Dust extinction measures for <i>z</i> ∼ 8 galaxies using machine learning on <i>JWST</i> imaging
Bibliographic record
Abstract
ABSTRACT We present the results of a Machine Learning study to measure the dust content of galaxies observed with JWST at z &gt; 6 through the use of trained neural networks based on high-resolution IllustrisTNG simulations. Dust is an important unknown in the evolution and observability of distant galaxies and is degenerate with other stellar population features through spectral energy fitting. As such, we develop and test a new spectral energy distribution (SED)-independent Machine Learning method to predict dust attenuation and sSFR of high redshift (z &gt; 6) galaxies. Simulated galaxies were constructed using the IllustrisTNG model, with a variety of dust contents parametrized by E(B–V) and A(V) values. These simulated galaxies were then used to train Convolutional Neural Network (CNN) models using supervised learning through a regression model. We demonstrate that within the context of these simulations, our single and multiband models are able to predict dust content of distant galaxies to within a 1$\sigma$ dispersion of A(V) $\sim 0.1$. On spectroscopically confirmed z &gt; 6 galaxies from JADES and CEERS programmes, our models predicted attenuation values of A(V) &lt; 0.7 for all systems, with a lower average [A(V) = 0.28]. The predictions of dust attenuation values that have an average error of 0.26 ($\sigma$ = 0.36) larger than SED fitted values, but for star formation an average error of 0.18 ($\sigma$ = 0.2) smaller. Both results show that distant galaxies at $z &gt; 6$ with confirmed spectroscopy are not very dusty, although this sample is potentially biased. We discuss these issues and present ideas on how to accurately measure dust features at the highest redshifts using a combination of Machine Learning and SED fitting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".