Bibliographic record
Abstract
This release marks the completion of two years work implementing JAX (https://docs.jax.dev/en/latest/notebooks/thinking_in_jax.html) in PyAutoLens. With JAX, any lens modeling analysis can be run on GPU, with speed up of ~x50 or more for all lens modeling. Core Release The core PyAutoLens API does not change significantly, however existing users redownload the new autolens workspace, which has new configs and examples: https://github.com/Jammy2211/autolens_workspace New user should checkout the start_here.ipynb notebook, which can be read via a Google Colab by clicking the hyperlink. GPU Modeling Examples The following Juypter Notebooks, which run via Google Colab, illustrate < 10 minute lens modeling for different science cases: start_here_imaging.ipynb: Galaxy-scale strong lenses observed with CCD imaging (e.g. Hubble, James Webb). start_here_interferometer.ipynb: Galaxy scale strong lenses observed with interferometer data (e.g. ALMA). start_here_point_source.ipynb: Galaxy scale strong lenses with a lensed point source (e.g. lensed quasars). start_here_group.ipynb: Group scale strong lenses where there are 2-10 lens galaxies. Performance Of Other Features Pixelized sources run ~x5 - x20 faster on modern HPC GPU clusters, with lens modeling times typically ~10 - 20 minutes. Pixelized source performance depends on the available GPU VRAM. In November 2025 a release will make GPU performance of pixelized sources for all GPU hardware approach < 10 minute lens models. Interferometer with many Visibilities: Above ~ 100,000 visibilities interferometer performance suffers significant slow down. **In December 2025 a new release will make all interferometer modeling efficient irrespective of the number of visibilities. CPU Performance: For pixelized sources CPU performance is worse than the previous PyAutoLens, as JAX is not optimized for CPUs. A future release will restore performance to be on par with previous versions, but users seeking to perform pixelized source modeling without GPU may wish to use the previous PyAutoLens. Strong Lens Galaxy Clusters This release can perform strong lens cluster calculations and lens modeling on GPU. For those familiar with cluster lensing, this includes performing an image-plane chi-squared multiple image calculation for clusters with over 100s of cluster members, with full support for multi-plane ray tracing of the entire cluster! Initial profiling shows it runs 50 or more times faster than other strong lens cluster codes run on CPU. Documentation and examples for cluster modeling are actively being developed but not yet mature. You can find the most up to date examples at the following links: https://github.com/Jammy2211/autolens_workspace/blob/release/start_here_cluster.ipynb https://github.com/Jammy2211/autolens_workspace/tree/release/scripts/simulators/cluster https://github.com/Jammy2211/autolens_workspace/tree/release/scripts/modeling/cluster
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.254 | 0.146 |
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".