bestChiSq > 100 and ngoodobs > 100


In [22]:
import matplotlib.pyplot as plt
%matplotlib notebook
import numpy as np
from astropy.table import Table as tbl

In [12]:
source = tbl.read('allsky4.tbl', format = 'ipac')

In [24]:
ret = plt.hist(source['bestmeanmag'], range = (12,27), bins = 50)
plt.xlabel('Mean Mag')
plt.ylabel('Counts')


Out[24]:
<matplotlib.text.Text at 0x270a7550b38>

This is showing the best mean magnitude observed for all sources. There appears to be a linear trend for the counts between ~14 and ~18.5 magnitude stars. Then, the distrubtion turns into a more typical Gaussian-like shape. However, this may be a exponential trend upward that just falls off at higher magnitudes because of the telescope being used and the time it takes to observe fainter stars. The latter seems like a more probable explanation as the number of stars increases at higher magnitudes. Additionally, I am somewhat concerned about the area surrounding the 26th magnitude as this seems fainter than what could be measured, and there is a broad area in which very few observations were made. These seem to be extraneous points due to some unknown source.

Below are the counts and cent


In [25]:
print(ret)


(array([  2.00000000e+00,   6.00000000e+00,   4.00000000e+00,
         2.10000000e+01,   3.00000000e+01,   9.80000000e+01,
         1.92000000e+02,   1.53700000e+03,   6.94400000e+03,
         7.98200000e+03,   9.14200000e+03,   1.01630000e+04,
         1.11310000e+04,   1.21330000e+04,   1.27970000e+04,
         1.38130000e+04,   1.45230000e+04,   1.53730000e+04,
         1.65060000e+04,   1.82880000e+04,   2.05690000e+04,
         2.36810000e+04,   2.79400000e+04,   3.29920000e+04,
         3.79310000e+04,   4.09930000e+04,   4.24330000e+04,
         3.76950000e+04,   2.96940000e+04,   1.99350000e+04,
         8.52900000e+03,   4.30600000e+03,   2.33600000e+03,
         7.50000000e+02,   2.97000000e+02,   1.40000000e+02,
         5.30000000e+01,   3.80000000e+01,   5.20000000e+01,
         5.50000000e+01,   6.30000000e+01,   7.70000000e+01,
         6.90000000e+01,   1.15000000e+02,   1.30000000e+02,
         1.80000000e+02,   1.71000000e+02,   1.98000000e+02,
         9.70000000e+01,   3.00000000e+01]), array([ 12. ,  12.3,  12.6,  12.9,  13.2,  13.5,  13.8,  14.1,  14.4,
        14.7,  15. ,  15.3,  15.6,  15.9,  16.2,  16.5,  16.8,  17.1,
        17.4,  17.7,  18. ,  18.3,  18.6,  18.9,  19.2,  19.5,  19.8,
        20.1,  20.4,  20.7,  21. ,  21.3,  21.6,  21.9,  22.2,  22.5,
        22.8,  23.1,  23.4,  23.7,  24. ,  24.3,  24.6,  24.9,  25.2,
        25.5,  25.8,  26.1,  26.4,  26.7,  27. ]), <a list of 50 Patch objects>)

In [ ]: