예제 #1
0
파일: pyNEST.py 프로젝트: attdobi/iPyNb
def genBands(
    nSim=1e5,
    maxS1=50,
    f_drift=700,
    g1=0.075,
    SPE_res=0.5,
    eff_extract=0.95,
    SE_size=50,
    SE_res=10,
    e_lifetime=1000,
    dt0=500,
    minSpikePE=0.25,
    min_NS1_coin=3,
    min_Ne_ext=5,
    Det="LZ",
    S2raw_min=250,
    xe_density=2.888,
):
    # Setup the LUX or LZ detector:
    pt = 0  # start with NR

    NEST = libNEST.NEST(pt, 10, f_drift, xe_density)  # 0 is NR, 1 is ER ... Energy, EField(V/cm), density
    if Det == "LZ":
        myDet = libNEST.Detector()
        myDet.LZSettings()
        NEST.SetDetectorParameters(myDet)
    elif Det == "LUX":
        myDet = libNEST.Detector()
        myDet.LUXSettings()
        NEST.SetDetectorParameters(myDet)
    else:
        print("Invalid detector, defulting to LZ")
        myDet = libNEST.Detector()
        myDet.LZSettings()
        NEST.SetDetectorParameters(myDet)

    # Calculate the NR band, and count below that for acceptance ########
    maxEr = 100  # keVnr, for flat spectrum... DD
    Flat_Er = maxEr * st.uniform.rvs(size=nSim)
    # 0-100 keVnr

    ## Generate Signal in the detector ##
    Nph = []
    Ne = []
    S1 = []
    S2 = []
    S1c = []
    S2c = []

    for en in Flat_Er:
        NEST.SetEnergy(en)
        NEST.DetectorResponse()
        Nph.append(NEST.GetNumPhotons())
        Ne.append(NEST.GetNumElectrons())
        S1.append(NEST.GetS1())
        S2.append(NEST.GetS2())
        S1c.append(NEST.GetS1c())
        S2c.append(NEST.GetS2c())

    Nph = np.array(Nph)
    Ne = np.array(Ne)
    S1 = np.array(S1)
    S2 = np.array(S2)
    S1c = np.array(S1c)
    S2c = np.array(S2c)

    S1_bins = linspace(1, maxS1, maxS1)
    S1_bin_cen_n = empty_like(S1_bins)
    mean_S2oS1_n = empty_like(S1_bins)
    std_S2oS1_n = empty_like(S1_bins)
    # Find the NR S2/S1 band at each S1
    det_cuts = (S1c > 0) & (S2c >= S2raw_min)
    for index, S1s in enumerate(S1_bins):
        cut = det_cuts & inrange(S1c, [S1s - 0.5, S1s + 0.5])
        S1_bin_cen_n[index] = mean(S1c[cut])
        mean_S2oS1_n[index] = mean(log10(S2c[cut] / S1c[cut]))
        std_S2oS1_n[index] = std(log10(S2c[cut] / S1c[cut]))

    # Calc Nr Efficiency vs E
    E_bins = linspace(1, maxEr, maxEr)
    E_bin_cen_n = empty_like(E_bins)
    Eff_n = empty_like(E_bins)
    det_cuts = (S1c > 0) & (S2c >= S2raw_min)
    for index, Es in enumerate(E_bins):
        cut = det_cuts & inrange(Flat_Er, [Es - 0.5, Es + 0.5])
        E_bin_cen_n[index] = mean(Flat_Er[cut])
        Eff_n[index] = sum(cut) / sum(inrange(Flat_Er, [Es - 0.5, Es + 0.5]))  # cut/total_in_bin

    # Calculate the ER band ####################################################
    maxEe = 100  # keVee, for flat ER spectrum
    Flat_Ee = maxEe * st.uniform.rvs(size=nSim)
    # 0-100 keVee
    ## Generate Signal in the detector ##
    Nph = []
    Ne = []
    S1 = []
    S2 = []
    S1c = []
    S2c = []

    NEST.SetParticleType(1)  # set to ER events
    for en in Flat_Er:
        NEST.SetEnergy(en)
        NEST.DetectorResponse()
        Nph.append(NEST.GetNumPhotons())
        Ne.append(NEST.GetNumElectrons())
        S1.append(NEST.GetS1())
        S2.append(NEST.GetS2())
        S1c.append(NEST.GetS1c())
        S2c.append(NEST.GetS2c())

    Nph = np.array(Nph)
    Ne = np.array(Ne)
    S1 = np.array(S1)
    S2 = np.array(S2)
    S1c = np.array(S1c)
    S2c = np.array(S2c)

    S1_bins = linspace(1, maxS1, maxS1)
    S1_bins = linspace(1, maxS1, maxS1)
    S1_bin_cen_e = empty_like(S1_bins)
    mean_S2oS1_e = empty_like(S1_bins)
    stdev_S2oS1_e = empty_like(S1_bins)
    # Find the ER S2/S1 band at each S1
    det_cuts = (S1c > 0) & (S2c >= S2raw_min)
    for index, S1s in enumerate(S1_bins):
        cut = det_cuts & inrange(S1c, [S1s - 0.5, S1s + 0.5])
        S1_bin_cen_e[index] = mean(S1c[cut])
        mean_S2oS1_e[index] = mean(log10(S2c[cut] / S1c[cut]))
        stdev_S2oS1_e[index] = std(log10(S2c[cut] / S1c[cut]))

    # Calc Nr Efficiency vs E
    E_bins = linspace(1, maxEe, maxEe)
    E_bin_cen_e = empty_like(E_bins)
    Eff_e = empty_like(E_bins)
    det_cuts = (S1c > 0) & (S2c >= S2raw_min)
    for index, Es in enumerate(E_bins):
        cut = det_cuts & inrange(Flat_Ee, [Es - 0.5, Es + 0.5])
        E_bin_cen_e[index] = mean(Flat_Ee[cut])
        Eff_e[index] = sum(cut) / sum(inrange(Flat_Ee, [Es - 0.5, Es + 0.5]))  # cut/total_in_bin

    return (
        S1_bin_cen_n,
        mean_S2oS1_n,
        std_S2oS1_n,
        S1_bin_cen_e,
        mean_S2oS1_e,
        stdev_S2oS1_e,
        E_bin_cen_e,
        Eff_e,
        E_bin_cen_n,
        Eff_n,
    )
예제 #2
0
파일: pyNEST.py 프로젝트: attdobi/iPyNb
def genBands(NEST=NEST_setup(),nSim=2e5, maxS1=50, S2raw_min=450, Ermin=0, mWmp=50):
    '''input: NEST obj, nSim, maxS1, S2raw_min, Ermin, mWmp.    
   output: S1_bin_cen_n, mean_S2oS1_n, std_S2oS1_n, S1_bin_cen_e, mean_S2oS1_e, std_S2oS1_e, E_bin_cen_e, Eff_e, E_S1_e(average E for a given S1), E_bin_cen_n, Eff_n, E_S1_n(average E for a given S1), num_leak_e(vs S1 bin), num_total_e(vs S1 bin), leak_gauss_e(vs S1 bin), sNR(spline NR band) ''' 
    #start with NR
    NEST.SetParticleType(0)
    
    maxEr=100 #keVnr, for flat spectrum
    #Default, use a 50 GeV WIMP for discrimnation
    if mWmp==-1:
        Er = maxEr*st.uniform.rvs(size=nSim); #0-100 keVnr
    else:
        Er = rates.WIMP.genRandEnergies(nSim, mW=mWmp)
    
    ## Generate Signal in the detector ##
    Nph=[]
    Ne=[]
    S1=[]
    S2=[]
    S1c=[]
    S2c=[]
    
    for en in Er:
        NEST.SetEnergy(en)
        NEST.DetectorResponse()
        Nph.append(NEST.GetNumPhotons())
        Ne.append(NEST.GetNumElectrons())
        S1.append(NEST.GetS1())
        S2.append(NEST.GetS2())
        S1c.append(NEST.GetS1c())
        S2c.append(NEST.GetS2c())
        
    Nph=np.array(Nph)
    Ne=np.array(Ne)
    S1=np.array(S1)
    S2=np.array(S2)
    S1c=np.array(S1c)
    S2c=np.array(S2c)
    
    S1_bins=linspace(1,maxS1,maxS1)
    S1_bin_cen_n=empty_like(S1_bins)
    mean_S2oS1_n=empty_like(S1_bins)
    std_S2oS1_n=empty_like(S1_bins)
    E_S1_n=empty_like(S1_bins)
    coeff_n=[]
    var_matrix_n=[]
    #Find the NR S2/S1 band at each S1
    det_cuts= (S1c>0) & (S2c>0) & (S2>=S2raw_min) & (Er>=Ermin)
    for index, S1s in enumerate(S1_bins):
        cut=det_cuts & inrange(S1c,[S1s-1/2,S1s+1/2])
        S1_bin_cen_n[index]=mean(S1c[cut])
        mean_S2oS1_n[index]=mean(log10(S2c[cut]/S1c[cut]))
        std_S2oS1_n[index]=std(log10(S2c[cut]/S1c[cut]))
        E_S1_n[index]=mean(Er[cut]) #average E for a given S1  
        #fit Gauss to distibution
        # p0 is the initial guess for the fitting coefficients (A, mu and sigma above)
        #hist, bin_edges = np.histogram(log10(S2c[cut]/S1c[cut]), 20, density=True)
        #bin_centres = (bin_edges[:-1] + bin_edges[1:])/2
        #p0 = [max(hist), mean_S2oS1_n[index], std_S2oS1_n[index]]
        #coeff, var_matrix = curve_fit(gauss, bin_centres, hist, p0=p0)
        #coeff_n.append(coeff)
        #var_matrix_n.append(var_matrix)
    #convert to numpy array. 3xN matrix
    #coeff_n=np.array(coeff_n) #indexed as: amp, mean, sigma
    #var_matrix_n=np.array(var_matrix_n)
   
    #get NR mean, with a smooth spline(need this for discrimination calculation)
    if nSim>=5e5:
        sNR = ip.UnivariateSpline(S1_bin_cen_n, mean_S2oS1_n,s=.0001) #essentially linear interp
    else:
        sNR = ip.UnivariateSpline(S1_bin_cen_n, mean_S2oS1_n,s=.01) #essentially linear interp
    #Use gaussian fit
    #sNR = ip.UnivariateSpline(S1_bin_cen_n, coeff_n[:,1],s=0.005)
    
    #Calc Nr Efficiency vs E
    E_bins=linspace(0,maxEr,maxEr)
    E_bin_cen_n=empty_like(E_bins)
    Eff_n=empty_like(E_bins)
    det_cuts= (S1c>0) & (S2>=S2raw_min)
    for index, Es in enumerate(E_bins):
        cut=det_cuts & inrange(Er,[Es-0.5,Es+0.5])
        E_bin_cen_n[index]=mean(Er[cut])
        Eff_n[index]=sum(cut)/sum(inrange(Er,[Es-0.5,Es+0.5])) #cut/total_in_bin
   
    
    #Calculate the ER band and discrimination ####################################################
    maxEe=30 #keVee, for flat ER spectrum
    Flat_Ee = maxEe*st.uniform.rvs(size=nSim); #0-50 keVee
    ## Generate Signal in the detector ##
    Nph=[]
    Ne=[]
    S1=[]
    S2=[]
    S1c=[]
    S2c=[]
    
    NEST.SetParticleType(1) #set to ER events
    for en in Flat_Ee:
        NEST.SetEnergy(en)
        NEST.DetectorResponse()
        Nph.append(NEST.GetNumPhotons())
        Ne.append(NEST.GetNumElectrons())
        S1.append(NEST.GetS1())
        S2.append(NEST.GetS2())
        S1c.append(NEST.GetS1c())
        S2c.append(NEST.GetS2c())
        
    Nph=np.array(Nph)
    Ne=np.array(Ne)
    S1=np.array(S1)
    S2=np.array(S2)
    S1c=np.array(S1c)
    S2c=np.array(S2c)
    
    S1_bins=linspace(1,maxS1,maxS1)
    S1_bin_cen_e=empty_like(S1_bins)
    mean_S2oS1_e=empty_like(S1_bins)
    std_S2oS1_e=empty_like(S1_bins)
    num_leak_e=empty_like(S1_bins)
    num_total_e=empty_like(S1_bins)
    leak_gauss_e=empty_like(S1_bins)
    E_S1_e=empty_like(S1_bins)
    #Find the ER S2/S1 band at each S1
    det_cuts= (S1c>0) & (S2c>0) & (S2>=S2raw_min)
    for index, S1s in enumerate(S1_bins):
        cut=det_cuts & inrange(S1c,[S1s-1/2,S1s+1/2])
        S1_bin_cen_e[index]=mean(S1c[cut])
        mean_S2oS1_e[index]=mean(log10(S2c[cut]/S1c[cut]))
        std_S2oS1_e[index]=std(log10(S2c[cut]/S1c[cut]))
        #calculate discrimination
        num_leak_e[index]=sum(log10(S2c[cut]/S1c[cut])<sNR(S1c[cut])) #num below NR mean
        num_total_e[index]=sum(cut)
        #Gaussian overlap
        nsig=(mean_S2oS1_e[index]-sNR(S1_bin_cen_e[index]))/std_S2oS1_e[index] #(meanER-meanNR)/sigER
        leak_gauss_e[index]=sp.special.erfc(nsig/sqrt(2))/2
        E_S1_e[index]=mean(Flat_Ee[cut]) #average E for a given S1  
    
    #Calc Er Efficiency vs E
    E_bins=linspace(0,maxEe,maxEe)
    E_bin_cen_e=empty_like(E_bins)
    Eff_e=empty_like(E_bins)
    det_cuts= (S1c>0) & (S2>=S2raw_min)
    for index, Es in enumerate(E_bins):
        cut=det_cuts & inrange(Flat_Ee,[Es-0.5,Es+0.5])
        E_bin_cen_e[index]=mean(Flat_Ee[cut])
        Eff_e[index]=sum(cut)/sum(inrange(Flat_Ee,[Es-0.5,Es+0.5])) #cut/total_in_bin
        
    return S1_bin_cen_n, mean_S2oS1_n, std_S2oS1_n, S1_bin_cen_e, mean_S2oS1_e, std_S2oS1_e, E_bin_cen_e, Eff_e, E_S1_e, E_bin_cen_n, Eff_n, E_S1_n, num_leak_e, num_total_e, leak_gauss_e, sNR