dunkenj/pyrus

Python implementation of redshift PDF close-pair analysis

Pyrus

Python implementation of redshift PDF close-pair analysis - specifically that of López-Sanjuan et al. (2014) - modified for stellar mass selected close-pairs in flux-limited surveys.

(Pyrus is the genus of trees from which Pears grow.)

Requirements

Pyrus requires Python 2.7+ and the following modules:

• numpy
• scipy
• astropy

The following modules are optional if only performing simple, uncorrected pair/merger fraction analysis:

• matplotlib
• photutils
• aplpy

Pyrus, as presented here, is tested with the following module versions:

• Python (2.7.11), numpy (1.10.2), scipy (0.16.0), astropy (1.0.3), matplotlib (1.4.3), photutils (0.1), aplpy (1.0)

Quick Start

Below is a quick start guide to using the script. This will calculate the major (mu = 4) pair/merger fraction for galaxies above a stellar mass of log(M*) > 11, at physical separations between 5 < r [kpc] < 30, between the redshift range 1.0 < z < 1.5. This example does not take into account spatial masks or mass completeness corrections. These are described in the functions below.

from PyPairs import Pairs
import astropy.units as u
import numpy as np

# Set up pairs object
p = Pairs( zgrid, pzs, mzs,
           
           # Catalogue options
           photometry = './UDS_photometry.fits',
           catalogue_format = 'fits',
           idcol = 'id',
           racol = 'ra',
           deccol = 'dec',
           
           # Odds function arguments
           K = 0.05,
           dz = 0.001
           band = 'flux_tot_K',
           mags = False,
           abzp = 23.90,
           OSRlim = 0.3,
           mag_min = 16.0,
           mag_max = 25.0,
           mag_step = 0.2,
  )

# Set the intended stellar mass ratio, often denoted mu
p.massRatio = 4.
# Set the physical separation requirements
p.setSeparation( r_min = 5*u.kpc, r_max = 30*u.kpc )

# Provide initial indices of galaxies you wish to analyse
# In this case, use all galaxies already given to Pairs()
p.initialSample( np.arange(p.oddsCut.sum()) )

# Find initial pairs
p.findInitialPairs( z_min = 0.3, z_max = 2.5 )

# Make all necessary masks in mass, redshift, position
p.makeMasks( min_mass = 11.0,           # Minimum log(mass) for the primary sample
              max_mass = 12.0,          # Maximum log(mass) for any galaxy
              mass_ratio = p.massRatio
  )
  
# Retrieve the merger fraction for galaxies between z = 1.0 and z = 1.5
p.mergerFraction(1.0, 1.5)
fm = p.fm

Class function descriptions

genCutOuts()

Call this function after mergerFraction() to generate postage stamp cutouts of the identfied close pairs above some integrated pair probability. This functions required the aplpy python plotting library.

p.genCutOuts(
        imagepath,                # path to the image used for cutouts
        outpath = './',           # path to dir in which figures will be saved
        Npair_lim = 0.01,         # limit of integrated weighted PPF above which to select pairs
        cutoutsize = 25*u.arcsec, # side length of cutout
        imghduid = 0,             # FITS HDU index corresponding to the image data
        outprefix = 'stamp',      # prefix of output file name
        z_mean = False            # redshift of pair required to draw search area
  )

Doing so will output single PDF files containing images such as this for each identified close-pair system.


bootstrapMergers()

Call this function with or in place of mergerFraction() to perform a bootstrap error analysis of the pair/merger fraction. Random samples from the full galaxy list are drawn nsamples times and the analysis performed again. The mean and standard deviation in the returned pair/merger fractions are returned.

p.bootstrapMergers(
        zmin = 1.0,         # Lower redshift of range in which to calculate fm
        zmax = 1.5,         # Higher redshift of range in which to calculate fm
        nsamples = 100      # Number of bootstrap samples (100+ suggested)
  )