A Census of NUV M-Dwarf Flares Using Archival GALEX Data and the gPhoton2 Pipeline
Bibliographic record
Abstract
M-dwarfs are common stellar hosts of habitable-zone exoplanets. NUV radiation can severely impact the atmospheric and surface conditions of such planets, making characterization of NUV flaring activity a key aspect in determining habitability. We use archival data from the GALEX and XMM-Newton telescopes to study the flaring activity of M-dwarfs in the NUV. The GALEX observations form the most extensive dataset of M-dwarfs in the NUV to date, with exploitation of this data possible due to the new gPhoton2 pipeline. We run a dedicated algorithm to detect flares in the pipeline produced lightcurves and find some of the most energetic flares observed to date within the NUV bandpass, with energies of $\sim10^{34}$ ergs. Using GALEX data, we constrain flare frequency distributions for stars from M0 to M6 in the NUV up to $10^5\,$s in equivalent duration and $10^{34}$ ergs in energy, orders of magnitude above any previous study in the UV. We speculate the combined effect of NUV luminosities and flare rates of M4 and later stars could potentially allow abiogenesis on their habitable zone exoplanets. As a counterpoint, we estimate that the high frequencies of energetic UV flares and associated coronal mass ejections would inhibit the formation of an ozone layer, possibly preventing genesis of complex Earth-like lifeforms due to sterilizing levels of surface UV radiation. We also provide a framework for future observations of M-dwarfs with ULTRASAT, a wide FoV NUV telescope to be launched in 2027.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".