Evidence for the kinematic Sunyaev-Zel’dovich effect with the Atacama Cosmology Telescope and velocity reconstruction from the Baryon Oscillation Spectroscopic Survey
Bibliographic record
Abstract
We use microwave temperature maps from two seasons of data from the Atacama Cosmology Telescope at 146 GHz, together with the ``Constant Mass'' CMASS galaxy sample from the Baryon Oscillation Spectroscopic Survey to measure the kinematic Sunyaev-Zel'dovich (kSZ) effect over the redshift range $z=0.4--0.7$. We use galaxy positions and the continuity equation to obtain a reconstruction of the line-of-sight velocity field. We stack the microwave temperature at the location of each halo, weighted by the corresponding reconstructed velocity. We vary the size of the aperture photometry filter used, thus probing the free electron profile of these halos from within the virial radius out to three virial radii, on the scales relevant for investigating the missing baryons problem. The resulting best fit kSZ model is preferred over the no-kSZ hypothesis at 3.3 and $2.9\ensuremath{\sigma}$ for two independent velocity reconstruction methods, using 25,537 galaxies over 660 square degrees. The data suggest that the baryon profile is shallower than the dark matter in the inner regions of the halos probed here, potentially due to energy injection from active galactic nucleus or supernovae. Thus, by constraining the gas profile on a wide range of scales, this technique will be useful for understanding the role of feedback in galaxy groups and clusters. The effect of foregrounds that are uncorrelated with the galaxy velocities is expected to be well below our signal, and residual thermal Sunyaev-Zel'dovich contamination is controlled by masking the most massive clusters. Finally, we discuss the systematics involved in converting our measurement of the kSZ amplitude into the mean free electron fraction of the halos in our sample.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".