We lifted 13 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| markusbonse/applefy | pwc_unofficial | 0 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| collect_all_contrast_grid_configs | Not yet run | markusbonse/applefy/applefy/utils/file_handling.py code served (permissive licence) · get_code("30f78ab37be6c451") |
| create_checkpoint_folders | Not yet run | markusbonse/applefy/applefy/utils/file_handling.py code served (permissive licence) · get_code("92c48136077ade31") |
| draw_mc_sample | Not yet run | markusbonse/applefy/applefy/utils/mc_simulations.py code served (permissive licence) · get_code("1b25e631f62226ae") |
| draw_noise | Not yet run | markusbonse/applefy/applefy/utils/mc_simulations.py code served (permissive licence) · get_code("857011d351d6da32") |
| flux_ratio2mag | Not yet run | markusbonse/applefy/applefy/utils/photometry.py code served (permissive licence) · get_code("95c305a276bd9d53") |
| fpf_2_gaussian_sigma | Not yet run | markusbonse/applefy/applefy/statistics/general.py code served (permissive licence) · get_code("c39d261ac01c16ff") |
| gaussian_r2 | Not yet run | markusbonse/applefy/applefy/gaussianity/residual_tests.py code served (permissive licence) · get_code("5af946c9507df6ad") |
| gaussian_sigma_2_fpf | Not yet run | markusbonse/applefy/applefy/statistics/general.py code served (permissive licence) · get_code("a6444ab1487a6105") |
| generate_fake_planet_experiments | Not yet run | markusbonse/applefy/applefy/utils/fake_planets.py code served (permissive licence) · get_code("5c15841d6746ee4e") |
| mag2flux_ratio | Not yet run | markusbonse/applefy/applefy/utils/photometry.py code served (permissive licence) · get_code("a6b9b6b36842b449") |
| search_for_config_and_residual_files | Not yet run | markusbonse/applefy/applefy/utils/file_handling.py code served (permissive licence) · get_code("3936c27a886d93f4") |
| sort_fake_planet_results | Not yet run | markusbonse/applefy/applefy/utils/fake_planets.py code served (permissive licence) · get_code("4b44747b7c3436ca") |
| t_statistic_vectorized | Not yet run | markusbonse/applefy/applefy/statistics/parametric.py code served (permissive licence) · get_code("02e429993c81ef64") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
In this paper, we review the impact of small sample statistics on detection thresholds and corresponding confidence levels (CLs) in high contrast imaging at small angles. When looking close to the star, the number of resolution elements decreases rapidly towards small angles. This reduction of the number of degrees of freedom dramatically affects CLs and false alarm probabilities. Naively using the same ideal hypothesis and methods as for larger separations, which are well understood and commonly assume Gaussian noise, can yield up to one order of magnitude error in contrast estimations at fixed CL. The statistical penalty exponentially increases towards very small inner working angles. Even at 5-10 resolution elements from the star, false alarm probabilities can be significantly higher than expected. Here we present a rigorous statistical analysis which ensures robustness of the CL, but also imposes a substantial limitation on corresponding achievable detection limits (thus contrast) at small angles. This unavoidable fundamental statistical effect has a significant impact on current coronagraphic and future high contrast imagers. Finally, the paper concludes with practical recommendations to account for small number statistics when computing the sensitivity to companions at small angles and when exploiting the results of direct imaging planet surveys.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1407.2247")
get_code_for_paper("1407.2247")
have("1407.2247")
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