Clustering of the AKARI NEP deep field 24iμ/im selected galaxies
Bibliographic record
Abstract
piAims./i We present a method of selection of 24 iμ/im galaxies from the AKARI north ecliptic pole (NEP) deep field down to 150 iμ/iJy and measurements of their two-point correlation function. We aim to associate various 24 iμ/im selected galaxy populations with present day galaxies and to investigate the impact of their environment on the direction of their subsequent evolution./p piMethods./i We discuss using of Support Vector Machines (SVM) algorithm applied to infrared photometric data to perform star-galaxy separation, in which we achieve an accuracy higher than 80%. The photometric redshift information, obtained through the CIGALE code, is used to explore the redshift dependence of the correlation function parameter (rsub0/sub) as well as the linear bias evolution. This parameter relates galaxy distribution to the one of the underlying dark matter. We connect the investigated sources to their potential local descendants through a simplified model of the clustering evolution without interactions./p piResults./i We observe two different populations of star-forming galaxies, at zsubmed/sub ∼ 0.25, zsubmed/sub ∼ 0.9. Measurements of total infrared luminosities (iL/isubTIR/sub) show that the sample at zsubmed/sub ∼ 0.25 is composed mostly of local star-forming galaxies, while the sample at zsubmed/sub ∼ 0.9 is composed of luminous infrared galaxies (LIRGs) with iL/isubTIR/sub ∼ 10sup11.62/sup iL/isub⨀/sub. We find that dark halo mass is not necessarily correlated with the iL/isubTIR/sub: for subsamples with iL/isubTIR/sub = 10sup11.15/sup iL/isub⨀/sub at zsubmed/sub ∼ 0.7 we observe a higher clustering length (ir/isub0/sub = 6.21 ± 0.78 [ih/isup−1/supMpc]) than for a subsample with mean iL/isubTIR/sub = 10sup11.84/sup iL/isub⨀/sub at zsubmed/sub ∼ 1.1 (ir/isub0/sub = 5.86 ± 0.69 ih/isup−1/supMpc). We find that galaxies at zsubmed/sub ∼ 0.9 can be ancestors of present day iL/isub∗/sub early type galaxies, which exhibit a very high ir/isub0/sub ∼ 8ih/isup−1/sup Mpc./p
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".