Allan Variance Professor Hans Schuessler 689 Modern Atomic Physics Aysenur Bicer.

Slides:



Advertisements
Similar presentations
Memory Aid Help.  b 2 = c 2 - a 2  a 2 = c 2 - b 2  “c” must be the hypotenuse.  In a right triangle that has 30 o and 60 o angles, the longest.
Advertisements

BPT2423 – STATISTICAL PROCESS CONTROL
Simple Linear Regression and Correlation (Part II) By Asst. Prof. Dr. Min Aung.
Statistical properties of Random time series (“noise”)
AOSC 634 Air Sampling and Analysis Lecture 1 Measurement Theory Performance Characteristics of instruments Nomenclature and static response Copyright Brock.
Analyzing the Results of a Simulation and Estimating Errors Jason Cooper.
Error Propagation. Uncertainty Uncertainty reflects the knowledge that a measured value is related to the mean. Probable error is the range from the mean.
Statistics.
CE 428 LAB IV Error Analysis (Analysis of Uncertainty) Almost no scientific quantities are known exactly –there is almost always some degree of uncertainty.
Chapter 6 Random Error The Nature of Random Errors
On the Accuracy of Modal Parameters Identified from Exponentially Windowed, Noise Contaminated Impulse Responses for a System with a Large Range of Decay.
V. Rouillard  Introduction to measurement and statistical analysis ASSESSING EXPERIMENTAL DATA : ERRORS Remember: no measurement is perfect – errors.
Vibrationdata 1 Unit 5 The Fourier Transform. Vibrationdata 2 Courtesy of Professor Alan M. Nathan, University of Illinois at Urbana-Champaign.
Memory Aid Help.  b 2 = c 2 - a 2  a 2 = c 2 - b 2  “c” must be the hypotenuse.  In a right triangle that has 30 o and 60 o angles, the longest.
Lecture 1 Signals in the Time and Frequency Domains
Simulation of Random Walk How do we investigate this numerically? Choose the step length to be a=1 Use a computer to generate random numbers r i uniformly.
Chapter 4 Statistics. 4.1 – What is Statistics? Definition Data are observed values of random variables. The field of statistics is a collection.
Physics 114: Exam 2 Review Lectures 11-16
Wireless Communication Technologies 1 Phase noise A practical oscillator does not produce a carrier at exactly one frequency, but rather a carrier that.
Medicaps Institute of Technology & Management Submitted by :- Prasanna Panse Priyanka Shukla Savita Deshmukh Guided by :- Mr. Anshul Shrotriya Assistant.
Sampling Error SAMPLING ERROR-SINGLE MEAN The difference between a value (a statistic) computed from a sample and the corresponding value (a parameter)
Time-dependent Schrodinger Equation Numerical solution of the time-independent equation is straightforward constant energy solutions do not require us.
1 Chapter 9 Detection of Spread-Spectrum Signals.
LEAST MEAN-SQUARE (LMS) ADAPTIVE FILTERING. Steepest Descent The update rule for SD is where or SD is a deterministic algorithm, in the sense that p and.
Radiation Detection and Measurement, JU, First Semester, (Saed Dababneh). 1 Counting Statistics and Error Prediction Poisson Distribution ( p.
Long Term Stability in CW Cavity Ring-Down Experiments
Statistics 1: Introduction to Probability and Statistics Section 3-2.
Radiation Detection and Measurement, JU, 1st Semester, (Saed Dababneh). 1 Radioactive decay is a random process. Fluctuations. Characterization.
Electronic Noise Noise phenomena Device noise models
11 Stochastic age-structured modelling: dynamics, genetics and estimation Steinar Engen, Norwegian University of Science and Technology Abstract In his.
ECE-7000: Nonlinear Dynamical Systems 2. Linear tools and general considerations 2.1 Stationarity and sampling - In principle, the more a scientific measurement.
A Comparison of Burst Gravitational Wave Detection Algorithms for LIGO Amber L. Stuver Center for Gravitational Wave Physics Penn State University.
CHAPTER- 3.1 ERROR ANALYSIS.  Now we shall further consider  how to estimate uncertainties in our measurements,  the sources of the uncertainties,
Lecture 8. Comparison of modulaters sizeCapacitanceInsertion loss Chirping Electro- absorption smalllowerhighersome Electro-optic type (LiNbO3) largehigherlowernone.
Chapter 6 Random Processes
Basic Counting StatisticsBasic Counting StatisticsBasic Counting StatisticsBasic Counting Statistics.
Systematic errors of MC simulations Equilibrium error averages taken before the system has reached equilibrium  Monitor the variables you are interested.
Oscillator models of the Solar Cycle and the Waldmeier-effect
Quantitative Data Continued
EE599-2 Audio Signals and Systems
Software Based Separation of Amplitude and Phase Noises in Time Domain
Physics 114: Exam 2 Review Weeks 7-9
Lesson 2: Performance of Control Systems
Unit 5 The Fourier Transform.
The break signal in climate records: Random walk or random deviations
Volume 89, Issue 2, Pages (August 2005)
Physics 114: Exam 2 Review Material from Weeks 7-11
NTP Clock Discipline Modelling and Analysis
General Properties of Radiation
Introduction to Instrumentation Engineering
Lecture 2 – Monte Carlo method in finance
Chapter 3D Chapter 3, part D Fall 2000.
Volume 34, Issue 5, Pages (May 2002)
Electronic Noise Noise phenomena Device noise models
Statistics 1: Introduction to Probability and Statistics
CHAPTER- 3.1 ERROR ANALYSIS.
Volume 5, Issue 4, Pages e4 (October 2017)
Volume 34, Issue 5, Pages (May 2002)
H.Sebastian Seung, Daniel D. Lee, Ben Y. Reis, David W. Tank  Neuron 
Slow Chromatin Dynamics Allow Polycomb Target Genes to Filter Fluctuations in Transcription Factor Activity  Scott Berry, Caroline Dean, Martin Howard 
Testing the Fit of a Quantal Model of Neurotransmission
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Chapter 6 Random Processes
Volume 89, Issue 2, Pages (August 2005)
Daniel Krofchick, Mel Silverman  Biophysical Journal 
Quantum phase magnification
The Mars Pathfinder Atmospheric Structure Investigation/Meteorology (ASI/MET) Experiment by J. T. Schofield, J. R. Barnes, D. Crisp, R. M. Haberle, S.
Threshold Autoregressive
Basic descriptions of physical data
CHAPTER – 1.2 UNCERTAINTIES IN MEASUREMENTS.
Presentation transcript:

Allan Variance Professor Hans Schuessler 689 Modern Atomic Physics Aysenur Bicer

History of Allan Variance (1966) D.W. Allan proposed M- sample variance..Determine the stability of atomic clocks. Describe the statistics of atomic frequency standards (1993) Werle et. Al detailed gave description of analysis of optical spectrometer

Figure 1.is a simulated plot of the time fluctuations, x(t) between a pair of oscillators and of the corresponding fractional frequencies calculated from the time fluctuations each averaged over a sample time t. At the bottom are the equations for the standard deviation (left) and for the time-domain measure of frequency stability as recommended by the IEEE (right) Fractional frequency If the time difference or the time fluctuations are available then the frequency or the fractional frequency fluctuations can be calculated from one period of sampling to the next over the data length

EXAMPLE : Find the two-sample (Allan) variance, s y 2 (t ), of the following sequence of fractional frequency fluctuation values y k, each value averaged over one second.

Allan Variance for CRDS Allan variance can be use to characterize the slow drift of a near-IR distributed feedback laser-based continuous wave cavity ring-down spectroscopy (CW-CRDS) system. In CRDS the accurate measurement of decay time constant can be limited by slow drift of setup. Allan variance can be used to analyze the stability of instrument. For a N time- series data xi the Allan variance is given by:

k is the subgroup size m+1 is the number of subgroups The integration time T equals to k/f, where f is sample rate. When white noise is dominant in the system (uncorrelated decays), Allan variance is proportional to 1/T and averaging data can improve the signal to noise ratio. When the drift appears Allan variance will become larger. The longest T during which the instrument can be regarded stable is determined by the drift of the system. The minimum of Allan variance gives the smallest detectable change during the longest integration time period.

Figure 2. Allan variance plot of the error in the absorbance of 13CH4 as a function of the number of ring-downs.

References IV. ANALYSIS OF TIME DOMAIN DATA Y. Chen,*,† Kevin.†K. Lehmann,‡ J. Kessler,§ B. Sherwood Lollar, ∇ G. Lacrampe Couloume, ∇ and T. C. Onstott Measurement of the 13C/12C of Atmospheric CH4 Using Near-Infrared (NIR) Cavity Ring-Down Spectroscopy Long-term stability in continuous wave cavity ringdown spectroscopy experiments Haifeng Huang and Kevin K. Lehmann*

How to calculate Allan Variance A time series of data is recorded successively and has N points: x i, i = 1...N The time interval between two successive data points is Δt. For an average size of data points of p, this N data series can be divided into m subgroups successively, with m equal to the largest integer less than or equal to N/p. For each subgroup, we have Each pair of successive A n (p) gives one sample of the Allan variance corresponding to the averaging size of p

The time averaged Allan variance of averaging size of p is defined as Evaluation of t requires ~ N-p/2+2N/p floating point operations ~ N(p max +2ϒ+2ln (p max +1))- p max (p max +1)/4 for p = 1...p max, with γ = γ the Euler–Mascheroni constant

If we assume the system is ergodic, this time averaged Allan variance t is equal to the ensemble averaged Allan variance When p= 1, the Allan variance is close to the short-term ensemble variance of the data series. If we have data without drift and uncorrelated Gaussian noise of variance σ 2, then the ensemble mean value for the Allan variance of length p is, e = σ 2 / p thus giving the straight line with a slope of −1 on a log-log plot. For calculation from a finite data series, there will be statistical fluctuations in the calculated values of t given by the variance

The slope of the Allan plot is −1 for p ≪ p min and +1 for p ≫ p min. The variance of t can be calculated in this case as

Figure.2 Allan plots of two algorithms. The modified algorithm (thinner curves) generates smoother Allan plots. For clarity, the Allan plots have been separated for (A) k1, (B) k2, and (C) k1 − k2