The Michelson Interferometer

Similar documents
Fizeau s Experiment with Moving Water. New Explanation. Gennady Sokolov, Vitali Sokolov

The Fizeau Experiment with Moving Water. Sokolov Gennadiy, Sokolov Vitali

Activity 3: Length Measurements with the Four-Sided Meter Stick

Section 19. Dispersing Prisms

Section 19. Dispersing Prisms

Types of Waves Transverse Shear. Waves. The Wave Equation

Sample Size Determination (Two or More Samples)

Exam II Covers. STA 291 Lecture 19. Exam II Next Tuesday 5-7pm Memorial Hall (Same place as exam I) Makeup Exam 7:15pm 9:15pm Location CB 234

Analysis of Experimental Data

9.5 Young s Double-Slit Experiment

Linear Regression Models

Statistics 511 Additional Materials

CHAPTER 2. Mean This is the usual arithmetic mean or average and is equal to the sum of the measurements divided by number of measurements.

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

3. Newton s Rings. Background. Aim of the experiment. Apparatus required. Theory. Date : Newton s Rings

Final Examination Solutions 17/6/2010

Topic 9: Sampling Distributions of Estimators

Economics 250 Assignment 1 Suggested Answers. 1. We have the following data set on the lengths (in minutes) of a sample of long-distance phone calls

Lecture 1 Probability and Statistics

Probability, Expectation Value and Uncertainty

Chapter 35 - Refraction

Topic 9: Sampling Distributions of Estimators

Optics. n n. sin. 1. law of rectilinear propagation 2. law of reflection = 3. law of refraction

Topic 9: Sampling Distributions of Estimators

Assessment and Modeling of Forests. FR 4218 Spring Assignment 1 Solutions

EXPERIMENT OF SIMPLE VIBRATION

λ = ) = d sin! n Analysis: Rearrange the equation dsinθn to solve for wavelength; # d sin! n = n " 1 & $ ( ) n " 1 2 Solution: ( m) sin 3.

NCSS Statistical Software. Tolerance Intervals

Statistical Intervals for a Single Sample

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Lecture 1 Probability and Statistics

Overview. p 2. Chapter 9. Pooled Estimate of. q = 1 p. Notation for Two Proportions. Inferences about Two Proportions. Assumptions

TABLES AND FORMULAS FOR MOORE Basic Practice of Statistics

Lecture 6 Chi Square Distribution (χ 2 ) and Least Squares Fitting

Confidence Intervals

Data Analysis and Statistical Methods Statistics 651

Lecture 5. Materials Covered: Chapter 6 Suggested Exercises: 6.7, 6.9, 6.17, 6.20, 6.21, 6.41, 6.49, 6.52, 6.53, 6.62, 6.63.

PHYS-3301 Lecture 3. EM- Waves behaving like Particles. CHAPTER 3 The Experimental Basis of Quantum. CHAPTER 3 The Experimental Basis of Quantum

1 Inferential Methods for Correlation and Regression Analysis

MATH/STAT 352: Lecture 15

Estimating the Population Mean - when a sample average is calculated we can create an interval centered on this average

Data Description. Measure of Central Tendency. Data Description. Chapter x i

ANALYSIS OF EXPERIMENTAL ERRORS

Median and IQR The median is the value which divides the ordered data values in half.

Ray Optics Theory and Mode Theory. Dr. Mohammad Faisal Dept. of EEE, BUET

a b c d e f g h Supplementary Information

INF-GEO Solutions, Geometrical Optics, Part 1

A statistical method to determine sample size to estimate characteristic value of soil parameters

ENGI 4421 Probability and Statistics Faculty of Engineering and Applied Science Problem Set 1 Solutions Descriptive Statistics. None at all!

Statistical Fundamentals and Control Charts

Lecture 2: Poisson Sta*s*cs Probability Density Func*ons Expecta*on and Variance Es*mators

Worksheet 23 ( ) Introduction to Simple Linear Regression (continued)

Expectation and Variance of a random variable

Describing the Relation between Two Variables

7-1. Chapter 4. Part I. Sampling Distributions and Confidence Intervals

FACULTY OF MATHEMATICAL STUDIES MATHEMATICS FOR PART I ENGINEERING. Lectures

Open book and notes. 120 minutes. Cover page and six pages of exam. No calculators.

2λ, and so on. We may write this requirement succinctly as

Statistical Analysis on Uncertainty for Autocorrelated Measurements and its Applications to Key Comparisons

Response Variable denoted by y it is the variable that is to be predicted measure of the outcome of an experiment also called the dependent variable

Homework 7 Due 5 December 2017 The numbers following each question give the approximate percentage of marks allocated to that question.

PH 425 Quantum Measurement and Spin Winter SPINS Lab 1

t distribution [34] : used to test a mean against an hypothesized value (H 0 : µ = µ 0 ) or the difference

Error & Uncertainty. Error. More on errors. Uncertainty. Page # The error is the difference between a TRUE value, x, and a MEASURED value, x i :

is also known as the general term of the sequence

Statistics and Chemical Measurements: Quantifying Uncertainty. Normal or Gaussian Distribution The Bell Curve

Chapter 8: Estimating with Confidence

ENGI 4421 Confidence Intervals (Two Samples) Page 12-01

Example: Find the SD of the set {x j } = {2, 4, 5, 8, 5, 11, 7}.

3/3/2014. CDS M Phil Econometrics. Types of Relationships. Types of Relationships. Types of Relationships. Vijayamohanan Pillai N.

Random Variables, Sampling and Estimation

Continuous Data that can take on any real number (time/length) based on sample data. Categorical data can only be named or categorised

TRACEABILITY SYSTEM OF ROCKWELL HARDNESS C SCALE IN JAPAN

Frequentist Inference

The standard deviation of the mean

R is a scalar defined as follows:

Census. Mean. µ = x 1 + x x n n

University of California, Los Angeles Department of Statistics. Simple regression analysis

October 25, 2018 BIM 105 Probability and Statistics for Biomedical Engineers 1

Statistical Inference (Chapter 10) Statistical inference = learn about a population based on the information provided by a sample.

9.3 constructive interference occurs when waves build each other up, producing a resultant wave of greater amplitude than the given waves

Math 152. Rumbos Fall Solutions to Review Problems for Exam #2. Number of Heads Frequency

4.1 Sigma Notation and Riemann Sums

II. Descriptive Statistics D. Linear Correlation and Regression. 1. Linear Correlation

STA Learning Objectives. Population Proportions. Module 10 Comparing Two Proportions. Upon completing this module, you should be able to:

Section A assesses the Units Numerical Analysis 1 and 2 Section B assesses the Unit Mathematics for Applied Mathematics

Measures of Spread: Standard Deviation

Stat 139 Homework 7 Solutions, Fall 2015

University of California, Los Angeles Department of Statistics. Hypothesis testing

Lecture 2: Monte Carlo Simulation

Provläsningsexemplar / Preview TECHNICAL REPORT INTERNATIONAL SPECIAL COMMITTEE ON RADIO INTERFERENCE

n but for a small sample of the population, the mean is defined as: n 2. For a lognormal distribution, the median equals the mean.

MIDTERM 3 CALCULUS 2. Monday, December 3, :15 PM to 6:45 PM. Name PRACTICE EXAM SOLUTIONS

Confidence Interval for one population mean or one population proportion, continued. 1. Sample size estimation based on the large sample C.I.

4.1 SIGMA NOTATION AND RIEMANN SUMS

6.867 Machine learning, lecture 7 (Jaakkola) 1

PHYS-3301 Lecture 10. Wave Packet Envelope Wave Properties of Matter and Quantum Mechanics I CHAPTER 5. Announcement. Sep.

Analysis of Experimental Measurements

Read through these prior to coming to the test and follow them when you take your test.

Chapter 2 Descriptive Statistics

Transcription:

The Michelso Iterferometer Ady Chmileko, 0310799 Istructor: Jeff Gardier Sectio 1 (Dated: :30 pm Tuesday October, 013) I. PURPOSE The purpose of the experimet is to use the Michelso Iterferometer, oce calibrated, to measure the wavelegth of mercury (Hg) gree light, as well as measure the separatio of the Hg yellow lies. We will also use the Michelso Iterferometer alog with a small vacuum chamber to determie the approximate refractive idex of air ad compare it to our predictios of light travellig through a medium compared to a vacuum. We will also observe the iterferece patter of white light ad try to apply theory to our observatios. II. ANALYSIS A. Calibratio of the Iterferometer i Sodium (Na) Yellow Light D 1 (± 0.005 mm) D (± 0.005 mm) K 5.000 5.160 0.184 ± 0.011 6.50 6.400 0.1964 ± 0.0137 TABLE I: Measuremets made for the displacemet of the micrometer for 100 diffractio lies ( ) of Na yellow light. Sample Calculatios for K usig row 1 of Table I usig a λ of 5893Å K = λ ( (D 1 D ) K = 100 5893Å ( (5.000 5.160) K = 0.184 Sample Calculatios for K usig row 1 of Table I K = K ( D) D 1 D + ( ) ( 0.005) K = 0.184 0.16 + () 100 K = ±0.011 Sample Calculatios for K usig row 1 of Table I. i K = Ki K = 0.184+0.1964 K = 0.1903

Sample Calculatios for K usig row 1 of Table I. i Ki K = K = 0.011+0.0137 K = ±0.019 K was foud to be 0.1903 ± 0.019, takig 0.005mm as the ucertaity i the micrometer measuremet ad as the ucertaity, misreadig at most the positio of the first couted ad last couted diffractio lies. B. Determiatio of the Wavelegth of the Mercury (Hg) Gree Light D 1 (± 0.005 mm) D (± 0.005 mm) λ (m) 6.300 6.445 551.9 ± 53.4 3.090 3.30 53.8 ± 5.5 TABLE II: Measuremets made for the displacemet of the micrometer for 100 diffractio lies ( ) of Hg gree light. Sample Calculatios for K usig row 1 of Table II usig a λ of 5893Å λ = K ( (D1 D) λ = 0.1903 ( (6.300 6.445) 100 λ = 551.9m Sample Calculatios for λ usig row 1 of Table II λ = λ ( D) λ = 551.9 D 1 D + K K ( 0.005) 0.145 + (0.019) 0.1903 λ = ±53.4 Sample Calculatios for λ usig row 1 of Table II. i λ = λi λ = 551.9+53.8 λ = 54.35 Sample Calculatios for λ usig row 1 of Table II. i λi λ = λ = 53.4+5.5 λ = ±53.0 Sample Calculatios for % deviatio of observed measured wavelegth of gree light from Hg with the accepted value of 546.074 m % deviatio = 546.074 54.35 546.074 100% % deviatio = 0.7% λ for the gree light emitted by Hg was foud to be 54.35 ± 53.0 m, which oly differed by 0.7% from the accepted value of 546.074m ad was well withi our calculated ucertaity.

3 Separatio of the Hg Yellow Lies D 1 10 (± 0.005 mm): 3.330, 3.740, 4.140, 4.570, 4.980, 5.380, 5.810, 6.0, 6.630, 7.050 δd 1 9 (± 0.005 mm): 0.410, 0.400, 0.430, 0.410, 0.400, 0.430, 0.410, 0.410, 0.40 Sample Calculatios for δd i δdi δd = δd = 0.410+0.400+0.430+0.410+0.400+0.430+0.410+0.410+0.40 9 δd = ±0.413 Sample Calculatios for δd δd = ± δd δd = ± 0.005 9 δd = ±0.0036mm Sample Calculatios for λ λ usig λ as 578.0 m λ λ = λ K δd λ λ 578.0m = 0.1903 0.413mm λ λ =.15m Sample Calculatios for (λ λ ) (λ λ ) = λ λ (λ λ ) =.15m ( K K ) ( δd δd ) ( 0.019 0.1903 ) ( 0.0036 0.413 ) (λ λ ) = 0.14m Sample Calculatios for % deviatio of observed measured wavelegth differece of Hg Yellow I ad II lies with the accepted differece of 579.065m-576.959m =.106m % deviatio =.106.15.106 100% % deviatio = 0.9% λ λ for the Hg Yellow I ad II lies was measured to be.15 ± 0.14m, which oly differed by 0.9% compared to the accepted values of 579.065m-576.959m =.106m, the accepted value was also withi our estimated ucertaity. C. The Idex of Refractio of Air (for Na Yellow Light) P 0 (± 0.5 cmhg) P i (± 0.5 cmhg) 75.0 5 66.0 58 48 39 30 14 74.0 5 65 56.5 49 41 33 4 15 7 74.0 5 65 60 50 41 31 1 13 TABLE III: Measuremets take recordig the cell pressure P i for each iterval of 5 ( ) rigs disappearig.

4 FIG. 1: Refractive idex of air - 1 versus the iteral cell pressure i mmhg show i blue. 3 outlier values idicated i red are ot used i the regressio. Sample Calculatios for air usig regressio costats a = 6.81 10 7 ad b = 0.000343 at stadard atmosphere pressure of 760mmHg for Na yellow light air 1 = ax + b air 1 = 6.81 10 7 760mm + 0.000343 air = 1.00034 Sample Calculatios for air air = ( air 1) air = 0.00034 ( a a ) ( b b ) (.54 6.81 ) ( 6.53 343. ) air = 0.00013 Usig the regressio costats a ad b idicated i Fig.1, we ca calculate the refractive idex i air for Na yellow light (5893Å) at stadard atmospheric pressure (760 mmhg) was foud to be 1.00034 ± 0.00013 which is cosistet with theory as it should be greater tha 1 (refractive idex at a vacuum). D. Observatio of White Light Friges Trial 1: D 1 = 14.14 ±0.005 mm Trial : D 1 = 14.14 ±0.005 mm D = 14.11 ±0.005 mm D = 14.11 ±0.005 mm Sample Calculatios carriage distace travelled. δd carriage = K δd micrometer δd carriage = 0.1903 (14.14mm 14.11mm) δd carriage = 5.709 10 6 m

5 Sample Calculatios for δd carriage δd carriage = δd carriage ( D) δd carriage = 5.709µm ( 0.005) δd carriage = 0.34µm D 1 D + K K 0.03 + 0.019 0.1903 The white light friges was observed to be a white bad, with a purpler colour, the blue, gree, yellow, orage, red, the the patter bega to repeat i the fashio. The approximate distace over which friges are observed was betwee 14.14 mm ad 14.11 mm o the micrometer, which traslates to 5.709 µm of actual carriage distace. Frige diameters are so large ear zero path differece because the iterferece patter is a result of a superpositio of wave crests, at ear zero path differece the wave crests is small so the for two crests or troughs to alig up takes a greater agle, or larger diameter friges. You ca see so may colours because the white light from the icadescet bulb cotais all (or most) of the wavelegths of light, as certai parts of the light iterfere with each other as the carriage is moves, other wavelegths costruct resultig i the separatio of colours cotaied withi the white light. Whe the path legth is zero, that meas the path legth is the same for all wavelegths ad that is where the patter appears early black. At large optical path differeces the radom separatio of all the wavelegths ad the o-exact aligmet of the mirrors you caot observe the vibrat colours except ear zero path legth differeces. III. CONCLUSION First the iterferometer was calibrated ad the costat K was foud, relatig the micrometer movemet to the actual carriage travel of the mirror ad usig a kow source wavelegth was foud to be 0.1903 ± 0.019, ad this value was used throughout for the rest of our calculatios. Usig the same method above with the measured K, we measured the wavelegth of the gree like spectral lie of Hg, ad foud it to me 54.35 ± 53.0 m, which oly differed by 0.7% of the accepted value of 546.074 m, which is also withi out ucertaity estimates. Similarly we calculated the separatio of the Hg yellow lies. We measured the separatio to be.14 ± 0.14 m, which oly differed by 0.9% compared to the accepted value of.106 m. Usig a vacuum chamber ad by varyig the pressure (ad by extesio the umber of particles i the chamber), we were able to measure the refractive idex of air, air. Usig the Lorez-Loretz law we were able to plot our results ad calculate a umber for air at STP, 760 mmhg at room temperature for Na yellow light to be 1.00034 ± 0.00013. This is cosistet with theory as the refractive idex of materials should be greater tha 1 (the refractive idex of a vacuum).