Challenges in cosmic magnification reconstruction by magnification response
Bibliographic record
Abstract
Cosmic magnification on the observed galaxy overdensity is a promising weak gravitational lensing tracer. Current cosmic magnification reconstruction algorithms, the analytical method of blind separation (ABS) and constrained internal linear combination (cILC), intend to disentangle the weak lensing signal using the magnification response in various flux bins. In this work, we reveal an unrecognized systematic bias arising from the difference between galaxy bias and the galaxy-lensing cross-correlation bias, due to the mismatch between the weak lensing kernel and the redshift distribution of photometric objects. It results into a galaxy-lensing degeneracy, which invalidates ABS as an exact solution. Based on the simulated cosmoDC2 galaxies, we verify that the recovered weak lensing amplitude by ABS is biased low by $\ensuremath{\sim}10%$. cILC, including a modified version proposed here, also suffers from systematic bias of comparable amplitude. Combining flux and color information leads to significant reduction in statistical errors, but fails to eliminate the aforementioned bias. With the presence of this newly found systematic, it remains a severe challenge in blindly and robustly separating the cosmic magnification from the galaxy intrinsic clustering.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".