Jake Grigorian

ORCID: 0000-0002-2466-865X
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About
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Research Areas
  • Stellar, planetary, and galactic studies
  • Astronomy and Astrophysical Research
  • Isotope Analysis in Ecology
  • Astronomical Observations and Instrumentation
  • Geophysics and Gravity Measurements
  • Gamma-ray bursts and supernovae
  • Food Quality and Safety Studies

University of Southern California
2024

Abstract After decades of brown dwarf discovery and follow-up, we can now infer the functional form mass distribution within 20 pc, which serves as a constraint on star formation theory at lowest masses. Unlike objects main sequence that have clear luminosity-to-mass correlation, dwarfs lack correlation between an observable parameter (luminosity, spectral type, or color) mass. A measurement function must therefore be procured through proxy measurements theoretical models. We utilize various...

10.3847/1538-4357/ad62fc article EN cc-by The Astrophysical Journal 2024-10-01

After decades of brown dwarf discovery and follow-up, we can now infer the functional form mass distribution within 20 parsecs, which serves as a constraint on star formation theory at lowest masses. Unlike objects main sequence that have clear luminosity-to-mass correlation, dwarfs lack correlation between an observable parameter (luminosity, spectral type, or color) mass. A measurement function must therefore be procured through proxy measurements theoretical models. We utilize various...

10.48550/arxiv.2406.09690 preprint EN arXiv (Cornell University) 2024-06-13

We present the discovery of 118 new ultracool dwarf candidates, discovered using a machine learning tool, named \texttt{SMDET}, applied to time series images from Wide-field Infrared Survey Explorer. gathered photometric and astrometric data estimate each candidate's spectral type, distance, tangential velocity. This sample has photometrically estimated class distribution 28 M dwarfs, 64 L 18 T dwarfs. also identify subdwarf candidate, two extreme candidate young Five objects did not have...

10.48550/arxiv.2408.14447 preprint EN arXiv (Cornell University) 2024-08-26

Abstract We present the discovery of 118 new ultracool dwarf candidates, discovered using a machine-learning tool, named SMDET , applied to time-series images from Wide-field Infrared Survey Explorer. gathered photometric and astrometric data estimate each candidate’s spectral type, distance, tangential velocity. This sample has photometrically estimated class distribution 28 M dwarfs, 64 L 18 T dwarfs. also identify T-subdwarf candidate, two extreme candidate young Five objects did not have...

10.3847/1538-3881/ad77d2 article EN cc-by The Astronomical Journal 2024-10-17
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