Linking High-z and Low-z: Are We Observing the Progenitors of the Milky Way with JWST?
Bibliographic record
Abstract
Abstract The recent JWST observation of the Firefly Sparkle at z = 8.3 offers a unique opportunity to link the high- and the low-z Universe. Indeed, the claim of it being a Milky Way (MW) type of assembly at the cosmic dawn opens the possibility of interpreting the observation with locally calibrated galaxy-formation models. Here, we use the a state-of-the-art MW-evolution model to perform forward modeling of our Galaxy's progenitors at high-z. We build a set of mock spectra for the MW building blocks to make predictions for JWST and to interpret the Firefly Sparkle observation. First, we find that the most massive MW progenitor becomes detectable in a deep survey like JADES from z ≈ 8.2, meaning that we could have already observed MW analogs that still need interpretation. Second, we provide predictions for the number of detectable MW progenitors in lensed surveys like the CAnadian NIRISS Unbiased Cluster Survey, and interpret the Firefly Sparkle as a group of MW building blocks. Both the number of detections and the observed NIRCam photometry are consistent with our predictions. By identifying the MW progenitors whose mock photometry best fits the data, we find bursty and extended star formation histories, lasting >150–300 Myr, and estimate their properties: M h ≈ 108−9 M ⊙, M ⋆ ≈ 106.2−7.5 M ⊙, SFR ≈ 0.04–0.20 M ⊙ yr−1, and Z gas ≈ 0.04–0.24 Z ⊙. Uncovering the properties of MW analogs at cosmic dawn by combining JWST observations and locally constrained models will allow us to understand our Galaxy's formation, linking the high- and low-z perspectives.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".