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IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors

ArXiv 2501.02473 (2025)

Authors:

Noé Dia, MJ Yantovski-Barth, Alexandre Adam, Micah Bowles, Laurence Perreault-Levasseur, Yashar Hezaveh, Anna Scaife
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Supernova remnants on the outskirts of the Large Magellanic Cloud

Astronomy & Astrophysics EDP Sciences 693 (2025) l15

Authors:

Manami Sasaki, Federico Zangrandi, Miroslav Filipović, Rami ZE Alsaberi, Jordan D Collier, Frank Haberl, Ian Heywood, Patrick Kavanagh, Bärbel Koribalski, Roland Kothes, Sanja Lazarević, Pierre Maggi, Chandreyee Maitra, Sean Points, Zachary J Smeaton, Velibor Velović
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What We Don't C: Representations for scientific discovery beyond VAEs

Machine Learning and the Physical Sciences workshop at NeurIPS 2025

Authors:

Brian Rogers, Micah Bowles, Chris J. Lintott, Steve Croft

Abstract:

Accessing information in learned representations is critical for scientific discovery in high-dimensional domains. We introduce a novel method based on latent flow matching with classifier-free guidance that disentangles latent subspaces by explicitly separating information included in conditioning from information that remains in the residual representation. Across three experiments -- a synthetic 2D Gaussian toy problem, colored MNIST, and the Galaxy10 astronomy dataset -- we show that our method enables access to meaningful features of high dimensional data. Our results highlight a simple yet powerful mechanism for analyzing, controlling, and repurposing latent representations, providing a pathway toward using generative models for scientific exploration of what we don't capture, consider, or catalog.
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Anomaly Detection and RFI Classification with Unsupervised Learning in Narrowband Radio Technosignature Searches

ArXiv 2411.16556 (2024)

Authors:

Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson, Daniel Bautista, Vishal Gajjar, Matthew Lebofsky, David HE MacMahon, Caleb Painter, Andrew PV Siemion
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MIGHTEE: the continuum survey Data Release 1

Monthly Notices of the Royal Astronomical Society Oxford University Press 536:3 (2024) 2187-2211

Authors:

Catherine Hale, Ian Heywood, Matthew Jarvis, Imogen Whittam, Philip Best, Fangxia An, Rebecca Bowler, Ian Harrison, Allison Matthews, Dan Smith, Russ Taylor, Mattia Vaccari

Abstract:

The MeerKAT International GHz Tiered Extragalactic Exploration Survey (MIGHTEE) is one of the large survey projects using the MeerKAT telescope, covering four fields that have a wealth of ancillary data available. We present Data Release 1 of the MIGHTEE continuum survey, releasing total intensity images and catalogues over ∼20 deg2, across three fields at ∼1.2-1.3 GHz. This includes 4.2 deg2 over the Cosmic Evolution Survey (COSMOS) field, 14.4 deg2 over the XMM Large-Scale Structure (XMM-LSS) field and deeper imaging over 1.5 deg2 of the Extended Chandra Deep Field South (CDFS). We release images at both a lower resolution (7–9 arcsec) and higher resolution (∼5 arcsec). These images have central rms sensitivities of ∼1.3 −2.7 μJy beam−1 (∼1.2 −3.6 μJy beam−1) in the lower (higher) resolution images respectively. We also release catalogues comprised of ∼144 000 (∼114 000) sources using the lower (higher) resolution images. We compare the astrometry and flux-density calibration with the Early Science data in the COSMOS and XMM-LSS fields and previous radio observations in the CDFS field, finding broad agreement. Furthermore, we extend the source counts at the ∼10 μJy level to these larger areas (∼20 deg2) and, using the areal coverage of MIGHTEE we measure the sample variance for differing areas of sky. We find a typical sample variance of 10-20percnt for 0.3 and 0.5 sq. deg. sub-regions at S1.4 ≤ 200 μJy, which increases at brighter flux densities, given the lower source density and expected higher galaxy bias for these sources.
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