The SRG/eROSITA All-Sky Survey
Bibliographic record
Abstract
Aims. We aim to participate in the calibration of the X-ray photon count rate to halo mass scaling relation of galaxy clusters selected in the first eROSITA All-Sky Survey on the western Galactic hemisphere (eRASS1) using weak-lensing (WL) data from the fourth data release of the Kilo-Degree Survey (KiDS-1000). We therefore measured the radial shear profiles around eRASS1 galaxy clusters using background galaxies in KiDS-1000 as well as the cluster member contamination. Furthermore, we provide consistency checks with the other stage-III weak-lensing surveys that take part in the eRASS1 mass calibration, the Dark Energy Survey Year 3 (DES Y3) and Hyper Suprime-Cam Year 3 (HSC-Y3), as KiDS-1000 has overlap with both surveys. Methods. We determined the cluster member contamination of eRASS1 clusters present in KiDS-1000 based on background galaxy number density profiles, where we accounted for the optical obscuration caused by cluster galaxies. The extracted shear profiles, together with the result of the contamination model and the lens sample selection, were then analysed through a Bayesian population model. We calibrated the WL mass bias parameter by analysing realistic synthetic shear profiles from mock cluster catalogues. Our consistency checks between KiDS-1000 and DES Y3 and HSC-Y3 include the comparison of contamination-corrected density contrast profiles and amplitudes by employing the union of background sources around common clusters as well as the individual scaling relation results. Results. We present a global contamination model for eRASS1 clusters in KiDS-1000 and the calibration results of the X-ray photon count rate to halo mass relation. The results of the WL mass bias parameter bWL obtained through mock observations show that hydro-dynamical modelling uncertainties only play a sub-dominant role in KiDS-1000. The uncertainty of the multiplicative shear bias dominates the systematic error budget at low cluster redshifts, while the uncertainty of our contamination model does so at high ones. The crosschecks between the three WL surveys show that they are for the most part statistically consistent with each other. This enables, for the first time, cosmological constraints from clusters calibrated by three state-of-the-art weak-lensing surveys.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.007 |
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".