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Identifying and Characterizing Very Low-mass Spectral Blend Binaries with Machine Learning Methods

  • Authors: Juan Diego Draxl Giannoni, Malina Desai, Adam J. Burgasser, A. Camille Dunning, Christian Aganze, Luke McDermott, Christopher A. Theissen, Daniella C. Bardalez Gagliuffi

Juan Diego Draxl Giannoni et al 2026 The Astronomical Journal 171 .

  • Provider: AAS Journals

Caption: Figure 12.

Component spectral type distributions for a simulated sample of VLM dwarf binaries assuming a uniform age distribution over 0.5–8 Gyr, a power-law mass distribution ﹩\frac{dN}{dM}\propto {M}^{-0.6}﹩ for 0.01 MM ≤ 0.1 M, a power-law mass ratio distribution ﹩\frac{dN}{dq}\propto {q}^{4}﹩ for 0.2 ≤ q ≤ 1, I. Baraffe et al. (2003) evolutionary models, and the M. J. Pecaut & E. E. Mamajek (2013) spectral type/effective temperature relation. Color shading indicates the relative density distribution for each spectral type combination.

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