SPECTRAL CLASSIFICATION OF GALAXIES AT 0.5 ⩽<i>z</i>⩽ 1 IN THE CDFS: THE ARTIFICIAL NEURAL NETWORK APPROACH
Bibliographic record
Abstract
The aim of this work is to combine spectral energy distribution (SED) fitting with artificial neural network techniques to assign spectral characteristics to a sample of galaxies at 0.5 < z < 1. The sample is selected from the spectroscopic campaign of the ESO/GOODS-South field, with 142 sources having photometric data from the GOODS-MUSIC catalog covering bands between ∼0.4 and 24 μm in 10–13 filters. We use the CIGALE code to fit photometric data to Maraston's synthesis spectra to derive mass, specific star formation rate, and age, as well as the best SED of the galaxies. We use the spectral models presented by Kinney et al. as targets in the wavelength interval ∼1200–7500 Å. Then a series of neural networks are trained, with average performance ∼90%, to classify the best SED in a supervised manner. We consider the effects of the prominent features of the best SED on the performance of the trained networks and also test networks on the galaxy spectra of Coleman et al., which have a lower resolution than the target models. In this way, we conclude that the trained networks take into account all the features of the spectra simultaneously. Using the method, 105 out of 142 galaxies of the sample are classified with high significance. The locus of the classified galaxies in the three graphs of the physical parameters of mass, age, and specific star formation rate appears consistent with the morphological characteristics of the galaxies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".