| _version_ | 1866901667214000128 |
|---|---|
| author | Lightholder, Jack Hardgrove, Craig |
| author_facet | Lightholder, Jack Hardgrove, Craig |
| contents | <h1>DAN Two-Layer Retrieval Data Release (Sols 3501–4100)</h1> <p>This repository contains processed Dynamic Albedo of Neutrons (DAN) two-layer retrieval products and supporting summary products for Curiosity observations spanning sols 3501–4100. The release is organized into region-level summary products and per-location retrieval products. File names use a consistent `site / drive / start_sol / stop_sol` convention so that products from different subdirectories can be matched directly. Retrievals are performed on coadds of all observations at a given site/drive, producing one retrieval per location.</p> <h2>Repository structure</h2> <p>2_layer_DAN_sols_3501_4100/<br>└── retrieval_products/</p> <h3>retrieval_products/</h3> <p>This directory contains the observation-level retrieval products and diagnostics.</p> <p>retrieval_products/<br>├── background_subtracted_coadd/<br>├── coadd_observations/<br>├── coadd_product/<br>├── corner_plots/<br>├── GMMs/<br>│ ├── unmix_2/<br>│ │ └── gmm_mix/<br>│ └── unmix_variable/<br>│ ├── gmm_mix/<br>│ └── gmm_selection/<br>├── MCMC_backend/<br>├── SNR/<br>├── times/<br>└── walker_plots/<br><br></p> <h3>Naming convention</h3> <p>Most files share a common stem:</p> <p>site_<SITE>_drive_<DRIVE>_start_sol_<START>_stop_sol_<STOP></p> <p>For example:</p> <p>site_026_drive_1274_start_sol_542_stop_sol_542<br>This stem is followed by a product-specific suffix, for example:</p> <p>- _bg_dat.npy<br>- _label_matched.txt<br>- _coadded.npy<br>- _corner_plot.png<br>- _gmm_mix.png<br>- _gmm_selection.png<br>- _MCMC.h5.zip<br>- _CETN_SNR.npy<br>- _CTN_SNR.npy<br>- _times.npy<br>- _walker_plot.png</p> <p>This convention allows all products associated with a given coadded retrieval to be aligned by filename.</p> <h3>Retrieval content</h3> <p>The products in this release correspond to a two-layer retrieval framework. The primary retrieved physical parameters are:</p> <p>- <strong>Bottom Layer WEH</strong>: bottom-layer water-equivalent hydrogen, in wt%<br>- <strong>Top Layer WEH</strong>: top-layer water-equivalent hydrogen, in wt%<br>- <strong>Bottom Layer Σ_abs</strong>: bottom-layer bulk macroscopic neutron absorption cross section (BNACS), in cm^2/g<br>- <strong>Top Layer Σ_abs</strong>: top-layer bulk macroscopic neutron absorption cross section (BNACS), in cm^2/g<br>- <strong>Depth</strong>: modeled interface depth between the two layers, in cm</p> <p>Posterior diagnostics may also include an additional logf[counts] fit parameter in the MCMC products.</p> <h2>Observation-level products</h2> <p>Each retrieval generally includes one file in each of the subdirectories below.</p> <h3>background_subtracted_coadd/</h3> <p>Files ending in _bg_dat.npy contain the background-subtracted count data used in retrieval processing.</p> <h3>coadd_observations/</h3> <p>Files ending in _label_matched.txt list the DAN observation label or labels contributing to the coadded retrieval product. These files provide the traceability link between the retrieval and the original contributing observation set.</p> <h3>coadd_product/</h3> <p>Files ending in _coadded.npy contain the coadded observation-space product used as the retrieval input.</p> <h3>corner_plots/</h3> <p>Files ending in _corner_plot.png are posterior diagnostic figures showing parameter distributions and pairwise covariances.</p> <h3>GMMs/</h3> <p>This directory contains Gaussian mixture model post-processing products.</p> <p>- unmix_2/ contains fixed two-component mixture summaries.<br>- unmix_variable/ contains variable-component mixture exploration products.</p> <p>Within these directories:</p> <p>- gmm_mix/ contains posterior unmixing summary plots.<br>- gmm_selection/ contains model-selection plots used to compare mixture counts.</p> <p>These products support interpretation of multimodal posterior structure.</p> <h3>MCMC_backend/</h3> <p>Files ending in _MCMC.h5.zip are compressed HDF5 backends containing the archived MCMC chains. These are the primary reproducibility products for users who want to regenerate posterior summaries, diagnostics, or alternate post-processing results.</p> <h3>SNR/</h3> <p>This directory contains per-observation signal-to-noise products:</p> <p>- *_CETN_SNR.npy<br>- *_CTN_SNR.npy</p> <p>These arrays store time-bin-level SNR values associated with the retrieval input data.</p> <h3>times/</h3> <p>Files ending in _times.npy contain the time-bin definitions associated with the DAN die-away measurement.</p> <h3>walker_plots/</h3> <p>Files ending in _walker_plot.png show the evolution of the MCMC walkers as a function of iteration number and are intended for quality control and convergence assessment.</p> <h2>Recommended use</h2> <p>A typical use pattern is:</p> <p>1. Start with regional_summaries/ to review region-scale behavior and identify observations of interest.<br>2. Use coadd_observations/ to trace a retrieval back to the contributing DAN observation labels.<br>3. Use coadd_product/, background_subtracted_coadd/, SNR/, and times/ for observation-space analysis.<br>4. Use corner_plots/, walker_plots/, and GMMs/ for posterior interpretation and quality control.<br>5. Use MCMC_backend/ when full reproducibility or custom post-processing is required.</p> <h2>Acknowledgment</h2> <p>If you use these products in published work, please cite the associated data release and the relevant scientific publications describing the DAN retrieval methodology.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19243766 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | DAN Two Layer Retrievals - Sols 3501-4100 Lightholder, Jack Hardgrove, Craig <h1>DAN Two-Layer Retrieval Data Release (Sols 3501–4100)</h1> <p>This repository contains processed Dynamic Albedo of Neutrons (DAN) two-layer retrieval products and supporting summary products for Curiosity observations spanning sols 3501–4100. The release is organized into region-level summary products and per-location retrieval products. File names use a consistent `site / drive / start_sol / stop_sol` convention so that products from different subdirectories can be matched directly. Retrievals are performed on coadds of all observations at a given site/drive, producing one retrieval per location.</p> <h2>Repository structure</h2> <p>2_layer_DAN_sols_3501_4100/<br>└── retrieval_products/</p> <h3>retrieval_products/</h3> <p>This directory contains the observation-level retrieval products and diagnostics.</p> <p>retrieval_products/<br>├── background_subtracted_coadd/<br>├── coadd_observations/<br>├── coadd_product/<br>├── corner_plots/<br>├── GMMs/<br>│ ├── unmix_2/<br>│ │ └── gmm_mix/<br>│ └── unmix_variable/<br>│ ├── gmm_mix/<br>│ └── gmm_selection/<br>├── MCMC_backend/<br>├── SNR/<br>├── times/<br>└── walker_plots/<br><br></p> <h3>Naming convention</h3> <p>Most files share a common stem:</p> <p>site_<SITE>_drive_<DRIVE>_start_sol_<START>_stop_sol_<STOP></p> <p>For example:</p> <p>site_026_drive_1274_start_sol_542_stop_sol_542<br>This stem is followed by a product-specific suffix, for example:</p> <p>- _bg_dat.npy<br>- _label_matched.txt<br>- _coadded.npy<br>- _corner_plot.png<br>- _gmm_mix.png<br>- _gmm_selection.png<br>- _MCMC.h5.zip<br>- _CETN_SNR.npy<br>- _CTN_SNR.npy<br>- _times.npy<br>- _walker_plot.png</p> <p>This convention allows all products associated with a given coadded retrieval to be aligned by filename.</p> <h3>Retrieval content</h3> <p>The products in this release correspond to a two-layer retrieval framework. The primary retrieved physical parameters are:</p> <p>- <strong>Bottom Layer WEH</strong>: bottom-layer water-equivalent hydrogen, in wt%<br>- <strong>Top Layer WEH</strong>: top-layer water-equivalent hydrogen, in wt%<br>- <strong>Bottom Layer Σ_abs</strong>: bottom-layer bulk macroscopic neutron absorption cross section (BNACS), in cm^2/g<br>- <strong>Top Layer Σ_abs</strong>: top-layer bulk macroscopic neutron absorption cross section (BNACS), in cm^2/g<br>- <strong>Depth</strong>: modeled interface depth between the two layers, in cm</p> <p>Posterior diagnostics may also include an additional logf[counts] fit parameter in the MCMC products.</p> <h2>Observation-level products</h2> <p>Each retrieval generally includes one file in each of the subdirectories below.</p> <h3>background_subtracted_coadd/</h3> <p>Files ending in _bg_dat.npy contain the background-subtracted count data used in retrieval processing.</p> <h3>coadd_observations/</h3> <p>Files ending in _label_matched.txt list the DAN observation label or labels contributing to the coadded retrieval product. These files provide the traceability link between the retrieval and the original contributing observation set.</p> <h3>coadd_product/</h3> <p>Files ending in _coadded.npy contain the coadded observation-space product used as the retrieval input.</p> <h3>corner_plots/</h3> <p>Files ending in _corner_plot.png are posterior diagnostic figures showing parameter distributions and pairwise covariances.</p> <h3>GMMs/</h3> <p>This directory contains Gaussian mixture model post-processing products.</p> <p>- unmix_2/ contains fixed two-component mixture summaries.<br>- unmix_variable/ contains variable-component mixture exploration products.</p> <p>Within these directories:</p> <p>- gmm_mix/ contains posterior unmixing summary plots.<br>- gmm_selection/ contains model-selection plots used to compare mixture counts.</p> <p>These products support interpretation of multimodal posterior structure.</p> <h3>MCMC_backend/</h3> <p>Files ending in _MCMC.h5.zip are compressed HDF5 backends containing the archived MCMC chains. These are the primary reproducibility products for users who want to regenerate posterior summaries, diagnostics, or alternate post-processing results.</p> <h3>SNR/</h3> <p>This directory contains per-observation signal-to-noise products:</p> <p>- *_CETN_SNR.npy<br>- *_CTN_SNR.npy</p> <p>These arrays store time-bin-level SNR values associated with the retrieval input data.</p> <h3>times/</h3> <p>Files ending in _times.npy contain the time-bin definitions associated with the DAN die-away measurement.</p> <h3>walker_plots/</h3> <p>Files ending in _walker_plot.png show the evolution of the MCMC walkers as a function of iteration number and are intended for quality control and convergence assessment.</p> <h2>Recommended use</h2> <p>A typical use pattern is:</p> <p>1. Start with regional_summaries/ to review region-scale behavior and identify observations of interest.<br>2. Use coadd_observations/ to trace a retrieval back to the contributing DAN observation labels.<br>3. Use coadd_product/, background_subtracted_coadd/, SNR/, and times/ for observation-space analysis.<br>4. Use corner_plots/, walker_plots/, and GMMs/ for posterior interpretation and quality control.<br>5. Use MCMC_backend/ when full reproducibility or custom post-processing is required.</p> <h2>Acknowledgment</h2> <p>If you use these products in published work, please cite the associated data release and the relevant scientific publications describing the DAN retrieval methodology.</p> |
| title | DAN Two Layer Retrievals - Sols 3501-4100 |
| url | https://doi.org/10.5281/zenodo.19243766 |