queue: land the filings for #71, #74 and #78

bibliography/pyautomemory.bib

@@ -16167,3 +16167,27 @@ @misc{Krizic2026
   url = {https://arxiv.org/abs/2609.04312},
   year = {2026}
 }
+
+@misc{Jarugula2026,
+  archivePrefix = {arXiv},
+  author = {Jarugula, Sreevani and Nord, Brian D. and {\'{C}}iprijanovi{\'{c}}, Aleksandra and Trivedi, Shubhendu},
+  eprint = {2608.23534},
+  note = {Submitted to Machine Learning: Science and Technology; Report number: FERMILAB-PUB-26-0340-CSAID},
+  primaryClass = {astro-ph.CO},
+  title = {{Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation}},
+  url = {https://arxiv.org/abs/2608.23534},
+@article{Williams2026,
+  arxivId = {2608.27566},
+  author = {Williams, D. M. and Treu, T. and Johnson, D. P. and Mozumdar, P. and Knabel, S. and Birrer, S. and Fassnacht, C. D. and Galan, A. and Shajib, A. J. and Wong, K. C. and Cappellari, M. and Courbin, F. and Morishita, T. and Motta, V. and Sluse, D. and Stiavelli, M.},
+  doi = {10.48550/arXiv.2608.27566},
+  eprint = {2608.27566},
+  note = {Submitted to Physical Review D},
+  title = {{TDCOSMO. XXVII. JWST-based Lens Models and H$_0$ Measurement of WFI2033, HE0435, and PG1115}},
+  url = {https://arxiv.org/abs/2608.27566},
+@misc{Galan2026,
+  author = {Galan, A. and Schweinfurth, A. G. and Suyu, S. H. and Johnson, D. P. and Chawla, A. and Sluse, D. and Kharchilava, G. V. and Buckley-Geer, E. and Lin, H. and Knabel, S. and Sheu, W. and Ertl, S. and Anguita, T. and Birrer, S. and Courbin, F. and Dux, F. and Millon, M. and Motta, V. and Schuldt, S. and Treu, T. and Williams, D. M. and Cappellari, M. and Wong, K. C. and Eckert, D.},
+  eprint = {2609.00179},
+  title = {{TDCOSMO XXVIII. The Hubble constant from the quadruply lensed quasar J1537$-$3010 with precise time delays}},
+  url = {https://arxiv.org/abs/2609.00179},
+  year = {2026}
+}

reading-queue.md

@@ -89,11 +89,13 @@ On what scales does strong lensing robustly constrain the mass profile of low-ma
 Measuring Extragalactic Microlens Masses and Motions in Strongly Lensed Quasar Systems with Intensity Interferometry — 2608.27577
 TDCOSMO XXIX: JWST/NIRSpec IFU Spatially Resolved Kinematics of Three Time-delay Lenses — 2609.03089
 Host Dependence and Line-of-Sight Effects on Galaxy-Galaxy Strong Lensing in Clusters — 2608.30079
-TDCOSMO XXVIII. The Hubble constant from the quadruply lensed quasar J1537$-$3010 with precise time delays — 2609.00179
+DONE 2026-09-11 — TDCOSMO XXVIII. The Hubble constant from the quadruply lensed quasar J1537$-$3010 with precise time delays — 2609.00179
 HOLISMOKES -- XVIII. Cosmology with strongly lensed type II supernovae: Effects of instrumental setups on $H_0$ — 2608.26331
 DONE 2026-09-01 — Mapping the Information Geometry of an Unresolved Dark Matter Population using a Differentiable Strong Lensing Simulator — 2608.18224
 DONE 2026-09-03 — Follow-up of SN 2025wny I: Space-based Observations of the First Multiply-imaged Superluminous Supernova — 2608.28413
 DONE 2026-09-07 — ALPACA I: Controlling source and PSF systematics in JWST time-delay cosmography with differentiable lens modeling — 2609.04312
+DONE 2026-09-11 — Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation — 2608.23534
+DONE 2026-09-11 — TDCOSMO. XXVII. JWST-based Lens Models and H$_0$ Measurement of WFI2033, HE0435, and PG1115 — 2608.27566
 
 
 ## SMBHs

wiki/lensing/sources/deep-learning-lensing.md

@@ -73,6 +73,36 @@ status: stub
 - Evidence until the paper identity and canonical key are verified.
 
 
+## Jarugula 2026 — calibrated NRE cosmology
+
+**Canonical BibTeX key:** `Jarugula2026`
+**Reference:** Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation; arXiv:2608.23534; submitted to Machine Learning: Science and Technology
+**Concepts:** [[deep-learning-lensing]], [[lens-statistics]]
+
+**Supports:**
+- Neural Ratio Estimation (NRE) can combine per-lens likelihood ratios into a
+  population-level posterior, jointly inferring the dark energy
+  equation-of-state parameter w and the matter density Ω_m from lensing
+  images plus spectroscopic data.
+- A posterior-coverage calibration step corrects the overconfidence typical
+  of neural density/ratio estimators, giving honest uncertainties at
+  population scale (~23% on w, ~3% on Ω_m for 100 lenses).
+- A simulation-based-inference example of population-level lensing cosmology
+  with a cosmological target (dark energy, matter density) rather than the
+  dark-matter-substructure target most SBI lensing studies use.
+
+**Use when:**
+- Citing calibrated NRE/SBI as a route to population-level cosmological
+  inference (w, Ω_m) from galaxy-galaxy strong lens samples.
+- Citing the coverage-calibration approach for correcting neural
+  density-estimator overconfidence in lensing inference.
+
+**Do not use for:**
+- Substructure/dark-matter population inference — this paper's target is
+  dark energy and matter density, not subhalo populations.
+- Real-survey results — validated on simulations only at time of writing.
+
+
 ## See also
 
 - [[deep-learning-lensing]]

wiki/lensing/sources/time-delay-cosmography.md

@@ -156,6 +156,18 @@ status: stub
 - Non-JWST PSF systematics or non-time-delay lens-modelling contexts without checking applicability.
 
 
+## Williams 2026 — TDCOSMO XXVII (JWST H0: WFI2033, HE0435, PG1115)
+
+**Canonical BibTeX key:** `Williams2026`
+
+If the cosmology natural-language paper ever happens, this is a good work to
+refer to for how H0 analysis is performed currently.
+
+## Galan 2026 — TDCOSMO XXVIII (H0 from J1537-3010)
+
+**Canonical BibTeX key:** `Galan2026`
+
+
 ## See also
 
 - [[time-delay-cosmography]]