6 papers
Inferring Planet and Disk Parameters from Protoplanetary Disk Images Using a Variational Autoencoder
Sayed Shafaat Mahmud, Sayantan Auddy, Neal Turner +1
Dust-continuum observations of many protoplanetary disks reveal rings and gaps that are widely interpreted as evidence of ongoing planet formation. Here we present the first framew…
From Images to Physics: Probabilistic Inference of Galaxy Parameters and Emission Lines via VAE & Normalizing Flows
Adiba Amira Siddiqa, Sayed Shafaat Mahmud, Rafael Martinez-Galarza
We introduce a Variational Autoencoder (VAE)--Normalizing Flow (NF) framework for rapid probabilistic inference of galaxy properties and emission line fluxes at from S…
Neural Network identification of Dark Star Candidates. II. Spectroscopy
Adiba Amira Siddiqa, Sayed Shafaat Mahmud, Cosmin Ilie
Some of the first stars in the Universe might be powered by Dark Matter (DM) annihilations, rather than nuclear fusion. Those objects, i.e. Dark stars (DS), offer a unique window i…
Neural Network identification of Dark Star Candidates. I. Photometry
Sayed Shafaat Mahmud, Adiba Amira Siddiqa, Cosmin Ilie
The formation of the first stars in the universe could be significantly impacted by the effects of Dark Matter (DM). Namely, if DM is in the form of Weakly Interacting Massive Part…
VADER: A Variational Autoencoder to Infer Planetary Masses and Gas-Dust Disk Properties Around Young Stars
Sayed Shafaat Mahmud, Sayantan Auddy, Neal Turner +1
We present \textbf{VADER} (Variational Autoencoder for Disks Embedded with Rings), for inferring both planet mass and global disk properties from high-resolution ALMA dust continuu…
Spectroscopic Supermassive Dark Star candidates
Cosmin Ilie, Sayed Shafaat Mahmud, Jillian Paulin +1
Dark Stars, i.e. early stars composed almost entirely of hydrogen and helium but powered by Dark Matter, could form in zero metallicity clouds located close to the center of high r…