You will evaluate PyAutoLens model fits and the segmentation maps used as input. For each object you will see the modeling outputs, the RGB cutout, segmentation map, and Einstein radius.
Criteria for Success
1. You have high confidence that the object is a genuine strong gravitational lens (otherwise classify as Unsure If Lens).
2. The system is not a double source-plane lens (otherwise classify as Double Source Plane Lens (DSPL)).
3. The system contains only one lens galaxy within its Einstein radius (otherwise classify as Multi Galaxy Lens (MGL)).
4. The lens model provides an accurate and reliable measurement of the Einstein radius suitable for publication in a peer-reviewed scientific article (otherwise classify as Failure).
5. The critical curve morphology and source reconstruction are physically plausible (otherwise classify as Failure).
6. If all criteria above are satisfied, classify the system as Success.
Failure classifications
If you select Failure, you will be asked where in the fitting pipeline things first went wrong. If the segmentation/masking is incorrect, everything downstream also fails, so always pick the earliest failure.
Use Back to revise, Jump to to navigate by number, and Criteria for examples and full evaluation criteria.