Arcminute Microkelvin Imager Observations at 15.5 GHz of Multiple Outbursts of Cygnus X-3 in 2024
Research Notes of the American Astronomical Society American Astronomical Society 9:2 (2025) 35
Abstract:
We report radio monitoring of Cygnus X-3 at 15.5 GHz during 2024 with the Arcminute Microkelvin Imager. Observations were made on 296 days throughout the year, and reveal five radio outbursts to multi-jansky levels, peaking in February, April, June, July and August. The brightest peak, with ≈16 Jy, was on June 27th.The Observed Phase Space of Mass-loss History from Massive Stars Based on Radio Observations of a Large Supernova Sample
The Astrophysical Journal American Astronomical Society 979:2 (2025) 189
Abstract:
In this work, we study the circumstellar material (CSM) around massive stars, and the mass-loss rates depositing this CSM, using a large sample of radio observations of 325 core-collapse supernovae (CCSNe; only ~22% of them being detected). This sample comprises both archival data and our new observations of 99 CCSNe conducted with the AMI-LA radio array in a systematic approach devised to constrain the mass loss at different stages of stellar evolution. In the supernova (SN)–CSM interaction model, observing the peak of the radio emission of an SN provides the CSM density at a given radius (and therefore the mass-loss rate that deposited this CSM). On the other hand, limits on the radio emission, and/or on the peak of the radio emission provide a region in the CSM phase space that can be ruled out. Our analysis shows a discrepancy between the values of mass-loss rates derived from radio-detected and radio-nondetected SNe. Furthermore, we rule out mass-loss rates in the range of 2 × 10−6–10−4 M⊙ yr−1 for different epochs during the last 1000 yr before the explosion (assuming wind velocity of 10 km s−1) for the progenitors of ~80% of the Type II supernovae (SNe II) in our sample. In addition, we rule out the ranges of mass-loss rates suggested for red supergiants for ~50% of the progenitors of SNe II in our sample. We emphasize here that these results take a step forward in constraining mass loss in winds from a statistical point of view.IRIS: A Bayesian Approach for Image Reconstruction in Radio Interferometry with expressive Score-Based priors
ArXiv 2501.02473 (2025)
What We Don't C: Representations for scientific discovery beyond VAEs
Machine Learning and the Physical Sciences workshop at NeurIPS 2025
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.