Using Narrowband Photometry to Classify Stars and Brown Dwarfs
Bibliographic record
Abstract
We present a new system of narrowband filters in the near-infrared that can be used to classify stars and brown dwarfs. This set of four filters, spanning the H band, can be used to identify molecular features unique to brown dwarfs, such as H 2 O and CH 4 . The four filters are centered at 1.495 μm (H 2 O), 1.595 μm (continuum), 1.66 μm (CH 4 ), and 1.75 μm (H 2 O). Using two H 2 O filters allows us to solve for the reddening of individual objects. This can be accomplished by constructing a color-color-color cube and rotating it until the reddening vector disappears. We created a model of predicted color-color-color values for different spectral types by integrating filter bandpass data with the spectra of known stars and brown dwarfs. We validated this model by making photometric measurements of seven known L and T dwarfs, ranging from L1 to T7.5. The photometric measurements agree with the model to within ±0.1 mag, allowing us to create spectral indexes for different spectral types. We can classify A through early M stars to within ±2 spectral types, late-type M and L dwarfs to within ±0.3 spectral types, and T dwarfs to within ±0.1 spectral types (1 σ). Thus, we can distinguish between a T1 and a T3 dwarf. The four physical bands can be converted into two reddening-free indexes μ 1 and μ 2 and an extinction A V for the individual objects. This technique, which is equivalent to extremely low resolution spectroscopy, can be used to survey large areas to provide rough spectral classifications for all the stars in the area, ranging down to the coolest brown dwarfs. It should prove particularly useful in young clusters where reddening can be high.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".