A versatile tool for cluster lensing source reconstruction – I. Methodology and illustration on sources in the Hubble Frontier Field Cluster MACS J0717.5+3745
Bibliographic record
Abstract
ABSTRACT We describe a general-purpose method to reconstruct the intrinsic properties of sources lensed by the gravitational potential of foreground clusters of galaxies. The tool lenstruction is implemented in the publicly available multipurpose gravitational lensing software lenstronomy, in order to provide an easy and fast solution to this common astrophysical problem. The tool is based on forward modelling the appearance of the source in the image plane, taking into account the distortion by lensing and the instrumental point spread function. For singly imaged sources, a global lens model in the format of the Hubble Frontier Fields (HFF) lensing maps is required as a starting point. For multiply imaged sources, the tool can also fit and apply first- (deflection), second- (shear, convergence), and third-order (flexion) corrections to the local gravitational potential to improve the reconstruction, depending on the quality of the data. We illustrate the performance and features of the code with two examples of multiply imaged systems taken from the HFF, starting from five different publicly available cluster models. We find that, after our correction, the relative magnification – and other lensing properties – between the multiple images becomes robustly constrained. Furthermore, we find that scatter between models of the reconstructed source size and magnitude is reduced. The code and Jupyter notebooks are publicly available.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".