Going beyond galaxy ages with dense basis star formation history reconstruction
Bibliographic record
Abstract
Abstract Panchromatic SED fitting allows us to better resolve degeneracies between quantities like the star formation rate and dust. This in turn allows us to more robustly extract information about the different stellar populations that comprise a galaxy’s Star Formation History (SFH). Using the Dense Basis SED fitting method (Iyer & Gawiser 2017), we reconstruct the SFHs with uncertainties for a large sample of galaxies using an atlas of SEDs corresponding to a physically motivated basis of SFHs. Using Gaussian Process Regression, we encode the parameters describing these SFHs in a functionally independent form. This give us more robust estimates for quantities like Stellar Masses and Star Formation Rates, that directly depend on the SFH. These SFHs can additionally be used to answer questions like the time at which a galaxy’s star formation peaked, and how many major episodes of star formation occurred in a galaxy’s past, allowing us to go beyond the traditionally estimated ‘Galaxy Age’, which is often poorly constrained. They also allow us to probe the high-redshift low-stellar mass regime of the SFR-M* correlation by constructing trajectories in SFR-M* space for each galaxy.
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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".