Abstract Type Ia supernovae (SNe Ia) are securely understood to come from the thermonuclear explosion of a white dwarf as a result of binary interaction, but the nature of that binary interaction and the secondary object is uncertain. Recently, a double white dwarf model known as the dynamically driven double-degenerate double-detonation (D6) model has become a promising explanation for these events. One realization of this scenario predicts that the companion may survive the explosion and reside within the remnant as a fast moving (V_peculiar> 1000 km s⁻¹), overluminous (L> 0.1L_⊙) white dwarf. Recently, three objects that appear to have these unusual properties have been discovered in the Gaia survey. We obtained photometric observations of the SN Ia remnant SN 1006 with the Dark Energy Camera over four years to attempt to discover a similar star. We present a deep, high-precision astrometric proper-motion survey of the interior stellar population of the remnant. We rule out the existence of a high-proper-motion object consistent with our tested realization of the D6 scenario (V_transverse> 600 km s⁻¹withm_r< 21 corresponding to an intrinsic luminosity ofL> 0.0176L_⊙). We conclude that such a star does not exist within the remnant or is hidden from detection by either strong localized dust or the unlikely possibility of ejection from the binary system almost parallel to the line of sight.