Bayesian Analysis for 4 7 Be + p 5 8 B + γ Based on Effective Field Theory

Similar documents
SPANC -- SPlitpole ANalysis Code User Manual

PHYS 450 Spring semester Lecture 02: Dealing with Experimental Uncertainties. Ron Reifenberger Birck Nanotechnology Center Purdue University

Electron-Impact Double Ionization of the H 2

SUPPLEMENTARY INFORMATION

8/25/17. Data Modeling. Data Modeling. Data Modeling. Patrice Koehl Department of Biological Sciences National University of Singapore

Complex Atoms; The Exclusion Principle and the Periodic System

improve a reaction calculation

U-Pb Geochronology Practical: Background

Neutral-Current Neutrino-Nucleus Inelastic Reactions for Core Collapse Supernovae

Uncertainty and auto-correlation in. Measurement

Measurement of the photon structure function F 2 γ (x,q 2 ) with the LUMI detector at L3

Quantifying Uncertainty

Lecture 6. p+p, Helium Burning and Energy Generation. ) 2 H (+0.42 MeV) p( p,e + ν e. Proton-proton reaction:

Applied Nuclear Physics (Fall 2004) Lecture 23 (12/3/04) Nuclear Reactions: Energetics and Compound Nucleus

Canonical description of charm and bottom

Uncertainty as the Overlap of Alternate Conditional Distributions

Moderator & Moderator System

7 Be(p,γ) 8 B S-factor from ab initio wave functions

Experimental Studies of Quasi-fission Reactions. B.B.Back, B.G.Glagola, D.Henderson, S.B.Kaufman, J.G.Keller, S.J.Sanders, F.Videbaek, and B.D.

Chapter 13: Multiple Regression

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations

A REVIEW OF ERROR ANALYSIS

Linear Approximation with Regularization and Moving Least Squares

Measurement of the D 0 π e + ν Branching Fraction, Form Factor and Implications for V ub

is the calculated value of the dependent variable at point i. The best parameters have values that minimize the squares of the errors

Computing MLE Bias Empirically

Entropy generation in a chemical reaction

Monte Carlo method II

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Negative Binomial Regression

Likelihood Methods: A Companion to the NEPPSR analysis project. Colin Gay, Yale University

Dose Calculation Algorithms and Commissioning. Particles contributing to dose. Dose Algorithms 7/24/2014. Primary protons. Secondary particles

Level Crossing Spectroscopy

Lecture 14: Forces and Stresses

Non-interacting Spin-1/2 Particles in Non-commuting External Magnetic Fields

3D Estimates of Analysis and Short-Range Forecast Error Variances

Basic Statistical Analysis and Yield Calculations

T E C O L O T E R E S E A R C H, I N C.

Probability Theory (revisited)

The Feynman path integral

Appendix B: Resampling Algorithms

Combined Limits on First Generation. Leptoquarks from the CDF and D. Experiments. Leptoquark Limit Combination Working Group 1

Number of cases Number of factors Number of covariates Number of levels of factor i. Value of the dependent variable for case k

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

On an Extension of Stochastic Approximation EM Algorithm for Incomplete Data Problems. Vahid Tadayon 1

SIMPLE REACTION TIME AS A FUNCTION OF TIME UNCERTAINTY 1

Some basic statistics and curve fitting techniques

Y = β 0 + β 1 X 1 + β 2 X β k X k + ε

Relaxation laws in classical and quantum long-range lattices

JAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger

Comparison of Regression Lines

Rate of Absorption and Stimulated Emission

THE COUPLED LES - SUBGRID STOCHASTIC ACCELERATION MODEL (LES-SSAM) OF A HIGH REYNOLDS NUMBER FLOWS

Lecture 20: Noether s Theorem

Outline for today. Markov chain Monte Carlo. Example: spatial statistics (Christensen and Waagepetersen 2001)

A Robust Method for Calculating the Correlation Coefficient

Phase I Monitoring of Nonlinear Profiles

Physics 181. Particle Systems

Transverse-velocity scaling of femtoscopy in proton proton collisions

Chapter 9: Statistical Inference and the Relationship between Two Variables

INVESTIGATION OF ANGULAR DISTRIBUTIONS IN THE INTERACTION OF COSMIC-RAY PARTICLES

Cokriging Partial Grades - Application to Block Modeling of Copper Deposits

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

How its computed. y outcome data λ parameters hyperparameters. where P denotes the Laplace approximation. k i k k. Andrew B Lawson 2013

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

Modeling of Electron Transport in Thin Films with Quantum and Scattering Effects

The convergent close-coupling method. CCC method for electron-atom scattering

THE VIBRATIONS OF MOLECULES II THE CARBON DIOXIDE MOLECULE Student Instructions

Statistical models with uncertain error parameters

Laboratory 3: Method of Least Squares

Thermo-Calc Software. Modelling Multicomponent Precipitation Kinetics with CALPHAD-Based Tools. EUROMAT2013, September 8-13, 2013 Sevilla, Spain

Investigating Quasi-Continuum Decay to Discrete Levels

r i r j 3. (2) Gm j m i r i (r i r j ) r i r j 3. (3)

Deep Inelastic Neutron Scattering Studies of Atomic Momentum Distributions in Condensed Argon and Neon*

Least-Squares Fitting of a Hyperplane

x = , so that calculated

Advanced Quantum Mechanics

Linear Correlation. Many research issues are pursued with nonexperimental studies that seek to establish relationships among 2 or more variables

Deterministic and Monte Carlo Codes for Multiple Scattering Photon Transport

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Mechanics Physics 151

princeton univ. F 13 cos 521: Advanced Algorithm Design Lecture 3: Large deviations bounds and applications Lecturer: Sanjeev Arora

Uncertainty in measurements of power and energy on power networks

Relativistic Mean-Field Models with Different Parametrizations of Density Dependent Couplings

Laboratory 1c: Method of Least Squares

THE IGNITION PARAMETER - A quantification of the probability of ignition

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules

Calculations of Energy Levels Using the Weakest Bound Electron Potential Model Theory

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Aging model for a 40 V Nch MOS, based on an innovative approach F. Alagi, R. Stella, E. Viganò

The Analysis Procedure An Overview

PHY688, Statistical Mechanics

Topic 7: Analysis of Variance

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force.

Aerosols, Dust and High Spectral Resolution Remote Sensing

NANYANG TECHNOLOGICAL UNIVERSITY SEMESTER I EXAMINATION MTH352/MH3510 Regression Analysis

Learning Objectives for Chapter 11

Unification Paradigm

> To construct a potential representation of E and B, you need a vector potential A r, t scalar potential ϕ ( F,t).

A New Method for Estimating Overdispersion. David Fletcher and Peter Green Department of Mathematics and Statistics

Transcription:

Bayesan Analyss for 4 7 Be + p 5 8 B + γ Based on Effectve Feld Theory Xln Zhang Unversty of Washngton In collaboraton wth K. Nollett (San Dego State U.) and D. Phllps (Oho U.) INT Program INT-16-a, Bayesan Methods n Nuclear Physcs, June, 016

Outlne Motvaton Be7 capture n EFT: next-to-leadng order (NLO) Bayesan analyss Questons 6/8/016

Radatve Capture Reacton 4 7 Be + p 8 5 B + γ 6/8/016 3

Radatve Capture Reacton 4 7 Be + p 8 5 B + γ 6/8/016 3

Radatve Capture Reacton 4 7 Be + p 8 5 B + γ Knetc energy (E) between core (C) and nucleon(n) Photon takes away all the energy: Q value + E Partcles carry spn ( channels sets of parameters) Electromagnetc dpole radaton (charge separaton), and governed by strong nteracton 6/8/016 3

Radatve Capture Reacton 6/8/016 4

Radatve Capture Reacton 6/8/016 4

Radatve Capture Reacton 6/8/016 4

Radatve Capture Reacton Statstcal & systematcal uncertantes 6/8/016 4

Radatve Capture Reacton Statstcal & systematcal uncertantes Goal s to nfer the S factor and ts uncertanty at near-zero energes based on theory 6/8/016 4

Motvatons W.C. Haxton, R.G. Hamsh Robertson, and Aldo M. Serenell, Annu.Rev. Astron. Astrophys. 51, 1 (013) 6/8/016 5

Solar neutrno generaton 6/8/016 6

Solar neutrno generaton Radatve Capture 6/8/016 6

Solar neutrno generaton 6/8/016 7

Solar neutrno generaton However, Be7+p (7.5%), He3+He4 (5.4%) 6/8/016 7

Solar neutrno generaton Not expermentally accessble (0 kev CM energy). Involve theoretcal uncertanty However, Be7+p (7.5%), He3+He4 (5.4%) 6/8/016 7

The capture reacton cross sectons mpact solar neutrno oscllaton experments, and solar modelng. 6/8/016 8

Solar abundance problem 6/8/016 9

Solar abundance problem Based on surface propertes from 1-D convecton zone smulaton Based on surface propertes from 3-D convecton zone smulaton 6/8/016 9

Solar abundance problem Based on surface propertes from 1-D convecton zone smulaton Hgh metallcty Hgh core T Large neutrno flux Based on surface propertes from 3-D convecton zone smulaton Low metallcty Low core T Small neutrno flux 6/8/016 9

Solar abundance problem: Helosesmology 6/8/016 10

Solar abundance problem: Helosesmology The 6/8/016 revsed SSM does NOT agree wth Helosesmology measurements 10

Solar abundance problem: Neutrnos 6/8/016 11

Solar abundance problem: Neutrnos 6/8/016 11

Solar abundance problem: Neutrnos Two models could be dfferentated IF the theoretcal errors and those of solar neutrno experments on 8B neutrno flux can be reduced. 6/8/016 11

EFT at NLO A smple pcture due to scale separaton; systematc expanson (Lagrangan); uncertanty estmate X.Z., K. Nollett and D. Phllps, PRC 89, 05160 (014) PLB 751, 535(015); EPJ Web Conf. 113, 06001 (016). 6/8/016 1

Then and now Tombrello(1965), Aurdal(1970), Rev.Mod.Phys.(1998), Rev.Mod.Phys(011) 6/8/016 13

Then and now X.Z, K. Nollett, and D. Phllps (015) Tombrello(1965), Aurdal(1970), Rev.Mod.Phys.(1998), Rev.Mod.Phys(011) Based on the same data 6/8/016 13

Scale separaton: spectrum 6/8/016 14

Scale separaton: spectrum 6/8/016 14

Scale separaton: spectrum B8: a shallow bound state n terms of proton+be7 Proton-Be7 s-wave has large scatterng lengths Length scale ~ 1/(momentum scale) 6/8/016 14

Scale separaton: spectrum 6/8/016 15

Scale separaton: spectrum 6/8/016 15

Scale separaton: spectrum * E Shallow bound state k, k k C 0. ~ 1 6/8/016 15

Scale separaton: spectrum * E Be and proton total spn can be 1 or, gvng two ndependent reacton channels two sets of parameters 6/8/016 16

Scale separaton: reacton LO NLO 6/8/016 17

Scale separaton: reacton LO NLO EFT quantfes ths pcture, 6/8/016 17

Scale separaton: reacton LO NLO EFT quantfes ths pcture, low by expandng S-matrx n terms of 0.. 6/8/016 17 Q

EFT: NLO LO: 4 parameters ncludng C 3, C 5 P ) ( P ) (, a, a 5 ( 3 S1 ) ( S ) 6/8/016 18

EFT: NLO LO: 4 parameters ncludng C 3, C 5 P ) ( P ) (, a, a 5 ( 3 S1 ) ( S ) r,, L, ( 3 S ) ( S ) E 1 LE NLO: another 5 parameters ncludng, r 5 1 6/8/016 1 18

EFT: NLO LO: 4 parameters ncludng C 3, C 5 P ) ( P ) (, a, a 5 ( 3 S1 ) ( S ) r,, L, ( 3 S ) ( S ) E 1 LE NLO: another 5 parameters ncludng, r 5 1 Core exctaton 6/8/016 1 18

Model ndependence Potental models: B. Davds and S. Typel (003) Mcroscopc calculaton: P. Descouvemont (004) 6/8/016 19

Model ndependence Potental models: B. Davds and S. Typel (003) Mcroscopc calculaton: P. Descouvemont (004) 6/8/016 EFT reproduces other models 19

Bayesan Analyss 6/8/016 0

Bayesan Analyss 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Data. Here only En<0.5 MeV drect capture data are used, ncludng Junghans, Flppone, Hammache, Baby (3 n total) 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Data. Here only En<0.5 MeV drect capture data are used, ncludng Junghans, Flppone, Hammache, Baby (3 n total) 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Theory, here S factor 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } EFT parameters 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Systematc error varables 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Pr D g,{ }; T Exp ; # data 1 S g; E 1 D 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Pr D g,{ }; T Exp ; # data 1 S g; E 1 D 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Pr D g,{ }; T Exp ; # data 1 S g; E 1 D Pr # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j j l g g g l 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Pr D g,{ }; T Exp ; # data 1 S g; E 1 D Pr g g l j gl d Pr g,{ } D T # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j Pr g D; T ; 6/8/016 1

Pr Bayesan Analyss g,{ } D; T PrD g,{ }; T Prg,{ T } Pr D g,{ }; T Exp ; # data 1 S g; E 1 D Pr g g l j gl d Pr g,{ } D T # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j Pr g D; T ; Monte-Carlo Markov-Chan ensemble of parameters accordng to the parameter dstrbutons 6/8/016 1

Junghans BE1 and BE3 (flled crcle), Flppone (open crcle), Baby (flled damond), Hammache (flled box) Green band s our 1- standard devaton error band: 3% error 6/8/016

S(0 kev) [S(0 kev)] E. G. Adelberger, et.al., Rev. Mod. Phys. 83, 195 (011) recommend: S( 0) 0.8 0.7 ( ) 1.4 ( expt theor) ev b 6/8/016 3

S(0 kev) [S(0 kev)] E. G. Adelberger, et.al., Rev. Mod. Phys. 83, 195 (011) recommend: S( 0) 0.8 0.7 ( ) 1.4 ( expt theor) ev b We reduce the error by more than 50%! 6/8/016 3

PDF for and C 3 C C 3, 5 ( P ) ( P ) C 5. ( P ) ( P ) Tabacaru et.al., measurements by transfer reacton (large eclpse) Nollett et.al., ab nto calculaton (small eclpse) 6/8/016 4

PDF for and C 3 C C 3, 5 ( P ) ( P ) C 5. ( P ) ( P ) C 3 C 5 0.564 ( P ) ( P ) (3) Drect capture reacton constrans total squared ANCs! Tabacaru et.al., measurements by transfer reacton (large eclpse) Nollett et.al., ab nto calculaton (small eclpse) 6/8/016 4

PDF for and.33 0 L 3 ( P ) L, ( 3 P ) 6/8/016 5

PDF for and.33 0 L 3 ( P ) L, ( 3 P ) Core exctaton and short range term 6/8/016 not dstngushed by low energy data 5

PDFs Red for S=1, Blue for S=. 6/8/016 6

PDFs Red for S=1, Blue for S=. 6/8/016 6

PDFs Red for S=1, Blue for S=. L 1/ 3 fm 6/8/016 6

PDFs Red for S=1, Blue for S=. L 1/ 3 fm 6/8/016 6 From left to rght: Junghans (BE1and BE3 ) Baby, Hammache, Flpponne

Choce of data sets 6/8/016 7

Choce of data sets 6/8/016 7 Include data: Flppone(1983), Baby (003), Hammache (001)

Choce of data sets Include data: Flppone(1983), Baby (003), Hammache (001) Add Junghans BE3 (010) 6/8/016 7

Choce of data sets Add Junghans BE3 and BE1 (010) Include data: Flppone(1983), Baby (003), Hammache (001) Add Junghans BE3 (010) 6/8/016 7

EFT NLO correctons 6/8/016 8

EFT NLO correctons E, M1 contrbutons (S factor): < 0.01% 6/8/016 8

EFT NLO correctons E, M1 contrbutons (S factor): < 0.01% Radatve correctons: ~0.1% 6/8/016 8

EFT NLO correctons E, M1 contrbutons (S factor): < 0.01% Radatve correctons: ~0.1% EFT s-wave scatterng: ~0.8% 6/8/016 8

EFT NLO correctons E, M1 contrbutons (S factor): < 0.01% Radatve correctons: ~0.1% EFT s-wave scatterng: ~0.8% EFT NLO currents: ~0.8% 6/8/016 8

EFT NLO correctons E, M1 contrbutons (S factor): < 0.01% Radatve correctons: ~0.1% EFT s-wave scatterng: ~0.8% EFT NLO currents: ~0.8% Notce B8 BE=136.4(1.0) kev: ~ 0.8% 6/8/016 8

EFT NLO correctons E, M1 contrbutons (S factor): < 0.01% Radatve correctons: ~0.1% EFT s-wave scatterng: ~0.8% EFT NLO currents: ~0.8% Notce B8 BE=136.4(1.0) kev: ~ 0.8% Recall EFT ftted to varous potental model and RGM calculaton results: devaton <~1% up to 1MeV (cm E). 6/8/016 8

NLO mpact on Bayesan analyss N3LO NLO NLO: L L + L k Λ N3LO: L L + L k Λ NLO 6/8/016 9

NLO mpact on Bayesan analyss N3LO NLO NLO: L L + L k Λ N3LO: L L + L k Λ NLO Addng NLO shfts S(0) by << 1%. 6/8/016 9

NLO: L L + L k Λ N3LO: L L + L k Λ Data couldn t gve more nformaton 6/8/016 30

A few questons 6/8/016 31

Questons data D E g S Exp T D g # 1 1 ; ; };,{ Pr ds. or flat para l g l l err sys j j l j g g Exp Exp T g # 0 # },{ Pr 6/8/016 3

Pr Pr D g,{ }; T Questons Exp ; g; E 1 How to deal wth normalzaton floatng parameter? [P.S.Baranov, A.L.L vov et.al.,physcs of Partcles and Nucleus, 3, 376 (001)] # data # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j j 1 S l g g g l D 6/8/016 3

Pr Pr D g,{ }; T Questons Exp ; g; E 1 How to deal wth normalzaton floatng parameter? [P.S.Baranov, A.L.L vov et.al.,physcs of Partcles and Nucleus, 3, 376 (001)] Assgn flat prors for parameters? (red for Gaussan a0, blue and green wth flat a0 pror) # data # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j j 1 S l g g g l D 6/8/016 3

Pr Pr D g,{ }; T Questons Exp ; g; E 1 How to deal wth normalzaton floatng parameter? [P.S.Baranov, A.L.L vov et.al.,physcs of Partcles and Nucleus, 3, 376 (001)] Assgn flat prors for parameters? (red for Gaussan a0, blue and green wth flat a0 pror) # data # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j j 1 S l g g g l D 6/8/016 3

Pr Pr D g,{ }; T Questons Exp ; g; E 1 How to deal wth normalzaton floatng parameter? [P.S.Baranov, A.L.L vov et.al.,physcs of Partcles and Nucleus, 3, 376 (001)] Assgn flat prors for parameters? (red for Gaussan a0, blue and green wth flat a0 pror) Over fttng? Is there a best ft? # data # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j j 1 S l g g g l D 6/8/016 3

Pr Pr D g,{ }; T Questons Exp ; g; E 1 How to deal wth normalzaton floatng parameter? [P.S.Baranov, A.L.L vov et.al.,physcs of Partcles and Nucleus, 3, 376 (001)] Assgn flat prors for parameters? (red for Gaussan a0, blue and green wth flat a0 pror) Over fttng? Is there a best ft? # data # sys err # para 0 j l l g,{ } T Exp Exp or flat ds. j j 1 S l g g g l D 6/8/016 3

About MCMC 6/8/016 33

About MCMC Acceptance s 15%, good? 6/8/016 33

About MCMC Acceptance s 15%, good? How about auto-correlaton length? 6/8/016 33

About MCMC Acceptance s 15%, good? How about auto-correlaton length? Is 7000-samples enough? 6/8/016 33

About MCMC Acceptance s 15%, good? How about auto-correlaton length? Is 7000-samples enough? How to estmate the error of error? 6/8/016 33

About MCMC Acceptance s 15%, good? How about auto-correlaton length? Is 7000-samples enough? How to estmate the error of error? Comment on VEGAS algorthm due to Lepage? 6/8/016 33

Summary EFT works for ths reacton Bayesan analyss s used to quantfy uncertantes Choce of data sets, theoretcal error, and choce of prors have been tested Questons 6/8/016 34

Back up 6/8/016 35

Solar abundance problem: Neutrnos 6/8/016 36

Solar abundance problem: Neutrnos Extract C+N abundance W. C. Haxton et.al. (013) 6/8/016 36

Solar abundance problem: Neutrnos A-few-percent measurements Extract C+N abundance W. C. Haxton et.al. (013) 6/8/016 36

Solar abundance problem: Neutrnos To be measured A-few-percent measurements Extract C+N abundance W. C. Haxton et.al. (013) 6/8/016 36

Solar abundance problem: Neutrnos To be measured A-few-percent measurements Extract C+N abundance Knowng C+N can also be used to dfferentate solar models. However nuclear cross secton error agan needs to be reduced. W. C. Haxton et.al. (013) 6/8/016 36

Solar abundance problem: Neutrnos To be measured A-few-percent measurements Extract C+N abundance Knowng C+N can also be used to dfferentate solar models. However nuclear cross secton error agan needs to be reduced. W. C. Haxton et.al. (013) 6/8/016 36

Solar abundance problem: Debate! 6/8/016 37

Solar abundance problem: Debate! arxv:1603.05960, 1604.05318 6/8/016 37

Solar abundance problem: Debate! arxv:1603.05960, 1604.05318 Capture reacton study wll make an mpact! 6/8/016 37