Varstar Detect

Python package optimized for variable star detection in TESS Secctor 1 data.

Initialization:

To install repository from PyPi: from varstardectect import Star

Looking for stellar variability:

Use the amplitude_test() function, with the following documentation, to determine objects which are variable. In which you have to determine the directory of the archive file with the sector info, it must be a csv.

amplitude_test(min, max, amp, directory):
"""                
                   amplitude_test DOCUMENTATION
---------------------------------------------------------------------
Detects variable stars with amplitude higher than threshold.
---------------------------------------------------------------------
INPUTS:     - min: lower star search (TESS) range delimiter
            - max: higher star search (TESS) range delimiter
            - amp: amplitude threshold
            - directory: directory with TESS sector 1 files csv. You can
              download from
              https://tess.mit.edu/observations/target-lists/
        -------------------------------------------------------------
OUTPUTS:    - candidates: 1D numpy array with variable candidate 
              target IDs
            - chis: 1D numpy array with the chi^2 parameter of each
              approximation.
            - degree: 1D numpy array with the degree of the optimal
              degree of the approximation.
            - periods: 1D numpy array with the period of each 
              approimating function.
            - period_errors: 1D numpy array with the period 
              uncertainty for each candidate.
            - amplitudes: 1D numpy array with the amplitude of each
              approximation.
            - amplitude_errors: 1D numpy array with the uncertainty 
              of the amplitude of each candidate.
----------------------------------------------------------------------
PROCEDURE:
            1. Calculates amplitude for an observed star.
            2. Calculates if amplitude is bigger than threshold.
            3. Returns candidates and their characteristics.
----------------------------------------------------------------------
"""

Background:

The function uses several numerical and statistical methods to filter and interpret the data obtained form TESS, providing the characteristics of each star through phenomenological analysis of the lightcurve, given that it has passed the amplitude test.

DISCLAIMER:

This is a Beta state of the program. It is unstable therefore itcan have bugs. It has not been optimized correctly yet.