<i>Euclid</i> : The first statistical census of dusty and massive objects in the ERO/Perseus field
Bibliographic record
Abstract
Our comprehension of the history of star formation at z > 3 strongly relies on rest-frame ultraviolet observations. However, this selection systematically misses the dustiest and most massive sources, resulting in an incomplete census at earlier times. Infrared facilities such as Spitzer and the James Webb Space Telescope have shed light on a hidden population lying at z = 3 − 6 characterised by extreme red colours named HIEROs (HST-to-IRAC extremely red objects), identified by the colour criterion H E − ch2 > 2.25. Recently, Euclid Early Release Observations (EROs) have opened the possibility to further study such objects, exploiting the comparison between Euclid and ancillary Spitzer /IRAC observations. The aim of this study was to investigate the effectiveness of this synergy in characterising the population of a small test area of 232 arcmin 2 . We utilised catalogues in the Perseus field across the VIS and NISP bands, supplemented by data from the four Spitzer channels and several ground-based MegaCam bands ( u , g , r , H α , i , and z ) already included in the ERO catalogue. We selected 121 HIEROs by applying the H E − ch2 > 2.25 colour cut, cleaned this sample of globular clusters and brown dwarfs, and then inspected by eye the multi-band cutouts of each source, ending with 42 reliable HIEROs. Photometric redshifts and other physical properties of the final sample were estimated using the spectral-energy-distribution-fitting software Bagpipes . From the z phot and M * values, we computed the galaxy stellar mass function at 3.5 < z < 5.5. When we exclude all galaxies that could host an active galactic nucleus, or whose stellar masses might be overestimated, we still find that the high-mass end of the galaxy stellar mass function is similar to previous estimates, indicating that the true value could be even higher. This investigation highlights the importance of a deeper study of this still mysterious population, in particular to assess its contribution to the cosmic star-formation rate density and its agreement with current galaxy evolution and formation models. These early results demonstrate Euclid ’s capabilities to push the boundaries of our understanding of obscured star formation across a wide range of epochs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".