PanSTARRS - WISE reddening

Apply the reddenign to the relevant magnitudes of our samples. This is done before computing the $Q_0$ or applying the ML cross-matching.



In [1]:

    
import numpy as np
from astropy.table import Table
from astropy import units as u
from astropy.coordinates import SkyCoord



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from mltier1 import Field
from extinction import FILTER_EXT, get_eb_v



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%load_ext autoreload



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%autoreload



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%pylab inline









    



Populating the interactive namespace from numpy and matplotlib



In [6]:

    
field = Field(170.0, 190.0, 45.5, 56.5)
field_full = Field(160.0, 232.0, 42.0, 62.0)

Load the data



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panstarrs = Table.read("panstarrs_u1.fits")



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wise = Table.read("wise_u1.fits")

As we will use the coordinates to retrieve the extinction in their positions



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coords_panstarrs = SkyCoord(panstarrs['raMean'], panstarrs['decMean'], unit=(u.deg, u.deg), frame='icrs')



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coords_wise = SkyCoord(wise['raWise'], wise['decWise'], unit=(u.deg, u.deg), frame='icrs')

Get the extinction for the positions of the sources in the catalogues.



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ext_panstarrs = get_eb_v(coords_panstarrs.ra.deg, coords_panstarrs.dec.deg)









    



100%|██████████| 60/60 [13:29<00:00, 13.59s/it]



In [9]:

    
ext_wise = get_eb_v(coords_wise.ra.deg, coords_wise.dec.deg)









    



100%|██████████| 30/30 [05:51<00:00, 11.42s/it]

Apply the correction to each position



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i_correction = ext_panstarrs * FILTER_EXT["i"]



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w1_correction = ext_wise * FILTER_EXT["W1"]



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hist(i_correction, bins=100);



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hist(w1_correction, bins=100);



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panstarrs.rename_column("i", 'iUncor')



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wise.rename_column("W1mag", 'W1magUncor')



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panstarrs["i"] = panstarrs["iUncor"] - i_correction



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wise["W1mag"] = wise["W1magUncor"] - w1_correction



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columns_save = ['objID', 'raMean', 'decMean', 'raMeanErr', 'decMeanErr', 'i', 'iErr']



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panstarrs[columns_save].write('panstarrs_u2.fits', format="fits")



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panstarrs["ext"] = ext_panstarrs



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panstarrs[['objID', "ext"]].write('panstarrs_extinction.fits', format="fits")



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# Free memory
del panstarrs



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columns_save = ['AllWISE', 'raWise', 'decWise', 'raWiseErr', 'decWiseErr', 'W1mag', 'W1magErr']



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wise[columns_save].write('wise_u2.fits', format="fits")



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wise["ext"] = ext_wise



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wise[['AllWISE', "ext"]].write('wise_extinction.fits', format="fits")



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