activity
20132026
most citedSuperRAENN: A Semi-supervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium Deep Survey Supernovae

92 citations · 800 across the 37 of their papers we have counts for

collaborators

36 papers

astro-ph.GA2026

LoVoCCS. III. Third Generation Pipeline & The Hercules Supercluster

Anthony M. Englert, Shenming Fu, Ian Dell'Antonio +29

The Local Volume Complete Cluster Survey (LoVoCCS) is a volume-complete survey of over one-hundred nearby (), X-ray luminous ($L_{500} > 10^{44} \text{ erg s}^{-1}…

astro-ph.IM2026

How to Craft the Right Language AI Policy For Your Research Group (Some Assembly Required)

Michelle Ntampaka, S. Burke-Spolaor, Ioana Ciucă +14

Language AI is rapidly becoming part of the astronomy research ecosystem, prompting research teams to develop policies governing its use. But resources and advice for AI adoption a…

astro-ph.CO2025

Estimating the triaxiality of massive clusters from 2D observables in MillenniumTNG with machine learning

Ana Maria Delgado, Michelle Ntampaka, Sownak Bose +7

Properties of massive galaxy clusters, such as mass abundance and concentration, are sensitive to cosmology, making cluster statistics a powerful tool for cosmological studies. How…

astro-ph.IM2025

The Importance of Being Adaptable: An Exploration of the Power and Limitations of Domain Adaptation for Simulation-Based Inference with Galaxy Clusters

Michelle Ntampaka, A. Ciprijanovic, Ana Maria Delgado +4

The application of deep machine learning methods in astronomy has exploded in the last decade, with new models showing remarkably improved performance on benchmark tasks. Not nearl…

astro-ph.CO2024

A Multi-Wavelength Technique for Estimating Galaxy Cluster Mass Accretion Rates

John Soltis, Michelle Ntampaka, Benedikt Diemer +7

The mass accretion rate of galaxy clusters is a key factor in determining their structure, but a reliable observational tracer has yet to be established. We present a state-of-the-…

astro-ph.CO2024★ 1 cited

Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling

Alan Hsu, Matthew Ho, Joyce Lin +4

We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ…