Predicting the spectrum of UGC 2885, Rubin's Galaxy with machine\n learning
Bibliographic record
Abstract
Wu & Peek (2020) predict SDSS-quality spectra based on Pan-STARRS broad-band\n\\textit{grizy} images using machine learning (ML). In this letter, we test\ntheir prediction for a unique object, UGC 2885 ("Rubin's galaxy"), the largest\nand most massive, isolated disk galaxy in the local Universe ($D<100$ Mpc).\nAfter obtaining the ML predicted spectrum, we compare it to all existing\nspectroscopic information that is comparable to an SDSS spectrum of the central\nregion: two archival spectra, one extracted from the VIRUS-P observations of\nthis galaxy, and a new, targeted MMT/Binospec observation. Agreement is\nqualitatively good, though the ML prediction prefers line ratios slightly more\ntowards those of an active galactic nucleus (AGN), compared to archival and\nVIRUS-P observed values. The MMT/Binospec nuclear spectrum unequivocally shows\nstrong emission lines except H$\\beta$, the ratios of which are consistent with\nAGN activity. The ML approach to galaxy spectra may be a viable way to identify\nAGN supplementing NIR colors. How such a massive disk galaxy ($M^* = 10^{11}$\nM$_\\odot$), which uncharacteristically shows no sign of interaction or mergers,\nmanages to fuel its central AGN remains to be investigated.\n
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".