In fact, three new spectral types (L, T, and Y) were added to the Harvard sequence to accommodate the spectral features of brown dwarfs.
During the cooling process, some brown dwarfs unexpectedly become bluer instead of redder (at optical and near-infrared wavelengths).
The aim of this work is to approximate the spectral type of brown dwarfs using only photometric data, in particular 2MASS and WISE magnitudes.
After validating the reliability of the algorithms, we applied them to 21 isolated brown dwarfs without prior spectral type determinations.
Machine learning algorithms, combined with multi-band photometry, are a powerful tool for estimating the spectral types of brown dwarfs.
Top and middle panels: Colour–colour diagrams (J−K vs. J−H) as a function of spectral type. The spectral types highlighted in each panel are as follows: M0-M4 (a), M5-M9 (b), L0-L4 (c), L5-L9 (d), T0-T4 (e), T5-Y (f). The remaining objects in the sample are shown in grey for reference. Bottom panels: colour–magnitude diagrams, 𝑀𝐽 versus (𝐽 − 𝐻), (𝐻 − 𝐾𝑠 ), and (𝐽 − 𝐾𝑠 ), corresponding to panels (g), (h), and (i). The black point with error bars in the lower-right corner of each panel represents the median uncertainty for all objects in the sample. Note: Types M0–M5 are shown for contextual continuity but are not part of the spectral classes defined in this work. The T5–Y group in panel (f) is further subdivided into Class 4 (T5–T9) and Class 5 (Y) in our classification scheme (see Table 1). — astro-ph.SR
Brown dwarfs are compact objects that do not reach temperatures high enough to produce sustained hydrogen fusion. Consequently, they cool over time, gradually evolving through later spectral types.
In fact, three new spectral types (L, T, and Y) were added to the Harvard sequence to accommodate the spectral features of brown dwarfs. During the cooling process, some brown dwarfs unexpectedly become bluer instead of redder (at optical and near-infrared wavelengths).
This phenomenon, known as the bluing effect, is particularly noticeable at the L/T spectral transition. The aim of this work is to approximate the spectral type of brown dwarfs using only photometric data, in particular 2MASS and WISE magnitudes. We used two machine learning algorithms, Random Forest and Gaussian Processes, which were evaluated using a 70/30 train/test split.
Both models were trained using 5-fold cross-validation and achieved F1-scores of 0.84 and 0.87, respectively, on the test set. After validating the reliability of the algorithms, we applied them to 21 isolated brown dwarfs without prior spectral type determinations.
Our results indicate that 5 of these objects have a spectral type between L0 and L4, while the remaining 16 fall within the M6-M9 range. Machine learning algorithms, combined with multi-band photometry, are a powerful tool for estimating the spectral types of brown dwarfs.
A.R. Callen, I.H. Bustos Fierro, M. Gómez
Subjects: Solar and Stellar Astrophysics (astro-ph.SR); Instrumentation and Methods for Astrophysics (astro-ph.IM)
Cite as: arXiv:2605.20146 [astro-ph.SR] (or arXiv:2605.20146v1 [astro-ph.SR] for this version)
https://doi.org/10.48550/arXiv.2605.20146
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From: Iván Bustos Fierro
[v1] Tue, 19 May 2026 17:34:37 UTC (6,362 KB)
https://arxiv.org/abs/2605.20146
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