The Hubble Sequence at z ∼ 0 in the IllustrisTNG simulation with deep learning
Bibliographic record
Abstract
ABSTRACT We analyse the optical morphologies of galaxies in the IllustrisTNG simulation at z ∼ 0 with a convolutional neural network trained on visual morphologies in the Sloan Digital Sky Survey. We generate mock SDSS images of a mass complete sample of $\sim 12\, 000$ galaxies in the simulation using the radiative transfer code SKIRT and include PSF and noise to match the SDSS r-band properties. The images are then processed through the exact same neural network used to estimate SDSS morphologies to classify simulated galaxies in four morphological classes (E, S0/a, Sab, Scd). The CNN model classifies simulated galaxies in one of the four main classes with the same uncertainty as for observed galaxies. The mass–size relations of the simulated galaxies divided by morphological type also reproduce well the slope and the normalization of observed relations which confirms a reasonable diversity of optical morphologies in the TNG suite. However we find a weak correlation between optical morphology and Sersic index in the TNG suite as opposed to SDSS which might require further investigation. The stellar mass functions (SMFs) decomposed into different morphologies still show some discrepancies with observations especially at the high-mass end. We find an overabundance of late-type galaxies ($\sim 50{{\ \rm per\ cent}}$ versus $\sim 20{{\ \rm per\ cent}}$) at the high-mass end [log(M*/M⊙) > 11] of the SMF as compared to observations according to the CNN classifications and a lack of S0 galaxies ($\sim 20{{\ \rm per\ cent}}$ versus $\sim 40{{\ \rm per\ cent}}$) at intermediate masses. This work highlights the importance of detailed comparisons between observations and simulations in comparable conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".