A recently quenched galaxy 700 million years after the Big Bang
(2024)
Double, double, toil, and trouble: The tails, bubbles, and knots of the local compact obscured nucleus galaxy NGC4418
(2024)
Peeling Back the Layers of Extinction of Dusty Galaxies in the Era of JWST: Modelling Joint NIRSpec + MIRI Spectra at rest-frame 1.5-28 $\mu$m
(2024)
Searching for Strong Gravitational Lenses
Space Science Reviews Springer 220:2 (2024) 23
Abstract:
Abstract Strong gravitational lenses provide unique laboratories for cosmological and astrophysical investigations, but they must first be discovered – a task that can be met with significant contamination by other astrophysical objects and asterisms. Here we review strong lens searches, covering various sources (quasars, galaxies, supernovae, FRBs, GRBs, and GWs), lenses (early- and late-type galaxies, groups, and clusters), datasets (imaging, spectra, and lightcurves), and wavelengths. We first present the physical characteristics of the lens and source populations, highlighting relevant details for constructing targeted searches. Search techniques are described based on the main lensing feature that is required for the technique to work, namely one of: (i) an associated magnification, (ii) multiple spatially-resolved images, (iii) multiple redshifts, or (iv) a non-zero time delay between images. To use the current lens samples for science, and for the design of future searches, we list several selection biases that exist due to these discovery techniques. We conclude by discussing the future of lens searches in upcoming surveys and the new population of lenses that will be discovered.
The Weird and the Wonderful in Our Solar System: Searching for Serendipity in the Legacy Survey of Space and Time
The Astronomical Journal, 167:118 (14pp), 2024 March
Abstract:
We present a novel method for anomaly detection in solar system object data in preparation for the Legacy Survey of Space and Time. We train a deep autoencoder for anomaly detection and use the learned latent space to search for other interesting objects. We demonstrate the efficacy of the autoencoder approach by finding interesting examples, such as interstellar objects, and show that by using the autoencoder, further examples of interesting classes can be found. We also investigate the limits of classic unsupervised approaches to anomaly detection through the generation of synthetic anomalies and evaluate the feasibility of using a supervised learning approach. Future work should consider expanding the feature space to increase the variety of anomalies that can be uncovered during the survey using an autoencoder.