171 citations · 801 across the 65 of their papers we have counts for
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JWST Observations of the Metal-poor T Dwarf WISEA J155349.96+693355.2: The First Brown Dwarf Associated with the Gaia-Enceladus Milky Way Substructure
Aaron M. Meisner, Adam J. Burgasser, Chih-Chun Hsu +15
We present JWST NIRSpec and MIRI observations of the metal-poor T dwarf WISEA J155349.96+693355.2. The combined NIRSpec/prism plus MIRI/LRS spectrum (R ~ 100) provides 0.6-12 m…
Metal-poor Brown Dwarf Kinematics from JWST NIRSpec Spectroscopy
Chih-Chun Hsu, Christopher A. Theissen, Adam J. Burgasser +14
Galactic archaeology relies on stellar kinematics and chemical abundances to identify various stellar populations and associations. With JWST, Galactic archaeology of ancient metal…
The Dyn-Atmo Survey: JWST/NIRSpec spectroscopy of dynamical benchmark GJ 758 B
Alexander Madurowicz, Emily Rickman, Daniella Bardalez Gagliuffi +22
We present new JWST/NIRSpec IFU high contrast spectroscopic observations of the brown dwarf companion GJ 758 B. Extensive radial velocity monitoring of the primary has enabled high…
The Dyn-Atmo Survey: High-contrast Imaging Spectroscopy of the Substellar Companion HD 13724 B with the JWST NIRSpec IFU
Kielan K. W. Hoch, Alex Madurowicz, Evert Nasedkin +22
We present the first spectral analysis from the JWST Cycle 3 GO Program #6362, the Dyn-Atmo Survey of dynamical-atmospheric benchmarks, characterizing the atmospheres of directly-i…
EncoTESS: Age-Sensitive Encodings from Raw TESS Light Curves
Phil R. Van-Lane, Joshua S. Speagle, Ryan Cloutier +3
Main sequence stars of spectral types late F through M exhibit systematic variability in photometric light curves, particularly when they are young. Rotational modulation of starsp…
Identifying and Characterizing Very Low Mass Spectral Blend Binaries with Machine Learning Methods
Juan Diego Draxl Giannoni, Malina Desai, Adam J. Burgasser +5
We present an approach to identifying and characterizing unresolved, very low mass spectral blend binaries composed of late-M, L, and T dwarfs using machine learning methodologies.…