About
Last updated: 2024-07-06
Checks: 7 0
Knit directory: MATHPOP/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20240702) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version a38fbed. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .DS_Store
Ignored: analysis/.DS_Store
Ignored: data/.DS_Store
Ignored: data/GC_prob/.DS_Store
Ignored: data/point_source_data/.DS_Store
Ignored: data/prob_GC_data/.DS_Store
Ignored: data/sim/.DS_Store
Ignored: data/v10acs/.DS_Store
Ignored: data/v10wfc3/.DS_Store
Ignored: data/v11acs/.DS_Store
Ignored: data/v11wfc3/.DS_Store
Ignored: data/v12acs/.DS_Store
Ignored: data/v12wfc3/.DS_Store
Ignored: data/v13acs/.DS_Store
Ignored: data/v13wfc3/.DS_Store
Ignored: data/v14acs/.DS_Store
Ignored: data/v14wfc3/.DS_Store
Ignored: data/v15acs/.DS_Store
Ignored: data/v15wfc3/.DS_Store
Ignored: data/v6acs/.DS_Store
Ignored: data/v6wfc3/.DS_Store
Ignored: data/v7acs/.DS_Store
Ignored: data/v8acs/.DS_Store
Ignored: data/v9acs/.DS_Store
Ignored: data/v9wfc3/.DS_Store
Unstaged changes:
Modified: analysis/_site.yml
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/about.Rmd) and HTML
(docs/about.html) files. If you’ve configured a remote Git
repository (see ?wflow_git_remote), click on the hyperlinks
in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| html | 64686bd | david.li | 2024-07-06 | Build site. |
| Rmd | 0b7fd29 | david.li | 2024-07-06 | wflow_git_commit(all = T) |
| html | 0b7fd29 | david.li | 2024-07-06 | wflow_git_commit(all = T) |
| html | 0a9d403 | david.li | 2024-07-05 | Build site. |
| html | c1fd386 | david.li | 2024-07-04 | Build site. |
| html | a513a79 | david.li | 2024-07-04 | Build site. |
| html | 6ff1ccc | david.li | 2024-07-04 | Build site. |
| html | 0b819e1 | david.li | 2024-07-03 | Build site. |
| html | 44c0586 | david.li | 2024-07-03 | Build site. |
| html | 345edbd | david.li | 2024-07-03 | Build site. |
| html | 957c2e2 | david.li | 2024-07-03 | Build site. |
| Rmd | 199856c | david.li | 2024-07-03 | wflow_publish(c("analysis/index.Rmd", "analysis/vignette.Rmd", |
| html | 3030b0c | david.li | 2024-07-03 | Build site. |
| Rmd | db0f025 | david.li | 2024-07-03 | Initial Build |
| html | db0f025 | david.li | 2024-07-03 | Initial Build |
Introduction
This page gives a brief introduction of the assumptions and construction of the MATHPOP methodology to infer globular cluster (GCs) counts in LSBGs/UDGs. For more details, see the original paper by Li et al. (2024).
MATHPOP uses a marked point process model to jointly model the spatial location \(\mathbf{X}\) (point process) of observed GCs and their magnitudes \(\mathbf{M}\) (mark). It is assumed that the GC locations in galaxies, either bright, normal galaxies or UDGs, arise from a Sersic profile, while the GC magnitude distribution is described by a Gaussian distribution, which is the GCLF. Moreover, the background GC population, i.e., GCs in the intergalatic medium (IGM), are uniformly distributed (a homogeneous Poisson process). Note that we assume that the GCLF of each GC sub-population (normal galaxy, UDG, or IGM) have their own GCLF, and we infer them using the data.
Below is a graphical model that illustrates the MATHPOP model framework:
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Sonoma 14.1.1
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
time zone: America/Toronto
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] vctrs_0.6.5 httr_1.4.7 cli_3.6.1 knitr_1.45
[5] rlang_1.1.4 xfun_0.41 stringi_1.8.4 processx_3.8.2
[9] promises_1.2.1 jsonlite_1.8.7 glue_1.6.2 rprojroot_2.0.4
[13] git2r_0.33.0 htmltools_0.5.8.1 httpuv_1.6.12 ps_1.7.5
[17] sass_0.4.7 fansi_1.0.6 rmarkdown_2.25 jquerylib_0.1.4
[21] tibble_3.2.1 evaluate_0.23 fastmap_1.2.0 yaml_2.3.7
[25] lifecycle_1.0.4 whisker_0.4.1 stringr_1.5.1 compiler_4.3.2
[29] fs_1.6.3 pkgconfig_2.0.3 Rcpp_1.0.11 rstudioapi_0.15.0
[33] later_1.3.1 digest_0.6.36 R6_2.5.1 utf8_1.2.4
[37] pillar_1.9.0 callr_3.7.3 magrittr_2.0.3 bslib_0.5.1
[41] tools_4.3.2 cachem_1.0.8 getPass_0.2-4