Last check-in: 2026-10-17T11:18:53Z.
Active campaigns
| Campaign | Recent progress | Next steps |
|---|---|---|
| HST imaging, clean slate: Nautilus base on A100 and jax_cpu at a pinned release, mass stage with linear lens light and with it fixed, then other samplers on the mass stage | Opened 2026-10-17 as a fresh start by human decision: the base is re-measured from scratch on PyAuto release 2026.10.17.1 (PyAutoLens tag v2026.10.17.1, released 2026-10-17) in fresh output trees; the 2026-09-24 rows and both earlier campaigns are superseded history. | Phase 1 via Mind start_dev: lens-light mode as a run variant (--lens-light linear|fixed|both, fixed = ordinary profiles with intensities from the light[1] fit), both rows from one chain, --export-prepared for both modes, library revisions and SLURM job id recorded in every row, submit scripts for A100 dense/sparse and ral jax_cpu dense/sparse against a private pinned checkout; then phase 2 compute by separate authorisation. |
| Point-source sampler admission | 2 October project record reports five Nautilus seeds; no gradient-sampler admission measurement. | Assess a bounded gradient-sampler admission proposal with the human; no new job is authorized. |
| Graphical and EP inference coordination | Mind campaign map and two scoping contracts preserved verbatim; implementation work remains in Mind. | Reconcile current Cortex observations and shipped fixes, then select one bounded phase. |
| Analytic Gaussian ensemble and control calibration | Cortex records accepted wave-1 baseline and 30 September independent-review caveats. | Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there. |
| N=50 NUTS versus three EP repeats | Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission. | Present the existing wave-2 proposal with current dependency evidence for decision. |
| Hierarchical slope recovery at scale | Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; Mind records later moments fix. | Verify the moments fix release and project mirror; recover missing summaries from existing samples before a proposed rerun. |
| IC50 EP and graphical scale ladder | Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat. | Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field diagnostics. |
| PyAutoFit search benchmark on gaussian_x3 (autofit_inference) | Wave 2 closed (B5b-2, 2026-10-15, autofit_inference#12) — RAL 409930 pulled, 12,750 rows admitted at the pin df6d218f10cd with 0 missing; NSS 50-step leads both evidence rankings; the catalogue ranks only legs with success rate >= 50 % over >= 20 attempts (post-hoc presentation convention); fit records published as inference-summary@3 (101 record shards). | B5c-2b (draft/research/autofit_inference/search_ext_b5c2b_scored_wave_submit.md): realisations 2-3 and their exact references, the correlated_nd@2 scored-wave manifest (83 cells x 50 seeds x 3 realisations, 12 450 runs) and its probe for 8 probe-first cells, then the submit on RAL --partition=ral from a private pinned copy with the A2 memory requests; correlated_nd@2 is frozen (autofit_inference 44c5e77f1fb0); the family is catalogued apart from gaussian_x3 and ranks nothing across families. |
Active tasks
| Task | Campaign | Status | Priority | Next steps |
|---|---|---|---|---|
| Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmark | slam-hst-base | blocked | normal | Verify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient witness. Carried into hst-imaging-mass-samplers phase 1 as a prior-identity question for the frozen model. |
| EP campaign — phase map for the 2026 Q3 graphical/EP push | graphical-ep | needs-decision | high | Reconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled. |
| Expectation Propagation Scale-Up — Scoping | graphical-ep | needs-slicing | high | Reconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining inference phase. |
| Graphical Model Scale-Up — Scoping | graphical-ep | needs-slicing | high | Reconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness. |
| NUTS/HMC trial on validated likelihoods | point-source-admission | needs-decision | normal | An unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing candidate targets. |
| gaussian_x3 search benchmark — wave 1 (the pilot) | gaussian-x3-search-benchmark | blocked | normal | Pilot stopped at 223 of 520 runs and is not restarted; freeze gaussian_x3@2 from its 17-row calibration before any scored run; the deferred cells move to scored wave 2 (after A2 and A4). |
| gaussian_x3 search benchmark — wave 2 (scored) | gaussian-x3-search-benchmark | running | high | Wave 2 is closed and judged (B5b-2); the fit snapshot is captured from main under inference-summary@3 after the B5b-2 PRs merge. |
| HST imaging mass stage, clean slate: Nautilus base on A100 and jax_cpu at release 2026.10.17.1, linear and fixed lens light | hst-imaging-mass-samplers | needs-slicing | high | Phase 1 via Mind start_dev: --lens-light linear|fixed|both, both rows from one chain, --export-prepared for both modes, library revisions and SLURM job id in every row, A100 and ral jax_cpu submit scripts against a private pinned checkout; no compute until phase 1 ships and phase 2 is authorised. |
| HST imaging mass stage: sampler comparison, wave 1 | hst-imaging-mass-samplers | needs-slicing | normal | Depends on the frozen mass_total[1] problems (both light modes, A100 and jax_cpu) from the pinned base in hst_mass_base_light_modes; candidate samplers from the experiments runner (nautilus, emcee, nuts, smc) and the promoted PyAutoFit catalogue (NSS, DynestyStatic/Dynamic, Zeus) are not yet sliced. |
Inference Results
Decision History
No decisions recorded yet.
Sampler candidates
Literature expectations, not measured PyAuto recommendations. Review a candidate and copy its investigation prompt.
dynesty
dynamic nested sampling · reviewed 2026-10-08
Expected uses
- Evidence and posterior baseline with adjustable effort allocation.
Limitations
- Prior transforms and likelihood-contour exploration need validation; no universal dimensional scaling advantage.
Integration: integrated: DynestyStatic and DynestyDynamic
Benchmark evidence: not_assessed: this literature review establishes no benchmark result
Investigation prompt
Nautilus
neural-network importance nested sampling · reviewed 2026-10-08
Expected uses
- Evidence and posterior estimation for expensive gradient-free likelihoods.
Limitations
- Learned proposal coverage and importance-weight stability need checking on unseen modes.
Integration: integrated: Nautilus
Benchmark evidence: not_assessed: this literature review establishes no benchmark result