Revisiting Physical Parameters of the Benchmark Brown Dwarf LHS 6343 C through a Hubble Space Telescope/WFC3 Secondary-eclipse Observation
Bibliographic record
Abstract
Abstract The LHS 6343 system consists of a resolved M-dwarf binary with an evolved, negligibly irradiated brown dwarf (BD), LHS 6343 C, orbiting the primary star. Such BD eclipsing binaries present rare and unique opportunities to calibrate substellar evolutionary and atmosphere models since mass, radius, temperature, and luminosity can be directly measured. We update this BD’s mass (62.6 ± 2.2 M Jup) and radius (0.788 ± 0.043 R Jup) using empirical stellar relations and a Gaia Data Release 3 distance. We use Hubble Space Telescope/Wide Field Camera 3 (WFC3) observations of an LHS 6343 C secondary eclipse to obtain a near-IR emission spectrum, which matches to a spectral type of T1.5 ± 1. We combine this spectrum with existing Kepler and Spitzer/IRAC secondary-eclipse photometry to perform atmospheric characterization using the ATMO-2020, Sonora-Bobcat, and BT-Settl model grids. ATMO-2020 models with strong nonequilibrium chemistry yield the best fit to observations across all modeled bandpasses while predicting physical parameters consistent with Gaia-dependent analogs. BT-Settl predicts values slightly more consistent with such analogs but offers a significantly poorer fit to the WFC3 spectrum. Finally, we obtain a semi-empirical measurement of LHS 6343 C’s apparent luminosity by integrating its observed and modeled spectral energy distribution. Applying knowledge of the system’s distance yields a bolometric luminosity of log(L bol/L ☉) = −4.77 ± 0.03 and, applying the Stefan–Boltzmann law for the known radius, an effective temperature of 1303 ± 29 K. We also use the ATMO-2020 and Sonora-Bobcat evolutionary model grids to infer an age for LHS 6343 C of 2.86 − 0.33 + 0.40 Gyr and 3.11 − 0.38 + 0.50 Gyr respectively.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".