Cluster

Cluster#

class asteca.Cluster(ra: ndarray | None = None, dec: ndarray | None = None, magnitude: ndarray | None = None, e_mag: ndarray | None = None, color: ndarray | None = None, e_color: ndarray | None = None, color2: ndarray | None = None, e_color2: ndarray | None = None, plx: ndarray | None = None, e_plx: ndarray | None = None, pmra: ndarray | None = None, e_pmra: ndarray | None = None, pmde: ndarray | None = None, e_pmde: ndarray | None = None, N_clust_min: int = 25, N_clust_max: int = 5000, verbose: int = 1)#

Bases: object

Define a Cluster object.

This object contains the basic data required to load a group of observed stars that could represent a cluster or an entire field.

Parameters:

  • ra (np.ndarray | None) – Array that contains the right ascension (RA), defaults to None

  • dec (np.ndarray | None) – Array that contains the declination (DEC), defaults to None

  • magnitude (np.ndarray | None) – Array that contains the magnitude, defaults to None

  • e_mag (np.ndarray | None) – Array that contains the magnitude’s uncertainty, defaults to None

  • color (np.ndarray | None) – Array that contains the color, defaults to None

  • e_color (np.ndarray | None) – Array that contains the color’s uncertainty, defaults to None

  • color2 (np.ndarray | None) – Array that contains the second color, defaults to None

  • e_color2 (np.ndarray | None) – Array that contains the second color’s uncertainty, defaults to None

  • plx (np.ndarray | None) – Array that contains the parallax, defaults to None

  • e_plx (np.ndarray | None) – Array that contains the parallax uncertainty, defaults to None

  • pmra (np.ndarray | None) – Array that contains the RA proper motion, defaults to None

  • e_pmra (np.ndarray | None) – Array that contains the RA proper motion’s uncertainty, defaults to None

  • pmde (np.ndarray | None) – Array that contains the DEC proper motion, defaults to None

  • e_pmde (np.ndarray | None) – Array that contains the DEC proper motion’s uncertainty, defaults to None

  • N_clust_min (int) – Minimum number of cluster members, defaults to 25

  • N_clust_max (int) – Maximum number of cluster members, defaults to 5000

  • verbose (int) – Verbose level. A value of 0 hides all output, defaults to 1

Methods Summary

get_center([algo, data_2d, radec_c, pms_c, ...])

Estimate center coordinates for the cluster

get_nmembers([algo, eq_to_gal])

Estimate the number of members for the cluster.

Methods Documentation

get_center(algo: str = 'knn_5d', data_2d: str = 'radec', radec_c: tuple[float, float] | None = None, pms_c: tuple[float, float] | None = None, plx_c: float | None = None) → None#

Estimate center coordinates for the cluster

Use the available data (ra, dec, pmra, pmde, plx) to estimate a cluster’s center coordinates as the point(s) of maximum density. Algorithms:

  • knn_5d: Estimates the 5-dimensional center values (in ra, dec, pmra, pmde, plx) as the median position of the k (k=N_clust_min) stars with the largest nearest-neighbor density to a 5D center value that can be either given (fully or partially) or estimated.

  • kde_2d: Estimates the 2-dimensional center values (either in (ra, dec) or in (pmra, pmde); determined by the data_2d argument) using a Kernel Density Estimator (KDE).

Parameters:

  • algo (str) – Algorithm used to estimate center values, one of (knn_5d, kde_2d), defaults to knn_5d

  • data_2d (str) – String indicating the data to be used to estimate the center value, either: radec or pms, defaults to radec

  • radec_c (tuple[float, float] | None) – Estimated value for the (RA, DEC) center, defaults to None

  • pms_c (tuple[float, float] | None) – Estimated value for the (pmRA, pmDE) center, defaults to None

  • plx_c (float | None) – Estimated value for the plx center, defaults to None

Raises:

ValueError – If required data is missing from the Cluster object

get_nmembers(algo: str = 'ripley', eq_to_gal: bool = True) → None#

Estimate the number of members for the cluster. Algorithms:

  • ripley: Originally introduced with the fastMP membership method in Perren et al. (2023). Requires (ra, dec, pmra, pmde, plx) and their center estimates.

  • density: Simple algorithm that counts the number of stars within the cluster region (center+radius) and subtracts the expected number of field stars within that region. Requires the (ra, dec) center and the radius of the cluster to be defined.

Parameters:

  • algo (str) – Algorithm used to estimate center values, one of (ripley, density); defaults to ripley

  • eq_to_gal (bool) – Convert (ra, dec) to (lon, lat). Useful for clusters with large dec values to reduce the frame’s distortion, defaults to False

Raises:

  • ValueError – If algo argument is not recognized

  • AttributeError – If required attributes are missing from the Cluster object

Contents