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In [2]:

    
import numpy as np

Finding rough minimum flux level required to detect



In [3]:

    
sig = 34. # roughly 
time = 6406. / 60. # hours 
sens = sig / np.sqrt(time) # sensitivity 
t = (5./sens)**2 # time required to find 5 sigma 

print(sens)
print(t)









    



3.29049378665
2.30896770473

$$S = s / \sqrt{t}$$$$t = \left( \frac{s}{S} \right) ^2$$

Poisson distribution can be approximated as Gaussian for $n>10$ events



In [4]:

    
gam_rate = 0.1582244 * 60 # gamma / hour 
gam_err = 0.006188022 * 60 
#bg_rate = 
f = 1.75

print(gam_rate)
print(gam_err)
print(gam_rate*f)









    



9.493464
0.37128131999999997
16.613561999999998



In [27]:

    
max_exp = 3.0 # hours / night 
exposure = 3
assert(exposure <= max_exp)
num_evts = exposure * gam_rate

print(num_evts)
print(np.sqrt(num_evts))

# if flux increased 
f = 1.75
print(num_evts*f)
stdev_f = np.sqrt(num_evts*f)
print(stdev_f)
n_sigma = (num_evts*f-num_evts)/stdev_f

print(" a flux level of " + str(f) + " measured for " + str(exposure) + " hours"\
      " times the average rate would be " + str(n_sigma) \
      + " stdevs away from the average rate")









    



28.480392
5.33670235258
49.840686
7.05979362305
 a flux level of 1.75 measured for 3 hours times the average rate would be 3.02562583845 stdevs away from the average rate

$$ \frac{fN-N}{\sqrt{fN}} = t $$$$ \left(f-1\right)^2N^2 = t^2fN $$



In [18]:









    



42.720588
6.53609883646
 a flux level of 1.5 measured for 3.0 hours times the average rate would be 2.17869961215 standard deviations away from the average rate



In [13]:

    
print(np.sqrt(gam_rate*85))
print(gam_err*85)









    



28.4067675035
31.558912199999998