Candidate

class compass.model.Candidate(df_survey, index_candidate, host_star, band, catalogue, std_min)

Bases: object

Model, true data and likelihoods, p_ratios of one candidate.

cc_true_data

True data from df_survey.

Type:

dict

cc_true_data

Model data based on host star fits.

Type:

dict

g2d_model

2D Gaussian of the model.

Type:

astropy.modeling.functional_models.Gaussian2D

g2d_conv

2D Gaussian of the convolution.

Type:

astropy.modeling.functional_models.Gaussian2D

g2d_cc

2D Gaussian of the candidate.

Type:

astropy.modeling.functional_models.Gaussian2D

g2d_pmuM1

2D Gaussian of the candidate at (0,0).

Type:

astropy.modeling.functional_models.Gaussian2D

cov_model

2x2 covariance matrix.

Type:

numpy.array

cov_cc

2x2 covariance matrix.

Type:

numpy.array

cov_conv

2x2 covariance matrix.

Type:

numpy.array

cov_pmuM1

2x2 covariance matrix.

Type:

numpy.array

p_b

Odd for being a background object.

Type:

float

p_ratio

Odds ratio.

Type:

float

p_tc

Odd for being a true companion.

Type:

float

back_true

true companion or background object.

Type:

str

mean_measured_positions

Measured position of candidate.

Type:

numpy.darray

mean_true_companion

Calculated position of candidate by pm and plx of star.

Type:

numpy.darray

mean_background_object

Calculated position of candidate by pm and plx of backgorund model.

Type:

numpy.darray

cov_measured_positions

Covariance matrix of measured position of candidate

Type:

numpy.darray

cov_true_companion

Covariance matrix of candidate by pm and plx of star.

Type:

numpy.darray

cov_background_object

Covariance matrix of candidate by pm and plx of backgorund model.

Type:

numpy.darray

r_tcb_2Dnmodel

log10(P_tc / P_b).

Type:

float

r_tcb_pmmodel

log10(P_tc / P_b).

Type:

float

Init candidates.

Parameters:

• df_survey (pandas.DataFrame) – Data of the candidates of a single host star.

• index_candidate (int) – index integer of the candidate in df_survey.

• host_star (Class Object) – Previously initiated class for the host star.

• band (str) – Band which the candidate was observed in df_survey (columnname).

• catalogue (str) – Name of the catalogue the model is based on: gaia, gaiacalctmass or tmass.

Methods Summary

calc_likelihoods_2Dnmodel(host_star[, ...])	Attributes the likelihoods to the candidate object in terms of the means and covariance matrices.
calc_likelihoods_pmmodel(host_star, ...)	Attributes the likelihoods to the candidate object in terms of the means and covariance matrices.
calc_prob_ratio_2Dnmodel()	Calculates the odds ratio based on the pm and plx model.
calc_prob_ratio_pmmodel()	Calculates the odds ratio based on the modelled g2d functions.

Methods Documentation

calc_likelihoods_2Dnmodel(host_star, catalogue_name='gaiacalctmass')

Attributes the likelihoods to the candidate object in terms of the means and covariance matrices.

Parameters:

• host_star (class object) – Use of proper motion and parallax of the star.

• background (class object) – Use of proper motion and parallax of the backgorund distribution.

calc_likelihoods_pmmodel(host_star, sigma_model_min, sigma_cc_min)

Attributes the likelihoods to the candidate object in terms of the means and covariance matrices.

Parameters:

• host_star (class) – Previously initiated class for the host star.

• sigma_model_min (float) – The inflating factor for the model likelihood.

• sigma_cc_min (float) – The inflating factor for its likelihood.

calc_prob_ratio_2Dnmodel()

Calculates the odds ratio based on the pm and plx model.

calc_prob_ratio_pmmodel()

Calculates the odds ratio based on the modelled g2d functions.