BLM Trip Limits Revisited May 28, 2004 Peter Kasper.

Slides:



Advertisements
Similar presentations
What is the sum of the following infinite series 1+x+x2+x3+…xn… where 0
Advertisements

Normal Distributions: Finding Probabilities
Time Series Building 1. Model Identification
Regression Analysis Module 3. Regression Regression is the attempt to explain the variation in a dependent variable using the variation in independent.
LHC Collimation Working Group – 19 December 2011 Modeling and Simulation of Beam Losses during Collimator Alignment (Preliminary Work) G. Valentino With.
Exponential Smoothing Methods
Section 4.2 Fitting Curves and Surfaces by Least Squares.
9. SIMPLE LINEAR REGESSION AND CORRELATION
Introduction to Production and Resource Use Chapter 6.
Petter Mostad Linear regression Petter Mostad
2: Population genetics Break.
F.Brinker, DESY, July 17 st 2008 Injection to Doris and Petra Fitting the detector in the IP-region Radiation issues Beam optic, Target cell Polarisation.
© John M. Abowd 2005, all rights reserved Modeling Integrated Data John M. Abowd April 2005.
An Example of Fitting ODE Parameters to Data Using Excel Narrated by Kerry Braxton-Andrew And Josh Katzenstein.
Chapter 11 Multiple Regression.
April 1, Beam measurement with -Update - David Jaffe & Pedro Ochoa 1)Reminder of proposed technique 2)Use of horn-off data 3)Use of horn2-off data?
Correlation and Regression Analysis
1 BABS 502 Moving Averages, Decomposition and Exponential Smoothing Revised March 11, 2011.
RLSELE Adaptive Signal Processing 1 Recursive Least-Squares (RLS) Adaptive Filters.
Diane Stockton Trend analysis. Introduction Why do we want to look at trends over time? –To see how things have changed What is the information used for?
Inference for regression - Simple linear regression
Copyright © Cengage Learning. All rights reserved. 13 Linear Correlation and Regression Analysis.
Time-Series Analysis and Forecasting – Part V To read at home.
The Examination of Residuals. The residuals are defined as the n differences : where is an observation and is the corresponding fitted value obtained.
880.P20 Winter 2006 Richard Kass 1 Confidence Intervals and Upper Limits Confidence intervals (CI) are related to confidence limits (CL). To calculate.
880.P20 Winter 2006 Richard Kass 1 Maximum Likelihood Method (MLM) Does this procedure make sense? The MLM answers this question and provides a method.
ESTIMATES AND SAMPLE SIZES
1 What Is Forecasting? Sales will be $200 Million!
Ch4 Describing Relationships Between Variables. Section 4.1: Fitting a Line by Least Squares Often we want to fit a straight line to data. For example.
Chapter 17 Time Series Analysis and Forecasting ©.
Bayesian Methods I: Parameter Estimation “A statistician is a person who draws a mathematically precise line from an unwarranted assumption to a foregone.
Lecture 8 Simple Linear Regression (cont.). Section Objectives: Statistical model for linear regression Data for simple linear regression Estimation.
Dr. Harris Lecture 18 HW: Ch 17: 5, 11, 18, 23, 41, 50 Ch 17: Kinetics Pt 1.
Copyright © 2015 Chris J Jewell 1 Mechanics M1 (Slide Set 11) Core Mathematics for Mechanics M1 Mechanics M1.
1-Compartment Oral Dosing 400 mg of moxifloxacin is administered orally to Mr BB, a 68 yr old male who weighs 75 kg. Blood samples were drawn following.
CHEM 312: Lecture 3 Radioactive Decay Kinetics
© 1999 Prentice-Hall, Inc. Chap Chapter Topics Component Factors of the Time-Series Model Smoothing of Data Series  Moving Averages  Exponential.
Predicting Residual Radiation using Beam Loss Data – Early Results Bruce C. Brown 10 March 2010 Main Injector/Recycler Group Meeting.
Overview: Primary Sensitivities Nov S. Childress Page 1 NuMI Overview: NuMI Primary Beamline Sensitivities NuMI requirements are for a very large.
Estimating Volatilities and Correlations Chapter 21.
E02-017: Lifetime of Heavy Hypernuclei Introduction and Status Xiyu Qiu Lanzhou University Hall C meeting Jan 13, 2012.
Residual Radiation Cooldown: Main Injector Bruce C. Brown Fermilab All Experimenters Meeting 19 March 2012.
Measurements of Top Quark Properties at Run II of the Tevatron Erich W.Varnes University of Arizona for the CDF and DØ Collaborations International Workshop.
One Madison Avenue New York Reducing Reserve Variance.
T-TestsSlide #1 2-Sample t-test -- Examples Do mean test scores differ between two sections of a class? Does the average number of yew per m 2 differ between.
Error Modeling Thomas Herring Room ;
1 BABS 502 Moving Averages, Decomposition and Exponential Smoothing Revised March 14, 2010.
Responsive Reserve Service Deliverability Review September 15,
SYSTEMS Identification Ali Karimpour Assistant Professor Ferdowsi University of Mashhad Reference: “System Identification Theory.
Penny Kasper Fermilab Heavy Quarkonium Workshop 21 June Upsilon production DØ Penny Kasper Fermilab (DØ collaboration) 29 June 2006 Heavy Quarkonium.
CHAPTER 4 ESTIMATES OF MEAN AND ERRORS. 4.1 METHOD OF LEAST SQUARES I n Chapter 2 we defined the mean  of the parent distribution and noted that the.
CS203 – Advanced Computer Architecture Dependability & Reliability.
MODELLING SPOT PRICE OF ELECTRICITY IN NSW Hilary Green with Nino Kordzakhia and Ruben Thoplan 15th International Conference Computing in Economics.
Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.
Practice Problems Actex 3, 4, 5. Section 3 -- #3 A box contains 4 red balls and 6 white balls. A sample of size 3 is drawn without replacement from the.
Lecture 9 Forecasting. Introduction to Forecasting * * * * * * * * o o o o o o o o Model 1Model 2 Which model performs better? There are many forecasting.
Chapter Eight Estimation.
Perfect (or pure) Competition
Activities on straw tube simulation
S. Roesler (on behalf of DGS-RP)
Forecasting Methods Dr. T. T. Kachwala.
4 Components of Every Exercise Session/MEASURING HEART RATE
Chapter 4: Seasonal Series: Forecasting and Decomposition
Update on TB 2007 Xtal Irradiation Studies at H4
Normalization p-Be 40 GeV (2001 data)
Current status Minjung Kim.
ENM 310 Design of Experiments and Regression Analysis Chapter 3
Samuel T. Hess, Watt W. Webb  Biophysical Journal 
Geometric Sequences and Series
Jun’ichi Wakayama, Takumi Tamura, Naoto Yagi, Hiroyuki Iwamoto 
Presentation transcript:

BLM Trip Limits Revisited May 28, 2004 Peter Kasper

2 Relating Measured Losses to Measured Activation Measured activations A(t) and losses L(t) are related as follows.. A(T) =  i [ A(0). X i. e -K i T + F i.  T L(t). e -K i( T-t). dt ] where the sum is over produced isotopes i.. X i is the initial fraction of isotope i K i is the decay constant for isotope i and F i is a geometry dependent conversion factor for isotope i The maximum activation A max from running at a constant loss rate L max for an infinite time is given by.. A max =  i ( L max. F i / K i ) If we can determine X i, F i, and K i, we can use L max to limit A max.

3 Determining K i Cool down data from 13-Jan-03 to 27-Jan-03 plus measurements during the long shutdown (10-Sep-03 and 03-Nov-03) Data from each location normalized to have the same average Fit to both a single and a double exponential

4 Lifetime Measurements Fit a double exponential to averaged, normalized data assuming long-lived isotope is 54 Mn ( half life = 303 days) Fitted lifetime of 2 nd exponential is 5.6 days. This is very close to that of 18 Fe (5.7 days) Choice of long-lived isotope is not important; good fits can also be obtained with 57 Co (282 days) or 22 Na (2.6 years) Short-lived isotopes affect measurements taken within an hour or so of beam Assume a short-lived component due to (I forget) with half life 1.8 hours Thus model for a given location has three isotopes and 6 free parameters X i and F i.

5 Determining Maximum Activations Assume only two isotopes K 1 = 1.24E-1 and K 2 = 2.46E-3. also X 1 = 1 – X 2 Assume that the asymptotic isotope mixture is 50:50 for all locations i.e. F 1 / K 1 = F 2 / K 2 => F 2 = F 1. ( K 2 / K 1 ) Use two activation measurements to constrain F 1 … F 1 = [ A T – A O.(1 - X 2 ). e -K 1 T - A O. X 2. e -K 1 T ] / [ S 1 T + S 2 T. K 2 /K 1 ] S i T =  T L(t). e -K i( T-t). dt S i T is determined by using D44 to obtain BLM readings (B:BLxxx0) at ~1 minute intervals and then calculating weighted sums Either set X 2 = 0.5 (asymptotic assumption) or fit to the recent series of weekly activation measurements Calculate A max for each location using current trip points

6 Fit Residuals

7 Fit Quality vs Location

8 Fit Summary - I Predicted maximum activation is inversely correlated with X 2. (X 0 ) 1 st column: X 2 fixed at 0.5 or value where A max is less than the maximum measured activation. 2 nd column: X 2 is fitted. is r.m.s. of fractional fit residuals not constrained to be zero Red numbers correspond to A max > 200 mr/hr and good fit ( < 0.2 )

9 Fit Summary - II Predicted maximum activation is inversely correlated with X 2. (X 0 ) 1 st column: X 2 fixed at 0.5 or value where A max is less than the maximum measured activation. 2 nd column: X 2 is fitted. is r.m.s. of fractional fit residuals not constrained to be zero Red numbers correspond to A max > 200 mr/hr and good fit ( < 0.2 )

10 Suggested Changes I RF sections – limit to 200 mr/hr assuming fitted value of X 2 B:BLL → 50< 15 last week B:BLL160No action Low A max B:BLL → 25< 10 last week B:BLL190No action Low A max B:BLL → 65< 25 last week B:BLL → 170< 65 last week B:BLL → 90< 15 last week Fit quality dominated by one dubious measurement B:BLL → 70< 50 last week RF sections – limit to 200 mr/hr assuming X 2 = 0.5 (fitted A max is OK) B:BLL → 55< 49 last week

11 Suggested Changes II Other areas with good fits – limit to 200 mr/hr assuming fitted value of X 2 B:BL → 105< 86 last week B:BL → 21< 26 & averaged 15 last week B:BLL → 140< 30 last week B:BLL → 55< 12 last week B:BLL → 85< 23 last week B:BLS → 590< 300 last week B:BLS → 345< 245 last week B:BLS → 570< 700 & averaged 464 last week B:BLS → 255< 27 last week B:BLS → 940< 600 last week B:BLS → 355< 35 last week B:BLS → 365< 20 last week B:BLS → 370< 30 last week B:BLS → 865< 35 last week

12 Suggested Changes III Other areas – limit to 200 mr/hr assuming fitted value of X 2 B:BLS → 130< 75 last week Fit quality dominated by one dubious measurement B:BLS → 205< 15 last week Fit better than indicated due to low activation levels B:BL → 50< 100 & averaged 36 last week Fit borderline OK Other areas – limit to 200 mr/hr assuming X 2 = 0.5 (fitted A max is OK) B:BLS → 170< 100 last week B:BLS → 570< 75 last week Extraction regions - limit to 300 mr/hr assuming X 2 = 0.5 B:BL → 195< 300 & averaged 190 last week B:BLL → 605< 500 last week B:BL → 95< 70 last week B:BL → 135averaged 36 last week L10 - limit to 300 mr/hr assuming X 2 = 0.5 B:BLL → 200 < 300 & averaged 211 last week !!

13 The Rest I B:BLL010No actionPoor fit B:BLL020No actionPoor fit & Low A max B:BLL030No actionPoor fit & Low A max for X 2 =0.5 B:BLL050No action Poor fit & Low A max B:BLL060No actionPoor fit B:BLL070No action Low A max B:BLL080No actionPoor fit & Low A max B:BLL090No action Low A max B:BLL120No actionPoor fit & Low A max B:BLL200No action Low A max B:BL0250No action A max < 300 mr/hr B:BL0510No actionPoor fit & Low A max B:BL0520No actionPoor fit B:BL0610No actionPoor fit B:BL0710No actionPoor fit

14 The Rest II B:BLS020No actionMarginal fit & Low A max B:BLS040No action Low A max B:BLS070No action Poor fit B:BLS080No actionLow A max B:BLS090No actionPoor fit & Low A max B:BLS120No actionA max OK B:BLS190No action Poor fit & Low A max B:BLS210No action Low A max B:BLS220No action Low A max B:BLS230No action Poor fit & Low A max B:BLS240No action Low A max