Error From Fwhm
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institution loginHelpJournalsBooksRegisterJournalsBooksRegisterSign inHelpcloseSign in using your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or password?Sign in via your institutionOpenAthens loginOther institution login Purchase Loading... Export You have selected 1 citation for export. Help Direct export fwhm gaussian Save to Mendeley Save to RefWorks Export file Format RIS (for EndNote, fwhm lorentzian ReferenceManager, ProCite) BibTeX Text Content Citation Only Citation and Abstract Export Advanced search Close This document does not fwhm resolution have an outline. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Nuclear Instruments and Methods in Physics Research Section A: fwhm xrd Accelerators, Spectrometers, Detectors and Associated Equipment Volume 283, Issue 1, 20 October 1989, Pages 72-77 Comparison among methods for calculating FWHM Author links open the overlay panel. Numbers correspond to the affiliation list which can be exposed by using the show more link. Opens overlay N. Markevich, Opens overlay I. Gertner Department of Physics, Technion, Haifa, Israel Received 16
Fwhm Equation
May 1989, Available online 28 October 2002 Show more Choose an option to locate/access this article: Check if you have access through your login credentials or your institution. Check access Purchase Sign in using your ScienceDirect credentials Username: Password: Remember me Not Registered? Forgotten username or password? OpenAthens login Login via your institution Other institution login doi:10.1016/0168-9002(89)91258-8 Get rights and content AbstractFive methods for calculating full width at half maximum (FWHM) are presented and applied to computer generated pseudorandom normal distributions of known parameters. The methods employ: the definition of FWHM, the relation between FWHM and the area under a Gaussian, the second moment of the peak, least-squares fit of the logarithm of the distribution to a parabola, and a least-squares fit to a straight line using the logarithm of the ratios of consecutive and equally spaced points on the Gaussian. The dependence of the error in the calculated FWHM on the Gaussian parameters - amplitude, mean and variance - is presented and discussed for each method. A comparison among the methods shows the superiority
Community Forums > Physics > General Physics > Dismiss Notice Join Physics Forums Today! The friendliest, high quality science and math community on the planet! Everyone who loves science is here! Observational error and FWHM Tags: accuracy error fwhm Mar 29, 2015 #1 TadyZ Hello, i have a
Fwhm Calculator
optical signal(x is length in pixels and y is amplitude ) and i have found a number fwhm astronomy of FWHM values of peaks. The width is measured in pixels, let's say the average width of 50 peaks is 8,7 pixels, standard deviation is fwhm matlab 0,6 and the average amplitude of peaks is 38 and standard deviation is 1,2. I want to find what's the error for the average value if i say that accuracy is 1 pixel. I want to plot a graph with all the http://www.sciencedirect.com/science/article/pii/0168900289912588 peaks, average value and to show if deviations may be caused by observational errors or something else. I think that my attempt to find it is too simple. I just take 1/8,7 and find that the error is 11,5%. TadyZ, Mar 29, 2015 Phys.org - latest science and technology news stories on Phys.org •Metamaterial uses light to control its motion •Stable molecular state of photons and artificial atom discovered •Self-learning computer tackles problems beyond the reach of previous systems Mar 29, 2015 #2 https://www.physicsforums.com/threads/observational-error-and-fwhm.805628/ mfb Insights Author 2015 Award Staff: Mentor All your peaks are supposed to be the same (repeated measurements of the same thing)? How did you get FWHM and amplitude? If that was a fit result, the fit result should also include the mean value and its uncertainty for every peak. Without background, the uncertainty on the mean will follow the FWHM divided by the square root of the number of events used to determine it - with some prefactor depending on what exactly you do. mfb, Mar 29, 2015 Mar 29, 2015 #3 TadyZ No, it's not repeated measurements, in theory values suppose to be the same, but they have deviations. Amplitude is brightness of a pixel, i got FWHM using matlab function, this one to be exact. If you want background: i'm measuring width of a white space(if it's telling you something, it's picket fence test for QA of LINAC MLC) in the picture that i've attached. I need to measure it on every horizontal line and find which line has the highest deviation with respect to average value. I already did that. But i also need to find out if the deviations are not caused by observational error. In practice i know that it's not, because measurements with slightly different setting show similar results, but i need to prove it in theory. Attached Files: FiguraRI.111_1_18.png File size: 110.4 KB Views: 513 TadyZ, Mar 29, 2015 Mar 29, 2015 #4 mfb Insights Author 2015 Award Staff: Mentor Matla
of parameters, used in the same functional forms, applied to the same set of data, with the same initial and final conditions will result in the same set of parameters http://www.casaxps.com/help_manual/error_analysis.htm on termination. Vary any of the above conditions and the result from the optimization routine will change in some respect. One method for assessing the uncertainty in the parameters for a peak model is to vary these optimization conditions by repeating an experiment on, what are hoped to be, identical samples. Then for each set of data apply the error from same optimization routine to the same synthetic model and so determine a distribution for the set of parameters used to quantify a sample. Such a procedure will vary almost every aspect of the measurement process and so result in a distribution for the parameters that truly represent the nature of the experiment. The basis for such an approach as error from fwhm described above lies in the assumption that there exists a set of parameters (only known to nature) that does not depend on any optimization routine nor any other errors introduced into the measurement process, and these true values will lie inside the region of the N-dimensional parameter space defined by the set of outcomes to this sequence of experiments. Obviously, if the synthetic model does not describe a set of parameters in tune with nature, the results may be in more doubt than the measured distribution might suggest. However, given that all is well then the task is to offer a means of understanding the uncertainties in the peak parameters within the context of these parameter distributions. Peak identification in XPS spectra represents a challenge since synthetic models more often than not involve overlapping line-shapes (Figure 1), the consequence of which is correlated optimization parameters. That is to say, if a single data envelope results from two overlapping peaks and if one of these underlying peaks is reduced in intensity then in order to describe the sam