afm-spirals

Analytical flyby framework (AFM) for spirals

This is an analytical framework based on multi-variate normal distribuiton from Gaia DR3 outputs. See Shuai et al. (2022) for the mathematical derivation and corresponding paper (ApJS accepted).

Step 0: Query the Gaia DR3 archive using ADQL

Query the neighboring stars that are located within 10 pc from a given star. See Step0_ADQL.txt for an example using the MWC 758 system. Export the querying results in a .csv file in folder ./data_gaia_query/.

Step 1: Compute the frequentist results using the Gaia DR3 values

Run python Step1_Frequentist.py SR21 at Step1_Frequentist.py for the SR21 system (no blank spaces because the code reads sys.argv parameters).

The code reads the .csv file exported from Step 0, then writes the frequentist results in a .csv file in folder ./data_fequentist/.

Step 2: Perform Bayesian calculation for closest approach time for selected flyby candidates

Run python Step2_Bayesian.py SR21 68 at Step2_Bayesian.py for the SR21 system (no blank spaces because the code reads sys.argv parameters), where 68 is the row index of for SR21B in the corresponding .csv file from Step 1.

The code reads the .csv file exported from Step 1, then write the Bayesian results using emcee on closest-approach time in an .h5 file in folder ./data_mcmc/.

Step 3: Perform Monte Carlo sampling for closest approach distance for selected flyby candidates

Run python Step3_MC_distance.py SR21 68 at Step3_MC_distance.py to sample the distribution for closest approach distance. The distance samples will be stored at ./data_mcmc/ in a .npy file, see distance_posterior_SR21.npy for the corresponding example for SR21 and SR21B.

Citation

@software{linling_shuai_2022_7403480,
  author       = {Linling Shuai and
                  Bin Ren},
  title        = {slinling/afm-spirals: release v1.0},
  month        = dec,
  year         = 2022,
  publisher    = {Zenodo},
  version      = {release v1.0},
  doi          = {10.5281/zenodo.7403480},
  url          = {https://doi.org/10.5281/zenodo.7403480}
}