Checking the resolved resonance region in EXFOR database

Similar documents
ENSC Discrete Time Systems. Project Outline. Semester

, which yields. where z1. and z2

Chapter 3: Cluster Analysis

CS 477/677 Analysis of Algorithms Fall 2007 Dr. George Bebis Course Project Due Date: 11/29/2007

INFLUENCE OF SYSTEMATIC EXPERIMENTAL UNCERTAINTIES IN THE EVALUATION OF NUCLEAR DATA

Hypothesis Tests for One Population Mean

Part 3 Introduction to statistical classification techniques

Math Foundations 20 Work Plan

MATHEMATICS SYLLABUS SECONDARY 5th YEAR

Section 6-2: Simplex Method: Maximization with Problem Constraints of the Form ~

You need to be able to define the following terms and answer basic questions about them:

PSU GISPOPSCI June 2011 Ordinary Least Squares & Spatial Linear Regression in GeoDa

Least Squares Optimal Filtering with Multirate Observations

Tree Structured Classifier

Bootstrap Method > # Purpose: understand how bootstrap method works > obs=c(11.96, 5.03, 67.40, 16.07, 31.50, 7.73, 11.10, 22.38) > n=length(obs) >

LCAO APPROXIMATIONS OF ORGANIC Pi MO SYSTEMS The allyl system (cation, anion or radical).

7 TH GRADE MATH STANDARDS

A New Evaluation Measure. J. Joiner and L. Werner. The problems of evaluation and the needed criteria of evaluation

T Algorithmic methods for data mining. Slide set 6: dimensionality reduction

Computational modeling techniques

Modelling of Clock Behaviour. Don Percival. Applied Physics Laboratory University of Washington Seattle, Washington, USA

THERMAL-VACUUM VERSUS THERMAL- ATMOSPHERIC TESTS OF ELECTRONIC ASSEMBLIES

CAUSAL INFERENCE. Technical Track Session I. Phillippe Leite. The World Bank

Time, Synchronization, and Wireless Sensor Networks

x 1 Outline IAML: Logistic Regression Decision Boundaries Example Data

Performance Bounds for Detect and Avoid Signal Sensing

Resampling Methods. Cross-validation, Bootstrapping. Marek Petrik 2/21/2017

CHAPTER 4 DIAGNOSTICS FOR INFLUENTIAL OBSERVATIONS

The Kullback-Leibler Kernel as a Framework for Discriminant and Localized Representations for Visual Recognition

CHAPTER 3 INEQUALITIES. Copyright -The Institute of Chartered Accountants of India

Internal vs. external validity. External validity. This section is based on Stock and Watson s Chapter 9.

SUPPLEMENTARY MATERIAL GaGa: a simple and flexible hierarchical model for microarray data analysis

February 28, 2013 COMMENTS ON DIFFUSION, DIFFUSIVITY AND DERIVATION OF HYPERBOLIC EQUATIONS DESCRIBING THE DIFFUSION PHENOMENA

Public Key Cryptography. Tim van der Horst & Kent Seamons

Quantum Harmonic Oscillator, a computational approach

COMP 551 Applied Machine Learning Lecture 9: Support Vector Machines (cont d)

Perfrmance f Sensitizing Rules n Shewhart Cntrl Charts with Autcrrelated Data Key Wrds: Autregressive, Mving Average, Runs Tests, Shewhart Cntrl Chart

Early detection of mining truck failure by modelling its operation with neural networks classification algorithms

IAML: Support Vector Machines

Intelligent Pharma- Chemical and Oil & Gas Division Page 1 of 7. Global Business Centre Ave SE, Calgary, AB T2G 0K6, AB.

NGSS High School Physics Domain Model

Session #22: Homework Solutions

k-nearest Neighbor How to choose k Average of k points more reliable when: Large k: noise in attributes +o o noise in class labels

ELECTRON CYCLOTRON HEATING OF AN ANISOTROPIC PLASMA. December 4, PLP No. 322

Distributions, spatial statistics and a Bayesian perspective

BLAST / HIDDEN MARKOV MODELS

NUROP CONGRESS PAPER CHINESE PINYIN TO CHINESE CHARACTER CONVERSION

[COLLEGE ALGEBRA EXAM I REVIEW TOPICS] ( u s e t h i s t o m a k e s u r e y o u a r e r e a d y )

Preparation work for A2 Mathematics [2018]

Fall 2013 Physics 172 Recitation 3 Momentum and Springs

Turing Machines. Human-aware Robotics. 2017/10/17 & 19 Chapter 3.2 & 3.3 in Sipser Ø Announcement:

Statistics Statistical method Variables Value Score Type of Research Level of Measurement...

Cambridge Assessment International Education Cambridge Ordinary Level. Published

Multiple Source Multiple. using Network Coding

A Frequency-Based Find Algorithm in Mobile Wireless Computing Systems

making triangle (ie same reference angle) ). This is a standard form that will allow us all to have the X= y=

( ) kt. Solution. From kinetic theory (visualized in Figure 1Q9-1), 1 2 rms = 2. = 1368 m/s

Simple Linear Regression (single variable)

Mathematics Methods Units 1 and 2

Supporting information

Preparation work for A2 Mathematics [2017]

Collocation Map for Overcoming Data Sparseness

Slide04 (supplemental) Haykin Chapter 4 (both 2nd and 3rd ed): Multi-Layer Perceptrons

Statistics, Numerical Models and Ensembles

the results to larger systems due to prop'erties of the projection algorithm. First, the number of hidden nodes must

Green economic transformation in Europe: territorial performance, potentials and implications

Standard Title: Frequency Response and Frequency Bias Setting. Andrew Dressel Holly Hawkins Maureen Long Scott Miller

Emphases in Common Core Standards for Mathematical Content Kindergarten High School

Pattern Recognition 2014 Support Vector Machines

Sequential Allocation with Minimal Switching

NAME: Prof. Ruiz. 1. [5 points] What is the difference between simple random sampling and stratified random sampling?

Admin. MDP Search Trees. Optimal Quantities. Reinforcement Learning

ELT COMMUNICATION THEORY

Chapter Summary. Mathematical Induction Strong Induction Recursive Definitions Structural Induction Recursive Algorithms

Materials Engineering 272-C Fall 2001, Lecture 7 & 8 Fundamentals of Diffusion

A Polarimetric Survey of Radio Frequency Interference in C- and X-Bands in the Continental United States using WindSat Radiometry

Resampling Methods. Chapter 5. Chapter 5 1 / 52

Contributions to the Theory of Robust Inference

Building to Transformations on Coordinate Axis Grade 5: Geometry Graph points on the coordinate plane to solve real-world and mathematical problems.

Determining Optimum Path in Synthesis of Organic Compounds using Branch and Bound Algorithm

Determining the Accuracy of Modal Parameter Estimation Methods

4th Indian Institute of Astrophysics - PennState Astrostatistics School July, 2013 Vainu Bappu Observatory, Kavalur. Correlation and Regression

Differentiation Applications 1: Related Rates

SURVIVAL ANALYSIS WITH SUPPORT VECTOR MACHINES

Churn Prediction using Dynamic RFM-Augmented node2vec

The blessing of dimensionality for kernel methods

UN Committee of Experts on Environmental Accounting New York, June Peter Cosier Wentworth Group of Concerned Scientists.

MATCHING TECHNIQUES. Technical Track Session VI. Emanuela Galasso. The World Bank

MACHINE LEARNING FOR CLUSTER- GALAXY CLASSIFICATION

Name AP CHEM / / Chapter 1 Chemical Foundations

CHAPTER 24: INFERENCE IN REGRESSION. Chapter 24: Make inferences about the population from which the sample data came.

We say that y is a linear function of x if. Chapter 13: The Correlation Coefficient and the Regression Line

COMP 551 Applied Machine Learning Lecture 11: Support Vector Machines

Function notation & composite functions Factoring Dividing polynomials Remainder theorem & factor property

Methods and approaches to provide feedback from nuclear and covariance data adjustment for improvement of nuclear data files

Math 302 Learning Objectives

Snow avalanche runout from two Canadian mountain ranges

Math 10 - Exam 1 Topics

Section 5.8 Notes Page Exponential Growth and Decay Models; Newton s Law

Transcription:

Checking the reslved resnance regin in EXFOR database Gttfried Bertn Sciété de Calcul Mathématique (SCM) Oscar Cabells OECD/NEA Data Bank JEFF Meetings - Sessin JEFF Experiments Nvember 0-4, 017 Bulgne-Billancurt, France

1. Presentatin f the SCM activities Mathematical mdelling cmpany, established in 1995; Creates mathematical tls fr decisin help; Specialized in rbust mdelling; Main branches f activities: energy, envirnment, health, transprtatin, scientific assistance t large prjects Our wrk in nuclear sectr: Malfunctins in sensr netwrks; Outlier detectin, recnstructing missing infrmatin; Lking fr znes with highest risk; Evaluating the perfrmance f a netwrk f sensrs (e.g TELERAY); Taking int accunt the uncertainties in cmputatinal cdes.

. Objectives Crss-checking the experimental data (EXFOR) with the evaluated nes Prviding a list f suspicius data Ranking the entries t see which data are ptentially errneus and which are reliable Applying the wrk n mst nuclear data: Istpes and natural elements Threshld reactins, ismeric transitins, angular distributins, etc. Neutrn reactins. Taking the uncertainties f bth EXFOR and ENDF int accunt

3. Methdlgy in 016 Cmpute the distance between a curve (PENDF) and a set f pints (EXFOR) The distance is the interval between tw 95% vertical cnfidence intervals fr EXFOR and ENDF Cmpute the min distance ver the discretized hrizntal cnfidence interval Fig. 1. General principle f the methd Definitin f a Ranking value t identify the ptential prblems in EXFOR r ENDF: distance rati = max σ EXFOR, σ ENDF

4. Implementatin 1) Finding the right scale fr abscissa and discretizing it in 50 intervals ) Cnstructing the resnance indicatr as the relative variance 3) Cmputing the distance ratis fr each intervals: In a n-resnance interval: average f the pintwise distances In a resnance interval: difference between integral f EXFOR and ENDF

4. Implementatin 4) Averaging the ratis f the 50 intervals and cmputing the final ranking in A, B,, E 5) Cmpute the rank f the wrst single pint t detect single utliers (Fig. ) Fig.. Single pint aberrant in Carbn natural element

4. Implementatin This methd has limitatins in the resnance intervals Effect f reslutin bradening in regin f high variability: the crss-sectin measured is an averaging f the theretical crss-sectins at different energies

5. Results (016)

6. SCM s Methdlgy applied in RRR (017) Recver the reslutin functin in rder t: Cmpare PENDF and EXFOR in resnance regin Assess the shape f the reslutin functin (fr n_tof and GELINA entries) Verify if the reslutin changes with energy Detect islated sets f pints and utliers: impssibility t find a reslutin functin, abnrmally high reslutin, etc. Find missing peak in ENDF (r cntaminatin in EXFOR) Check nrmalizatin

6. SCM s Methdlgy applied in RRR (017) Discretize the energy dmain s that there are 50 resnance peaks in each energy bin Fr each energy bin: Checking Nrmalizatin by cmputing the rati between integral f EXFOR and PENDF Calculate the reslutin functin: the EXFOR curve is a mving average f the ENDF curve: find the cefficients x j f this averaging

6. SCM s Methdlgy applied in RRR (017) Discretize the energy dmain s that there are 50 resnance peaks in each energy bin Fr each energy bin: Crss-sectin (b) ENDF b a x a x a x 1 1,1 1 1, 1,3 3 b a x a x a x,1 1,,3 3 Energy (ev) x x x 1 3???

6. SCM s Methdlgy applied in RRR (017) Discretize the energy dmain s that there are 50 resnance peaks in each energy bin Fr each energy bin: Calculate the reslutin ΔE: hw spread is the reslutin functin is Check this value against resnance energy ΔE/E Is it an abnrmally high value? Des this rati change fr the different energy bins?

7. Finding a reslutin functin T find the cefficients x i, slving the system: n x jai, j bi, i 1,..., N j 0, 1,..., j 1 x j n b i is the crss-sectin f EXFOR a i,j crss-sectin f ENDF at energies arund EXFOR energy n the number f cefficients, N the number f EXFOR measures

7. Finding a reslutin functin Slving using the least squares methd T take int accunt uncertainties, use prbabilistic methd: Archimedes methd Allws t calculate fr each cefficient the expectatin (blue), and lwer/upper bunds (black) ΔE Calculate the uncertainty upn the reslutin t btain ΔE ± δ

7. Finding a reslutin functin Archimedes methd: 1. Generating a candidate reslutin functin, i.e a set f cefficients x j. At each EXFOR energy i Applying the bradening n the PENDF curve using the reslutin functin x j Cmparing this PENDF value t the EXFOR crss-sectin b i : define prbability p i t be arund b i using the uncertainty n each measure 3. Calculating the weight f the candidate slutin as Φ = p 1 p p N 4. Nrmalizing by the sum f the weights when generating all the slutins

7. Finding a reslutin functin Each candidate slutin has a prbability assciated Fr each candidate we can calculate the reslutin ΔE Eventually, we btain a prbability law upn the reslutin, and each cefficient x j It wrks als fr nn-linear systems and any kind f uncertainty (nt nly Gaussian)

7. Finding a reslutin functin Hw t generate the candidates slutins? Numerical example: b 1 = 10b with standard deviatin b, 95% prbability t be in [4; 16] b = 1b with standard deviatin 1b 95% prbability t be in [9; 15] We btain a system f inequalities: Crss-sectin (b) ENDF 4 9.1x 9.5x 10.x 16 1 3 9 10.1x 9.4x 11.3x 15 1 3 Energy (ev)

7. Finding a reslutin functin These inequalities are the intersectin f half-spaces, and frm a cnvex space 4 9.1x 9.5x 10.x 16 1 3 9 10.1x 9.4x 11.3x 15 1 3 We generate nly candidate slutins in this cnvex subspace by perfrming a randm walk in it Checking existence f a slutin t this system abve using simplex algrithm. N slutin culd mean: t small EXFOR uncertainties with respect t the distance with ENDF islated set f pints

8. Results Example f reslutin functin btained fr n_tof data (figure at right) Green line: ENDF Pink line: Bradened ENDF using cefficients n figure at right Blue: EXFOR Usually small reslutin (0.8% apprximately in the example abve)

8. Results Added cnstraint n the shape (cnvlutin f expnential and multiple gaussians as used in SAMMY cde) Adding such cnstraint ften leads t pr match between bradened ENDF and EXFOR

9. Data Data prcessed at rm temperature (Dppler bradening) Applied t large entries (TOF measurements frm GELINA and CERN) Reslutin functin defined n an interval energy f 50% arund the resnance energy Hrizntal shift between ENDF and EXFOR: recenter afterwards the reslutin functin btained t align ENDF t EXFOR.

10. Find missing peak Case 1: cntaminatin frm anther istpe in EXFOR

10. Find missing peak Case 1: cntaminatin frm anther istpe in EXFOR

10. Find missing peak Case : missing resnance in ENDF Methd t detect it: Bradening f each ENDF using the RF Fr each lcal maximum in EXFOR, calculate the distance EXFOR/ENDF If large distance, cunt the number f resnance peaks arund this peak fr each evaluatr If there is disagreement between the evaluatrs n the number f peaks: reprt JEFF? Peak fr ENDF but nt fr JEFF

10. Find missing peak Case : missing resnance in ENDF One mre example ENDF (OK) TENDL (??)

11. Find prblem in nrmalizatin First case: integrals dn t match, n ambiguity Smething wrng independently f reslutin Secnd case: is the vertical shift due t nrmalizatin prblem r reslutin bradening? Can we say it visually? N, t verify: check the existence f a reslutin functin having sum equal t ne? yes: the shift is due t reslutin effect n: the shift is due t a nrmalizatin prblem

1. Change in reslutin Is there a change in reslutin ΔE at a certain energy?

1. Change in reslutin Energy <0eV (green line) Energy >0eV

1. Change in reslutin Take int accunt relative reslutin ΔE/E The rati ΔE/E remains the same at left and right Energy 5 50 ev Energy 670 710 ev

1. Change in reslutin Plt fr each energy bin, the reslutin btained. Fr tw different entries: Entry #1: N change in reslutin Entry #: Change in reslutin at 1 MeV

13. Find utliers After crrectin fr reslutin bradening, pintwise cmparisn allws t detect utliers in resnance regin (n_tof data): subentry 33.

13. Find utliers Check als situatins f strange reslutin functin r impssible t calculate: Generally, decreasing when away frm the center f the distributin

14. Other remark Is there a physical cnstraint n the reslutin functin that shuld be added? Why shuld be Gaussian? Different reslutin functins can lead t the same result (pink and red)

15. Cnclusin This wrk allwed t cmpare the ENDF and EXFOR in the reslved resnance zne Checking missing peak in ENDF Detecting islated sets f pints and ptential utliers Assessing the reslutin functin fr n_tof and GELINA data per energy bin and hw the reslutin changes with energy