The 2023 Outburst of the Gaia Alerted EXor Gaia23bab
Bibliographic record
Abstract
Abstract Episodic accretion is a fundamental process in the build-up of the stellar mass. EX Lupi-type eruptive young stars (EXors) represent one of the main types of episodic accretion. We study the recently discovered EXor Gaia23bab during its 2023 outburst. We obtained optical and near-infrared photometry and spectroscopy to probe the variation of the physical properties of Gaia23bab during its recent outburst. We also collected archival photometry to study a previous outburst of the star. We used several accretion tracers, including the Ca ii triplet, He i, and various hydrogen lines from the Paschen and Brackett series, to measure the accretion rate during the outburst. The accretion rate is consistent with ∼2.0 × 10−7 M ⊙ yr−1. Comparing the line fluxes of the hydrogen Brackett series to predictions of Case B theory suggests excitation temperatures of 5000–10,000 K and electron densities of 109–1010 cm−3. Comparison to the predictions of a model for T Tauri stars revealed that the fluxes of the Balmer series are consistent with temperatures of 5000–12,500 K and a hydrogen density of 108 cm−3, while the fluxes of the Paschen series are consistent with temperatures in the range between 10,000 and 12,500 K and a hydrogen density of 1011 cm−3. The derived temperatures and densities confirm that Gaia23bab is a prototypical EXor, not only due to its accretion rate, but also based on the best-fit temperatures and densities revealed by the detected hydrogen lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".