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Abstract:

The division of the Milky Way’s disk into “thin” and “thick” components is a common practice, but one that is often ambiguously defined. The two ways of dividing stars are not equivalent: many stars belonging to the stellar population at high [α/Fe] (the “chemical thick disk”) do not belong to the larger-scale-height exponential density component of G. Gilmore & N. Reid (the “kinematic thick disk”). Furthermore, the existence of two distinct kinematic components is debated. This issue has surfaced in discussions about the recently discovered interstellar object 3I/ATLAS, which likely originated around an old star, and has been variously classified as a member of both the “thin disk” and the “thick disk” by different works. We illustrate that the origins of interstellar objects should be discussed with care, and relative to specific, identified populations of stars.

Abstract:

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.

Abstract:

We introduce a new kinematic disturbance parameter, (pronounced ‘DVSG’), which takes advantage of integral field spectroscopy (IFS) to quantify differences between a galaxy’s stellar and gas velocity maps. The motivation behind is to capture disturbances in the kinematics of a galaxy that might be missed by alternative methods, while also attempting to minimize bias towards galaxy properties or features of the IFS data. We first detail the reasons for introducing this parameter and explain how the value of a galaxy can be calculated. We then present initial results using to quantify the kinematic disturbance of obscured active galactic nuclei (AGNs) found in the MaNGA (Mapping Nearby Galaxies at Apache Point Observatory) survey. We find that there is no statistically significant difference between the distributions of AGN and a control sample (matched in mass and redshift) of inactive galaxies. This suggests that AGN triggering may not be preferentially caused by any distinct kinematic disturbance process, or combination of processes, beyond those observed in inactive galaxies.