PSF and Field of View characteristics of imaging and nulling interferometers

Size: px
Start display at page:

Download "PSF and Field of View characteristics of imaging and nulling interferometers"

Transcription

1 PSF ad FoV characteristics of imagig ad ullig iterferometers PSF ad Field of View characteristics of imagig ad ullig iterferometers Fraçois Héault UMR CNRS 6525 H. Fizeau UNS, CNRS, CA Aveue Nicolas Coperic Grasse - Frace Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

2 PSF ad FoV characteristics of imagig ad ullig iterferometers peig questios I the frame of Darwi/TPF-I exoplaet fidig space missios - Do ullig maps deped o the type of combiig optics (axial vs. multi-axial combiatio schemes)? - Has it some cosequece o their ullig imagig capacity? Previous publicatios Simple Fourier optics formalism for high agular resolutio systems ad ullig iterferometry, JSA A 27, p (2010) Fibered ullig telescope for extra-solar coroagraphy, ptics Letters 34, 7, p (2009) Computig extictio maps of star ullig iterferometers, ptics Express 16, (2008) Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

3 PSF ad FoV characteristics of imagig ad ullig iterferometers V -sky agular coordiates Sky object u v U s s Y P 1 Etrace pupil plae (P) X D Y Exit pupil plae (P ) P 2 P 3 B D P 4 P 1 P 2 B P 3 P 4 s s X F Y Detectio plae X Coordiates systems M Z M Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

4 PSF ad FoV characteristics of imagig ad ullig iterferometers Image of a sky object (s) projected back o sky I( s) = s T Ω = 1 ( s ) PSF ( s - s ) N a exp [ iφ ] exp[ ik ( s P s' P' / m) ] 2 dω with PSF T (s) : PSF of oe idividual collectig telescope, beig projected back o-sky a : amplitude trasmissio factor of the th telescope ϕ : phase-shift alog the th iterferometer arm for cophasig or ullig purpose k = 2π/λ : waveumber of moochromatic electro-magetic field m : optical compressio factor betwee telescopes ad their relay optics Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

5 PSF ad FoV characteristics of imagig ad ullig iterferometers Geeral layout of a multi-aperture iterferometer B P 1 P 2 (P) Telescope 1 F C Telescope 2 Relay optics 1 APS 1 APS 2 Metrology Metrology beam 1 B beam 2 Relay optics 2 Covergig optics Divergig optics Fold mirror Beamsplitter Acromatic Phase Shifter Combiig optics (P ) F Focal plae All telescopes assumed to be idetical All exit pupils optically cojugated with etrace pupils Coferece 7734 ptical ad Ifrared Iterferometry II Z Sa Diego, Jue 30 th

6 PSF ad FoV characteristics of imagig ad ullig iterferometers PSF ad Field of View characteristics Geeralized Poit Spread Fuctio (PSF) PSF [ ] [ ] [ ( )] 2 iφ exp ik s'p' / m exp ik s P ' P' N G ( s, s ) PSFT ( s) a exp / = 1 = m Chagig over the whole istrumet Field of View bject-image relatioship is ot a covolutio product Maximal achievable Field of View (FoV) Neglectig ay kid of apertures or stops, Neglectig geometrical aberratios ad diffractio effects N = 1 [ ] [ ( )] 2 iφ exp ik s P ' P' FoV( s) = a m exp / Suitable for fast polychromatic FoV computatios: FoV δλ ( s) = δλ FoV ( s) λ B δλ (λ) dλ δλ B δλ (λ) dλ Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

7 PSF ad FoV characteristics of imagig ad ullig iterferometers Golde rule of Fizeau iterferometers ly occurs whe: P = m P Geeralized PSF becomes: Pupil I Pupil ut PSF N G ( s, xs ) PSFT ( s) a exp / = 1 [ ] [ ] 2 iφ exp ik s' P' = m Costat over the whole FoV Classical bject-image relatioship I(s) = (s) * PSF(s) holds Maximal achievable Field of View: Becomes ifiite whatever the wavelegth Costat trasmissio equal to: x FoV( s) = a exp i N = 1 [ ] 2 φ Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

8 PSF ad FoV characteristics of imagig ad ullig iterferometers Numerical simulatios: a 8-telescope Fizeau iterferometer i imagig ad ullig modes Iput pupils Y D X PSF (FoV ceter) PSF (half FoV) Maximal achievable FoV B 5 arcsec Y 0 π 0 D π π X 0 π 0 B Golde rule exteds destructive frige over the whole FoV, killig the cetral star ad all its surroudig plaets (this is ot what we wat ) Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th arcsec 8

9 PSF ad FoV characteristics of imagig ad ullig iterferometers Sheared-Pupil Telescopes (SPT) Secodary Mirror Moolithic telescope Relay optics Primary Mirror (P) APS 1 APS 2 ad PD compesatio Metrology beam 1 Beam desifier May be replaced with a modified Mach-Zehder combier Beam combier (exit pupil plae) P 1 P 2 [or Michelso B equipped with cube-corers] Metrology beam 2 F (P ) Metrology beams F Z (P ) Focal plae Coferece 7734 ptical ad Ifrared Iterferometry II Z Focal plae Sa Diego, Jue 30 th

10 PSF ad FoV characteristics of imagig ad ullig iterferometers Two differet types of Sheared-Pupil Telescope Iput pupil plae Y utput pupil plae Y Umasked output sub-pupils X D X D Y Moolithic pupil telescope B Y Lyot stop o Exit pupil Masked output sub-pupils D X D X B B Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

11 PSF ad FoV characteristics of imagig ad ullig iterferometers Iterest ad limitatios of ullig SPTs Usable for exploratory sciece missios: exo-zodi characterizatio, Jupiter-like plaets If rotatig, allow to validate most of the Darwi/TPF-I algorithms evisaged for plaets fidig ad characterizatio Whe umasked, they cocetrate eergy i very small core, overcomig Rayleigh s diffractio limit But o real super-resolvig power, sice PSF are sharpeed after sub-aperture filterig: Specific bject- Image relatioship: N [ iφ ] exp[ iks'p' / m] [ PSF ( s) *( s) ] Ι( s) = a exp T = 1 2 Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

12 PSF ad FoV characteristics of imagig ad ullig iterferometers PSF ad Field of View simulatios of SPTs Masked SPT B = 1 m D = 3 m 5 arcsec Masked SPT B = 0.5 m D = 4 m Sheared-Pupil Telescope D = 5 m Umasked SPT: high throughput, residual star leakage Masked SPT: o leakage, elarged ulled area of low throughput Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

13 PSF ad FoV characteristics of imagig ad ullig iterferometers Axially Combied Iterferometer (ACI) Telescope 1 Relay optics 1 P 1 Metrology beam 1 Axial beam combier Coferece 7734 ptical ad Ifrared Iterferometry II F B Z Telescope 2 APS 1 APS 2 Frige tracker (P ) Focal plae Metrology beam 2 Ι( s) = P 2 Relay optics 2 PSF T (P) ( s) * Co-axial recombiatio by meas of a balaced set of beamsplitters Equivalet to the previous moolithic, masked SPT Nulls all diffracted light origiatig from cetral star Specific bject-image relatioship: N = 1 a exp Sa Diego, Jue 30 th 2010 [ iφ ] exp[ iksp ] 2 ( s) 13

14 PSF ad FoV characteristics of imagig ad ullig iterferometers Nullig imagig capacities of SPT Masked SPT B = 1 m D = 3 m Masked SPT B = 0.5 m D = 4 m Fictitious sky object 2 arcsec No gai i agular resolutio, but diffracted starlight cleaed before fial image blurrig Progressive leakage from cetral objects (to be traded agaist throughput) Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

15 PSF ad FoV characteristics of imagig ad ullig iterferometers Nullig imagig capacities of ACI ACI B = 20 m D = 5 m ACI B = 10 m D = 5 m Fictitious sky object 2 arcsec For loger baselies, ullig ACI behaves as a sigle-dish telescope Nullig capacity seems to be lost Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

16 PSF ad FoV characteristics of imagig ad ullig iterferometers Coclusios Nullig maps ad ulled images produced by differet types of multi-aperture optical systems ca be rapidly evaluated by meas of a simple Fourier optics formalism From the results of theory ad first umerical computatios: Golde rule for iterferometric imagig exteds the destructive frige patter of ullig iterferometers over their whole Field of View A ullig moolithic, sheared-pupil telescope is a attractive solutio Requires further tradeoff o throughput/leakage Theoretical bject-image relatioship of the Bracewell iterferometer allows full extictio of diffracted starlight, but o super-resolutio is possible [ I the Please case retai: of fibered this is ullig all very iterferometers, prelimiary, evetually best aecdotal throughput are ] achieved ad usig somewhat axial heuristic. recombiatio Further schemes work is required (ullig ad ACIs) cooperatios are welcome Coferece 7734 ptical ad Ifrared Iterferometry II Sa Diego, Jue 30 th

Imaging and nulling properties of sparse-aperture Fizeau interferometers Imaging and nulling properties of sparse-aperture Fizeau interferometers

Imaging and nulling properties of sparse-aperture Fizeau interferometers Imaging and nulling properties of sparse-aperture Fizeau interferometers Imagig ad ullig properties of sparse-aperture Fizeau iterferometers Imagig ad ullig properties of sparse-aperture Fizeau iterferometers Fraçois Héault Istitut de Plaétologie et d Astrophysique de Greoble,

More information

PSF and field of view characteristics of imaging and nulling interferometers

PSF and field of view characteristics of imaging and nulling interferometers PSF ad field of view characteristics of imagig ad ullig iterferometers Fraçois Héault UMR 6525 CRS H. Fizeau US, CA, Aveue icolas Coperic, 06130 Grasse Frace ASTRACT I this commuicatio are preseted some

More information

Imaging and nulling properties of sparse-aperture Fizeau interferometers

Imaging and nulling properties of sparse-aperture Fizeau interferometers Imagig ad ullig properties of sparse-aperture Fizeau iterferometers Fraçois Héault Istitut de Plaétologie et d Astrophysique de Greoble Uiversité Joseph Fourier, Cetre Natioal de la Recherche Scietifique.P.

More information

Cork Institute of Technology Bachelor of Science (Honours) in Applied Physics and Instrumentation-Award - (NFQ Level 8)

Cork Institute of Technology Bachelor of Science (Honours) in Applied Physics and Instrumentation-Award - (NFQ Level 8) ork Istitute of Techology Bachelor of Sciece (Hoours) i Applied Physics ad Istrumetatio-Award - (NFQ Level 8) Istructios Aswer Four questios, at least TWO questios from each Sectio. Use separate aswer

More information

Coma aberration. Lens Design OPTI 517. Prof. Jose Sasian

Coma aberration. Lens Design OPTI 517. Prof. Jose Sasian Coma aberratio Les Desig OPTI 517 Coma 0.5 wave 1.0 wave.0 waves 4.0 waves Spot diagram W W W... 040 0 H,, W 4 H W 131 W 00 311 H 3 H H cos W 3 W 00 W H cos W 400 111 H H cos cos 4 Coma though focus Cases

More information

Cheapest nuller in the World: Crossed beamsplitter cubes

Cheapest nuller in the World: Crossed beamsplitter cubes Cheapest nuller in the World: François Hénault Institut de Planétologie et d Astrophysique de Grenoble, Université Joseph Fourier, CNRS, B.P. 53, 38041 Grenoble France Alain Spang Laboratoire Lagrange,

More information

Overview of Aberrations

Overview of Aberrations Overview of Aberratios Les Desig OPTI 57 Aberratio From the Lati, aberrare, to wader from; Lati, ab, away, errare, to wader. Symmetry properties Overview of Aberratios (Departures from ideal behavior)

More information

Section 19. Dispersing Prisms

Section 19. Dispersing Prisms Sectio 9 Dispersig Prisms 9- Dispersig Prism 9- The et ray deviatio is the sum of the deviatios at the two surfaces. The ray deviatio as a fuctio of the iput agle : si si si cossi Prism Deviatio - Derivatio

More information

Section 19. Dispersing Prisms

Section 19. Dispersing Prisms 19-1 Sectio 19 Dispersig Prisms Dispersig Prism 19-2 The et ray deviatio is the sum of the deviatios at the two surfaces. The ray deviatio as a fuctio of the iput agle : 1 2 2 si si si cossi Prism Deviatio

More information

Name Solutions to Test 2 October 14, 2015

Name Solutions to Test 2 October 14, 2015 Name Solutios to Test October 4, 05 This test cosists of three parts. Please ote that i parts II ad III, you ca skip oe questio of those offered. The equatios below may be helpful with some problems. Costats

More information

Seidel sums and a p p l p icat a ion o s f or o r s imp m l p e e c as a es

Seidel sums and a p p l p icat a ion o s f or o r s imp m l p e e c as a es Seidel sums ad applicatios for simple cases Aspheric surface Geerally : o spherical rotatioally symmetric surfaces but ca be off-axis coic sectios Greatly help to improve performace, ad reduce the umber

More information

Statistical Pattern Recognition

Statistical Pattern Recognition Statistical Patter Recogitio Classificatio: No-Parametric Modelig Hamid R. Rabiee Jafar Muhammadi Sprig 2014 http://ce.sharif.edu/courses/92-93/2/ce725-2/ Ageda Parametric Modelig No-Parametric Modelig

More information

Infinite Sequences and Series

Infinite Sequences and Series Chapter 6 Ifiite Sequeces ad Series 6.1 Ifiite Sequeces 6.1.1 Elemetary Cocepts Simply speakig, a sequece is a ordered list of umbers writte: {a 1, a 2, a 3,...a, a +1,...} where the elemets a i represet

More information

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 +

62. Power series Definition 16. (Power series) Given a sequence {c n }, the series. c n x n = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + 62. Power series Defiitio 16. (Power series) Give a sequece {c }, the series c x = c 0 + c 1 x + c 2 x 2 + c 3 x 3 + is called a power series i the variable x. The umbers c are called the coefficiets of

More information

Lecture 3. Properties of Summary Statistics: Sampling Distribution

Lecture 3. Properties of Summary Statistics: Sampling Distribution Lecture 3 Properties of Summary Statistics: Samplig Distributio Mai Theme How ca we use math to justify that our umerical summaries from the sample are good summaries of the populatio? Lecture Summary

More information

Formation of A Supergain Array and Its Application in Radar

Formation of A Supergain Array and Its Application in Radar Formatio of A Supergai Array ad ts Applicatio i Radar Tra Cao Quye, Do Trug Kie ad Bach Gia Duog. Research Ceter for Electroic ad Telecommuicatios, College of Techology (Coltech, Vietam atioal Uiversity,

More information

Time-Domain Representations of LTI Systems

Time-Domain Representations of LTI Systems 2.1 Itroductio Objectives: 1. Impulse resposes of LTI systems 2. Liear costat-coefficiets differetial or differece equatios of LTI systems 3. Bloc diagram represetatios of LTI systems 4. State-variable

More information

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5

Olli Simula T / Chapter 1 3. Olli Simula T / Chapter 1 5 Sigals ad Systems Sigals ad Systems Sigals are variables that carry iformatio Systemstake sigals as iputs ad produce sigals as outputs The course deals with the passage of sigals through systems T-6.4

More information

Quantum Information & Quantum Computation

Quantum Information & Quantum Computation CS9A, Sprig 5: Quatum Iformatio & Quatum Computatio Wim va Dam Egieerig, Room 59 vadam@cs http://www.cs.ucsb.edu/~vadam/teachig/cs9/ Admiistrivia Do the exercises. Aswers will be posted at the ed of the

More information

1 Generating functions for balls in boxes

1 Generating functions for balls in boxes Math 566 Fall 05 Some otes o geeratig fuctios Give a sequece a 0, a, a,..., a,..., a geeratig fuctio some way of represetig the sequece as a fuctio. There are may ways to do this, with the most commo ways

More information

Salmon: Lectures on partial differential equations. 3. First-order linear equations as the limiting case of second-order equations

Salmon: Lectures on partial differential equations. 3. First-order linear equations as the limiting case of second-order equations 3. First-order liear equatios as the limitig case of secod-order equatios We cosider the advectio-diffusio equatio (1) v = 2 o a bouded domai, with boudary coditios of prescribed. The coefficiets ( ) (2)

More information

Lesson 10: Limits and Continuity

Lesson 10: Limits and Continuity www.scimsacademy.com Lesso 10: Limits ad Cotiuity SCIMS Academy 1 Limit of a fuctio The cocept of limit of a fuctio is cetral to all other cocepts i calculus (like cotiuity, derivative, defiite itegrals

More information

ES.182A Topic 40 Notes Jeremy Orloff

ES.182A Topic 40 Notes Jeremy Orloff ES.182A opic 4 Notes Jeremy Orloff 4 Flux: ormal form of Gree s theorem Gree s theorem i flux form is formally equivalet to our previous versio where the lie itegral was iterpreted as work. Here we will

More information

THE KALMAN FILTER RAUL ROJAS

THE KALMAN FILTER RAUL ROJAS THE KALMAN FILTER RAUL ROJAS Abstract. This paper provides a getle itroductio to the Kalma filter, a umerical method that ca be used for sesor fusio or for calculatio of trajectories. First, we cosider

More information

Types of Waves Transverse Shear. Waves. The Wave Equation

Types of Waves Transverse Shear. Waves. The Wave Equation Waves Waves trasfer eergy from oe poit to aother. For mechaical waves the disturbace propagates without ay of the particles of the medium beig displaced permaetly. There is o associated mass trasport.

More information

THE LEVEL SET METHOD APPLIED TO THREE-DIMENSIONAL DETONATION WAVE PROPAGATION. Wen Shanggang, Sun Chengwei, Zhao Feng, Chen Jun

THE LEVEL SET METHOD APPLIED TO THREE-DIMENSIONAL DETONATION WAVE PROPAGATION. Wen Shanggang, Sun Chengwei, Zhao Feng, Chen Jun THE LEVEL SET METHOD APPLIED TO THREE-DIMENSIONAL DETONATION WAVE PROPAGATION We Shaggag, Su Chegwei, Zhao Feg, Che Ju Laboratory for Shock Wave ad Detoatio Physics Research, Southwest Istitute of Fluid

More information

Robustness of spatial-coherence multiplexing under receiver misalignment

Robustness of spatial-coherence multiplexing under receiver misalignment Robustess of spatial-coherece multiplexig uder receiver misaligmet Lawrece J. Pelz ad Betty Lise Aderso It has bee show previously that the spatial coherece of a source ca be modulated ad demodulated;

More information

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution

Discrete-Time Systems, LTI Systems, and Discrete-Time Convolution EEL5: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we begi our mathematical treatmet of discrete-time s. As show i Figure, a discrete-time operates or trasforms some iput sequece x [

More information

17 Phonons and conduction electrons in solids (Hiroshi Matsuoka)

17 Phonons and conduction electrons in solids (Hiroshi Matsuoka) 7 Phoos ad coductio electros i solids Hiroshi Matsuoa I this chapter we will discuss a miimal microscopic model for phoos i a solid ad a miimal microscopic model for coductio electros i a simple metal.

More information

Fully achromatic nulling interferometer (FANI) for high SNR exoplanet characterization

Fully achromatic nulling interferometer (FANI) for high SNR exoplanet characterization Fully achromatic nulling interferometer (FANI) for high SNR exoplanet characterization François Hénault Institut de Planétologie et d Astrophysique de Grenoble Université Joseph Fourier Centre National

More information

Phys 102 Lecture 25 The quantum mechanical model of light

Phys 102 Lecture 25 The quantum mechanical model of light Phys 102 Lecture 25 The quatum mechaical model of light 1 Recall last time Problems with classical physics Stability of atoms Atomic spectra Photoelectric effect Quatum model of the atom Bohr model oly

More information

Answers to test yourself questions

Answers to test yourself questions Aswers to test yourself questios Optio C C Itroductio to imagig a The focal poit of a covergig les is that poit o the pricipal axis where a ray parallel to the pricipal axis refracts through, after passage

More information

2.710 Optics Spring 09 Solutions to Problem Set #3 Due Wednesday, March 4, 2009

2.710 Optics Spring 09 Solutions to Problem Set #3 Due Wednesday, March 4, 2009 MASSACHUSETTS INSTITUTE OF TECHNOLOGY.70 Optics Sprig 09 Solutios to Problem Set #3 Due Weesay, March 4, 009 Problem : Waa s worl a) The geometry or this problem is show i Figure. For part (a), the object

More information

Frequency Response of FIR Filters

Frequency Response of FIR Filters EEL335: Discrete-Time Sigals ad Systems. Itroductio I this set of otes, we itroduce the idea of the frequecy respose of LTI systems, ad focus specifically o the frequecy respose of FIR filters.. Steady-state

More information

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

Optics. n n. sin. 1. law of rectilinear propagation 2. law of reflection = 3. law of refraction Optics What is light? Visible electromagetic radiatio Geometrical optics (model) Light-ray: extremely thi parallel light beam Usig this model, the explaatio of several optical pheomea ca be give as the

More information

Shocks waves and discontinuities.

Shocks waves and discontinuities. Shocks waes ad discotiuities Laurece Rezeau http://www.lpp.fr/?laurece-rezeau OUTLINE Obseratios of discotiuities Jump coditios at the boudary Differet kids of discotiuities What about the boudaries aroud

More information

BHW #13 1/ Cooper. ENGR 323 Probabilistic Analysis Beautiful Homework # 13

BHW #13 1/ Cooper. ENGR 323 Probabilistic Analysis Beautiful Homework # 13 BHW # /5 ENGR Probabilistic Aalysis Beautiful Homework # Three differet roads feed ito a particular freeway etrace. Suppose that durig a fixed time period, the umber of cars comig from each road oto the

More information

Similarity between quantum mechanics and thermodynamics: Entropy, temperature, and Carnot cycle

Similarity between quantum mechanics and thermodynamics: Entropy, temperature, and Carnot cycle Similarity betwee quatum mechaics ad thermodyamics: Etropy, temperature, ad Carot cycle Sumiyoshi Abe 1,,3 ad Shiji Okuyama 1 1 Departmet of Physical Egieerig, Mie Uiversity, Mie 514-8507, Japa Istitut

More information

Chapter 10: Power Series

Chapter 10: Power Series Chapter : Power Series 57 Chapter Overview: Power Series The reaso series are part of a Calculus course is that there are fuctios which caot be itegrated. All power series, though, ca be itegrated because

More information

Analytic Continuation

Analytic Continuation Aalytic Cotiuatio The stadard example of this is give by Example Let h (z) = 1 + z + z 2 + z 3 +... kow to coverge oly for z < 1. I fact h (z) = 1/ (1 z) for such z. Yet H (z) = 1/ (1 z) is defied for

More information

Machine Learning for Data Science (CS 4786)

Machine Learning for Data Science (CS 4786) Machie Learig for Data Sciece CS 4786) Lecture & 3: Pricipal Compoet Aalysis The text i black outlies high level ideas. The text i blue provides simple mathematical details to derive or get to the algorithm

More information

Design of multiplexed phase diffractive optical elements for focal depth extension

Design of multiplexed phase diffractive optical elements for focal depth extension Desig of multiplexed phase diffractive optical elemets for focal depth extesio Hua Liu, Zhewu Lu,,* Qiag Su ad Hu Zhag,,2 Opto_electroics techology ceter, Chagchu Istitute of Optics ad Fie Mechaics ad

More information

Analysis of azimuthal phase mask coronagraphs

Analysis of azimuthal phase mask coronagraphs Aalysis of azimuthal phase mask coroagraphs Fraçois Héault Istitut de Plaétologie et d Astrophysique de Greoble Uiversité Greoble-Alpes, Cetre Natioal de la Recherche Scietifique B.P. 53, 384 Greoble Frace

More information

Signals & Systems Chapter3

Signals & Systems Chapter3 Sigals & Systems Chapter3 1.2 Discrete-Time (D-T) Sigals Electroic systems do most of the processig of a sigal usig a computer. A computer ca t directly process a C-T sigal but istead eeds a stream of

More information

1 Review of Probability & Statistics

1 Review of Probability & Statistics 1 Review of Probability & Statistics a. I a group of 000 people, it has bee reported that there are: 61 smokers 670 over 5 960 people who imbibe (drik alcohol) 86 smokers who imbibe 90 imbibers over 5

More information

Rotationally invariant integrals of arbitrary dimensions

Rotationally invariant integrals of arbitrary dimensions September 1, 14 Rotatioally ivariat itegrals of arbitrary dimesios James D. Wells Physics Departmet, Uiversity of Michiga, A Arbor Abstract: I this ote itegrals over spherical volumes with rotatioally

More information

(Figure 2.9), we observe x. and we write. (b) as x, x 1. and we write. We say that the line y 0 is a horizontal asymptote of the graph of f.

(Figure 2.9), we observe x. and we write. (b) as x, x 1. and we write. We say that the line y 0 is a horizontal asymptote of the graph of f. The symbol for ifiity ( ) does ot represet a real umber. We use to describe the behavior of a fuctio whe the values i its domai or rage outgrow all fiite bouds. For eample, whe we say the limit of f as

More information

Roberto s Notes on Infinite Series Chapter 1: Sequences and series Section 3. Geometric series

Roberto s Notes on Infinite Series Chapter 1: Sequences and series Section 3. Geometric series Roberto s Notes o Ifiite Series Chapter 1: Sequeces ad series Sectio Geometric series What you eed to kow already: What a ifiite series is. The divergece test. What you ca le here: Everythig there is to

More information

Commutativity in Permutation Groups

Commutativity in Permutation Groups Commutativity i Permutatio Groups Richard Wito, PhD Abstract I the group Sym(S) of permutatios o a oempty set S, fixed poits ad trasiet poits are defied Prelimiary results o fixed ad trasiet poits are

More information

6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer.

6 Integers Modulo n. integer k can be written as k = qn + r, with q,r, 0 r b. So any integer. 6 Itegers Modulo I Example 2.3(e), we have defied the cogruece of two itegers a,b with respect to a modulus. Let us recall that a b (mod ) meas a b. We have proved that cogruece is a equivalece relatio

More information

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

The Fizeau Experiment with Moving Water. Sokolov Gennadiy, Sokolov Vitali The Fizeau Experimet with Movig Water. Sokolov Geadiy, Sokolov itali geadiy@vtmedicalstaffig.com I all papers o the Fizeau experimet with movig water, a aalysis cotais the statemet: "The beams travel relative

More information

Computing the output response of LTI Systems.

Computing the output response of LTI Systems. Computig the output respose of LTI Systems. By breaig or decomposig ad represetig the iput sigal to the LTI system ito terms of a liear combiatio of a set of basic sigals. Usig the superpositio property

More information

Lecture 4. Hw 1 and 2 will be reoped after class for every body. New deadline 4/20 Hw 3 and 4 online (Nima is lead)

Lecture 4. Hw 1 and 2 will be reoped after class for every body. New deadline 4/20 Hw 3 and 4 online (Nima is lead) Lecture 4 Homework Hw 1 ad 2 will be reoped after class for every body. New deadlie 4/20 Hw 3 ad 4 olie (Nima is lead) Pod-cast lecture o-lie Fial projects Nima will register groups ext week. Email/tell

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES 9 SEQUENCES AND SERIES INTRODUCTION Sequeces have may importat applicatios i several spheres of huma activities Whe a collectio of objects is arraged i a defiite order such that it has a idetified first

More information

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

PHYS-3301 Lecture 10. Wave Packet Envelope Wave Properties of Matter and Quantum Mechanics I CHAPTER 5. Announcement. Sep. Aoucemet Course webpage http://www.phys.ttu.edu/~slee/3301/ PHYS-3301 Lecture 10 HW3 (due 10/4) Chapter 5 4, 8, 11, 15, 22, 27, 36, 40, 42 Sep. 27, 2018 Exam 1 (10/4) Chapters 3, 4, & 5 CHAPTER 5 Wave

More information

Filter banks. Separately, the lowpass and highpass filters are not invertible. removes the highest frequency 1/ 2and

Filter banks. Separately, the lowpass and highpass filters are not invertible. removes the highest frequency 1/ 2and Filter bas Separately, the lowpass ad highpass filters are ot ivertible T removes the highest frequecy / ad removes the lowest frequecy Together these filters separate the sigal ito low-frequecy ad high-frequecy

More information

You may work in pairs or purely individually for this assignment.

You may work in pairs or purely individually for this assignment. CS 04 Problem Solvig i Computer Sciece OOC Assigmet 6: Recurreces You may work i pairs or purely idividually for this assigmet. Prepare your aswers to the followig questios i a plai ASCII text file or

More information

Section 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations

Section 1.1. Calculus: Areas And Tangents. Difference Equations to Differential Equations Differece Equatios to Differetial Equatios Sectio. Calculus: Areas Ad Tagets The study of calculus begis with questios about chage. What happes to the velocity of a swigig pedulum as its positio chages?

More information

Lecture Chapter 6: Convergence of Random Sequences

Lecture Chapter 6: Convergence of Random Sequences ECE5: Aalysis of Radom Sigals Fall 6 Lecture Chapter 6: Covergece of Radom Sequeces Dr Salim El Rouayheb Scribe: Abhay Ashutosh Doel, Qibo Zhag, Peiwe Tia, Pegzhe Wag, Lu Liu Radom sequece Defiitio A ifiite

More information

ALGEBRAIC GEOMETRY COURSE NOTES, LECTURE 5: SINGULARITIES.

ALGEBRAIC GEOMETRY COURSE NOTES, LECTURE 5: SINGULARITIES. ALGEBRAIC GEOMETRY COURSE NOTES, LECTURE 5: SINGULARITIES. ANDREW SALCH 1. The Jacobia criterio for osigularity. You have probably oticed by ow that some poits o varieties are smooth i a sese somethig

More information

INF-GEO Solutions, Geometrical Optics, Part 1

INF-GEO Solutions, Geometrical Optics, Part 1 INF-GEO430 20 Solutios, Geometrical Optics, Part Reflectio by a symmetric triagular prism Let be the agle betwee the two faces of a symmetric triagular prism. Let the edge A where the two faces meet be

More information

Multi-spectral piston sensor for co-phasing giant segmented mirrors and multi-aperture interferometric arrays

Multi-spectral piston sensor for co-phasing giant segmented mirrors and multi-aperture interferometric arrays Multi-spectral pisto sesor for co-phasig giat segmeted mirrors Multi-spectral pisto sesor for co-phasig giat segmeted mirrors ad multi-aperture iterferometric arrays Fraçois Héault UMR 6525 CRS H. Fizeau

More information

C. Complex Numbers. x 6x + 2 = 0. This equation was known to have three real roots, given by simple combinations of the expressions

C. Complex Numbers. x 6x + 2 = 0. This equation was known to have three real roots, given by simple combinations of the expressions C. Complex Numbers. Complex arithmetic. Most people thik that complex umbers arose from attempts to solve quadratic equatios, but actually it was i coectio with cubic equatios they first appeared. Everyoe

More information

TMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences.

TMA4245 Statistics. Corrected 30 May and 4 June Norwegian University of Science and Technology Department of Mathematical Sciences. Norwegia Uiversity of Sciece ad Techology Departmet of Mathematical Scieces Corrected 3 May ad 4 Jue Solutios TMA445 Statistics Saturday 6 May 9: 3: Problem Sow desity a The probability is.9.5 6x x dx

More information

Properties and Hypothesis Testing

Properties and Hypothesis Testing Chapter 3 Properties ad Hypothesis Testig 3.1 Types of data The regressio techiques developed i previous chapters ca be applied to three differet kids of data. 1. Cross-sectioal data. 2. Time series data.

More information

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

Ray Optics Theory and Mode Theory. Dr. Mohammad Faisal Dept. of EEE, BUET Ray Optics Theory ad Mode Theory Dr. Mohammad Faisal Dept. of, BUT Optical Fiber WG For light to be trasmitted through fiber core, i.e., for total iteral reflectio i medium, > Ray Theory Trasmissio Ray

More information

Math 116 Second Midterm November 13, 2017

Math 116 Second Midterm November 13, 2017 Math 6 Secod Midterm November 3, 7 EXAM SOLUTIONS. Do ot ope this exam util you are told to do so.. Do ot write your ame aywhere o this exam. 3. This exam has pages icludig this cover. There are problems.

More information

Polynomials with Rational Roots that Differ by a Non-zero Constant. Generalities

Polynomials with Rational Roots that Differ by a Non-zero Constant. Generalities Polyomials with Ratioal Roots that Differ by a No-zero Costat Philip Gibbs The problem of fidig two polyomials P(x) ad Q(x) of a give degree i a sigle variable x that have all ratioal roots ad differ by

More information

A New Recursion for Space-Filling Geometric Fractals

A New Recursion for Space-Filling Geometric Fractals A New Recursio for Space-Fillig Geometric Fractals Joh Shier Abstract. A recursive two-dimesioal geometric fractal costructio based upo area ad perimeter is described. For circles the radius of the ext

More information

Solutions to Homework 1

Solutions to Homework 1 Solutios to Homework MATH 36. Describe geometrically the sets of poits z i the complex plae defied by the followig relatios /z = z () Re(az + b) >, where a, b (2) Im(z) = c, with c (3) () = = z z = z 2.

More information

Summary of formulas Summary of optical systems

Summary of formulas Summary of optical systems Summar of formulas Summar of optical sstems Les Desig OPTI 57 Imagig: cetral projectio X' Y' Z' a X b Y c Z d axbyczd 0 0 0 0 a X b Y c Z d axbyczd 0 0 0 0 a X byczd axbyczd 3 3 3 3 0 0 0 0 Colliear trasformatio

More information

NUMERICAL METHODS FOR SOLVING EQUATIONS

NUMERICAL METHODS FOR SOLVING EQUATIONS Mathematics Revisio Guides Numerical Methods for Solvig Equatios Page 1 of 11 M.K. HOME TUITION Mathematics Revisio Guides Level: GCSE Higher Tier NUMERICAL METHODS FOR SOLVING EQUATIONS Versio:. Date:

More information

Lecture 7: Fourier Series and Complex Power Series

Lecture 7: Fourier Series and Complex Power Series Math 1d Istructor: Padraic Bartlett Lecture 7: Fourier Series ad Complex Power Series Week 7 Caltech 013 1 Fourier Series 1.1 Defiitios ad Motivatio Defiitio 1.1. A Fourier series is a series of fuctios

More information

A. Basics of Discrete Fourier Transform

A. Basics of Discrete Fourier Transform A. Basics of Discrete Fourier Trasform A.1. Defiitio of Discrete Fourier Trasform (8.5) A.2. Properties of Discrete Fourier Trasform (8.6) A.3. Spectral Aalysis of Cotiuous-Time Sigals Usig Discrete Fourier

More information

Problem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t =

Problem Cosider the curve give parametrically as x = si t ad y = + cos t for» t» ß: (a) Describe the path this traverses: Where does it start (whe t = Mathematics Summer Wilso Fial Exam August 8, ANSWERS Problem 1 (a) Fid the solutio to y +x y = e x x that satisfies y() = 5 : This is already i the form we used for a first order liear differetial equatio,

More information

5.61 Fall 2013 Problem Set #3

5.61 Fall 2013 Problem Set #3 5.61 Fall 013 Problem Set #3 1. A. McQuarrie, page 10, #3-3. B. McQuarrie, page 10, #3-4. C. McQuarrie, page 18, #4-11.. McQuarrie, pages 11-1, #3-11. 3. A. McQuarrie, page 13, #3-17. B. McQuarrie, page

More information

Chapter 8. DFT : The Discrete Fourier Transform

Chapter 8. DFT : The Discrete Fourier Transform Chapter 8 DFT : The Discrete Fourier Trasform Roots of Uity Defiitio: A th root of uity is a complex umber x such that x The th roots of uity are: ω, ω,, ω - where ω e π /. Proof: (ω ) (e π / ) (e π )

More information

Statistics 511 Additional Materials

Statistics 511 Additional Materials Cofidece Itervals o mu Statistics 511 Additioal Materials This topic officially moves us from probability to statistics. We begi to discuss makig ifereces about the populatio. Oe way to differetiate probability

More information

Lecture 2 Appendix B: Some sample problems from Boas, Chapter 1. Solution: We want to use the general expression for the form of a geometric series

Lecture 2 Appendix B: Some sample problems from Boas, Chapter 1. Solution: We want to use the general expression for the form of a geometric series Lecture Appedix B: ome sample problems from Boas, Chapter Here are some solutios to the sample problems assiged for Chapter, 6 ad 9 : 5 olutio: We wat to use the geeral expressio for the form of a geometric

More information

SEQUENCES AND SERIES

SEQUENCES AND SERIES Sequeces ad 6 Sequeces Ad SEQUENCES AND SERIES Successio of umbers of which oe umber is desigated as the first, other as the secod, aother as the third ad so o gives rise to what is called a sequece. Sequeces

More information

Analysis of Experimental Measurements

Analysis of Experimental Measurements Aalysis of Experimetal Measuremets Thik carefully about the process of makig a measuremet. A measuremet is a compariso betwee some ukow physical quatity ad a stadard of that physical quatity. As a example,

More information

Sequences. A Sequence is a list of numbers written in order.

Sequences. A Sequence is a list of numbers written in order. Sequeces A Sequece is a list of umbers writte i order. {a, a 2, a 3,... } The sequece may be ifiite. The th term of the sequece is the th umber o the list. O the list above a = st term, a 2 = 2 d term,

More information

Algorithms for Clustering

Algorithms for Clustering CR2: Statistical Learig & Applicatios Algorithms for Clusterig Lecturer: J. Salmo Scribe: A. Alcolei Settig: give a data set X R p where is the umber of observatio ad p is the umber of features, we wat

More information

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j

The z-transform. 7.1 Introduction. 7.2 The z-transform Derivation of the z-transform: x[n] = z n LTI system, h[n] z = re j The -Trasform 7. Itroductio Geeralie the complex siusoidal represetatio offered by DTFT to a represetatio of complex expoetial sigals. Obtai more geeral characteristics for discrete-time LTI systems. 7.

More information

PAijpam.eu ON DERIVATION OF RATIONAL SOLUTIONS OF BABBAGE S FUNCTIONAL EQUATION

PAijpam.eu ON DERIVATION OF RATIONAL SOLUTIONS OF BABBAGE S FUNCTIONAL EQUATION Iteratioal Joural of Pure ad Applied Mathematics Volume 94 No. 204, 9-20 ISSN: 3-8080 (prited versio); ISSN: 34-3395 (o-lie versio) url: http://www.ijpam.eu doi: http://dx.doi.org/0.2732/ijpam.v94i.2 PAijpam.eu

More information

SOLID MECHANICS TUTORIAL BALANCING OF RECIPROCATING MACHINERY

SOLID MECHANICS TUTORIAL BALANCING OF RECIPROCATING MACHINERY SOLID MECHANICS TUTORIAL BALANCING OF RECIPROCATING MACHINERY This work covers elemets of the syllabus for the Egieerig Coucil Exam D5 Dyamics of Mechaical Systems. O completio of this tutorial you should

More information

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

Fizeau s Experiment with Moving Water. New Explanation. Gennady Sokolov, Vitali Sokolov Fizeau s Experimet with Movig Water New Explaatio Geady Sokolov, itali Sokolov Email: sokolov@vitalipropertiescom The iterferece experimet with movig water carried out by Fizeau i 85 is oe of the mai cofirmatios

More information

Basic Waves and Optics

Basic Waves and Optics Lasers ad appliatios APPENDIX Basi Waves ad Optis. Eletromageti Waves The eletromageti wave osists of osillatig eletri ( E ) ad mageti ( B ) fields. The eletromageti spetrum is formed by the various possible

More information

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

University of California, Los Angeles Department of Statistics. Hypothesis testing Uiversity of Califoria, Los Ageles Departmet of Statistics Statistics 100B Elemets of a hypothesis test: Hypothesis testig Istructor: Nicolas Christou 1. Null hypothesis, H 0 (claim about µ, p, σ 2, µ

More information

Classical Mechanics Qualifying Exam Solutions Problem 1.

Classical Mechanics Qualifying Exam Solutions Problem 1. Jauary 4, Uiversity of Illiois at Chicago Departmet of Physics Classical Mechaics Qualifyig Exam Solutios Prolem. A cylider of a o-uiform radial desity with mass M, legth l ad radius R rolls without slippig

More information

AIT. Blackbody Radiation IAAT

AIT. Blackbody Radiation IAAT 3 1 Blackbody Radiatio Itroductio 3 2 First radiatio process to look at: radiatio i thermal equilibrium with itself: blackbody radiatio Assumptios: 1. Photos are Bosos, i.e., more tha oe photo per phase

More information

Expectation and Variance of a random variable

Expectation and Variance of a random variable Chapter 11 Expectatio ad Variace of a radom variable The aim of this lecture is to defie ad itroduce mathematical Expectatio ad variace of a fuctio of discrete & cotiuous radom variables ad the distributio

More information

Waves and rays - II. Reflection and transmission. Seismic methods: Reading: Today: p Next Lecture: p Seismic rays obey Snell s Law

Waves and rays - II. Reflection and transmission. Seismic methods: Reading: Today: p Next Lecture: p Seismic rays obey Snell s Law Seismic methods: Waves ad rays - II Readig: Today: p7-33 Net Lecture: p33-43 Reflectio ad trasmissio Seismic rays obey Sell s Law (just like i optics) The agle of icidece equals the agle of reflectio,

More information

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018

Sequences, Mathematical Induction, and Recursion. CSE 2353 Discrete Computational Structures Spring 2018 CSE 353 Discrete Computatioal Structures Sprig 08 Sequeces, Mathematical Iductio, ad Recursio (Chapter 5, Epp) Note: some course slides adopted from publisher-provided material Overview May mathematical

More information

Lecture 2 Clustering Part II

Lecture 2 Clustering Part II COMS 4995: Usupervised Learig (Summer 8) May 24, 208 Lecture 2 Clusterig Part II Istructor: Nakul Verma Scribes: Jie Li, Yadi Rozov Today, we will be talkig about the hardess results for k-meas. More specifically,

More information

COURSE INTRODUCTION: WHAT HAPPENS TO A QUANTUM PARTICLE ON A PENDULUM π 2 SECONDS AFTER IT IS TOSSED IN?

COURSE INTRODUCTION: WHAT HAPPENS TO A QUANTUM PARTICLE ON A PENDULUM π 2 SECONDS AFTER IT IS TOSSED IN? COURSE INTRODUCTION: WHAT HAPPENS TO A QUANTUM PARTICLE ON A PENDULUM π SECONDS AFTER IT IS TOSSED IN? DROR BAR-NATAN Follows a lecture give by the author i the trivial otios semiar i Harvard o April 9,

More information

Math 116 Practice for Exam 3

Math 116 Practice for Exam 3 Math 6 Practice for Eam 3 Geerated April 4, 26 Name: SOLUTIONS Istructor: Sectio Number:. This eam has questios. Note that the problems are ot of equal difficulty, so you may wat to skip over ad retur

More information

Chapter 6 Overview: Sequences and Numerical Series. For the purposes of AP, this topic is broken into four basic subtopics:

Chapter 6 Overview: Sequences and Numerical Series. For the purposes of AP, this topic is broken into four basic subtopics: Chapter 6 Overview: Sequeces ad Numerical Series I most texts, the topic of sequeces ad series appears, at first, to be a side topic. There are almost o derivatives or itegrals (which is what most studets

More information

Jacob Hays Amit Pillay James DeFelice 4.1, 4.2, 4.3

Jacob Hays Amit Pillay James DeFelice 4.1, 4.2, 4.3 No-Parametric Techiques Jacob Hays Amit Pillay James DeFelice 4.1, 4.2, 4.3 Parametric vs. No-Parametric Parametric Based o Fuctios (e.g Normal Distributio) Uimodal Oly oe peak Ulikely real data cofies

More information

Calculus 2 - D. Yuen Final Exam Review (Version 11/22/2017. Please report any possible typos.)

Calculus 2 - D. Yuen Final Exam Review (Version 11/22/2017. Please report any possible typos.) Calculus - D Yue Fial Eam Review (Versio //7 Please report ay possible typos) NOTE: The review otes are oly o topics ot covered o previous eams See previous review sheets for summary of previous topics

More information