Thermal and quantum noise

Oliver, Bernard M.

doi:10.1109/proc.1965.3814

Cited by 163 publications

(47 citation statements)

References 9 publications

(7 reference statements)

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“…Straightforward, statistical proofs exist showing that if photon arrival rates are time-independent (i.e., they can be described as being a statistically stationary process), the total number of photons arriving during any time interval τ is Poissondistributed. 4 Theoretical studies have established the correspondence between the number of photons incident on the detector and the number of photoelectrons emitted, and thus the photoelectrons also have a Poisson distribution. 5 The probability of emitting n p photoelectrons during time τ is given by…”

Section: A Shot Noisementioning

confidence: 99%

“…The derivation of the NSF is based on the fact that when the intensity of an incident light field does not fluctuate during the time of observation (i.e., when it remains in a statistically stationary state), photons sampled during this time will follow a Poisson stochastic process. 4,5 In Section 2 of the paper we review the statistical basis of photodetection. The mathematical derivation of the NSF is presented in Section 3.…”

Section: Introductionmentioning

confidence: 99%

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Estimating random errors due to shot noise in backscatter lidar observations

et al. 2006

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In this paper, we discuss the estimation of random errors due to shot noise in backscatter lidar observations that use either photomultiplier tube (PMT) or avalanche photodiode (APD) detectors. The statistical characteristics of photodetection are reviewed, and photon count distributions of solar background signals and laser backscatter signals are examined using airborne lidar observations at 532 nm using a photon-counting mode APD. Both distributions appear to be Poisson, indicating that the arrival at the photodetector of photons for these signals is a Poisson stochastic process. For Poissondistributed signals, a proportional, one-to-one relationship is known to exist between the mean of a distribution and its variance. Although the multiplied photocurrent no longer follows a strict Poisson distribution in analog-mode APD and PMT detectors, the proportionality still exists between the mean and the variance of the multiplied photocurrent. We make use of this relationship by introducing the noise scale factor (NSF), which quantifies the constant of proportionality that exists between the rootmean-square of the random noise in a measurement and the square root of the mean signal. Using the NSF to estimate random errors in lidar measurements due to shot noise provides a significant advantage over the conventional error estimation techniques, in that with the NSF uncertainties can be reliably calculated from/for a single data sample.Methods for evaluating the NSF are presented. Algorithms to compute the NSF are developed for the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar and tested using data from the Lidar In-space Technology Experiment (LITE).

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Section: A Shot Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating random errors due to shot noise in backscatter lidar observations

et al. 2006

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“…For further reading on the general subject of thermal and quantum noise, see, for example, Oliver (1965) and Kerr et al (1997). Nityananda (1994) compares quantum issues in the radio and optical domains, and a discussion of basic concepts is given by Radhakrishnan (1999).…”

Section: Quantum Effectmentioning

confidence: 99%

Introduction and Historical Review

Thompson

Moran

Swenson

2017

Astronomy and Astrophysics Library

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“…This preserves the uncertainty relation at the expense of an added minimum noise power of hν at the input. Oliver (1965) has elaborated and generalized this argument to include all wavelength regimes, and has shown that the minimum total noise power spectral density ψ ν of an ideal amplifier (relative to the input) is…”

Section: Quantum Limits Of Amplifiersmentioning

confidence: 99%

Radio and Optical Interferometry: Basic Observing Techniques and Data Analysis

Monnier

Allen

2013

Planets, Stars and Stellar Systems

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Astronomers usually need the highest angular resolution possible when observing celestial objects, but the blurring effect of diffraction imposes a fundamental limit on the image quality from any single telescope. Interferometry allows light collected at widely-separated telescopes to be combined in order to synthesize an aperture much larger than an individual telescope thereby improving angular resolution by orders of magnitude. Because diffraction has the largest effect for long wavelengths, radio and millimeter wave astronomers depend on interferometry to achieve image quality on par with conventional large-aperture visible and infrared telescopes. Interferometers at visible and infrared wavelengths extend angular resolution below the milli-arcsecond level to open up unique research areas in imaging stellar surfaces and circumstellar environments.In this chapter the basic principles of interferometry are reviewed with an emphasis on the common features for radio and optical observing. While many techniques are common to interferometers of all wavelengths, crucial differences are identified that will help new practitioners to avoid unnecessary confusion and common pitfalls. The concepts essential for writing observing proposals and for planning observations are described, depending on the science wavelength, the angular resolution, and the field of view required. Atmospheric and ionospheric turbulence degrades the longest-baseline observations by significantly reducing the stability of interference fringes. Such instabilities represent a persistent challenge, and the basic techniques of phase-referencing and phase closure have been developed to deal with them. Synthesis imaging with large observing datasets has become a routine and straightforward process at radio observatories, but remains challenging for optical facilities. In this context the commonly-used image reconstruction algorithms CLEAN and MEM are presented. Lastly, a concise overview of current facilities is included as an appendix.

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Thermal and quantum noise

Cited by 163 publications

References 9 publications

Estimating random errors due to shot noise in backscatter lidar observations

Estimating random errors due to shot noise in backscatter lidar observations

Introduction and Historical Review

Radio and Optical Interferometry: Basic Observing Techniques and Data Analysis

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