We lifted 11 functions out of this paper's own repositories and ran 8 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| swagnercarena/paltas | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| calc_p_dlt | Ran | swagnercarena/paltas/paltas/Analysis/posterior_functions.py code served (permissive licence) · get_code("8dd78bfdeda299fe") |
| eval_lognormal_logpdf_approx | Ran | swagnercarena/paltas/paltas/Analysis/pdf_functions.py code served (permissive licence) · get_code("69dd5cb7afa36753") |
| eval_normal_logpdf_approx | Ran | swagnercarena/paltas/paltas/Analysis/pdf_functions.py code served (permissive licence) · get_code("678f2f28852a4b28") |
| gaussian_product_analytical | Ran | swagnercarena/paltas/paltas/Analysis/hierarchical_inference.py code served (permissive licence) · get_code("33c44db03da86bda") |
| log_p_omega | Ran | swagnercarena/paltas/paltas/Analysis/hierarchical_inference.py code served (permissive licence) · get_code("482acc8fc8a3f864") |
| log_p_xi_omega | Ran | swagnercarena/paltas/paltas/Analysis/hierarchical_inference.py code served (permissive licence) · get_code("11233485ab535344") |
| normalize_outputs | Ran | swagnercarena/paltas/paltas/Analysis/dataset_generation.py code served (permissive licence) · get_code("1f8988f30a73f827") |
| plot_calibration | Ran | swagnercarena/paltas/paltas/Analysis/posterior_functions.py code served (permissive licence) · get_code("aef692810b285d6c") |
| build_population_transformer | Not yet run | swagnercarena/paltas/paltas/Analysis/transformer_models.py code served (permissive licence) · get_code("a9d9d74d126bf8fa") |
| build_xresnet34 | Not yet run | swagnercarena/paltas/paltas/Analysis/conv_models.py code served (permissive licence) · get_code("fc74169535304902") |
| build_xresnet34_fc_inputs | Not yet run | swagnercarena/paltas/paltas/Analysis/conv_models.py code served (permissive licence) · get_code("acb3ce113d624745") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Constraining the distribution of small-scale structure in our universe allows us to probe alternatives to the cold dark matter paradigm. Strong gravitational lensing offers a unique window into small dark matter halos ($<10^{10} M_\odot$) because these halos impart a gravitational lensing signal even if they do not host luminous galaxies. We create large datasets of strong lensing images with realistic low-mass halos, Hubble Space Telescope (HST) observational effects, and galaxy light from HST's COSMOS field. Using a simulation-based inference pipeline, we train a neural posterior estimator of the subhalo mass function (SHMF) and place constraints on populations of lenses generated using a separate set of galaxy sources. We find that by combining our network with a hierarchical inference framework, we can both reliably infer the SHMF across a variety of configurations and scale efficiently to populations with hundreds of lenses. By conducting precise inference on large and complex simulated datasets, our method lays a foundation for extracting dark matter constraints from the next generation of wide-field optical imaging surveys.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.00690")
get_code_for_paper("2203.00690")
have("2203.00690")
Connect an agent — have() is free.