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NewConfigs_v3-DarkPhoton.py
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import numpy as np
from src.foresee import Foresee, Utility, Model
import os
from matplotlib import pyplot as plt
run_plotpions=False
run_setupmodel=True
run_LLPspectra=False
run_rateexample=False
run_setupscans=True
run_plotseparations=True
run_runscans=False
run_plotreach=False
scan_search=None
scan_search=["F2","F-default-sep"]
scan_search=["S1","F-default-sep"]
scan_search=["S2","F-default-sep"]
#scan_search=["S3","F-default-sep"]
scan_search=["F-default-sep-station0"]
#scan_search=["TEST_1m0T","TEST_1m1T","TEST_1p5m0p5T"]
scan_search=["TEST_1m0T","TEST_1p5m0p5T"]
#############
# Initialization
print("INFO : Initialise FORESEE")
foresee = Foresee()
#############
# Plot pions
if run_plotpions:
print("INFO : Plot pions")
plot=foresee.get_spectrumplot(pid="111", generator="EPOSLHC", energy="14")
#plot.show()
plot.savefig("NewConfigs_v3-DarkPhoton-EPOSLHC_Pion-Angle_vs_Momentum.pdf")
#############
# Specifying the Model: Dark Photons
if run_setupmodel:
print("INFO : Setting up Dark Photon model")
energy = "14"
modelname="DarkPhoton"
model = Model(modelname)
print("INFO : - Adding production modes")
## pion decay production
model.add_production_2bodydecay(
pid0 = "111",
pid1 = "22",
br = "2.*0.99 * coupling**2 * pow(1.-pow(mass/self.masses('111'),2),3)",
generator = "EPOSLHC",
energy = energy,
nsample = 10
)
## eta decay production
model.add_production_2bodydecay(
pid0 = "221",
pid1 = "22",
br = "2.*0.39 * coupling**2 * pow(1.-pow(mass/self.masses('221'),2),3)",
generator = "EPOSLHC",
energy = energy,
nsample = 10,
)
## Resonant mixing with SM V bosons production
model.add_production_mixing(
pid = "113",
mixing = "coupling * 0.3/5. * 0.77545**2/abs(mass**2-0.77545**2+0.77545*0.147*1j)",
generator = "EPOSLHC",
energy = energy,
)
## Bremsstrahlung production
model.add_production_direct(
label = "Brem",
energy = energy,
condition = "p.pt<1",
coupling_ref=1,
)
## Drell-Yan production
model.add_production_direct(
label = "DY",
energy = energy,
coupling_ref=1,
massrange=[1.5, 10.]
)
print("INFO : - Setting lifetimes")
## Lifetime
model.set_ctau_1d(
filename="files/models/"+modelname+"/ctau.txt",
coupling_ref=1
)
print("INFO : - Setting branching fractions")
## Branching ratio
model.set_br_1d(
modes=["e_e", "mu_mu"],
filenames=["files/models/"+modelname+"/br/e_e.txt","files/models/"+modelname+"/br/mu_mu.txt"]
)
## Set model just created
foresee.set_model(model=model)
masses = [
0.01 , 0.0126, 0.0158, 0.02 , 0.0251, 0.0316, 0.0398,
0.0501, 0.0631, 0.0794, 0.1 , 0.1122, 0.1259, 0.1413,
0.1585, 0.1778, 0.1995, 0.2239, 0.2512, 0.2818, 0.3162,
0.3548, 0.3981, 0.4467, 0.5012, 0.5623, 0.6026, 0.631 ,
0.6457, 0.6607, 0.6761, 0.6918, 0.7079, 0.7244, 0.7413,
0.7586, 0.7762, 0.7943, 0.8128, 0.8318, 0.8511, 0.871 ,
0.8913, 0.912 , 0.9333, 0.955 , 0.9772, 1. , 1.122 ,
1.2589, 1.4125, 1.5849, 1.7783, 1.9953, 2.2387, 2.5119,
2.8184, 3.1623, 3.9811, 5.0119, 6.3096, 7.9433, 10.
]
##########
# Generate LLP Spectra
if run_LLPspectra:
print("INFO : Generating LLP Spectra")
print("INFO : - Generating for m_A=100 MeV and epsilon=10^-5")
## Look at benchmark scenario with m_A=100 MeV and epsilon=10^-5
plt = foresee.get_llp_spectrum(0.1, coupling=10**(-5), do_plot=True)
#plt.show()
plt.savefig("NewConfigs_v3-DarkPhoton-EPOSLHC_LLP_m100_e10m5-Angle_vs_Momentum.pdf")
print("INFO : - Generating for all masses")
for mass in masses:
foresee.get_llp_spectrum(mass=mass,coupling=1)
#################
# Count Event Rate in Detector
if run_rateexample:
print("INFO : Count Event Rate in Detector")
## To count the #decays in detector volume need detector geometry
## These are FASER2 defaults
distance, selection, length, luminosity, channels, geo = 480, "np.sqrt(x.x**2 + x.y**2)< 1", 5, 3000, None, [(0.5,1.5)]
foresee.set_detector(distance=distance, selection=selection, length=length, luminosity=luminosity, channels=channels,geo=geo)
print("INFO : - For dark photon (m_A'=100 MeV) check #events in decay volume")
## For one dark photon (m_A'=100 MeV) look at how many particles decay inside the decay volume.
mass = 0.1
print(np.logspace(-8,-3,6))
output = foresee.get_events(mass=0.1, energy=energy, couplings=np.logspace(-8,-3,6), )
coups, ctaus, nsigs, energies, weights, seps, _ = output
for coup,ctau,nsig in zip(coups, ctaus, nsigs):
print ("epsilon =", '{:5.3e}'.format(coup), ": nsignal =", '{:5.3e}'.format(nsig))
print("INFO : - Plot energy distribution for different couplings")
## Look at energy distribution of the dark photons which decay inside the detector
fig = plt.figure(figsize=(7,5))
ax = plt.subplot(1,1,1)
for coup,en,weight in zip(coups,energies,weights):
print("en:",len(en),en[0])
print("coup:",coup)
print("weight:",len(weight),weight[0])
if sum(weight)<10**-5 : continue
ax.hist(en, weights=weight, bins=np.logspace(2,4, 20), histtype='step', label=r"$\epsilon=$"+str(coup))
ax.set_xscale("log")
ax.set_yscale("log")
ax.set_ylim(10**-7,10**5)
ax.set_xlabel("E(A') [GeV]")
ax.set_ylabel("Number of Events per bin")
ax.legend(frameon=False, labelspacing=0)
plt.tight_layout()
#plt.show()
plt.savefig("NewConfigs_v3-DarkPhoton-EPOSLHC_LLP_m100-Energy.pdf")
#fig = plt.figure(figsize=(7,5))
#ax = plt.subplot(1,1,1)
#for coup,sep,weight in zip(coups,seps,weights):
# tmp_s=[]
# tmp_w=[]
# #sep_cut=300.e-6
# sep_cut=0 # no cut
# for n,s in enumerate(sep):
# tmp_s.extend([e for e in s if e>sep_cut])
# tmp_w.extend([weight[n] for e in s if e>sep_cut])
# #sep=tmp_s
# #weight=tmp_w
# print(len(tmp_s),len(tmp_w))
# print("sep:",len(sep),sep[0])
# print("coup:",coup)
# print("weight:",len(weight),weight[0])
#
# if sum(weight)<10**-5 : continue
# ax.hist(tmp_s, weights=tmp_w, bins=np.logspace(-5,-1, 40), histtype='step', label=r"$\epsilon=$"+str(coup))
# ax.set_xscale("log")
# ax.set_yscale("linear")
# ax.set_ylim(0,7000)
# #ax.set_yscale("log")
# #ax.set_ylim(10**-7,10**5)
# ax.set_xlabel("Separation [m]")
# ax.set_ylabel("Number of Events per bin")
# ax.legend(frameon=False, labelspacing=0)
#plt.tight_layout()
##plt.show()
#plt.savefig("NewConfigs_v3-DarkPhoton-EPOSLHC_LLP_m100-Separation.pdf")
##########
# Parameter Scan
setup_dict={}
if run_setupscans:
print("INFO : Setup parameter scan for different detector configs")
## Get the LLP sensitivity reach for different detector configuraions. Just need to loop over different masses and use the previously introduced funtion get_events
setup_dict={
## "F-default":{
## "name":"F - No cut",
## "color":"maroon",
## "selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
## "length":1.5,
## "distance":480,
## "lumi":140,
## "channels": None,
## "geo": None,
## "separation":0
## },
## "F-default-test":{
## "name":"F - test",
## "color":"red",
## "selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
## "length":1.5,
## "distance":480,
## "lumi":140,
## "channels": None,
## "geo": [(0.55,1.5),(0,0.3),(0.55,1),(0,0.3),(0.55,1)],
## "separation":0
## },
# "F-default-sep":{
# "name":"F - 300 um",
# "color":"firebrick",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
# "length":1.5,
# "distance":480,
# "lumi":140,
# "channels": None,
# "geo":[(0.55,1.5),(0,0.3),(0.55,1),(0,0.3),(0.55,1)],
# "separation":300e-6,
# #"refsetup":"F-default-test"
## },
"TEST_1m0T":{
"name":"1m 0T",
"color":"firebrick",
"selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
"length":1,
"distance":480,
"lumi":140,
"channels": None,
"geo":[(0.,1)],
"separation":300e-6,
#"refsetup":"F-default-test"
},
"TEST_1m1T":{
"name":"1m 1T",
"color":"red",
"selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
"length":1,
"distance":480,
"lumi":140,
"channels": None,
"geo":[(1.,1)],
"separation":300e-6,
#"refsetup":"F-default-test"
},
"TEST_1p5m0p5T":{
"name":"1.5m 0.5T",
"color":"red",
"selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
"length":1.5,
"distance":480,
"lumi":140,
"channels": None,
"geo":[(0.5,1.5)],
"separation":300e-6,
#"refsetup":"F-default-test"
},
"F-default-sep-station0":{
"name":"F - Station1",
"color":"firebrick",
"selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
"length":1.5,
"distance":480,
"lumi":140,
"channels": None,
"geo":[(0.5,1.5)],
"separation":300e-6,
#"refsetup":"F-default-test"
},
## "F-500um":{
## "name":"F - 500 um",
## "color":"coral",
## "selection":"np.sqrt(x.x**2 + x.y**2)< 0.1",
## "length":1.5,
## "distance":480,
## "lumi":140,
## "channels": None,
## "geo":[(0.55,1.5),(0,0.3),(0.55,1),(0,0.3),(0.55,1)],
## "separation":500e-6,
## "refsetup":"F-default-test"
## },
# "F2_ee_default":{
# "name":"F2 no cut",
# "color":"maroon",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":5,
# "distance":480,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": None,
# "separation":0
# },
#
# "F2_0p55T5m-0T20m_300um":{
# "name":"F2 0.55T 5m",
# "color":"firebrick",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":5,
# "distance":480,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(0.55,5),(0,20)],
# "separation":300e-6
# },
#
# "F2_0T25m-300um":{
# "name":"F2 0T 25m",
# "color":"coral",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":5,
# "distance":480,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(0.,25)],
# "separation":300e-6
# },
#
# "S1_L1p5-D2_0p5T1p5m":{
# "name":"S1 0.5T 1.5m",
# "color":"rebeccapurple",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":1.5,
# "distance":497,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(0.5,1.5),(0,4.5)],
# "separation":300e-6
# },
#
# "S1_L1p5-D2_1T1m":{
# "name":"S1 1T 1m",
# "color":"darkorchid",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":1.5,
# "distance":497,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(1,1),(0,5)],
# "separation":300e-6
# },
#
# "S2_L2-D2_0p5T2m":{
# "name":"S2 0.5T 2m",
# "color":"darkorange",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":2,
# "distance":500,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(0.5,2),(0,5)],
# "separation":300e-6
# },
#
# "S2_L1p5-D2_1T1m":{
# "name":"S2 1T 2m",
# "color":"orange",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":2,
# "distance":500,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(1,2),(0,5)],
# "separation":300e-6
# },
#
# "S3_L1p5-D2_0p5T10m":{
# "name":"S3 0.5T 10m",
# "color":"darkgreen",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":10,
# "distance":615,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(0.5,10),(0,15)],
# "separation":300e-6
# },
#
# "S3_L1p5-D2_1T10m":{
# "name":"S3 1T 10m",
# "color":"forestgreen",
# "selection":"np.sqrt(x.x**2 + x.y**2)< 1",
# "length":10,
# "distance":615,
# "channels": ["e_e"],
# "lumi":3000,
# "geo": [(1,10),(0,15)],
# "separation":300e-6
# },
}
#################
# Plot separations
if run_plotseparations:
print("INFO : Plot particle separations")
## Look at separation distributions of the dark photons which decay inside the detector
fig = plt.figure(figsize=(7,5))
ax = plt.subplot(1,1,1)
maxy=0
for setup in setup_dict:
if scan_search and not sum([1 if s in setup else 0 for s in scan_search]):
continue
if not setup_dict[setup]["geo"]:
continue
print(f"INFO : - Running config: {setup}")
setup, selection, length, channels, distance, luminosity, geo = setup, setup_dict[setup]["selection"], setup_dict[setup]["length"], setup_dict[setup]["channels"], setup_dict[setup]["distance"], setup_dict[setup]["lumi"], setup_dict[setup]["geo"]
foresee.set_detector(selection=selection, channels=channels, length=length, distance=distance, luminosity=luminosity, geo=geo)
#### Get reach
list_nevents = []
list_seps = []
#for mass in [0.01,0.1,1.0]:
for mass in [0.1]:
print(f"INFO : - Mass {mass}")
coups=[1e-6]
linestyle="solid"
if mass == 0.1:
coups=[1e-5]
linestyle="dashed"
if mass == 0.01:
coups=[1e-4]
linestyle="dotted"
couplings, _, nevents, _, weights, seps, _ = foresee.get_events(mass=mass, energy=energy, couplings=coups)
for sep,weight in zip(seps,weights):
tmp_s=[]
tmp_w=[]
#sep_cut=300.e-6
sep_cut=0 # no cut
for n,s in enumerate(sep):
tmp_s.extend([e for e in s if e>sep_cut])
tmp_w.extend([weight[n] for e in s if e>sep_cut])
y, x, _ = ax.hist(tmp_s, weights=tmp_w/sum(tmp_w), bins=np.logspace(-5,-1, 40), histtype='step', label=f'{setup_dict[setup]["name"]}, m={mass} GeV', linestyle=linestyle,color=setup_dict[setup]["color"])#, density=True)
ax.set_xscale("log")
ax.set_yscale("linear")
if max(y)*1.2 > maxy:
maxy=max(y)*1.2
ax.set_ylim(0,maxy)
#ax.set_ylim(0,1)
#ax.set_yscale("log")
#ax.set_ylim(10**-7,10**5)
ax.set_xlabel("Separation [m]")
ax.set_ylabel("Number of Events per bin")
ax.legend(frameon=False, labelspacing=0)
plt.tight_layout()
#plt.show()
plt.savefig("NewConfigs_v3-DarkPhoton-EPOSLHC_LLP_m100-Separation%s.pdf"%("_"+scan_search[0] if scan_search else ""))
if run_runscans:
print("INFO : - Run each config")
done_scans=[]
for setup in setup_dict:
print(f"INFO : - Running config: {setup}")
#### Check if run already processed
if "refsetup" in setup_dict[setup]:
refsetup=setup_dict[setup]["refsetup"]
print(f"Using reference setup: {refsetup}. Skipping production")
continue
outfile="files/models/"+modelname+"/results/"+energy+"TeV_"+setup+".npy"
#### Specify setup
setup, selection, length, channels, distance, luminosity, geo = setup, setup_dict[setup]["selection"], setup_dict[setup]["length"], setup_dict[setup]["channels"], setup_dict[setup]["distance"], setup_dict[setup]["lumi"], setup_dict[setup]["geo"]
foresee.set_detector(selection=selection, channels=channels, length=length, distance=distance, luminosity=luminosity, geo=geo)
#### Get reach
list_nevents = []
list_seps = []
for mass in masses:
print(f"INFO : - Mass {mass}")
couplings, _, nevents, _, _, seps, _ = foresee.get_events(mass=mass, energy=energy)
#if setup_dict[setup]["separation"]:
# for n,nevent in enumerate(nevents):
list_nevents.append(nevents)
list_seps.append(seps)
#### Save results
np.save(outfile,[masses,couplings,list_nevents,list_seps])
##########
# Plot the Results - Configurations
if run_plotreach:
print("INFO : - Plotting reach")
#Now let's plot the results. We first specify all detector setups for which we want to show result (filename in model/results directory, label, color, linestyle, opacity alpha for filled contours, required number of events).
setups=[]
if not scan_search:
setups = [ ["14TeV_%s.npy"%(setup_dict[setup]["refsetup"] if "refsetup" in setup_dict[setup] else setup), setup_dict[setup]["name"],setup_dict[setup]["color"], "solid", 0., 3, setup_dict[setup]["separation"]] for setup in setup_dict ]
else:
setups = [ ["14TeV_%s.npy"%(setup_dict[setup]["refsetup"] if "refsetup" in setup_dict[setup] else setup), setup_dict[setup]["name"],setup_dict[setup]["color"], "solid", 0., 3, setup_dict[setup]["separation"]] for setup in setup_dict if scan_search in setup]
print(f"INFO : - Found {len(setups)} setups")
#Then we specify all the existing bounds (filename in model/bounds directory, label, label position x, label position y, label rotation)
bounds = [
["bounds_LHCb1.txt", "LHCb", 0.220, 2.8*10**-4, 0 ],
["bounds_LHCb2.txt", None , 0 , 0 , 0 ],
["bounds_LHCb3.txt", None , 0 , 0 , 0 ],
["bounds_E137.txt", "E137", 0.015, 1.2*10**-7, 0 ],
["bounds_CHARM.txt", "CHARM", 0.120, 1.4*10**-7, -5 ],
["bounds_NuCal.txt", "NuCal", 0.042, 1.3*10**-5, -28],
["bounds_E141.txt", "E141", 0.011, 5.8*10**-5, 33 ],
["bounds_NA64.txt", "NA64", 0.014, 3.6*10**-4, -35],
["bounds_BaBar.txt", "BaBar", 0.360, 1.4*10**-3, 0 ],
["bounds_NA48.txt", "NA48", 0.040, 1.4*10**-3, 0 ],
]
#We then specify other projected sensitivitities (filename in model/bounds directory, color, label, label position x, label position y, label rotation)
projections = [
["limits_SeaQuest.txt", "lime", "SeaQuest", 1.350, 2.1*10**-7, 0 ],
["limits_NA62.txt", "limegreen", "NA62" , 0.999, 1.1*10**-7, 0 ],
["limits_SHiP.txt", "forestgreen", "SHiP" , 1.750, 8.2*10**-7, 0 ],
["limits_HPS.txt", "deepskyblue", "HPS" , 0.050, 1.5*10**-4, 0 ],
["limits_HPS-1.txt", "deepskyblue", None , 0 , 0 , 0 ],
["limits_Belle2.txt", "blue", "Belle2" , 0.570, 1.3*10**-4, 0 ],
["limits_LHCb.txt", "dodgerblue", "LHCb" , 0.135, 2.8*10**-4, 0 ],
["limits_LHCb-mumu1.txt", "dodgerblue", None , 0 , 0 , 0 ],
["limits_LHCb-mumu2.txt", "dodgerblue", None , 0 , 0 , 0 ],
]
# Finally, we can plot everything using foresee.plot_reach(). It returns a matplotlib instance, to which we can add further lines and which we can show or save. Below, we add the dark matter relict target line for a specific benchmark.
plot = foresee.plot_reach(
setups=setups,
bounds=bounds,
projections=projections,
title="Dark Photons",
xlims = [0.01,3],
ylims=[10**-7,0.002],
xlabel=r"Dark Photon Mass $m_{A'}$ [GeV]",
ylabel=r"Kinetic Mixing $\epsilon$",
legendloc=(1,0.68),
figsize=(8,6),
)
data = foresee.readfile("files/models/"+modelname+"/lines/scalar_DM_Oh2_intermediate_eps_vs_mAprime.txt")
plot.plot(data.T[0], data.T[1], color="k", lw=2)
plot.text(0.010, 3.40*10**-5, "relic target", fontsize=13,color="k",rotation=25)
plot.text(0.011, 2.15*10**-5, r"$m_\chi\!=\!0.6 m_{A'}$",fontsize=13,color="k",rotation=25)
plot.text(0.013, 1.20*10**-5, r"$\alpha_D\!=\!0.6$",fontsize=13,color="k",rotation=25)
plot.subplots_adjust(left=0.12, right=0.97, bottom=0.10, top=0.95)
plot.savefig("NewConfigs_v3-DarkPhoton-EPOSLHC-Reach%s.pdf"%("_"+scan_search[0] if scan_search else ""))
#plot.show()