Giter Site home page Giter Site logo

bachyp_etal_ceresvesta_nhao's Introduction

Bach et al. (2024a, b) Archive

This is a repository to archive the results (but not all the required data; see the note below) from the codes, used in the following papers:

  • Title: Quantitative grain size estimation on airless bodies from the negative polarization branch
  • Paper1: Bach et al. (2024a) A&A, 684, 80. "I. Insights from experiments and lunar observations" (ADS, arXiv, A&A)
  • Paper2: Bach et al. (2024b) A&A, 684, 81. "II. Dawn mission targets, (4) Vesta and (1) Ceres" (ADS, arXiv, A&A)
  • Related Raw data: DOI (including skymonitor records, etc. Total 10+GB)
  • Part of the related codes: GitHub repo (here)

Important Notes

Unfortunately, I cannot open all the data I used in the papers. Hence:

  • โš ๏ธIMPORTANT: The codes will NOT run perfectly on your machine.
    • It is because not all files are included in this repo.
    • That is because (1) I am not the copyright holder and/or (2) the original data is too large.
  • Important raw data are accessible from Zenodo above.

Some parts will give, e.g., FileNotFoundError, so as noted, the codes will fail if you run them without any modification.

Contents

In codes_n_data directory:

  • <YYYYMMDD>_<OBJNAME>.ipynb: The reduction process for each object.
    • sky: The blank sky frames we took to test dome flat
    • SP/UP: Strongly Polarized and UnPolarized standard star exposures.
    • Especially using SP/UP/sky data, I spent 2 years for developing NICpolpy (GitHub; published Bach+2022 SAG). The files that I did not include in the repo (too dirty code) are:
      1. Finding the best algorithm to remove instrument artifacts (especially the Fourier pattern)
      2. Checking for bad pixels and possible uncertainties by preprocessing steps (including errors in flat, dark current, artifacts, and cosmic-ray rejection).
      3. Re-calibrating instrument parameters (gain and readout noise) and statistical testing to check whether there are pixel-to-pixel variations.
      4. Finding the best algorithm to remove the sky fringe patterns and quantify the possible errors (Bach et al. 2024, in prep)
      5. There are many more parts related to instrument artifacts, finding the best algorithm for object centering, statistical analyses, etc.
  • _phot.ipynb/_polr.ipynb: The (dirty) notebooks I used to do photometry and polarimetry, using multiple apmode, outlier, and other parameters (see Appendix A/B of Paper2).
    • Because of extensive testing, many parts are commented out, unused, and/or used in different orders.
  • figures-01-CeresVesta.ipynb: The code to plot PPCs in Paper2. Also it makes the final result as restuls/df_fit_all.csv. Some cells are deleted.
  • figures-part1.ipynb: The code to plot multiple PPC-related parameters (figs/). Multiple cells are deleted before adding here.

In codes_n_data/results directory:

  • figs/: The PPC and $q$-$u$ plane plots to see the data scatter depending on the algorithms (Appendix of Paper2).
  • figs-mcmc: The final PPC and MC (Monte Carlo) simulation corner plots (Paper2 Appendix C)
  • figs-time: The time-variation of the polarization degree (not included in Paper2)
  • <OBJNAME>_<OBSDATE>_<rapmode>.csv: The aperture radius and q/u-related parameters for multiple algorithms (Appendix of Paper2)
  • df_fin.csv: Extracted only the final results
  • df_fit_all.csv: All the fitted results after MC simulation

In codes_n_data/extdata directory:

  • A dirty collection of external data from other sources, excluding those I have no right to open to the public. I had to remove some columns in some files, including all_samples.xlsx (which does not include all the samples, unlike its name says...).
  • For PPCs that should be fitted to obtain polarimetric parameters, I am planning on (but cannot guarantee) uploading my codes and results at a separate repo.

In codes_n_data/figs directory:

  • Results from figures-part1.ipynb (figures used for Paper1).

History of Submission

Times in KST (GMT+0900)

  • 2023-08-28: Submission of the paper (initially Papers 1 & 2 were designed to be a single paper)
  • 2023-09-11: Received referee comment #1.
  • 2023-12-12: Splitted the manuscript into two and submitted (based on referee's comment).
  • 2023-12-22: Received referee comment #2 for both papers (minor revision). Editorial office recommended English proofread.
  • 2024-01-08: Resubmission of the manuscript after English proofread (AJE).
  • 2024-01-08: Paper 1 accepted.
  • 2024-01-10: Paper 1 minor language revision. Paper 1 available on arXiv.
  • 2024-01-11: Received referee reply for Paper 2. Paper 2 is accepted (2024-01-10 in Paris time).
  • 2024-xx-xx: Multiple minor revisions during the proofreading process.
  • 2024-04-05: Both papers appeared online.

bachyp_etal_ceresvesta_nhao's People

Contributors

ysbach avatar

Watchers

 avatar

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.