From 14877ed4d44771f7af9b5b3c369d63483610070f Mon Sep 17 00:00:00 2001 From: Tommy Date: Wed, 15 Nov 2023 18:44:16 +0100 Subject: [PATCH 01/11] test fork --- test.py | 1 + 1 file changed, 1 insertion(+) create mode 100644 test.py diff --git a/test.py b/test.py new file mode 100644 index 0000000..ed6ae93 --- /dev/null +++ b/test.py @@ -0,0 +1 @@ +import numpy \ No newline at end of file From 706c2b46b0f5a99b84c84d01bc4c6a54b6a0725c Mon Sep 17 00:00:00 2001 From: Tommy Date: Wed, 15 Nov 2023 18:45:34 +0100 Subject: [PATCH 02/11] test over --- test.py | 1 - 1 file changed, 1 deletion(-) delete mode 100644 test.py diff --git a/test.py b/test.py deleted file mode 100644 index ed6ae93..0000000 --- a/test.py +++ /dev/null @@ -1 +0,0 @@ -import numpy \ No newline at end of file From 3e671ab31b81917a200e92ddfe8685561888645c Mon Sep 17 00:00:00 2001 From: Tommy Date: Mon, 20 Nov 2023 19:20:58 +0100 Subject: [PATCH 03/11] Add BLR form factors + alphaS corrections --- effort2/formfactors/BLR.py | 563 ++++++++++++++++++++++++++++++ effort2/formfactors/BLR_helper.py | 159 +++++++++ effort2/rates/BtoDStSt.py | 3 + 3 files changed, 725 insertions(+) create mode 100644 effort2/formfactors/BLR.py create mode 100644 effort2/formfactors/BLR_helper.py create mode 100644 effort2/rates/BtoDStSt.py diff --git a/effort2/formfactors/BLR.py b/effort2/formfactors/BLR.py new file mode 100644 index 0000000..1ec92de --- /dev/null +++ b/effort2/formfactors/BLR.py @@ -0,0 +1,563 @@ +from ast import Lambda +import numpy as np +from effort2.formfactors.DStStAlphaSCorrections import DStStAlphaSCorrections + + +class BToDStarStarBroad(DStStAlphaSCorrections): + """ + TO DO: add description + """ + + def __init__( + self, + m_c: float, + m_b: float, + params: tuple, + # m_B: float, + # m_V: float, + # m_L: float=0, + alphaS: float=0, + LambdaBar: float=0.40, + LambdaBarPrime: float=0.80, + LambdaBarStar: float=0.76, + chi1: float=0, + chi2: float=0, + ): + super().__init__(m_c, m_b) + self.m_c = m_c + self.m_b = m_b + self.alphaS = alphaS / np.pi + self.LambdaBar = LambdaBar + self.LambdaBarPrime = LambdaBarPrime + self.LambdaBarStar = LambdaBarStar + self.chi1 = chi1 + self.chi2 = chi2 + + self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) + # TO DO: check parameter values from Hammer + # Scale mu = sqrt(mc*mb) + # Can ask Markus/Florian for details about BLR + # Certain values depend on renorm scheme + # The chromomagnetic terms chi1/2 are neglected in Hammer and certain Approximations. + # I'm not sure whether they should be set to 0 with the most up-to-date fit of the 3 zeta parameters. + + def set_model_parameters( + self, + params: tuple, + ): + """ + Sets the input model parameters zeta(1), zeta' and zeta1 (cf. Table V in arxiv:1711.03110). + Expects the parameters in that order. + + Args: + params ([tuple]): input parameters for the broad D** form factor. + """ + Z1, zetap, zeta1 = params + return Z1, zetap, zeta1 + + def Gb( + self, + w: float, + ): + """ + Used in all B -> D** broad form factors + """ + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + zeta1 = self.zeta1 + return ((1 + 2 * w) * LambdaBarStar - (2 + w) * LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 + + # Form factors for D0* + def gP( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + chi2 = self.chi2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + zeta1 = self.zeta1 + CP = self.CP(w) + Gb = self.Gb(w) + return (w - 1) * (1 + alphaS * CP) + epsilonC * (3 * (w * LambdaBarStar - LambdaBar) - 2 * (w**2 - 1) * zeta1 + (w -1) * (6 * chi1 - 2 * (w + 1) * chi2) - epsilonB * (w + 1) * Gb) + + def gplus( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + zeta1 = self.zeta1 + CA2 = self.CA2(w) + CA3 = self.CA3(w) + Gb = self.Gb(w) + return 0.5 * alphaS * (w - 1) * (CA2 + CA3) - epsilonC * (3 * (w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1) - epsilonB * Gb + + def gminus( + self, + w: float, + ): + alphaS = self.alphaS + epsilonC = 1 / (2 * self.m_c) + chi1 = self.chi1 + chi2 = self.chi2 + CA1 = self.CA1(w) + CA2 = self.CA2(w) + CA3 = self.CA3(w) + return 1 + alphaS * (CA1 + 0.5 * (w - 1) * (CA2 + CA3)) + epsilonC * (6 * chi1 - 2 * (w + 1) * chi2) + + def gT( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + chi2 = self.chi2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + zeta1 = self.zeta1 + Gb = self.Gb(w) + CT1 = self.CT1(w) + return 1 + alphaS * CT1 + epsilonC * (3 * (w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 + 6 * chi1 - 2 * (w + 1) * chi2) - epsilonB * Gb + + # Form factors for D1* + def gS( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + chi2 = self.chi2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + zeta1 = self.zeta1 + Gb = self.Gb(w) + CS = self.CS(w) + return 1 + alphaS * CS - epsilonC * ((w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 + 2 * chi1 - 2 * (w + 1) * chi2) - epsilonB * Gb + + def gV1( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + Gb = self.Gb(w) + CV1 = self.CV1(w) + return (w - 1) * (1 + alphaS * CV1) + epsilonC * (w * LambdaBarStar - LambdaBar - 2 * (w - 1) * chi1) - epsilonB * (w + 1) * Gb + + def gV2( + self, + w: float, + ): + alphaS = self.alphaS + chi2 = self.chi2 + epsilonC = 1 / (2 * self.m_c) + zeta1 = self.zeta1 + CV2 = self.CV2(w) + return - alphaS * CV2 + 2 * epsilonC * (zeta1 - chi2) + + def gV3( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + chi2 = self.chi2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + zeta1 = self.zeta1 + Gb = self.Gb(w) + CV1 = self.CV1(w) + CV3 = self.CV3(w) + return -1 - alphaS * (CV1 + CV3) - epsilonC * ((w * LambdaBarStar - LambdaBar) / (w + 1) + 2 * (zeta1 - chi1 + chi2)) + epsilonB * Gb + + def gA( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + Gb = self.Gb(w) + CA1 = self.CA1(w) + return 1 + alphaS * CA1 + epsilonC * ((w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * chi1) - epsilonB * Gb + + def gT1( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + Gb = self.Gb(w) + CT1 = self.CT1(w) + CT2 = self.CT2(w) + return -1 - alphaS * (CT1 + (w - 1) * CT2) + epsilonC * ((w * LambdaBarStar - LambdaBar) / (w + 1) + 2 * chi1) + epsilonB * Gb + + def gT2( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + chi1 = self.chi1 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + Gb = self.Gb(w) + CT1 = self.CT1(w) + CT3 = self.CT3(w) + return 1 + alphaS * (CT1 - (w - 1) * CT3) + epsilonC * ((w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * chi1) + epsilonB * Gb + + def gT3( + self, + w: float, + ): + alphaS = self.alphaS + zeta1 = self.zeta1 + chi2 = self.chi2 + epsilonC = 1 / (2 * self.m_c) + CT2 = self.CT2(w) + return -alphaS * CT2 + 2 * epsilonC * (zeta1 + chi2) + + +class BToDStarStarNarrow(DStStAlphaSCorrections): + """ + TO DO: add description + """ + + def __init__( + self, + m_c: float, + m_b: float, + params: tuple, + alphaS: float=0, + LambdaBar: float=0.40, + LambdaBarPrime: float=0.80, + LambdaBarStar: float=0.76, + eta1: float=0, + eta2: float=0, + eta3: float=0, + ): + super().__init__(m_c, m_b) + self.m_c = m_c + self.m_b = m_b + self.alphaS = alphaS / np.pi + self.LambdaBar = LambdaBar + self.LambdaBarPrime = LambdaBarPrime + self.LambdaBarStar = LambdaBarStar + self.eta1 = eta1 + self.eta2 = eta2 + self.eta3 = eta3 + + self.T1, self.taup, self.tau1, self.tau2 = self.set_model_parameters(params) + + def set_model_parameters( + self, + params: tuple, + ): + """ + Sets the input model parameters tau(1), tau', tau1 and tau2 (cf. Table V in arxiv:1711.03110). + Expects the parameters in that order. + + Args: + params ([tuple]): input parameters for the narrow D** form factor. + """ + T1, taup, tau1, tau2 = params + return T1, taup, tau1, tau2 + + def Fp( + self, + w: float, + ): + LambdaBar = self.LambdaBar + LambdaBarStar = self.LambdaBarStar + tau1 = self.tau1 + tau2 = self.tau2 + return LambdaBar + LambdaBarStar - (2 * w + 1) * tau1 - tau2 + + # Form factors for D1 + # The f form factors have to be multiplied by sqrt(6) + def fS( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarPrime = self.LambdaBarPrime + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CS = self.CS(w) + return -2 * (w + 1) * (1 + alphaS * CS) - 2 * epsilonB * (w - 1) * Fb - epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) - 2 * (w - 1) * ((2 * w + 1) * tau1 + tau2) + 2 * (w + 1) * (6 * eta1 + 2 * (w - 1) * eta2 - eta3)) + + def fV1( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarPrime = self.LambdaBarPrime + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta3 = self.eta3 + Fb = self.Fb(w) + CV1 = self.CV1(w) + return (1 - w**2) * (1 + alphaS * CV1) - epsilonB * (w**2 - 1) * Fb - epsilonC * (4 * (w + 1) * (w * LambdaBarPrime - LambdaBar) - (w**2 - 1) * (3 * tau1 - 3 * tau2 + 2 * eta1 + 3 * eta3)) + + def fV2( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CV1 = self.CV1(w) + CV2 = self.CV2(w) + return -3 - alphaS * (3 * CV1 + 2 * (w + 1) * CV2) - 3 * epsilonB * Fb - epsilonC * ((4 * w - 1) * tau1 + 5 * tau2 + 10 * eta1 + 4 * (w - 1) * eta2 - 5 * eta3) + + def fV3( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarPrime = self.LambdaBarPrime + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CV1 = self.CV1(w) + CV3 = self.CV3(w) + return w - 2 - alphaS * ((2 - w) * CV1 + 2 * (w + 1) * CV3) - epsilonB * (w + 2) * Fb + epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) + (w + 2)* tau1 + (2 + 3 * w) * tau2 - 2 (w + 6) * eta1 - 4 * (w - 1) * eta2 - (3 * w - 2) * eta3) + + def fA( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarPrime = self.LambdaBarPrime + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CA1 = self.CA1(w) + return -(w + 1) * (1 + alphaS * CA1) - epsilonB * (w - 1) * Fb - epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) - 3 * (w - 1) * (tau1 - tau2) - (w + 1) * (2 * eta1 + 3 * eta3)) + + def fT1( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarPrime = self.LambdaBarPrime + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta3 = self.eta3 + Fb = self.Fb(w) + CT1 = self.CT1(w) + CT2 = self.CT2(w) + return (w + 1) * (1 + alphaS * (CT1 + (w - 1) * CT2)) + epsilonB * (w - 1) * Fb - epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) - 3 * (w - 1) * (tau1 - tau2) + (w + 1) * (2 * eta1 + 3 * eta3)) + + def fT2( + self, + w: float, + ): + alphaS = self.alphaS + LambdaBar = self.LambdaBar + LambdaBarPrime = self.LambdaBarPrime + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CT1 = self.CT1(w) + CT3 = self.CT3(w) + return -(w + 1) * (1 + alphaS * (CT1 - (w - 1) * CT3)) + epsilonB * (w - 1) * Fb - epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) - 3 * (w - 1) * (tau1 - tau2) - (w + 1) * (2 * eta1 + 3 * eta3)) + + def fT3( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CT1 = self.CT1(w) + CT2 = self.CT2(w) + CT3 = self.CT3(w) + return 3 + alphaS * (3 * CT1 - (2 - w) * CT2 + 3 * CT3) + 3 * epsilonB * Fb - epsilonC * ((4 * w - 1) * tau1 + 5 * tau2 - 10 * eta1 - 4 * (w - 1) * eta2 + 5 * eta3) + + + # Form factors for D2* + def kP( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CP = self.CP(w) + return 1 + alphaS * CP + epsilonB * Fb + epsilonC * ((2 * w + 1) * tau1 + tau2 - 2 * eta1 - 2 * (w - 1) * eta2 + eta3) + + def kV( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta3 = self.eta3 + Fb = self.Fb(w) + CV1 = self.CV1(w) + return -1 - alphaS * CV1 - epsilonB * Fb - epsilonC * (tau1 - tau2 - 2 * eta1 + eta3) + + def kA1( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta3 = self.eta3 + Fb = self.Fb(w) + CA1 = self.CA1(w) + return -(w + 1) * (1 + alphaS * CA1) - epsilonB * (w - 1) * Fb - epsilonC * ((w - 1) * (tau1 - tau2) - (w + 1) * (2 * eta1 - eta3)) + + def kA2( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + epsilonC = 1 / (2 * self.m_c) + eta2 = self.eta2 + CA2 = self.CA2(w) + return alphaS * CA2 - 2 * epsilonC (tau1 + eta2) + + def kA3( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + eta1 = self.eta1 + eta2 = self.eta2 + eta3 = self.eta3 + Fb = self.Fb(w) + CA1 = self.CA1(w) + CA3 = self.CA3(w) + return 1 + alphaS * (CA1 + CA3) + epsilonB * Fb - epsilonC * (tau1 + tau2 + 2 * eta1 - 2 * eta2 - eta3) + + def kT1( + self, + w: float, + ): + alphaS = self.alphaS + epsilonC = 1 / (2 * self.m_c) + eta1 = self.eta1 + eta3 = self.eta3 + CT1 = self.CT1(w) + CT2 = self.CT2(w) + CT3 = self.CT3(w) + return 1 + alphaS * (CT1 + 0.5 * (w - 1) * (CT2 - CT3)) - epsilonC * (2 * eta1 - eta3) + + def kT2( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + tau2 = self.tau2 + epsilonC = 1 / (2 * self.m_c) + epsilonB = 1 / (2 * self.m_b) + Fb = self.Fb(w) + CT2 = self.CT2(w) + CT3 = self.CT3(w) + return 0.5 * alphaS * (w + 1) * (CT2 + CT3) + epsilonB * Fb - epsilonC * (tau1 - tau2) + + def kT3( + self, + w: float, + ): + alphaS = self.alphaS + tau1 = self.tau1 + epsilonC = 1 / (2 * self.m_c) + eta2 = self.eta2 + CT2 = self.CT2(w) + return -alphaS * CT2 + 2 * epsilonC * (tau1 - eta2) + + +if __name__ == "__main__": + pass diff --git a/effort2/formfactors/BLR_helper.py b/effort2/formfactors/BLR_helper.py new file mode 100644 index 0000000..5fe32db --- /dev/null +++ b/effort2/formfactors/BLR_helper.py @@ -0,0 +1,159 @@ +import numpy as np +from effort2.math.functions import diLog + + +class DStStAlphaSCorrections: + """ + Class to compute the C functions used in the BLR model for B -> D** form factors. + Taken from Phys. Rev. D 95, 115008 Appendix A. + """ + + def __init__( + self, + m_c: float, + m_b: float, + ) -> None: + self.z = m_c / m_b + self.wz = 0.5 (self.z + 1 / self.z) + + + def r( + self, + w: float, + ) -> float: + wp = w + np.sqrt(w**2 - 1) + return np.log(wp) / np.sqrt(w**2 - 1) + + + def Omega( + self, + w: float, + ): + z = self.z + r = self.r(w) + wp = w + np.sqrt(w**2 - 1) + wm = w - np.sqrt(w**2 - 1) + return w / (2 * np.sqrt(w**2 - 1)) * (2 * diLog(1 - wm * z) - 2 * diLog(1 - wp * z) + diLog(1 - wp**2) - diLog(1 - wm**2)) - w * r * np.log(z) + 1 + + + def CS( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + Omega = self.Omega(w) + return 1 / (3 * z * (w - wz)) * (2 * z * (w - wz) * Omega - (w - 1) * (z + 1) * (z + 1) * r + (z**2 -1) * np.log(z)) + + + def CP( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + Omega = self.Omega(w) + return 1 / (3 * z * (w - wz)) * (2 * z * (w - wz) * Omega - (w + 1) * (z - 1) * (z - 1) * r + (z**2 -1) * np.log(z)) + + + def CV1( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + Omega = self.Omega(w) + return 1 / (6 * z * (w - wz)) * (2 * (w + 1) * ((3 * w - 1) * z - z**2 - 1) * r + (12 * z * (wz - w) - (z**2 -1) * np.log(z)) + 4 * z * (w - wz) * Omega) + + def CV2( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + return -1 / (6 * z**2 * (w - wz) * (w - wz)) * (((4 * w**2 + 2 * w) * z**2 - (2 * w**2 + 5 * w -1) * z - (w + 1) * z**3 + 2) * r + z * ( 2 * (z - 1) * (wz - w) + (z**2 - (4 * w - 2) * z + (3 - 2 * w)) * np.log(z))) + + + def CV3( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + return 1 / (6 * z * (w - wz) * (w - wz)) * (((2 * w**2 + 5 * w - 1) * z**2 - (4 * w**2 + 2 * w) * z - 2 * z**3 + w + 1) * r + (2 * z * (z - 1) * (wz - w) + ((3 - 2 * w) * z**2 + (2 - 4 * w) * z + 1) * np.log(z))) + + + def CA1( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + Omega = self.Omega(w) + return 1 / (6 * z * (w - wz)) * (2 * (w - 1) * ((3 * w + 1) * z - z**2 - 1) * r + (12 * z * (wz - w) - (z**2 -1) * np.log(z)) + 4 * z * (w - wz) * Omega) + + + def CA2( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + return -1 / (6 * z**2 * (w - wz) * (w - wz)) * (((4 * w**2 - 2 * w) * z**2 - (2 * w**2 - 5 * w -1) * z - (w - 1) * z**3 + 2) * r + z * ( 2 * (z + 1) * (wz - w) + (z**2 - (4 * w + 2) * z + (3 + 2 * w)) * np.log(z))) + + + def CA3( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + return 1 / (6 * z * (w - wz) * (w - wz)) * ((2 * z**3 + (2 * w**2 - 5 * w -1) * z**2 + (4 * w**2 - 2 * w) * z - w + 1) * r + (2 * z * (z + 1) * (wz - w) - ((2 * w + 3) * z**2 - (4 * w + 2) * z + 1) * np.log(z))) + + + def CT1( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + Omega = self.Omega(w) + return 1 / (3 * z * (w - wz)) * ((w - 1) * ((4 * w + 2) * z - z**2 - 1) * r + (6 * z * (wz - w) - (z**2 - 1) * np.log(z)) + 2 * z * (w - wz) * Omega) + + + def CT2( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + return 2 / (3 * z * (w - wz)) * ((1 - w * z) * r + z * np.log(z)) + + + def CT3( + self, + w: float, + ): + z = self.z + wz = self.wz + r = self.r(w) + return 2 / (3 * (w - wz)) * ((w - z) * r + np.log(z)) + + +class IWFunctions: + """ + Class to define the Isgure-Wise functions used in the BLR model for B -> D** form factors + """ + + def __init__(self) -> None: + pass \ No newline at end of file diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py new file mode 100644 index 0000000..bc7d777 --- /dev/null +++ b/effort2/rates/BtoDStSt.py @@ -0,0 +1,3 @@ +# TO DO: add the rates here +# dGamma/dw can be found in arxiv:1711.03110 +# dGamma/dwdcosTheta can be found in arxiv:1606:09300 From cf9d7d82fa9893f005927114ea87345b73e34b12 Mon Sep 17 00:00:00 2001 From: Tommy Date: Tue, 21 Nov 2023 15:27:53 +0100 Subject: [PATCH 04/11] Small corrections --- effort2/formfactors/BLR.py | 23 ++++++++----------- ...LR_helper.py => DStStAlphaSCorrections.py} | 2 +- 2 files changed, 11 insertions(+), 14 deletions(-) rename effort2/formfactors/{BLR_helper.py => DStStAlphaSCorrections.py} (98%) diff --git a/effort2/formfactors/BLR.py b/effort2/formfactors/BLR.py index 1ec92de..6145d7c 100644 --- a/effort2/formfactors/BLR.py +++ b/effort2/formfactors/BLR.py @@ -10,19 +10,16 @@ class BToDStarStarBroad(DStStAlphaSCorrections): def __init__( self, - m_c: float, - m_b: float, - params: tuple, - # m_B: float, - # m_V: float, - # m_L: float=0, - alphaS: float=0, + m_c: float=1.31, + m_b: float=4.71, + params: tuple=(0.70, 0.2, 0.6), + alphaS: float=0.26, LambdaBar: float=0.40, LambdaBarPrime: float=0.80, LambdaBarStar: float=0.76, chi1: float=0, chi2: float=0, - ): + ) -> None: super().__init__(m_c, m_b) self.m_c = m_c self.m_b = m_b @@ -250,10 +247,10 @@ class BToDStarStarNarrow(DStStAlphaSCorrections): def __init__( self, - m_c: float, - m_b: float, - params: tuple, - alphaS: float=0, + m_c: float=1.31, + m_b: float=4.71, + params: tuple=(0.70, -1.6, -0.5, 2.9), + alphaS: float=0.26, LambdaBar: float=0.40, LambdaBarPrime: float=0.80, LambdaBarStar: float=0.76, @@ -369,7 +366,7 @@ def fV3( Fb = self.Fb(w) CV1 = self.CV1(w) CV3 = self.CV3(w) - return w - 2 - alphaS * ((2 - w) * CV1 + 2 * (w + 1) * CV3) - epsilonB * (w + 2) * Fb + epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) + (w + 2)* tau1 + (2 + 3 * w) * tau2 - 2 (w + 6) * eta1 - 4 * (w - 1) * eta2 - (3 * w - 2) * eta3) + return w - 2 - alphaS * ((2 - w) * CV1 + 2 * (w + 1) * CV3) + epsilonB * (w + 2) * Fb + epsilonC * (4 * (w * LambdaBarPrime - LambdaBar) + (w + 2)* tau1 + (2 + 3 * w) * tau2 - 2 * (w + 6) * eta1 - 4 * (w - 1) * eta2 - (3 * w - 2) * eta3) def fA( self, diff --git a/effort2/formfactors/BLR_helper.py b/effort2/formfactors/DStStAlphaSCorrections.py similarity index 98% rename from effort2/formfactors/BLR_helper.py rename to effort2/formfactors/DStStAlphaSCorrections.py index 5fe32db..588a7c6 100644 --- a/effort2/formfactors/BLR_helper.py +++ b/effort2/formfactors/DStStAlphaSCorrections.py @@ -14,7 +14,7 @@ def __init__( m_b: float, ) -> None: self.z = m_c / m_b - self.wz = 0.5 (self.z + 1 / self.z) + self.wz = 0.5 * (self.z + 1 / self.z) def r( From 88df66204a7204d86856f20fbd41ac2f0935b1f9 Mon Sep 17 00:00:00 2001 From: Tommy Date: Wed, 22 Nov 2023 10:54:19 +0100 Subject: [PATCH 05/11] Add rates for B -> D** l nu + small corrections --- effort2/formfactors/DStStAlphaSCorrections.py | 4 +- effort2/rates/BtoDStSt.py | 91 +++++++++++++++++++ 2 files changed, 93 insertions(+), 2 deletions(-) diff --git a/effort2/formfactors/DStStAlphaSCorrections.py b/effort2/formfactors/DStStAlphaSCorrections.py index 588a7c6..0142a02 100644 --- a/effort2/formfactors/DStStAlphaSCorrections.py +++ b/effort2/formfactors/DStStAlphaSCorrections.py @@ -20,7 +20,7 @@ def __init__( def r( self, w: float, - ) -> float: + ): wp = w + np.sqrt(w**2 - 1) return np.log(wp) / np.sqrt(w**2 - 1) @@ -96,7 +96,7 @@ def CA1( wz = self.wz r = self.r(w) Omega = self.Omega(w) - return 1 / (6 * z * (w - wz)) * (2 * (w - 1) * ((3 * w + 1) * z - z**2 - 1) * r + (12 * z * (wz - w) - (z**2 -1) * np.log(z)) + 4 * z * (w - wz) * Omega) + return 1 / (6 * z * (w - wz)) * (2 * (w - 1) * ((3 * w + 1) * z - z**2 - 1) * r + (12 * z * (wz - w) - (z**2 - 1) * np.log(z)) + 4 * z * (w - wz) * Omega) def CA2( diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index bc7d777..ffe1b31 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -1,3 +1,94 @@ # TO DO: add the rates here # dGamma/dw can be found in arxiv:1711.03110 # dGamma/dwdcosTheta can be found in arxiv:1606:09300 + +import numpy as np +from effort2.formfactors.kinematics import Kinematics +from effort2.formfactors.BLR import BToDStarStarBroad, BToDStarStarNarrow +from effort2.math.integrate import quad + +class BtoD0St(BToDStarStarBroad): + + def __init__( + self, + Vcb: float, + m_D: float, + m_B: float, + m_L:float=0, + G_F: float = 1.1663787e-5, + m_c: float=1.31, + m_b: float=4.71, + params: tuple=(0.70, 0.2, 0.6), + alphaS: float=0.26, + LambdaBar: float=0.40, + LambdaBarPrime: float=0.80, + LambdaBarStar: float=0.76, + chi1: float=0, + chi2: float=0, + ) -> None: + + super().__init__( + m_c, + m_b, + params, + alphaS, + LambdaBar, + LambdaBarPrime, + LambdaBarStar, + chi1, + chi2, + ) + self.m_c = m_c + self.m_b = m_b + self.alphaS = alphaS / np.pi + self.LambdaBar = LambdaBar + self.LambdaBarPrime = LambdaBarPrime + self.LambdaBarStar = LambdaBarStar + self.chi1 = chi1 + self.chi2 = chi2 + + self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) + + self.r = m_D / m_B + self.rho = m_L / m_B + + self.Gamma0 = (G_F**2 * Vcb**2 * m_B**5) / (192 * np.pi**3) + + self.kinematics = Kinematics(m_B, m_D, m_L) + self.w_min, self.w_max = self.kinematics.w_range_numerical_stable + + + def q2( + self, + w: float, + ): + r = self.r + return 1 + r**2 - 2 * r * w + + def dGamma_dw( + self, + w: float, + ) -> float: + Gamma0 = self.Gamma0 + rho = self.rho + r = self.r + q2 = self.q2(w) + zeta = self.Z1 * (1 + self.zetap * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 + gminus = self.gminus(w) * zeta + gplus = self.gplus(w) * zeta + return 4 * Gamma0 * r**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (gminus**2 * (w - 1) * (rho *((1 + r**2) * (2 * w - 1) + 2 * r * (w - 2)) + (1 - r)**2 * (w + 1) * q2) + gplus**2 * (w + 1) *(rho * ((1 + r**2) * (2 * w + 1) - 2 * r * (w + 2)) + (1 + r)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - r**2) * (w**2 - 1) * (q2 + 2 * rho)) + + def Gamma( + self, + wmin: float=None, + wmax: float=None, + ) -> float: + wmin = self.w_min if wmin is None else wmin + wmax = self.w_max if wmax is None else wmax + assert self.w_min <= wmin < wmax <= self.w_max, f"{wmin}, {wmax}" + + return quad(lambda w: self.dGamma_dw(w), wmin, wmax)[0] + + +if __name__ == "__main__": + pass From d1cc50ce5bf29bfedd59c0807d9d5ac47821bea1 Mon Sep 17 00:00:00 2001 From: Tommy Date: Wed, 22 Nov 2023 12:29:56 +0100 Subject: [PATCH 06/11] Fix small error in rate calculation --- effort2/rates/BtoDStSt.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index ffe1b31..68ba68f 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -49,7 +49,7 @@ def __init__( self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) - self.r = m_D / m_B + self.rm = m_D / m_B self.rho = m_L / m_B self.Gamma0 = (G_F**2 * Vcb**2 * m_B**5) / (192 * np.pi**3) @@ -57,21 +57,21 @@ def __init__( self.kinematics = Kinematics(m_B, m_D, m_L) self.w_min, self.w_max = self.kinematics.w_range_numerical_stable - + def q2( self, w: float, ): - r = self.r + r = self.rm return 1 + r**2 - 2 * r * w - + def dGamma_dw( self, w: float, ) -> float: Gamma0 = self.Gamma0 rho = self.rho - r = self.r + r = self.rm q2 = self.q2(w) zeta = self.Z1 * (1 + self.zetap * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 gminus = self.gminus(w) * zeta From 8d6421b2ea4ab35ca43b25a061bc54aa5ba8e565 Mon Sep 17 00:00:00 2001 From: Tommy Date: Thu, 23 Nov 2023 18:34:36 +0100 Subject: [PATCH 07/11] Fixes and tests --- effort2/formfactors/BLR.py | 7 ++++--- effort2/formfactors/DStStAlphaSCorrections.py | 11 +++-------- effort2/rates/BtoDStSt.py | 19 +++++++++++-------- 3 files changed, 18 insertions(+), 19 deletions(-) diff --git a/effort2/formfactors/BLR.py b/effort2/formfactors/BLR.py index 6145d7c..291c6ca 100644 --- a/effort2/formfactors/BLR.py +++ b/effort2/formfactors/BLR.py @@ -51,7 +51,7 @@ def set_model_parameters( """ Z1, zetap, zeta1 = params return Z1, zetap, zeta1 - + def Gb( self, w: float, @@ -62,6 +62,7 @@ def Gb( LambdaBar = self.LambdaBar LambdaBarStar = self.LambdaBarStar zeta1 = self.zeta1 + # TO DO: redefine zeta1 here ? return ((1 + 2 * w) * LambdaBarStar - (2 + w) * LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 # Form factors for D0* @@ -79,7 +80,7 @@ def gP( zeta1 = self.zeta1 CP = self.CP(w) Gb = self.Gb(w) - return (w - 1) * (1 + alphaS * CP) + epsilonC * (3 * (w * LambdaBarStar - LambdaBar) - 2 * (w**2 - 1) * zeta1 + (w -1) * (6 * chi1 - 2 * (w + 1) * chi2) - epsilonB * (w + 1) * Gb) + return (w - 1) * (1 + alphaS * CP) + epsilonC * (3 * (w * LambdaBarStar - LambdaBar) - 2 * (w**2 - 1) * zeta1 + (w -1) * (6 * chi1 - 2 * (w + 1) * chi2)) - epsilonB * (w + 1) * Gb def gplus( self, @@ -107,7 +108,7 @@ def gminus( CA1 = self.CA1(w) CA2 = self.CA2(w) CA3 = self.CA3(w) - return 1 + alphaS * (CA1 + 0.5 * (w - 1) * (CA2 + CA3)) + epsilonC * (6 * chi1 - 2 * (w + 1) * chi2) + return 1 + alphaS * (CA1 + 0.5 * (w - 1) * (CA2 - CA3)) + epsilonC * (6 * chi1 - 2 * (w + 1) * chi2) def gT( self, diff --git a/effort2/formfactors/DStStAlphaSCorrections.py b/effort2/formfactors/DStStAlphaSCorrections.py index 0142a02..5ba75b9 100644 --- a/effort2/formfactors/DStStAlphaSCorrections.py +++ b/effort2/formfactors/DStStAlphaSCorrections.py @@ -66,7 +66,7 @@ def CV1( wz = self.wz r = self.r(w) Omega = self.Omega(w) - return 1 / (6 * z * (w - wz)) * (2 * (w + 1) * ((3 * w - 1) * z - z**2 - 1) * r + (12 * z * (wz - w) - (z**2 -1) * np.log(z)) + 4 * z * (w - wz) * Omega) + return 1 / (6 * z * (w - wz)) * (2 * (w + 1) * ((3 * w - 1) * z - z**2 - 1) * r + (12 * z * (wz - w) - (z**2 - 1) * np.log(z)) + 4 * z * (w - wz) * Omega) def CV2( self, @@ -150,10 +150,5 @@ def CT3( return 2 / (3 * (w - wz)) * ((w - z) * r + np.log(z)) -class IWFunctions: - """ - Class to define the Isgure-Wise functions used in the BLR model for B -> D** form factors - """ - - def __init__(self) -> None: - pass \ No newline at end of file +if __name__ == "__main__": + pass diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index 68ba68f..935ff42 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -7,6 +7,7 @@ from effort2.formfactors.BLR import BToDStarStarBroad, BToDStarStarNarrow from effort2.math.integrate import quad + class BtoD0St(BToDStarStarBroad): def __init__( @@ -15,7 +16,7 @@ def __init__( m_D: float, m_B: float, m_L:float=0, - G_F: float = 1.1663787e-5, + G_F: float=1.1663787e-5, m_c: float=1.31, m_b: float=4.71, params: tuple=(0.70, 0.2, 0.6), @@ -50,8 +51,8 @@ def __init__( self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) self.rm = m_D / m_B - self.rho = m_L / m_B - + self.rho = (m_L / m_B)**2 + self.Gamma0 = (G_F**2 * Vcb**2 * m_B**5) / (192 * np.pi**3) self.kinematics = Kinematics(m_B, m_D, m_L) @@ -62,8 +63,8 @@ def q2( self, w: float, ): - r = self.rm - return 1 + r**2 - 2 * r * w + rm = self.rm + return 1 + rm**2 - 2 * rm * w def dGamma_dw( self, @@ -71,13 +72,15 @@ def dGamma_dw( ) -> float: Gamma0 = self.Gamma0 rho = self.rho - r = self.rm + rm = self.rm q2 = self.q2(w) zeta = self.Z1 * (1 + self.zetap * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 gminus = self.gminus(w) * zeta gplus = self.gplus(w) * zeta - return 4 * Gamma0 * r**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (gminus**2 * (w - 1) * (rho *((1 + r**2) * (2 * w - 1) + 2 * r * (w - 2)) + (1 - r)**2 * (w + 1) * q2) + gplus**2 * (w + 1) *(rho * ((1 + r**2) * (2 * w + 1) - 2 * r * (w + 2)) + (1 + r)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - r**2) * (w**2 - 1) * (q2 + 2 * rho)) - + # return 4 * Gamma0 * rm**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (gminus**2 * (w - 1) * (rho *((1 + rm**2) * (2 * w - 1) + 2 * rm * (w - 2)) + (1 - rm)**2 * (w + 1) * q2) + gplus**2 * (w + 1) * (rho * ((1 + rm**2) * (2 * w + 1) - 2 * rm * (w + 2)) + (1 + rm)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - rm**2) * (w**2 - 1) * (q2 + 2 * rho)) + # return np.sqrt(w**2 - 1) * (gminus**2 * (w - 1) * (rho *((1 + rm**2) * (2 * w - 1) + 2 * rm * (w - 2)) + (1 - rm)**2 * (w + 1) * q2) + gplus**2 * (w + 1) * (rho * ((1 + rm**2) * (2 * w + 1) - 2 * rm * (w + 2)) + (1 + rm)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - rm**2) * (w**2 - 1) * (q2 + 2 * rho)) + return (q2 - rho)**2 / q2**3 + def Gamma( self, wmin: float=None, From 18f872d11ec548f286a0fd8dff906ce4e8e8d2c6 Mon Sep 17 00:00:00 2001 From: Tommy Date: Fri, 24 Nov 2023 11:46:54 +0100 Subject: [PATCH 08/11] Tests to fix rates --- effort2/rates/BtoDStSt.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index 935ff42..1514e5c 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -77,9 +77,7 @@ def dGamma_dw( zeta = self.Z1 * (1 + self.zetap * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 gminus = self.gminus(w) * zeta gplus = self.gplus(w) * zeta - # return 4 * Gamma0 * rm**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (gminus**2 * (w - 1) * (rho *((1 + rm**2) * (2 * w - 1) + 2 * rm * (w - 2)) + (1 - rm)**2 * (w + 1) * q2) + gplus**2 * (w + 1) * (rho * ((1 + rm**2) * (2 * w + 1) - 2 * rm * (w + 2)) + (1 + rm)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - rm**2) * (w**2 - 1) * (q2 + 2 * rho)) - # return np.sqrt(w**2 - 1) * (gminus**2 * (w - 1) * (rho *((1 + rm**2) * (2 * w - 1) + 2 * rm * (w - 2)) + (1 - rm)**2 * (w + 1) * q2) + gplus**2 * (w + 1) * (rho * ((1 + rm**2) * (2 * w + 1) - 2 * rm * (w + 2)) + (1 + rm)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - rm**2) * (w**2 - 1) * (q2 + 2 * rho)) - return (q2 - rho)**2 / q2**3 + return 4 * Gamma0 * rm**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (gminus**2 * (w - 1) * (rho *((1 + rm**2) * (2 * w - 1) + 2 * rm * (w - 2)) + (1 - rm)**2 * (w + 1) * q2) + gplus**2 * (w + 1) * (rho * ((1 + rm**2) * (2 * w + 1) - 2 * rm * (w + 2)) + (1 + rm)**2 * (w - 1) * q2) - 2 * gminus * gplus * (1 - rm**2) * (w**2 - 1) * (q2 + 2 * rho)) def Gamma( self, From 4a848dd777f83dd75d06042b4351bc09a92668ce Mon Sep 17 00:00:00 2001 From: Tommy Date: Thu, 30 Nov 2023 17:57:35 +0100 Subject: [PATCH 09/11] Add rates for 3 remaining D** --- effort2/formfactors/BLR.py | 4 +- effort2/rates/BtoDStSt.py | 261 +++++++++++++++++++++++++++++++++++++ 2 files changed, 263 insertions(+), 2 deletions(-) diff --git a/effort2/formfactors/BLR.py b/effort2/formfactors/BLR.py index 291c6ca..6ce9236 100644 --- a/effort2/formfactors/BLR.py +++ b/effort2/formfactors/BLR.py @@ -65,7 +65,7 @@ def Gb( # TO DO: redefine zeta1 here ? return ((1 + 2 * w) * LambdaBarStar - (2 + w) * LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 - # Form factors for D0* + # Form factors for def gP( self, w: float, @@ -126,7 +126,7 @@ def gT( CT1 = self.CT1(w) return 1 + alphaS * CT1 + epsilonC * (3 * (w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 + 6 * chi1 - 2 * (w + 1) * chi2) - epsilonB * Gb - # Form factors for D1* + # Form factors for def gS( self, w: float, diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index 1514e5c..d582ed0 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -91,5 +91,266 @@ def Gamma( return quad(lambda w: self.dGamma_dw(w), wmin, wmax)[0] +class BtoD1St(BToDStarStarBroad): + + def __init__( + self, + Vcb: float, + m_D: float, + m_B: float, + m_L:float=0, + G_F: float=1.1663787e-5, + m_c: float=1.31, + m_b: float=4.71, + params: tuple=(0.70, 0.2, 0.6), + alphaS: float=0.26, + LambdaBar: float=0.40, + LambdaBarPrime: float=0.80, + LambdaBarStar: float=0.76, + chi1: float=0, + chi2: float=0, + ) -> None: + + super().__init__( + m_c, + m_b, + params, + alphaS, + LambdaBar, + LambdaBarPrime, + LambdaBarStar, + chi1, + chi2, + ) + self.m_c = m_c + self.m_b = m_b + self.alphaS = alphaS / np.pi + self.LambdaBar = LambdaBar + self.LambdaBarPrime = LambdaBarPrime + self.LambdaBarStar = LambdaBarStar + self.chi1 = chi1 + self.chi2 = chi2 + + self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) + + self.rm = m_D / m_B + self.rho = (m_L / m_B)**2 + + self.Gamma0 = (G_F**2 * Vcb**2 * m_B**5) / (192 * np.pi**3) + + self.kinematics = Kinematics(m_B, m_D, m_L) + self.w_min, self.w_max = self.kinematics.w_range_numerical_stable + + + def q2( + self, + w: float, + ): + rm = self.rm + return 1 + rm**2 - 2 * rm * w + + def dGamma_dw( + self, + w: float, + ) -> float: + Gamma0 = self.Gamma0 + rho = self.rho + rm = self.rm + q2 = self.q2(w) + zeta = self.Z1 * (1 + self.zetap * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 + gV1 = self.gV1(w) * zeta + gV2 = self.gV2(w) * zeta + gV3 = self.gV3(w) * zeta + gA = self.gA(w) * zeta + return 2 * Gamma0 * rm**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (gV1**2 * (2 * q2 * ((w - rm)**2 + 2 * q2) + rho * (4 * (w - rm)**2 - q2)) + (w**2 - 1) * (gV2**2 * (2 * rm**2 * q2 * (w**2 - 1) + rho * (3 * q2 + 4 * rm**2 * (w**2 - 1))) + gV3**2 * (2 * q2 * (w**2 - 1) + rho * (4 * (w - rm)**2 - q2)) + 2 * gA**2 * q2 * (2 * q2 + rho) + 2 * gV1 * gV2 * (2 * rm * q2 * (w - rm) + rho * (3 - rm**2 - 2 * rm * w)) + 4 * gV1 * gV3 * (w - rm) * (q2 + 2 * rho) + 2 * gV2 * gV3 * (2 * rm * q2 * (w**2 - 1) + rho * (3 * w * q2 + 4 * rm * (w**2 - 1))))) + + def Gamma( + self, + wmin: float=None, + wmax: float=None, + ) -> float: + wmin = self.w_min if wmin is None else wmin + wmax = self.w_max if wmax is None else wmax + assert self.w_min <= wmin < wmax <= self.w_max, f"{wmin}, {wmax}" + + return quad(lambda w: self.dGamma_dw(w), wmin, wmax)[0] + + +class BtoD1(BToDStarStarNarrow): + + def __init__( + self, + Vcb: float, + m_D: float, + m_B: float, + m_L:float=0, + G_F: float=1.1663787e-5, + m_c: float=1.31, + m_b: float=4.71, + params: tuple=(0.70, -1.6, -0.5, 2.9), + alphaS: float=0.26, + LambdaBar: float=0.40, + LambdaBarPrime: float=0.80, + LambdaBarStar: float=0.76, + eta1: float=0, + eta2: float=0, + eta3: float=0, + ) -> None: + + super().__init__( + m_c, + m_b, + params, + alphaS, + LambdaBar, + LambdaBarPrime, + LambdaBarStar, + eta1, + eta2, + eta3, + ) + self.m_c = m_c + self.m_b = m_b + self.alphaS = alphaS / np.pi + self.LambdaBar = LambdaBar + self.LambdaBarPrime = LambdaBarPrime + self.LambdaBarStar = LambdaBarStar + self.eta1 = eta1 + self.eta2 = eta2 + self.eta3 = eta3 + + self.T1, self.taup, self.tau1, self.tau2 = self.set_model_parameters(params) + + self.rm = m_D / m_B + self.rho = (m_L / m_B)**2 + + self.Gamma0 = (G_F**2 * Vcb**2 * m_B**5) / (192 * np.pi**3) + + self.kinematics = Kinematics(m_B, m_D, m_L) + self.w_min, self.w_max = self.kinematics.w_range_numerical_stable + + + def q2( + self, + w: float, + ): + rm = self.rm + return 1 + rm**2 - 2 * rm * w + + def dGamma_dw( + self, + w: float, + ) -> float: + Gamma0 = self.Gamma0 + rho = self.rho + rm = self.rm + q2 = self.q2(w) + tau = self.T1 * (1 + self.taup * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 + fV1 = self.fV1(w) * tau + fV2 = self.fV2(w) * tau + fV3 = self.fV3(w) * tau + fA = self.fA(w) * tau + return 2 * Gamma0 * rm**3 * np.sqrt(w**2 - 1) * (q2 - rho)**2 / q2**3 * (fV1**2 * (2 * q2 * ((w - rm)**2 + 2 * q2) + rho * (4 * (w - rm)**2 - q2)) + (w**2 - 1) * (fV2**2 * (2 * rm**2 * q2 * (w**2 - 1) + rho * (3 * q2 + 4 * rm**2 * (w**2 - 1))) + fV3**2 * (2 * q2 * (w**2 - 1) + rho * (4 * (w - rm)**2 - q2)) + 2 * fA**2 * q2 * (2 * q2 + rho) + 2 * fV1 * fV2 * (2 * rm * q2 * (w - rm) + rho * (3 - rm**2 - 2 * rm * w)) + 4 * fV1 * fV3 * (w - rm) * (q2 + 2 * rho) + 2 * fV2 * fV3 * (2 * rm * q2 * (w**2 - 1) + rho * (3 * w * q2 + 4 * rm * (w**2 - 1))))) + + def Gamma( + self, + wmin: float=None, + wmax: float=None, + ) -> float: + wmin = self.w_min if wmin is None else wmin + wmax = self.w_max if wmax is None else wmax + assert self.w_min <= wmin < wmax <= self.w_max, f"{wmin}, {wmax}" + + return quad(lambda w: self.dGamma_dw(w), wmin, wmax)[0] + + +class BToD2St(BToDStarStarNarrow): + + def __init__( + self, + Vcb: float, + m_D: float, + m_B: float, + m_L:float=0, + G_F: float=1.1663787e-5, + m_c: float=1.31, + m_b: float=4.71, + params: tuple=(0.70, -1.6, -0.5, 2.9), + alphaS: float=0.26, + LambdaBar: float=0.40, + LambdaBarPrime: float=0.80, + LambdaBarStar: float=0.76, + eta1: float=0, + eta2: float=0, + eta3: float=0, + ) -> None: + + super().__init__( + m_c, + m_b, + params, + alphaS, + LambdaBar, + LambdaBarPrime, + LambdaBarStar, + eta1, + eta2, + eta3, + ) + self.m_c = m_c + self.m_b = m_b + self.alphaS = alphaS / np.pi + self.LambdaBar = LambdaBar + self.LambdaBarPrime = LambdaBarPrime + self.LambdaBarStar = LambdaBarStar + self.eta1 = eta1 + self.eta2 = eta2 + self.eta3 = eta3 + + self.T1, self.taup, self.tau1, self.tau2 = self.set_model_parameters(params) + + self.rm = m_D / m_B + self.rho = (m_L / m_B)**2 + + self.Gamma0 = (G_F**2 * Vcb**2 * m_B**5) / (192 * np.pi**3) + + self.kinematics = Kinematics(m_B, m_D, m_L) + self.w_min, self.w_max = self.kinematics.w_range_numerical_stable + + + def q2( + self, + w: float, + ): + rm = self.rm + return 1 + rm**2 - 2 * rm * w + + def dGamma_dw( + self, + w: float, + ) -> float: + Gamma0 = self.Gamma0 + rho = self.rho + rm = self.rm + q2 = self.q2(w) + tau = self.T1 * (1 + self.taup * (w - 1)) # LO expansion of IW functions: Equation (36) in arxiv:1711.03110 + kA1 = self.kA1(w) * tau + kA2 = self.kA2(w) * tau + kA3 = self.kA3(w) * tau + kV = self.kV(w) * tau + return (2/3) * Gamma0 * rm**3 * (w**2 - 1)**(3/2) * (q2 - rho)**2 / q2**3 * (kA1**2 * (2 * q2 *(2 * (w - rm)**2 + 3 * q2) + rho * (8 * (w - rm)**2 - 3 * q2)) + 2 * (w**2 - 1) * (kA2**2 * (2 * rm**2 * q2 (w**2 - 1) + rho * (3 * q2 + 4 * rm**2 * (w**2 - 1))) + kA3**2 * (2 * q2 * (w**2 - 1) + rho * (4 * (w - rm)**2 - q2)) + 3 * kV**2 * q2 * (q2 + rho/2) + 2 * kA1 * kA2 * (2 * rm * q2 * (w - rm) + rho * (3 - rm**2 - 2 * rm * w)) + 4 * kA1 * kA3 * (w - rm) * (q2 + 2 * rho) + 2 * kA2 * kA3 * (2 * rm * q2 * (w**2 - 1) + rho * (3 * w * q2 + 4 * rm * (w**2 - 1))))) + + def Gamma( + self, + wmin: float=None, + wmax: float=None, + ) -> float: + wmin = self.w_min if wmin is None else wmin + wmax = self.w_max if wmax is None else wmax + assert self.w_min <= wmin < wmax <= self.w_max, f"{wmin}, {wmax}" + + return quad(lambda w: self.dGamma_dw(w), wmin, wmax)[0] + + if __name__ == "__main__": pass From 551e6c4c393ca4c2ce988c2c4e0b0753b0d802d3 Mon Sep 17 00:00:00 2001 From: Tommy Date: Mon, 8 Jan 2024 18:08:09 +0100 Subject: [PATCH 10/11] Fixing small bug for D2* and adding a notebook example --- ... and Form Factors for B --> D** l nu.ipynb | 273 ++++++++++++++++++ effort2/formfactors/BLR.py | 11 +- effort2/rates/BtoDStSt.py | 14 +- 3 files changed, 284 insertions(+), 14 deletions(-) create mode 100644 effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb diff --git a/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb b/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb new file mode 100644 index 0000000..f3a6231 --- /dev/null +++ b/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb @@ -0,0 +1,273 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "94a5cedf-b0c3-427a-8e05-88760121db96", + "metadata": {}, + "source": [ + "# How to Access Different Form Factors and Rates\n", + "\n", + "In this notebook we give an example for different form factor parameterizations and how to use them for $B$ to $D^{**}$ decays.\n", + "This will be a basic instructions and only concern itself with central values. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e6880e6f-1638-4f3e-8422-ff149a4eb011", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Welcome to JupyROOT 6.24/06\n", + "For optimal usage set `plt.style.use('belle2')`\n" + ] + } + ], + "source": [ + "# from effort2.rates.BtoDStSt import BtoD0St\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import uncertainties.unumpy as unp\n", + "import pdg\n", + "import b2plot as bp\n", + "import effort2.rates.BtoDStSt" + ] + }, + { + "cell_type": "markdown", + "id": "638ad31e-22ba-403d-ae22-546c88d81d94", + "metadata": {}, + "source": [ + "## Setting Up \n", + "\n", + "* We will require the masses of the contributing $B$ and $D^{**}$ mesons. We work with zero lepton masses, which is the default for effort. Non-zero lepton masses will be explored in a different notebook.\n", + "\n", + "* We will look at the BLR form factor parametrisation. Please note that the chosen values for the form factors might be outdated, and only serve for an explanatory purpose." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f8158d15-5f71-4ae6-a7e0-2955ca2dd9d8", + "metadata": {}, + "outputs": [], + "source": [ + "m_Bzero = pdg.get(511).Mass()\n", + "m_Bplus = pdg.get(521).Mass()\n", + "m_tau = pdg.get(15).Mass()" + ] + }, + { + "cell_type": "markdown", + "id": "06599e5d-8f5e-48bd-ad2a-a05494d0a442", + "metadata": {}, + "source": [ + "## Plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "51b0c954-21a7-4e78-9435-e208cd029844", + "metadata": {}, + "outputs": [], + "source": [ + "def add_watermark(\n", + " ax,\n", + " t: str = None,\n", + " logo: str = \"Belle II\",\n", + " px: float = 0.033,\n", + " py: float = 1.022, #0.915,\n", + " fontsize: int = 10,\n", + " alpha_logo=1,\n", + " shift: float = 0.15,\n", + " bstyle: str = \"normal\",\n", + " *args,\n", + " **kwargs,\n", + "):\n", + " ax.text(px, py, logo, ha=\"left\", transform=ax.transAxes, fontsize=fontsize, style=bstyle, alpha=alpha_logo, weight=\"bold\", *args, **kwargs,)\n", + " ax.text(px + shift, py, t, ha=\"left\", transform=ax.transAxes, fontsize=fontsize, alpha=alpha_logo, *args, **kwargs,)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a8de39bf", + "metadata": {}, + "outputs": [], + "source": [ + "dict_d = {\n", + " 'D0*': {\n", + " 'pdg': 10411,\n", + " 'rate': effort2.rates.BtoDStSt.BtoD0St,\n", + " 'str': r\"$B^0 \\to D^*_0 \\ell \\bar{\\nu}_\\ell$\",\n", + " },\n", + " 'D1*': {\n", + " 'pdg': 20413,\n", + " 'rate': effort2.rates.BtoDStSt.BtoD1St,\n", + " 'str': r\"$B^0 \\to D^*_1 \\ell \\bar{\\nu}_\\ell$\",\n", + " },\n", + " 'D1': {\n", + " 'pdg': 10413,\n", + " 'rate': effort2.rates.BtoDStSt.BtoD1,\n", + " 'str': r\"$B^0 \\to D_1 \\ell \\bar{\\nu}_\\ell$\",\n", + " },\n", + " 'D2*': {\n", + " 'pdg': 415,\n", + " 'rate': effort2.rates.BtoDStSt.BtoD2St,\n", + " 'str': r\"$B^0 \\to D^*_2 \\ell \\bar{\\nu}_\\ell$\",\n", + " },\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3ea235a8", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# B0, massless lepton\n", + "fig, ax = plt.subplots(dpi=200, figsize=(6.4, 2.2), nrows=1, ncols=1, sharex=False, sharey=False)\n", + "for key in dict_d.keys():\n", + " m_D = pdg.get(dict_d[key]['pdg']).Mass()\n", + " rate = dict_d[key]['rate'](\n", + " Vcb=40e-3,\n", + " m_D=m_D,\n", + " m_B=m_Bzero,\n", + " m_L=0,\n", + " )\n", + " w_range = np.linspace(*rate.kinematics.w_range_numerical_stable)\n", + "\n", + " total_rate = rate.Gamma()\n", + " w_rate = [rate.dGamma_dw(w) / total_rate * (max(w_range) - min(w_range)) for w in w_range]\n", + "\n", + " ax.plot(w_range, unp.nominal_values(w_rate), label=f\"BLR, {pdg.get(dict_d[key]['pdg']).GetName()}\", ls=\"solid\")\n", + "\n", + " ax.set_xlim(1, max(w_range))\n", + "\n", + " ax.set_ylim(0, 3)\n", + "\n", + " ax.set_xlabel(r\"$w$\")\n", + "\n", + " ax.set_ylabel(r\"$1 / \\Gamma \\times \\mathrm{d} \\Gamma / \\mathrm{d}w$\")\n", + "\n", + " ax.legend(loc=\"upper left\", frameon=False, fontsize=\"x-small\", ncol=1)\n", + "\n", + " add_watermark(ax, px=0.01, py=1.05, fontsize=8)\n", + "\n", + " plt.tight_layout()\n", + "\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "bd8d0e06", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# B0, tau lepton\n", + "fig, ax = plt.subplots(dpi=200, figsize=(6.4, 2.2), nrows=1, ncols=1, sharex=False, sharey=False)\n", + "for key in dict_d.keys():\n", + " m_D = pdg.get(dict_d[key]['pdg']).Mass()\n", + " rate = dict_d[key]['rate'](\n", + " Vcb=40e-3,\n", + " m_D=m_D,\n", + " m_B=m_Bzero,\n", + " m_L=m_tau,\n", + " )\n", + " w_range = np.linspace(*rate.kinematics.w_range_numerical_stable)\n", + "\n", + " total_rate = rate.Gamma()\n", + " w_rate = [rate.dGamma_dw(w) / total_rate * (max(w_range) - min(w_range)) for w in w_range]\n", + "\n", + " ax.plot(w_range, unp.nominal_values(w_rate), label=f\"BLR, {pdg.get(dict_d[key]['pdg']).GetName()}\", ls=\"solid\")\n", + "\n", + " ax.set_xlim(1, max(w_range))\n", + "\n", + " ax.set_ylim(0, 3)\n", + "\n", + " ax.set_xlabel(r\"$w$\")\n", + "\n", + " ax.set_ylabel(r\"$1 / \\Gamma \\times \\mathrm{d} \\Gamma / \\mathrm{d}w$\")\n", + "\n", + " ax.legend(loc=\"upper left\", frameon=False, fontsize=\"x-small\", ncol=1)\n", + "\n", + " add_watermark(ax, px=0.01, py=1.05, fontsize=8)\n", + "\n", + " plt.tight_layout()\n", + "\n", + "plt.show()\n", + "plt.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7afe4b10", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Belle2 (light-2311-nebelung)", + "language": "python", + "name": "belle2_light-2311-nebelung" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/effort2/formfactors/BLR.py b/effort2/formfactors/BLR.py index 6ce9236..2be10f5 100644 --- a/effort2/formfactors/BLR.py +++ b/effort2/formfactors/BLR.py @@ -5,7 +5,7 @@ class BToDStarStarBroad(DStStAlphaSCorrections): """ - TO DO: add description + A class to compute the form factors for the two broad D** states (D0* and D1') """ def __init__( @@ -33,7 +33,6 @@ def __init__( self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) # TO DO: check parameter values from Hammer # Scale mu = sqrt(mc*mb) - # Can ask Markus/Florian for details about BLR # Certain values depend on renorm scheme # The chromomagnetic terms chi1/2 are neglected in Hammer and certain Approximations. # I'm not sure whether they should be set to 0 with the most up-to-date fit of the 3 zeta parameters. @@ -126,7 +125,6 @@ def gT( CT1 = self.CT1(w) return 1 + alphaS * CT1 + epsilonC * (3 * (w * LambdaBarStar - LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 + 6 * chi1 - 2 * (w + 1) * chi2) - epsilonB * Gb - # Form factors for def gS( self, w: float, @@ -243,7 +241,7 @@ def gT3( class BToDStarStarNarrow(DStStAlphaSCorrections): """ - TO DO: add description + A class to compute the form factors for the two narrow D** states (D1 and D2) """ def __init__( @@ -286,7 +284,7 @@ def set_model_parameters( T1, taup, tau1, tau2 = params return T1, taup, tau1, tau2 - def Fp( + def Fb( self, w: float, ): @@ -381,7 +379,6 @@ def fA( epsilonC = 1 / (2 * self.m_c) epsilonB = 1 / (2 * self.m_b) eta1 = self.eta1 - eta2 = self.eta2 eta3 = self.eta3 Fb = self.Fb(w) CA1 = self.CA1(w) @@ -499,7 +496,7 @@ def kA2( epsilonC = 1 / (2 * self.m_c) eta2 = self.eta2 CA2 = self.CA2(w) - return alphaS * CA2 - 2 * epsilonC (tau1 + eta2) + return 1. #alphaS * CA2 - 2 * epsilonC (tau1 + eta2) def kA3( self, diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index d582ed0..5e8967c 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -264,7 +264,7 @@ def Gamma( return quad(lambda w: self.dGamma_dw(w), wmin, wmax)[0] -class BToD2St(BToDStarStarNarrow): +class BtoD2St(BToDStarStarNarrow): def __init__( self, @@ -283,7 +283,7 @@ def __init__( eta1: float=0, eta2: float=0, eta3: float=0, - ) -> None: + ) -> None: super().__init__( m_c, @@ -321,14 +321,14 @@ def __init__( def q2( self, w: float, - ): + ): rm = self.rm return 1 + rm**2 - 2 * rm * w def dGamma_dw( self, w: float, - ) -> float: + ) -> float: Gamma0 = self.Gamma0 rho = self.rho rm = self.rm @@ -338,13 +338,13 @@ def dGamma_dw( kA2 = self.kA2(w) * tau kA3 = self.kA3(w) * tau kV = self.kV(w) * tau - return (2/3) * Gamma0 * rm**3 * (w**2 - 1)**(3/2) * (q2 - rho)**2 / q2**3 * (kA1**2 * (2 * q2 *(2 * (w - rm)**2 + 3 * q2) + rho * (8 * (w - rm)**2 - 3 * q2)) + 2 * (w**2 - 1) * (kA2**2 * (2 * rm**2 * q2 (w**2 - 1) + rho * (3 * q2 + 4 * rm**2 * (w**2 - 1))) + kA3**2 * (2 * q2 * (w**2 - 1) + rho * (4 * (w - rm)**2 - q2)) + 3 * kV**2 * q2 * (q2 + rho/2) + 2 * kA1 * kA2 * (2 * rm * q2 * (w - rm) + rho * (3 - rm**2 - 2 * rm * w)) + 4 * kA1 * kA3 * (w - rm) * (q2 + 2 * rho) + 2 * kA2 * kA3 * (2 * rm * q2 * (w**2 - 1) + rho * (3 * w * q2 + 4 * rm * (w**2 - 1))))) - + return (2/3) * Gamma0 * rm**3 * (w**2 - 1)**(3/2) * (q2 - rho)**2 / q2**3 * (kA1**2 * (2 * q2 * (2 * (w - rm)**2 + 3 * q2) + rho * (8 * (w - rm)**2 - 3 * q2)) + 2 * (w**2 - 1) * (kA2**2 * (2 * rm**2 * q2 * (w**2 - 1) + rho * (3 * q2 + 4 * rm**2 * (w**2 - 1))) + kA3**2 * (2 * q2 * (w**2 - 1) + rho * (4 * (w - rm)**2 - q2)) + 3 * kV**2 * q2 * (q2 + rho/2) + 2 * kA1 * kA2 * (2 * rm * q2 * (w - rm) + rho * (3 - rm**2 - 2 * rm * w)) + 4 * kA1 * kA3 * (w - rm) * (q2 + 2 * rho) + 2 * kA2 * kA3 * (2 * rm * q2 * (w**2 - 1) + rho * (3 * w * q2 + 4 * rm * (w**2 - 1))))) + def Gamma( self, wmin: float=None, wmax: float=None, - ) -> float: + ) -> float: wmin = self.w_min if wmin is None else wmin wmax = self.w_max if wmax is None else wmax assert self.w_min <= wmin < wmax <= self.w_max, f"{wmin}, {wmax}" From d0adf7a504761bdf2c02eec91cf34014c342ee30 Mon Sep 17 00:00:00 2001 From: Tommy Date: Mon, 8 Jan 2024 18:23:18 +0100 Subject: [PATCH 11/11] Small fixes --- .../examples/Rates and Form Factors for B --> D** l nu.ipynb | 4 ++-- effort2/formfactors/BLR.py | 5 +---- effort2/rates/BtoDStSt.py | 1 - 3 files changed, 3 insertions(+), 7 deletions(-) diff --git a/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb b/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb index f3a6231..25a7529 100644 --- a/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb +++ b/effort2/examples/Rates and Form Factors for B --> D** l nu.ipynb @@ -136,7 +136,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -193,7 +193,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ] diff --git a/effort2/formfactors/BLR.py b/effort2/formfactors/BLR.py index 2be10f5..28f43e0 100644 --- a/effort2/formfactors/BLR.py +++ b/effort2/formfactors/BLR.py @@ -31,7 +31,6 @@ def __init__( self.chi2 = chi2 self.Z1, self.zetap, self.zeta1 = self.set_model_parameters(params) - # TO DO: check parameter values from Hammer # Scale mu = sqrt(mc*mb) # Certain values depend on renorm scheme # The chromomagnetic terms chi1/2 are neglected in Hammer and certain Approximations. @@ -61,10 +60,8 @@ def Gb( LambdaBar = self.LambdaBar LambdaBarStar = self.LambdaBarStar zeta1 = self.zeta1 - # TO DO: redefine zeta1 here ? return ((1 + 2 * w) * LambdaBarStar - (2 + w) * LambdaBar) / (w + 1) - 2 * (w - 1) * zeta1 - # Form factors for def gP( self, w: float, @@ -496,7 +493,7 @@ def kA2( epsilonC = 1 / (2 * self.m_c) eta2 = self.eta2 CA2 = self.CA2(w) - return 1. #alphaS * CA2 - 2 * epsilonC (tau1 + eta2) + return alphaS * CA2 - 2 * epsilonC * (tau1 + eta2) def kA3( self, diff --git a/effort2/rates/BtoDStSt.py b/effort2/rates/BtoDStSt.py index 5e8967c..0cfdd44 100644 --- a/effort2/rates/BtoDStSt.py +++ b/effort2/rates/BtoDStSt.py @@ -1,4 +1,3 @@ -# TO DO: add the rates here # dGamma/dw can be found in arxiv:1711.03110 # dGamma/dwdcosTheta can be found in arxiv:1606:09300