44 citations · 59 across the 9 of their papers we have counts for
11 papers
The Galaxy's Guide to the Tokenizer: A Benchmark for Scientific Foundation Models
Sogol Sanjaripour, Michael J. Smith, Manuel Pérez-Carrasco +3
Tokenization is central to adapting scientific data for transformer-based foundation models, yet its impact on learned representations remains poorly understood. We compare four to…
The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning
V. Samuel Pérez-Díaz, Vinay L. Kashyap, Joshua D. Ingram +6
We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use sou…
Modeling X-ray photon pile-up with a normalizing flow
Ole König, Daniela Huppenkothen, Douglas Finkbeiner +5
The dynamic range of imaging detectors flown on-board X-ray observatories often only covers a limited flux range of extrasolar X-ray sources. The analysis of bright X-ray sources i…
Learning Representations of Event Time Series with Sparse Autoencoders for Anomaly Detection, Similarity Search, and Unsupervised Classification
Steven Dillmann, Juan Rafael Martínez-Galarza
Event time series are sequences of discrete events occurring at irregular time intervals, each associated with a domain-specific observational modality. They are common in domains…
Hyperluminous Supersoft X-Ray Sources in the Chandra Catalog
Andrea Sacchi, Kevin Paggeot, Steven Dillmann +2
Hyperluminous supersoft X-ray sources, such as bright extragalactic sources characterized by particularly soft X-ray spectra, offer a unique opportunity to study accretion onto sup…
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TB of Astronomical Scientific Data
The Multimodal Universe Collaboration, Jeroen Audenaert, Micah Bowles +26
We present the MULTIMODAL UNIVERSE, a large-scale multimodal dataset of scientific astronomical data, compiled specifically to facilitate machine learning research. Overall, the MU…