def main():
ara_logeV, ara_200m_aeff = detector.get_aeff('ara_200m_1year_fromlimit')
phased_logeV, phased_aeff = detector.get_aeff('ara_phased_fromlimit')
fig = plt.figure(figsize=(2.*11,8.5))
ax_limit = fig.add_subplot(1,2,2)
ax_aeff.plot(np.power(10.,ara_logeV),ara_200m_aeff,'-<', linewidth=2.0,color='blue',label=r'1 ARA 200m Station @ SP')
beautify_aeff(ax_aeff)
fig.savefig("try_super_station.png",edgecolor='none',bbox_inches="tight") #save the figure
def main():
ara_logeV, ara_200m_limit = detector.get_limit('ara_200m_1year')
arianna_logeV, arianna_limit = detector.get_limit(
'arianna_hra3_singlestation_1year')
testbed_logeV, testbed_limit = detector.get_limit('ara_testbed_1year')
ara_100m_logeV, ara_100m_limit = detector.get_limit('ara_100m_1year')
phased_logeV, phased_limit = detector.get_limit('ara_phased_1year')
ara_logeV, ara_200m_aeff = detector.get_aeff('ara_200m_1year_fromlimit')
arianna_logeV, arianna_aeff = detector.get_aeff(
'arianna_hra3_single_fromlimit')
testbed_logeV, testbed_aeff = detector.get_aeff('ara_testbed_fromlimit')
ara_100m_logeV, ara_100m_aeff = detector.get_aeff(
'ara_100m_1year_fromlimit')
phased_logeV, phased_aeff = detector.get_aeff('ara_phased_fromlimit')
#set up some theory stuff
ahlers_data = np.genfromtxt("data/ahlers_2012.csv",
delimiter=',',
skip_header=1,
names=['energy', 'flux'])
ahlers_energy_logev = ahlers_data['energy']
ahlers_energy = np.power(10., ahlers_energy_logev)
ahlers_limit_log = ahlers_data['flux']
ahlers_limit = np.power(10., ahlers_limit_log)
ahlers_interpolator = splrep(ahlers_energy_logev, ahlers_limit_log, k=4)
icecube_energy_logev = np.array(
[15.5, 16, 16.5, 17, 17.5, 18, 18.5, 19, 19.5, 20, 20.5])
icecube_energy = np.power(10., icecube_energy_logev)
#we can first have the IceCube thru-mu fluxes
#we can have the nomina, and upper and lower 1 sigma possibilities
def icecube_thrumu_function(E):
return 3.03 * ((E / 1.e14)**-2.19) * 1e-27
def icecube_thrumu_upper_function(E):
return 3.81 * ((E / 1.e14)**-2.09) * 1e-27
def icecube_thrumu_lower_function(E):
return 2.34 * ((E / 1.e14)**-2.29) * 1e-27
def icecube_combined_function(E):
return 6.7 * ((E / 1.e14)**-2.50) * 1e-27
def icecube_combined_upper_function(E):
return 7.8 * ((E / 1.e14)**-2.41) * 1e-27
def icecube_combined_lower_function(E):
return 5.5 * ((E / 1.e14)**-2.59) * 1e-27
#to compute to a curve on EF(E) we have to multiply by energy
icecube_thrumu_efe = icecube_thrumu_function(
icecube_energy) * icecube_energy
icecube_thrumu_upper_efe = icecube_thrumu_upper_function(
icecube_energy) * icecube_energy
icecube_thrumu_lower_efe = icecube_thrumu_lower_function(
icecube_energy) * icecube_energy
icecube_combined_efe = icecube_combined_function(
icecube_energy) * icecube_energy
icecube_combined_upper_efe = icecube_combined_upper_function(
icecube_energy) * icecube_energy
icecube_combined_lower_efe = icecube_combined_lower_function(
icecube_energy) * icecube_energy
fig = plt.figure(figsize=(2. * 11, 8.5))
ax_limit = fig.add_subplot(1, 2, 2)
ax_aeff = fig.add_subplot(1, 2, 1)
# ax_limit.plot(ahlers_energy,ahlers_limit,'-', linewidth=3.0,color='gray',label="Ahlers 2012")
# ax_limit.fill_between(icecube_energy,icecube_thrumu_lower_efe,icecube_thrumu_upper_efe,facecolor='orange',alpha=0.3)
# ax_limit.fill_between(icecube_energy,icecube_combined_lower_efe,icecube_combined_upper_efe,facecolor='magenta',alpha=0.3)
# ax_limit.plot(icecube_energy,icecube_thrumu_efe,'-.', linewidth=3.0,color='orange',label=r'IceCube Thru-Mu (E$^{-2.19}$)')
# ax_limit.plot(icecube_energy,icecube_combined_efe,':', linewidth=3.0,color='magenta',label='IceCube Combined (E$^{-2.50}$)')
ax_limit.plot(np.power(10., phased_logeV),
phased_limit,
'-v',
linewidth=2.0,
color='magenta',
label=r'1 ARA Phased 200m Station @ SP, 1 Year')
ax_limit.plot(np.power(10., ara_logeV),
ara_200m_limit,
'-<',
linewidth=2.0,
color='blue',
label=r'1 ARA 200m Station @ SP, 1 Year')
ax_limit.plot(np.power(10., ara_100m_logeV),
ara_100m_limit,
'->',
linewidth=2.0,
color='black',
label=r'1 ARA 100m Station @ SP, 1 Year (approx)')
ax_limit.plot(np.power(10., arianna_logeV),
arianna_limit / 0.58,
'-o',
linewidth=2.0,
color='green',
label=r'1 ARIANNA Surface Station @ MB, 0.58 Year')
ax_limit.plot(np.power(10., testbed_logeV),
testbed_limit,
'-^',
linewidth=2.0,
color='red',
label=r'1 ARA Testbed Surface Station @ SP, 1 Year')
beautify_limit(ax_limit)
ax_aeff.plot(np.power(10., phased_logeV),
phased_aeff,
'-<',
linewidth=2.0,
color='magenta',
label=r'1 ARA Phased 200m Station @ SP')
ax_aeff.plot(np.power(10., ara_logeV),
ara_200m_aeff,
'-<',
linewidth=2.0,
color='blue',
label=r'1 ARA 200m Station @ SP')
ax_aeff.plot(np.power(10., ara_100m_logeV),
ara_100m_aeff,
'->',
linewidth=2.0,
color='black',
label=r'1 ARA 100m Station @ SP (approx)')
ax_aeff.plot(np.power(10., arianna_logeV),
arianna_aeff,
'-o',
linewidth=2.0,
color='green',
label=r'1 ARIANNA Surface Station @ MB')
ax_aeff.plot(np.power(10., testbed_logeV),
testbed_aeff,
'-^',
linewidth=2.0,
color='red',
label=r'1 ARA Testbed Surface Station @ SP')
beautify_aeff(ax_aeff)
fig.savefig("current_single_station_limits_and_aeff.png",
edgecolor='none',
bbox_inches="tight") #save the figure
#now,determine total sensitivity
#need to set up joing limit energy bins
total_energy_logeV = np.array(
[16., 16.5, 17., 17.5, 18., 18.5, 19, 19.5, 20., 20.5, 21.])
#need to insert two zeros in both ARA testbed and ARIANNA station
#for 16 and 16.5 energy bins (no data there)
testbed_aeff = np.insert(ara_200m_aeff, 0, 0., axis=0)
testbed_aeff = np.insert(ara_200m_aeff, 0, 0., axis=0)
#need to insert two zeros for 20.5 and 21 in phased array station
phased_aeff = np.append(phased_aeff, 0.)
phased_aeff = np.append(phased_aeff, 0.)
#need to insert a 21 bin in ARA200m, ARA100m, ARIANNA
ara_200m_aeff = np.append(ara_200m_aeff, 0.)
ara_100m_aeff = np.append(ara_100m_aeff, 0.)
# arianna_limit = np.insert(arianna_limit,0,0.,axis=0)
# arianna_limit = np.insert(arianna_limit,0,0.,axis=0)
# arianna_aeff = np.append(arianna_aeff,0.)
#number of stations of each type of station
num_testbed = 1.
num_100m = 1.
num_200m = 3.
num_phased = 1.
#livetime in seconds for each type of station
live_testbed = 632. * const.SecPerDay
live_100m = 731. * const.SecPerDay
live_200m = 2991. * const.SecPerDay
live_phased = 186. * const.SecPerDay
#we multiply the number of stations by the livetime
testbed_app_total = num_testbed * live_testbed * testbed_aeff
ara_100m_app_total = num_100m * live_100m * ara_100m_aeff
ara_200m_app_total = num_200m * live_200m * ara_200m_aeff
phased_app_total = num_phased * live_phased * phased_aeff
#units here are seconds * cm^2 * sr
total_aeff = (num_testbed * testbed_aeff) + (num_100m * ara_100m_aeff) + (
num_200m * ara_200m_aeff) + (num_phased * phased_aeff)
total_app = testbed_app_total + ara_100m_app_total + ara_200m_app_total + phased_app_total
total_limit = 1. / np.log(10) / 0.5 / total_app
arianna_total_aeff = 8. * arianna_aeff
arianna_total_app = ((3. * 170. * const.SecPerDay) +
(3. * 0.6 * const.SecPerYear) +
(4. * 170. * const.SecPerDay) +
(3. * 0.6 * const.SecPerYear) +
(7. * 0.6 * const.SecPerYear) +
(7. * 0.6 * const.SecPerYear) +
(7. / 12. * const.SecPerYear)) * arianna_aeff
arianna_total_limit = 1. / np.log(10) / 0.5 / arianna_total_app
fig2 = plt.figure(figsize=(2. * 11, 8.5))
ax2_aeff = fig2.add_subplot(1, 2, 1)
ax2_lim = fig2.add_subplot(1, 2, 2)
ax2_lim.plot(ahlers_energy,
ahlers_limit,
'-',
linewidth=3.0,
color='gray',
label="Ahlers 2012")
ax2_lim.fill_between(icecube_energy,
icecube_thrumu_lower_efe,
icecube_thrumu_upper_efe,
facecolor='orange',
alpha=0.3)
ax2_lim.fill_between(icecube_energy,
icecube_combined_lower_efe,
icecube_combined_upper_efe,
facecolor='magenta',
alpha=0.3)
ax2_lim.plot(icecube_energy,
icecube_thrumu_efe,
'-.',
linewidth=3.0,
color='orange',
label=r'IceCube Thru-Mu (E$^{-2.19}$)')
ax2_lim.plot(icecube_energy,
icecube_combined_efe,
':',
linewidth=3.0,
color='magenta',
label='IceCube Combined (E$^{-2.50}$)')
ax2_lim.plot(np.power(10., total_energy_logeV),
total_limit,
'-o',
linewidth=2.0,
color='blue',
label=r'Total Time-Integrated ARA Sensitivity')
ax2_lim.plot(np.power(10., arianna_logeV),
arianna_total_limit,
'-o',
linewidth=2.0,
color='green',
label=r'Total Time-Integrated ARIANNA Sensitivity')
ax2_aeff.plot(np.power(10., total_energy_logeV),
phased_app_total,
'-o',
linewidth=2.0,
color='magenta',
label=r'PA')
ax2_aeff.plot(np.power(10., total_energy_logeV),
total_aeff,
'-o',
linewidth=2.0,
color='blue',
label=r'Total Deployed ARA Sensitivity')
ax2_aeff.plot(np.power(10., arianna_logeV),
arianna_total_aeff,
'-o',
linewidth=2.0,
color='green',
label=r'Total Deployed ARIANNA Sensitivity')
beautify_aeff(ax2_aeff)
beautify_limit_withtheory(ax2_lim, 3)
ax2_aeff.set_ylim([1e2, 1e12])
ax2_lim.set_ylim([1e-22, 1e-10])
fig2.savefig("current_total_limits_and_aeff.png",
edgecolor='none',
bbox_inches="tight") #save the figure
#need interpolator for ARA deployed sensitivity
total_ara_app_interpolator = splrep(total_energy_logeV,
np.log10(total_app),
k=4)
#integrate up all the ARA sensitivity
counts_ara_icecubethrumu = []
counts_ara_icecubecombined = []
counts_ara_ahlers = []
energy_bins = []
bins = np.arange(16, 20.5, 0.5)
for bin in bins:
temp_logev = np.arange(bin, bin + 0.5, 0.01)
temp_energy = np.power(10., temp_logev)
#the ara sensitivity
temp_ara_aeff = np.power(10.,
splev(temp_logev, total_ara_app_interpolator))
#the flux estimates
temp_icecube_thrumu = icecube_thrumu_function(temp_energy)
temp_icecube_combined = icecube_combined_function(temp_energy)
temp_ahlers = np.power(10., splev(temp_logev,
ahlers_interpolator)) / temp_energy
#the counts
temp_counts_thrumu = np.trapz(temp_icecube_thrumu * temp_ara_aeff,
temp_energy)
temp_counts_combined = np.trapz(temp_icecube_combined * temp_ara_aeff,
temp_energy)
temp_counts_ahlers = np.trapz(temp_ahlers * temp_ara_aeff, temp_energy)
counts_ara_icecubethrumu.append(temp_counts_thrumu)
counts_ara_icecubecombined.append(temp_counts_combined)
counts_ara_ahlers.append(temp_counts_ahlers)
energy_bins.append(np.power(10., bin))
counts_ara_icecubethrumu = np.array(counts_ara_icecubethrumu)
counts_ara_icecubecombined = np.array(counts_ara_icecubecombined)
counts_ara_ahlers = np.array(counts_ara_ahlers)
energy_bins = np.array(energy_bins)
#what's the phased array contribution
pa_total_energy_logeV_for_interp = total_energy_logeV[:-2]
pa_total_app_for_interp = phased_app_total[:-2]
total_pa_app_interpolator = splrep(pa_total_energy_logeV_for_interp,
np.log10(pa_total_app_for_interp),
k=4)
counts_pa_icecubethrumu = []
counts_pa_icecubecombined = []
counts_pa_ahlers = []
pa_energy_bins = []
bins = np.arange(16, 20.5, 0.5)
for bin in bins:
temp_logev = np.arange(bin, bin + 0.5, 0.01)
temp_energy = np.power(10., temp_logev)
temp_pa_aeff = np.power(10.,
splev(temp_logev, total_pa_app_interpolator))
#the flux estimates
temp_icecube_thrumu = icecube_thrumu_function(temp_energy)
temp_icecube_combined = icecube_combined_function(temp_energy)
temp_ahlers = np.power(10., splev(temp_logev,
ahlers_interpolator)) / temp_energy
temp_counts_thrumu_pa = np.trapz(temp_icecube_thrumu * temp_pa_aeff,
temp_energy)
temp_counts_combined_pa = np.trapz(
temp_icecube_combined * temp_pa_aeff, temp_energy)
temp_counts_ahlers_pa = np.trapz(temp_ahlers * temp_pa_aeff,
temp_energy)
counts_pa_icecubethrumu.append(temp_counts_thrumu_pa)
counts_pa_icecubecombined.append(temp_counts_combined_pa)
counts_pa_ahlers.append(temp_counts_ahlers_pa)
pa_energy_bins.append(np.power(10., bin))
counts_pa_icecubethrumu = np.array(counts_pa_icecubethrumu)
counts_pa_icecubecombined = np.array(counts_pa_icecubecombined)
counts_pa_ahlers = np.array(counts_pa_ahlers)
print "total PA contributions %.4f, %.4f, %.4f" % (
counts_pa_icecubethrumu.sum(), counts_pa_icecubecombined.sum(),
counts_pa_ahlers.sum())
#need interpolator for ARIANNA deployed sensitivity
total_arianna_app_interpolator = splrep(arianna_logeV,
np.log10(arianna_total_app),
k=4)
counts_arianna_icecubethrumu = []
counts_arianna_icecubecombined = []
counts_arianna_ahlers = []
energy_bins_arianna = []
bins = np.arange(17, 20, 0.5)
for bin in bins:
temp_logev = np.arange(bin, bin + 0.5, 0.01)
temp_energy = np.power(10., temp_logev)
#the ara sensitivity
temp_arianna_aeff = np.power(
10., splev(temp_logev, total_arianna_app_interpolator))
#the flux estimates
temp_icecube_thrumu = icecube_thrumu_function(temp_energy)
temp_icecube_combined = icecube_combined_function(temp_energy)
temp_ahlers = np.power(10., splev(temp_logev,
ahlers_interpolator)) / temp_energy
#the counts
temp_counts_thrumu = np.trapz(temp_icecube_thrumu * temp_arianna_aeff,
temp_energy)
temp_counts_combined = np.trapz(
temp_icecube_combined * temp_arianna_aeff, temp_energy)
temp_counts_ahlers = np.trapz(temp_ahlers * temp_arianna_aeff,
temp_energy)
counts_arianna_icecubethrumu.append(temp_counts_thrumu)
counts_arianna_icecubecombined.append(temp_counts_combined)
counts_arianna_ahlers.append(temp_counts_ahlers)
energy_bins_arianna.append(np.power(10., bin))
counts_arianna_icecubethrumu = np.array(counts_arianna_icecubethrumu)
counts_arianna_icecubecombined = np.array(counts_arianna_icecubecombined)
counts_arianna_ahlers = np.array(counts_arianna_ahlers)
energy_bins_arianna = np.array(energy_bins_arianna)
fig3 = plt.figure(figsize=(2. * 11, 8.5))
ax3_count_ara = fig3.add_subplot(1, 2, 1)
ax3_count_arianna = fig3.add_subplot(1, 2, 2)
ax3_count_ara.set_title("ARA", fontsize=24)
ax3_count_arianna.set_title("ARIANNA", fontsize=24)
n, bins, patches = ax3_count_ara.hist(
energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_ara_ahlers,
label=r'Ahlers 2012: %.2f' % counts_ara_ahlers.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='red',
linewidth=4)
n, bins, patches = ax3_count_ara.hist(
energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_ara_icecubethrumu,
label=r'IceCube Thru-Mu E$^{-2.19}$: %.2f' %
counts_ara_icecubethrumu.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='blue',
linewidth=4)
n, bins, patches = ax3_count_ara.hist(
energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_ara_icecubecombined,
label=r'IceCube Combined E$^{-2.5}$: %.3f' %
counts_ara_icecubecombined.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='green',
linewidth=4)
n, bins, patches = ax3_count_arianna.hist(
energy_bins_arianna,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_arianna_ahlers,
label=r'Ahlers 2012: %.2f' % counts_arianna_ahlers.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='red',
linewidth=4)
n, bins, patches = ax3_count_arianna.hist(
energy_bins_arianna,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_arianna_icecubethrumu,
label=r'IceCube Thru-Mu E$^{-2.19}$: %.2f' %
counts_arianna_icecubethrumu.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='blue',
linewidth=4)
n, bins, patches = ax3_count_arianna.hist(
energy_bins_arianna,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_arianna_icecubecombined,
label=r'IceCube Combined E$^{-2.5}$: %.3f' %
counts_arianna_icecubecombined.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='green',
linewidth=4)
beautify_counts(ax3_count_ara)
beautify_counts(ax3_count_arianna)
ax3_count_ara.set_ylim([0, 3])
ax3_count_arianna.set_ylim([0, 0.08])
fig3.savefig("counts.png", edgecolor='none',
bbox_inches="tight") #save the figure
def main():
icecube_logeV, icecube_limit = detector.get_limit('icecube_2018')
anita_logeV, anita_limit = detector.get_limit('anita_2018')
icecube_exposure = 2.44 / np.log(10) / 0.5 / icecube_limit
anita_exposure = 2.44 / np.log(10) / 0.5 / anita_limit
target_exposure = icecube_exposure[:8]
target_energy = icecube_logeV[:8]
#this is the exposure we need to get our hands on
target_exposure = np.append(target_exposure, anita_exposure[4:5])
target_energy = np.append(target_energy, 20)
fig_tobeat = plt.figure(figsize=(2 * 11, 8.5))
ax_limit_tobeat = fig_tobeat.add_subplot(1, 2, 1)
ax_exposure_tobeat = fig_tobeat.add_subplot(1, 2, 2)
ax_limit_tobeat.plot(np.power(10., icecube_logeV),
icecube_limit,
'-^',
linewidth=2.0,
color='blue',
label=r'IceCube 2018',
markersize=10)
ax_limit_tobeat.plot(np.power(10., anita_logeV),
anita_limit,
'-v',
linewidth=2.0,
color='green',
label=r'ANITA 2018',
markersize=10)
ax_exposure_tobeat.plot(np.power(10., icecube_logeV),
icecube_exposure,
'-^',
linewidth=2.0,
color='blue',
label=r'IceCube 2018',
markersize=10)
ax_exposure_tobeat.plot(np.power(10., anita_logeV),
anita_exposure,
'-v',
linewidth=2.0,
color='green',
label=r'ANITA 2018',
markersize=10)
ax_exposure_tobeat.plot(np.power(10., target_energy),
target_exposure,
'--',
linewidth=8.0,
color='red',
label=r'Minimum Target Exposure for NGRA')
beautify_limit(ax_limit_tobeat)
beautify_aeff(ax_exposure_tobeat)
fig_tobeat.savefig("limit_to_beat.png",
edgecolor='none',
bbox_inches="tight") #save the figure
total_cost_1_5km = []
total_cost_1km = []
fixed_costs = []
station_number = np.arange(0, 100, 1)
for station in station_number:
total_cost_1_5km.append(cost(1.5, station))
total_cost_1km.append(cost(1, station))
fixed_costs.append(543000)
total_cost_1_5km = np.array(total_cost_1_5km)
total_cost_1km = np.array(total_cost_1km)
fixed_costs = np.array(fixed_costs)
average_cost_1_5km = total_cost_1_5km[1:] / station_number[1:]
average_cost_1km = total_cost_1km[1:] / station_number[1:]
num_cabled = 100.
cost_to_cable = cost(1.5, num_cabled)
num_extra_arianna = (cost_to_cable / 15000.)
# print "Cost to cable: ", cost_to_cable
print "Cost to cable: %.2f and cost per cabled station %.2f " % (
cost_to_cable, cost_to_cable / num_cabled)
print "Worth %.2f ARIANNA Stations " % (num_extra_arianna)
livetime = 5. * const.SecPerYear
arianna_energy, arianna_aeff = detector.get_aeff(
'arianna_icrc2017_fromlimit')
arianna_energy = arianna_energy[1:]
arianna_energy = arianna_energy[:-2]
arianna_aeff = arianna_aeff[1:]
arianna_aeff = arianna_aeff[:-2]
pa_energy, pa_eff = detector.get_aeff('ara_phased_fromlimit')
ara_exposure = pa_eff * 5. * const.SecPerYear
arianna_exposure = arianna_aeff * 5. * const.SecPerYear * 0.58 #arianna only gets 58% uptime
num_auto = 800.
num_auto_only = num_extra_arianna + num_auto
cabled_contribution = (ara_exposure * num_cabled)
auto_contribution = (arianna_exposure * num_auto)
auto_only_exposure = arianna_exposure * num_auto_only
#merged detector
total = cabled_contribution + auto_contribution
fig_thiscase = plt.figure(figsize=(11, 8.5))
ax_thiscase = fig_thiscase.add_subplot(1, 1, 1)
ax_thiscase.plot(np.power(10., target_energy),
target_exposure,
'--',
linewidth=4.0,
color='red',
label=r'Minimum Target Exposure for NGRA')
ax_thiscase.plot(np.power(10., pa_energy),
total,
'--',
linewidth=4.0,
color='black',
label=r'5 years, %d Cabled + %d Autonomous' %
(num_cabled, num_auto))
ax_thiscase.plot(np.power(10., arianna_energy),
auto_contribution,
'-v',
linewidth=4.0,
color='green',
label=r'Mixed: %d Auto Contribution' % num_auto,
markersize=10)
ax_thiscase.plot(np.power(10., pa_energy),
cabled_contribution,
'-^',
linewidth=4.0,
color='blue',
label=r'Mixed: %d Cabled Contribution' % num_cabled,
markersize=10)
ax_thiscase.plot(np.power(10., arianna_energy),
auto_only_exposure,
'-v',
linewidth=2.0,
color='magenta',
label=r'Auto Only: %d Station' % num_auto_only,
markersize=10)
beautify_aeff(ax_thiscase)
fig_thiscase.savefig("%dcabled_case.png" % num_cabled,
edgecolor='none',
bbox_inches="tight") #save the figure
fig = plt.figure(figsize=(2 * 11, 8.5))
ax_total = fig.add_subplot(1, 2, 1)
ax_average = fig.add_subplot(1, 2, 2)
ax_total.plot(station_number,
total_cost_1_5km / 1000.,
label='1.5 km Spacing',
linewidth=4)
ax_total.plot(station_number,
total_cost_1km / 1000.,
label='1 km Spacing',
linewidth=4)
ax_average.plot(station_number[1:],
average_cost_1_5km / 1000.,
label='1.5 km Spacing',
linewidth=4)
ax_average.plot(station_number[1:],
average_cost_1km / 1000.,
label='1 km Spacing',
linewidth=4)
ax_total.set_title("Total Cabling Cost", fontsize=24)
ax_average.set_title("Cabling Cost Per Station", fontsize=24)
beautify(ax_total)
beautify(ax_average)
sizer = 20
ax_total.set_ylabel('Thousands of USD', size=sizer)
ax_average.set_ylabel('Thousands of USD', size=sizer)
ax_average.set_ylim([0, 50]) #set the y limits of the plot
fig.savefig("cost.png", edgecolor='none',
bbox_inches="tight") #save the figure
def main():
'''
Load theory information
'''
ahlers_data = np.genfromtxt("data/ahlers_2012.csv",
delimiter=',',
skip_header=1,
names=['energy', 'flux'])
ahlers_energy_logev = ahlers_data['energy']
ahlers_energy = np.power(10., ahlers_energy_logev)
ahlers_limit_log = ahlers_data['flux']
ahlers_limit = np.power(10., ahlers_limit_log)
ahlers_interpolator = splrep(ahlers_energy_logev, ahlers_limit_log, k=4)
icecube_energy_logev = np.array(
[15.5, 16, 16.5, 17, 17.5, 18, 18.5, 19, 19.5, 20, 20.5])
icecube_energy = np.power(10., icecube_energy_logev)
#we can first have the IceCube thru-mu fluxes
#we can have the nomina, and upper and lower 1 sigma possibilities
def icecube_thrumu_function(E):
return 3.03 * ((E / 1.e14)**-2.19) * 1e-27
def icecube_thrumu_upper_function(E):
return 3.81 * ((E / 1.e14)**-2.09) * 1e-27
def icecube_thrumu_lower_function(E):
return 2.34 * ((E / 1.e14)**-2.29) * 1e-27
def icecube_combined_function(E):
return 6.7 * ((E / 1.e14)**-2.50) * 1e-27
def icecube_combined_upper_function(E):
return 7.8 * ((E / 1.e14)**-2.41) * 1e-27
def icecube_combined_lower_function(E):
return 5.5 * ((E / 1.e14)**-2.59) * 1e-27
#to compute to a curve on EF(E) we have to multiply by energy
icecube_thrumu_efe = icecube_thrumu_function(
icecube_energy) * icecube_energy
icecube_thrumu_upper_efe = icecube_thrumu_upper_function(
icecube_energy) * icecube_energy
icecube_thrumu_lower_efe = icecube_thrumu_lower_function(
icecube_energy) * icecube_energy
icecube_combined_efe = icecube_combined_function(
icecube_energy) * icecube_energy
icecube_combined_upper_efe = icecube_combined_upper_function(
icecube_energy) * icecube_energy
icecube_combined_lower_efe = icecube_combined_lower_function(
icecube_energy) * icecube_energy
'''
Load detector information
'''
ara_logeV, ara_200m_aeff = detector.get_aeff('ara_200m_1year_fromlimit')
phased_logeV, phased_aeff = detector.get_aeff('ara_phased_fromlimit')
#trim the ARA sensitivity to match the phased array in energy binning
ara_logeV = ara_logeV[:-1]
ara_200m_aeff = ara_200m_aeff[:-1]
app_ara = ara_200m_aeff * const.SecPerYear
app_pa = phased_aeff * const.SecPerYear
ara_app_interpolator = splrep(ara_logeV, np.log10(app_ara), k=4)
pa_app_interpolator = splrep(phased_logeV, np.log10(app_pa), k=4)
counts_ara_icecubethrumu = []
counts_ara_icecubecombined = []
counts_ara_ahlers = []
energy_bins = []
bins = np.arange(16, 19.5, 0.5)
for bin in bins:
temp_logev = np.arange(bin, bin + 0.5, 0.01)
temp_energy = np.power(10., temp_logev)
#the ara sensitivity
temp_ara_aeff = np.power(10., splev(temp_logev, ara_app_interpolator))
#the flux estimates
temp_icecube_thrumu = icecube_thrumu_function(temp_energy)
temp_icecube_combined = icecube_combined_function(temp_energy)
temp_ahlers = np.power(10., splev(temp_logev,
ahlers_interpolator)) / temp_energy
#the counts
temp_counts_thrumu = np.trapz(temp_icecube_thrumu * temp_ara_aeff,
temp_energy)
temp_counts_combined = np.trapz(temp_icecube_combined * temp_ara_aeff,
temp_energy)
temp_counts_ahlers = np.trapz(temp_ahlers * temp_ara_aeff, temp_energy)
#push back the counts
counts_ara_icecubethrumu.append(temp_counts_thrumu)
counts_ara_icecubecombined.append(temp_counts_combined)
counts_ara_ahlers.append(temp_counts_ahlers)
#save the energy bin
energy_bins.append(np.power(10., bin))
counts_ara_icecubethrumu = np.array(counts_ara_icecubethrumu)
counts_ara_icecubecombined = np.array(counts_ara_icecubecombined)
counts_ara_ahlers = np.array(counts_ara_ahlers)
energy_bins = np.array(energy_bins)
#what's the phased array contribution
counts_pa_icecubethrumu = []
counts_pa_icecubecombined = []
counts_pa_ahlers = []
pa_energy_bins = []
bins = np.arange(16, 19.5, 0.5)
for bin in bins:
temp_logev = np.arange(bin, bin + 0.5, 0.01)
temp_energy = np.power(10., temp_logev)
temp_pa_aeff = np.power(10., splev(temp_logev, pa_app_interpolator))
#the flux estimates
temp_icecube_thrumu = icecube_thrumu_function(temp_energy)
temp_icecube_combined = icecube_combined_function(temp_energy)
temp_ahlers = np.power(10., splev(temp_logev,
ahlers_interpolator)) / temp_energy
temp_counts_thrumu_pa = np.trapz(temp_icecube_thrumu * temp_pa_aeff,
temp_energy)
temp_counts_combined_pa = np.trapz(
temp_icecube_combined * temp_pa_aeff, temp_energy)
temp_counts_ahlers_pa = np.trapz(temp_ahlers * temp_pa_aeff,
temp_energy)
counts_pa_icecubethrumu.append(temp_counts_thrumu_pa)
counts_pa_icecubecombined.append(temp_counts_combined_pa)
counts_pa_ahlers.append(temp_counts_ahlers_pa)
pa_energy_bins.append(np.power(10., bin))
counts_pa_icecubethrumu = np.array(counts_pa_icecubethrumu)
counts_pa_icecubecombined = np.array(counts_pa_icecubecombined)
counts_pa_ahlers = np.array(counts_pa_ahlers)
pa_energy_bins = np.array(pa_energy_bins)
ratio_ahlers = counts_pa_ahlers / counts_ara_ahlers
ratio_icecubethrumu = counts_pa_icecubethrumu / counts_ara_icecubethrumu
ratio_icecubecombined = counts_pa_icecubecombined / counts_ara_icecubecombined
fig_counts = plt.figure(figsize=(3. * 11, 8.5))
ax_count_ara = fig_counts.add_subplot(1, 3, 1)
ax_count_pa = fig_counts.add_subplot(1, 3, 2)
ax_count_ratio = fig_counts.add_subplot(1, 3, 3)
ax_count_ara.set_title("ARA 200m", fontsize=24)
ax_count_pa.set_title("ARA 200m w/ PA", fontsize=24)
ax_count_ratio.set_title("Ratio ARA200/PA", fontsize=24)
n, bins, patches = ax_count_ara.hist(
energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_ara_ahlers,
label=r'Ahlers 2012: %.2f' % counts_ara_ahlers.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='red',
linewidth=4)
n, bins, patches = ax_count_ara.hist(
energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_ara_icecubethrumu,
label=r'IceCube Thru-Mu E$^{-2.19}$: %.2f' %
counts_ara_icecubethrumu.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='blue',
linewidth=4)
n, bins, patches = ax_count_ara.hist(
energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_ara_icecubecombined,
label=r'IceCube Combined E$^{-2.5}$: %.3f' %
counts_ara_icecubecombined.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='green',
linewidth=4)
n, bins, patches = ax_count_pa.hist(
pa_energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_pa_ahlers,
label=r'Ahlers 2012: %.2f' % counts_pa_ahlers.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='red',
linewidth=4)
n, bins, patches = ax_count_pa.hist(
pa_energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_pa_icecubethrumu,
label=r'IceCube Thru-Mu E$^{-2.19}$: %.2f' %
counts_pa_icecubethrumu.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='blue',
linewidth=4)
n, bins, patches = ax_count_pa.hist(
pa_energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=counts_pa_icecubecombined,
label=r'IceCube Combined E$^{-2.5}$: %.3f' %
counts_pa_icecubecombined.sum(),
fill=False,
stacked=True,
histtype='step',
edgecolor='green',
linewidth=4)
n, bins, patches = ax_count_ratio.hist(pa_energy_bins,
bins=np.power(
10., np.arange(16, 22, 1)),
weights=ratio_ahlers,
label=r'Ahlers 2012',
fill=False,
stacked=True,
histtype='step',
edgecolor='red',
linewidth=4)
n, bins, patches = ax_count_ratio.hist(
pa_energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=ratio_icecubethrumu,
label=r'IceCube Thru-Mu E$^{-2.19}$',
fill=False,
stacked=True,
histtype='step',
edgecolor='blue',
linewidth=4)
n, bins, patches = ax_count_ratio.hist(
pa_energy_bins,
bins=np.power(10., np.arange(16, 22, 1)),
weights=ratio_icecubecombined,
label=r'IceCube Combined E$^{-2.5}$',
fill=False,
stacked=True,
histtype='step',
edgecolor='green',
linewidth=4)
beautify_counts(ax_count_ara)
beautify_counts(ax_count_pa)
ratio_legend = beautify_counts(ax_count_ratio)
ax_count_ara.set_ylim([0, 0.2])
ax_count_pa.set_ylim([0, 0.2])
ax_count_ratio.set_ylim([0, 14])
ax_count_ratio.set_ylabel('Ratio', size=20) #give it a title
ratio_legend.set_title('')
fig_counts.savefig("ara200m_vs_pa_counts.png",
edgecolor='none',
bbox_inches="tight") #save the figure
def main():
'''
Load theory information
'''
ahlers_data = np.genfromtxt("data/ahlers_2012.csv",
delimiter=',',
skip_header=1,
names=['energy', 'flux'])
ahlers_energy_logev = ahlers_data['energy']
ahlers_energy = np.power(10., ahlers_energy_logev)
ahlers_limit_log = ahlers_data['flux']
ahlers_limit = np.power(10., ahlers_limit_log)
ahlers_interpolator = splrep(ahlers_energy_logev, ahlers_limit_log, k=4)
icecube_energy_logev = np.array(
[15.5, 16, 16.5, 17, 17.5, 18, 18.5, 19, 19.5, 20, 20.5])
icecube_energy = np.power(10., icecube_energy_logev)
#we can first have the IceCube thru-mu fluxes
#we can have the nomina, and upper and lower 1 sigma possibilities
def icecube_thrumu_function(E):
return 3.03 * ((E / 1.e14)**-2.19) * 1e-27
def icecube_thrumu_upper_function(E):
return 3.81 * ((E / 1.e14)**-2.09) * 1e-27
def icecube_thrumu_lower_function(E):
return 2.34 * ((E / 1.e14)**-2.29) * 1e-27
def icecube_combined_function(E):
return 6.7 * ((E / 1.e14)**-2.50) * 1e-27
def icecube_combined_upper_function(E):
return 7.8 * ((E / 1.e14)**-2.41) * 1e-27
def icecube_combined_lower_function(E):
return 5.5 * ((E / 1.e14)**-2.59) * 1e-27
#to compute to a curve on EF(E) we have to multiply by energy
icecube_thrumu_efe = icecube_thrumu_function(
icecube_energy) * icecube_energy
icecube_thrumu_upper_efe = icecube_thrumu_upper_function(
icecube_energy) * icecube_energy
icecube_thrumu_lower_efe = icecube_thrumu_lower_function(
icecube_energy) * icecube_energy
icecube_combined_efe = icecube_combined_function(
icecube_energy) * icecube_energy
icecube_combined_upper_efe = icecube_combined_upper_function(
icecube_energy) * icecube_energy
icecube_combined_lower_efe = icecube_combined_lower_function(
icecube_energy) * icecube_energy
'''
Load detector information
'''
ara_logeV, ara_100m_aeff = detector.get_aeff('ara_phased_100m')
arianna_logeV, arianna_aeff = detector.get_aeff(
'arianna_icrc2017_fromlimit')
ara_logeV, ara_100m_limit = detector.get_limit('ara_phased_100m_1year')
arianna_logeV, arianna_limit = detector.get_limit('arianna_2017icrc_1year')
#trim the ARIANNA sensitivity to match the ara array in energy binning
arianna_logeV = arianna_logeV[:-2]
arianna_aeff = arianna_aeff[:-2]
arianna_limit = arianna_limit[:-2]
#arianna_logeV = arianna_logeV[-1:]
#arianna_aeff = arianna_aeff[-1:]
#arianna_limit = arianna_limit[-1:]
app_ara = ara_100m_aeff * const.SecPerYear
app_arianna = arianna_aeff * const.SecPerYear * 0.58
exisiting_logeV, existing_best_exp = detector.get_exposure('best_existing')
target_logeV, target_exp = detector.get_exposure('target')
icecube_logeV, icecube_limit = detector.get_limit('icecube_2018')
anita_logeV, anita_limit = detector.get_limit('anita_2018')
fig = plt.figure(figsize=(2. * 11, 8.5))
ax_limit = fig.add_subplot(1, 2, 2)
ax_aeff = fig.add_subplot(1, 2, 1)
ax_limit.plot(ahlers_energy,
ahlers_limit,
'-',
linewidth=3.0,
color='gray',
label="Ahlers 2012")
ax_limit.fill_between(icecube_energy,
icecube_thrumu_lower_efe,
icecube_thrumu_upper_efe,
facecolor='orange',
alpha=0.3)
ax_limit.fill_between(icecube_energy,
icecube_combined_lower_efe,
icecube_combined_upper_efe,
facecolor='magenta',
alpha=0.3)
ax_limit.plot(icecube_energy,
icecube_thrumu_efe,
'-.',
linewidth=3.0,
color='orange',
label=r'IceCube Thru-Mu (E$^{-2.19}$)')
ax_limit.plot(icecube_energy,
icecube_combined_efe,
':',
linewidth=3.0,
color='magenta',
label='IceCube Combined (E$^{-2.50}$)')
ax_limit.plot(np.power(10., ara_logeV),
ara_100m_limit,
'-^',
linewidth=2.0,
color='blue',
label=r'1 ARA Phased 100m Station @ SP, 1 Year')
ax_limit.plot(np.power(10., arianna_logeV),
arianna_limit / 0.58,
'-v',
linewidth=2.0,
color='green',
label=r'1 ARIANNA Surface Station @ MB, 0.58 Year')
ax_limit.plot(np.power(10., icecube_logeV),
icecube_limit / 0.58,
'->',
linewidth=2.0,
color='black',
label=r'IceCube 2018')
ax_limit.plot(np.power(10., anita_logeV),
anita_limit / 0.58,
'-<',
linewidth=2.0,
color='brown',
label=r'ANITA 2018')
beautify_limit_withtheory(ax_limit, 3)
ax_aeff.plot(np.power(10., exisiting_logeV),
existing_best_exp,
'--',
linewidth=2.0,
color='grey',
label=r'Existing Best Exposure')
ax_aeff.plot(np.power(10., target_logeV),
target_exp,
'--',
linewidth=2.0,
color='red',
label=r'NGRA Target Exposure')
ax_aeff.plot(np.power(10., ara_logeV),
app_ara,
'-^',
linewidth=2.0,
color='blue',
label=r'1 ARA Phased 100m Station @ SP, 1 Year')
ax_aeff.plot(np.power(10., arianna_logeV),
app_arianna,
'-v',
linewidth=2.0,
color='green',
label=r'1 ARIANNA Surface Station @ MB, 0.58 Year')
beautify_aeff(ax_aeff)
fig.savefig("exposures_ara100phased_arianna.png",
edgecolor='none',
bbox_inches="tight") #save the figure