1 / 68

Astronomical Observational Techniques and Instrumentation

Astronomical Observational Techniques and Instrumentation. Professor Don Figer Noise. Aims for this lecture. review how to calculate signal-to-noise ratio describe processes that add noise to a detector signal give examples of how to combat noise. Lecture Outline. signal-to-noise ratio

dlarsen
Download Presentation

Astronomical Observational Techniques and Instrumentation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Astronomical Observational Techniques and Instrumentation Professor Don Figer Noise

  2. Aims for this lecture • review how to calculate signal-to-noise ratio • describe processes that add noise to a detector signal • give examples of how to combat noise

  3. Lecture Outline • signal-to-noise ratio • noise sources • shot noise • signal • dark current • background • read noise • thermal noise (Johnson) • kTC • 1/f • read noise in the electronics (summation of many contributors) • electronic crosstalk • popcorn noise • pickup/interference • how to combat noise, improving SNR

  4. Introduction • The ability to detect an object in an image is related to the amplitude of the signal and noise (and by contrast in some cases). • The signal can be increased by collecting more photons, e.g. • building a bigger lens, • using more efficient optics, • having a more efficient detector. • The noise can be reduced by making better electronics.

  5. Signal-to-Noise Ratio

  6. SNR and Sensitivity Definitions • SNR is the ratio of detected signal to uncertainty of the signal measurement. Higher is better. • Sensitivity is the flux level that corresponds to a given SNR for a particular system and integration time. Lower is better.

  7. SNR and Sensitivity • Both SNR and sensitivity depend on • signal • brightness of source • absorption of intervening material • gas, dust • particles in the atmosphere • optics • size of telescope • efficiency of detector • integration time • noise • detector read noise • shot noise from detector dark current • shot noise from background (zodiacal light, sky, telescope, instrument) • shot noise from source • imperfect calibrations

  8. Signal: definition • Signal is that part of the measurement which is contributed by the source.

  9. Noise - definition • Noise is that part of the measurement which is due to sources other than the object of interest. • In sensitivity calculations, the “noise” is usually equal to the standard deviation. • Random noise adds in quadrature.

  10. Sensitivity: definition • Sensitivity is the flux level that corresponds to a given SNR.

  11. SNR=10 read noise=0 e- SNR=9 read noise=5 e- SNR=7 read noise=10 e- SNR=4 read noise=20 e- SNR=2 read noise=50 e- SNR Example • These images show synthetic noise added to a real image of a star cluster obtained using Keck/LGSAO. • The maximum signal has been normalized to 100 and shot noise has been added. SNR values are for brightest pixel.

  12. Imaging at Night • Signal is diminished as light level is reduced. • Read noise is very important for object identification in this case.

  13. Read Noise vs. Telescope Size

  14. Noise and Performance • These plots show system performance for NGST (now called JWST). • Note that lower noise gives better performance.

  15. Tradeoff Example • An improvement in SNR due to one detector property can allow one to relax the performance in another property. • One last step in making such a trade is to convert improvements into dollars. Joint Dark Energy Mission, Brown 2007, PhD Thesis

  16. Noise Sources: Shot Noise in Signal

  17. Shot Noise Described • Photons arrive discretely, independently, and randomly. Their arrival times are described by Poisson statistics. • For Poisson statistics, the Root Mean Square uncertainty (RMS) in the number of photons per time interval is equal to the square root of the mean photon fluence (average number of photons detected per time interval). More simply, the signal equals the variance. • For example, a star is imaged onto a pixel and it produces on average 10 photo-electrons per second and we observe the star for 1 second, then the uncertainty of our measurement of its brightness will be the square root of 10 i.e. 3.2 electrons. This value is the ‘Photon Noise’. Increasing the exposure time to 10 seconds will increase the photon noise to 10 electrons (the square root of 100) but at the same time will increase the ‘Signal to Noise ratio’ (SNR). • This noise is often called “Shot Noise” because it is analogous to similar noise in electronics. The concept of shot noise was first introduced in 1918 by Walter Schottky who studied fluctuations of current in vacuum tubes. • In the absence of other noise sources, the SNR will increase as the square root of the exposure timedue to shot noise of the signal.

  18. Photon Shot Noise • The uncertainty in the source charge count is simply the square root of the collected charge. • Note that if this were the only noise source, then S/N would scale as t1/2. (Also true whenever noise dominated by a steady photon source.)

  19. Noise Sources: Shot Noise from Background

  20. Noise - sources: Noise from Background • Background photons come from everything but signal from the object of interest! • Note that the noise contribution is simply the uncertainty in the background level due to shot noise.

  21. Atmospheric emission: Blackbody Total power onto a detector: P = h AW Dn eskyBn(Tsky) h: transmission of all optics x Q.E. esky: emissivity of sky A: telescope area W: solid angle subtended by focal plane aperture Dn: bandwidth Bn(Tsky): Planck function At 10 mm, typically: h ~ 0.2, e ~ 0.1, AW ~ 3x10-10 m2 Sr Dn ~ 1.5 x 1013 Hz (10 mm filter), T ~ 270 K P ~ 10-9 W or ~ 4 x 1010g s-1

  22. q1 q0 Atelescope Asky detector x d D F Atmospheric emission: A Note on Solid Angle • Background from the sky can be calculated by multiplying the blackbody equation by the area of the sky seen by the detector and the solid angle subtended by the telescope aperture. • The AW product is invariant in a system.

  23. Telescope emission • The telescope emits blackbody radiation. • The emissivity is generally very low, ~0.05. • Each mirror contributes its own blackbody radiation.

  24. Noise Sources: Shot Noise from Dark Current

  25. Dark Current vs. Temperature in Silicon Shot Noise of Dark Current • Charge can also be generated in a pixel either by thermal motion or by the absorption of photons. • The two cases are indistinguishable. • Dark current can be reduced by cooling.

  26. Dark Current Mechanism

  27. Dark Current in Infrared Materials • Dark current is a function of temperature and cutoff wavelength.

  28. Noise Sources: Thermal Noise

  29. Properties of Johnson Noise • due to random thermal motion of electric charge in conductors • independent of current flow • noise is random at all frequencies (up to ~1/170 fs), i.e. it is “white” • For example, a 1 kΩ resistor at a temperature of 300 K has Johnson noise of

  30. Bandwidth • The equivalent bandwidth of a circuit is described by the illustration below. • In the case of an RC circuit, the bandwidth is given by:

  31. Noise Sources: Read Noise

  32. Read Noise • Read noise is the cumulative noise from sources associated with reading a signal.

  33. Noise Sources: kTC Noise

  34. kTC Noise • due to random thermal motion of electric charge in conductors, just like Johnson noise in resistors • act of “resetting” capacitor freezes in random fluctuation of charge • can be removed noiselessly through subtraction

  35. kTC Noise as Reset Noise

  36. Removing Reset Noise with CDS • The reset noise adds an upredictable offset voltage to the signal. • This offset can be removed by using correlated double sampling.

  37. Noise Sources: 1/f Noise

  38. 1/f noise • spectral density increases for lower frequencies • sometimes called “flicker noise” • caused by traps, often near surface interfaces • occurs in most devices • especially pronounced in FETs with small channels

  39. Noise in Time Domain white noise 1/f noise

  40. Noise in a MOSFET • Noise is a combination of thermal and 1/f noise, with the latter dominating at low frequencies.

  41. Noise Sources: Popcorn Noise

  42. Popcorn Noise • This is the minimum noise you can hear in a movie theatre during a tense scene. • This is noise produced by traps in FET channels that temporarily change the properties of the channel. • The summation of traps is thought to be a potential source of 1/f noise.

  43. Read Noise

  44. Read Noise • Read noise is produced by all the electronics in a detector system, e.g. • Johnson noise, • 1/f noise, • electronic current shot noise, • unstable power supplies • It is usually measured as the standard deviation in a sample of multiple reads taken with minimum exposure time and under dark conditions.

  45. Read Noise and SNR Example Fig. 13 - Text superposed on a tonal gradient, with superposed read noise. The contrast difference between text and background rises linearly across the image, while the noise stays constant; thus the S/N ratio rises linearly from zero to eight going from left to right.

  46. Noise Sources: Electronic Crosstalk

  47. Electronic Crosstalk signal Φ1 ΦR1 Φ2 ΦR2 Φ3 ΦR3

  48. Clocking Feedthrough (Crosstalk) • read time, 10.18 us • wait time for convert, 8.7 us • bandwidth ~ 160 kHz

  49. Noise Sources: Quantiziation Noise

  50. Quantization Noise • A digitizing system converts a continuous (analog) signal into quantized (digital) data. • The difference between the analog value and the digital value represents an error. • The error is the “quantization noise.” • For a random distribution, quantization noise is LSB/sqrt(12).

More Related