Euclid: Quick Data Release (Q1) -- Secondary nuclei in early-type galaxies
Bibliographic record
Abstract
Massive early-type galaxies (ETGs) are believed to form primarily through mergers of less massive progenitors, leaving behind numerous traces of violent formation histories, such as stellar streams and shells. A particularly striking signature of these mergers is the formation of supermassive black hole (SMBH) binaries, which can create depleted stellar cores through interactions with stars on radial orbits - a process known as core scouring. The secondary SMBH in such systems may still carry a dense stellar envelope and thereby remain observable for some time as a secondary nucleus, while it is sinking towards the shared gravitational potential of the merged galaxy. We leverage Euclid's Q1 Early Release data to systematically search for secondary nuclei in ETGs. We present a preliminary sample of 666 candidate systems distributed over 504 hosts (some of which contain multiple secondary nuclei). The vast majority of these fall at separations of 3 kpc to 15 kpc, indicative of normal mergers. 44 fall at projected separations of less than 2 kpc. We argue those candidates at very close angular separations are unlikely to be a consequence of chance alignments. We show that their stellar masses are mostly too large for them to be globular clusters and that a significant subset are unresolved even at Euclid's spatial resolution, rendering them too small to be dwarf galaxies. These may represent the highest-density nuclei of a previously merged galaxy, currently sinking into the centre of the new, common gravitational potential and thus likely to host a secondary SMBH. We then demonstrate that convolutional neural networks offer a viable avenue to detect multiple nuclei in the thirty-times larger sky coverage of the future Euclid DR1. Finally, we argue that our method could detect the remnants of a recoil event from two merged SMBHs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.022 |
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".