Changeset 304 in ETALON for reconstruction
- Timestamp:
- Nov 29, 2015, 4:00:37 PM (9 years ago)
- Location:
- reconstruction/long_paper3
- Files:
-
- 5 edited
Legend:
- Unmodified
- Added
- Removed
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reconstruction/long_paper3/phase_reconstruction_paper.aux
r299 r304 13 13 \citation{E203prstab} 14 14 \citation{E203prstab} 15 \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Example of profiles giving very different $\chi ^2$ despite being relatively similar. $\chi ^2_{\unhbox \voidb@x \hbox {sine noise}}=3.8219 e-08, \chi ^2_{\unhbox \voidb@x \hbox {offset}}=7.2661e-08$; For profile with sine noise: FW0.1M=0.0241, FW0.2M=0.044 FWHM=0.0621 FW0.8M=0.1849 FW0.9M=0.3619. For offset profile allFWXM=0. \relax }}{2}}15 \@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces Example of profiles giving very different $\chi ^2$ despite being relatively similar. $\chi ^2_{\unhbox \voidb@x \hbox {sine noise}}=3.8219\times 10^{-8}, \chi ^2_{\unhbox \voidb@x \hbox {offset}}=7.2661\times 10^{-8}$; For profile with sine noise: FW0.1M=0.0241, FW0.2M=0.044 FWHM=0.0621 FW0.8M=0.1849 FW0.9M=0.3619. As FWXM calculated from top of profile, for all profiles FWXM=0. \relax }}{2}} 16 16 \providecommand*\caption@xref[2]{\@setref\relax\@undefined{#1}} 17 17 \newlabel{Offsine}{{1}{2}} 18 \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces Effect of scaling the constraints on the parameters $\sigma _i$ (top) and $\mu _i$ (bottom) on the $\chi ^2$. For each point 1000 simulations wasmade.\relax }}{2}}18 \@writefile{lof}{\contentsline {figure}{\numberline {2}{\ignorespaces The constraints on the parameters $\sigma _i$ (top) and $\mu _i$ (bottom) due to effect of scaling in terms of the $\chi ^2$ and $Delta$FWHM ratio. For each point 1000 simulations were made.\relax }}{2}} 19 19 \newlabel{sigma_chi2}{{2}{2}} 20 20 \newlabel{eq:lamb}{{7}{3}} … … 26 26 \citation{VBthesis} 27 27 \citation{VBthesis,DESYthesis} 28 \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Comparison of different sampling with number of MDD with $\chi ^2$ criterium (top) and $\Delta _{FWHM}$ (bottom). Ls is linear sampling with $1^o,5^o,10^0$ MDD and Ts is Triple sine sampling; mx mean the reconstruction use the maximum number of detectors (blue and red dots on figure \ref {lin12}).\relax }}{4}} 28 \citation{LaiS} 29 \@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces Comparison of different sampling with number of MDD with $\chi ^2$ criterium (top) and $\Delta _{FWHM}$ (bottom). Ls is linear sampling with $1^o,5^o,10^0$ MDD and Ts is Triple sine sampling; mx mean the reconstruction use the maximum number of detectors (blue and red dots in figure \ref {lin12}).\relax }}{4}} 29 30 \newlabel{biglin}{{5}{4}} 30 31 \@writefile{lof}{\contentsline {figure}{\numberline {6}{\ignorespaces Effect of the sampling frequencies on the $\chi ^2$ (top) and $\Delta _{FWHM}$ (bottom). \relax }}{4}} 31 32 \newlabel{sampling_chi2}{{6}{4}} 32 \citation{LaiS} 33 \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Comparison of different LF extrapolation: example of spectrum (top) and profile (bottom) and histogram with mean $\chi ^2$ for each method (bottom). Gaussian and Taylorian methods are described in the text. "Real LF spectrum" means that the real LF spectrum is used. For this simulation was used the Hilbert method of phase recovery and $A\omega ^B$ high frequency extrapolation.\relax }}{5}} 33 \@writefile{lof}{\contentsline {figure}{\numberline {7}{\ignorespaces Comparison of different LF extrapolation: example of spectrum (top) and profile (bottom) and histogram with mean $\chi ^2$ for each method (bottom). Gaussian and Taylorian methods are described in the text. "Real LF spectrum" means that the real LF spectrum is used. For this simulation the Hilbert method of phase recovery and $A\omega ^B$ high frequency extrapolation were used.\relax }}{5}} 34 34 \newlabel{lf}{{7}{5}} 35 35 \@writefile{lof}{\contentsline {figure}{\numberline {8}{\ignorespaces Comparison of different LF extrapolation: histogram with mean $\chi ^2$ for each method (top) and $\Delta _{FWHM}$ (bottom). \relax }}{5}} 36 36 \newlabel{lf2}{{8}{5}} 37 \@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces Comparison of different HF extrapolation~: example of spectrum (top) and profile (bottom). For these simulation was used the Hilbert reconstruction method of phase recovery and Gaussian LF extrapolations.\relax }}{6}} 37 \@writefile{toc}{\contentsline {section}{Study of the reconstruction performance}{5}} 38 \@writefile{lof}{\contentsline {figure}{\numberline {9}{\ignorespaces Comparison of different HF extrapolations~: example of spectrum (top) and profile (bottom). For these simulations the Hilbert reconstruction method of phase recovery and Gaussian LF extrapolations were used.\relax }}{6}} 38 39 \newlabel{hf}{{9}{6}} 39 \@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Comparison of different HF extrapolation for Gaussian~: histogram with mean $\chi ^2$ (top) and $\Delta _{FWHM}$ (bottom).\relax }}{6}}40 \@writefile{lof}{\contentsline {figure}{\numberline {10}{\ignorespaces Comparison of different HF extrapolation for Gaussian~: histogram with mean $\chi ^2$ (top) and $\Delta _{FWHM}$ (bottom).\relax }}{6}} 40 41 \newlabel{hf2}{{10}{6}} 41 \@writefile{toc}{\contentsline {section}{Study of the reconstruction performance}{6}}42 42 \@writefile{lof}{\contentsline {figure}{\numberline {11}{\ignorespaces Examples of well reconstructed profile. The original profile is in blue and the profiles reconstructed with the Hilbert transform and the full Kramers-Kronig procedures are in red and black respectively.\relax }}{6}} 43 43 \newlabel{good_profiles}{{11}{6}} 44 \citation{Pelliccia:2014vba} 44 45 \@writefile{lof}{\contentsline {figure}{\numberline {12}{\ignorespaces Example of poorly reconstructed profile. The original profile is in blue and the profiles reconstructed with the Hilbert transform and the full Kramers-Kronig procedures are in red and black respectively.\relax }}{7}} 45 46 \newlabel{bad_profiles}{{12}{7}} 46 47 \@writefile{lof}{\contentsline {figure}{\numberline {13}{\ignorespaces {$\Delta _{FWHM}$ (top) and $\chi ^2$ (bottom) distribution of 1000 simulations reconstructed using the Hilbert transform method (black line) and Kramers-Kronig reconstruction method (red line). }\relax }}{7}} 47 48 \newlabel{profiles_stats_hilbert}{{13}{7}} 48 \@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces Example of reconstructed profile with zooms on the peak and tails. One can see that the profile reconstructed using the Kramers-Kronig method has a negative component. This will dominate the final $\chi ^2$ and explains why the $\chi ^2$ obtained by this method is higher as shown on figure~\ref {profiles_stats_hilbert}.\relax }}{7}}49 \@writefile{lof}{\contentsline {figure}{\numberline {14}{\ignorespaces Example of reconstructed profile with zooms on the peak and tails. One can see that the profile reconstructed using the Kramers-Kronig method has a negative component. This will dominate the final $\chi ^2$ and explains why the $\chi ^2$ obtained by this method is higher as shown in figure~\ref {profiles_stats_hilbert}.\relax }}{7}} 49 50 \newlabel{expKK}{{14}{7}} 50 \citation{Pelliccia:2014vba}51 51 \@writefile{lof}{\contentsline {figure}{\numberline {15}{\ignorespaces $\Delta _{FWXM}$ for 1000 profiles with both methods.\relax }}{8}} 52 52 \newlabel{fwxm}{{15}{8}} 53 53 \@writefile{lof}{\contentsline {figure}{\numberline {16}{\ignorespaces Original and reconstructed profile and their difference for bad profile (top) and good profile (bottom).\relax }}{8}} 54 54 \newlabel{mod}{{16}{8}} 55 \@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces Distribution of the $\chi ^2$ in the case of a lorenzian distribution. \relax }}{8}}55 \@writefile{lof}{\contentsline {figure}{\numberline {17}{\ignorespaces Distribution of the $\chi ^2$ in the case of a Lorenzian distribution. \relax }}{8}} 56 56 \newlabel{lorenz}{{17}{8}} 57 57 \@writefile{toc}{\contentsline {section}{Discussion}{8}} -
reconstruction/long_paper3/phase_reconstruction_paper.log
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<plot2210/61-eps-converted-to.pdf, id=96, 420.57124pt x 316.18124pt> 1581 File: plot2210/61-eps-converted-to.pdf Graphic file (type pdf) 1582 1583 <use plot2210/61-eps-converted-to.pdf> 1584 Package pdftex.def Info: plot2210/61-eps-converted-to.pdf used on input line 23 1585 2. 1586 (pdftex.def) Requested size: 199.16928pt x 149.73413pt. 1587 Package epstopdf Info: Source file: <plot2210/62.eps> 1588 (epstopdf) date: 2015-10-22 12:18:17 1589 (epstopdf) size: 11408 bytes 1590 (epstopdf) Output file: <plot2210/62-eps-converted-to.pdf> 1591 (epstopdf) date: 2015-10-22 12:21:08 1592 (epstopdf) size: 9444 bytes 1593 (epstopdf) Command: <repstopdf --outfile=plot2210/62-eps-converted- 1594 to.pdf plot2210/62.eps> 1595 (epstopdf) \includegraphics on input line 233. 1596 Package epstopdf Info: Output file is already uptodate. 1597 1598 <plot2210/62-eps-converted-to.pdf, id=98, 420.57124pt x 316.18124pt> 1599 File: plot2210/62-eps-converted-to.pdf Graphic file (type pdf) 1600 1601 <use 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1640 1646 (epstopdf) size: 9731 bytes 1641 1647 (epstopdf) Output file: <plots/81-eps-converted-to.pdf> 1642 (epstopdf) date: 2015-09-2 3 15:25:091643 (epstopdf) size: 6 578bytes1648 (epstopdf) date: 2015-09-29 08:49:25 1649 (epstopdf) size: 6910 bytes 1644 1650 (epstopdf) Command: <repstopdf --outfile=plots/81-eps-converted-to. 1645 1651 pdf plots/81.eps> 1646 (epstopdf) \includegraphics on input line 29 2.1652 (epstopdf) \includegraphics on input line 293. 1647 1653 Package epstopdf Info: Output file is already uptodate. 1648 1654 … … 1651 1657 1652 1658 <use plots/81-eps-converted-to.pdf> 1653 Package pdftex.def Info: plots/81-eps-converted-to.pdf used on input line 29 2.1659 Package pdftex.def Info: plots/81-eps-converted-to.pdf used on input line 293. 1654 1660 (pdftex.def) Requested size: 184.9429pt x 139.03815pt. 1655 1661 Package epstopdf Info: Source file: <plots/82.eps> 1656 (epstopdf) date: 2015-09-2 3 15:24:411662 (epstopdf) date: 2015-09-29 08:40:00 1657 1663 (epstopdf) 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reconstruction/long_paper3/phase_reconstruction_paper.tex
r299 r304 76 76 77 77 When it is only possible to measure the amplitude of the complex signal, it is necessary to recover the phase of the available data. 78 We assume that the function of the longitudinal beam density is analytical.%VH question 4 78 79 For an analytic function this is easier because the real and imaginary part are not completely independent. 79 80 The Kramers-Kronig relations~\cite{KK} helps restore the imaginary part of an analytic function $\varepsilon(\omega)$ from its real part and vice versa. … … 143 144 \includegraphics*[width=70mm]{plots/1.eps} 144 145 \caption{Example of profiles giving very different $\chi^2$ despite being relatively similar. \\ 145 $\chi^2_{\mbox{sine noise}}=3.8219 e-08, \chi^2_{\mbox{offset}}=7.2661e-08$; For profile with sine noise: FW0.1M=0.0241, FW0.2M=0.044 FWHM=0.0621 FW0.8M=0.1849 FW0.9M=0.3619. For offset profile all FWXM=0. }% VH change name of picture and unite with other146 $\chi^2_{\mbox{sine noise}}=3.8219\times 10^{-8}, \chi^2_{\mbox{offset}}=7.2661\times 10^{-8}$; For profile with sine noise: FW0.1M=0.0241, FW0.2M=0.044 FWHM=0.0621 FW0.8M=0.1849 FW0.9M=0.3619. As FWXM calculated from top of profile, for all profiles FWXM=0. }% VH quetion 5 146 147 \label{Offsine} 147 148 \end{figure} … … 149 150 150 151 151 To ensure that the choice of the parameters $\sigma_i$ and $\mu_i$ for the simulations does not bia is significantly the results, their value has been varied and this is shown on figure~\ref{sigma_chi2}.152 To ensure that the choice of the parameters $\sigma_i$ and $\mu_i$ for the simulations does not bias significantly the results, their value has been varied and this is shown in figure~\ref{sigma_chi2}. 152 153 153 154 \begin{figure}[htbp] … … 157 158 \includegraphics*[width=70mm]{plot2210/23.eps}\\ 158 159 \includegraphics*[width=70mm]{plot2210/24.eps}\\ 159 \caption{ Effect of scaling the constraints on the parameters $\sigma_i$ (top) and $\mu_i$ (bottom) on the $\chi^2$. For each point 1000 simulations was made.}160 \caption{The constraints on the parameters $\sigma_i$ (top) and $\mu_i$ (bottom) due to effect of scaling in terms of the $\chi^2$ and $Delta$FWHM ratio. For each point 1000 simulations were made.}%VH question 8 160 161 \label{sigma_chi2} 161 162 \end{figure} … … 165 166 166 167 Different distributions have been used for the frequencies $\omega_i$: linear, logarithmic, triple-sine. 167 In most sampling schemes we used 33 frequencies to make it comparable with the Triple-sine distribution used in~\cite{E203prstab}. These sampling scheme are defined as follow:168 In most sampling schemes we used 33 frequencies to make it comparable with the Triple-sine distribution used in~\cite{E203prstab}. These sampling schemes are defined as follow: 168 169 \begin{itemize} 169 170 \item \textit{Triple-sine} This sampling matches that of the E-203 experiment at FACET~\cite{E203prstab}. Eleven detectors are located every $10^o$ around the interaction point and 3 different sets of wavelengths are used, giving the following distribution: … … 185 186 %Further we will see, that due to space limitations of detector size, only Triple-sine sapmling is physical, but this study will give us information which sampling and further detector position are preferable. 186 187 \end{itemize} 187 {The study of the sampling is important, as it shows the best position of the detectors and also how to optimize the system. Linearly sampled spectrum gives the best result as shown on figure~\ref{samp}.188 {The study of the sampling is important, as it shows the best position of the detectors and also how to optimize the system. Linearly sampled spectrum gives the best result as shown in figure~\ref{samp}. 188 189 %XXX WE need to discuss this XXX This is not surprising, because in the process of profile recovery is present interpolation procedure for spectrum, which is well known works best with a uniform sampling. 189 190 } … … 199 200 {However, the linear sampling may not be practical to realize in a the real world. One needs to take into account the spatial size of the detectors (about 10 degrees in the case of E-203) and there is also a limit on the start and end points of detectors location (35-145 degrees for E-203). So linear sampling at a wide range of frequencies is impossible with this number of points. An investigation of how many linear sampling points can be used for a given angle difference between detectors shows that such physical constraints reduce strongly the number of detectors that can be used. 200 201 %For angle calculation and applying condition for first and final point was used formula ~\ref{eq:lamb}. 201 Figure~\ref{lin12} shows examples of detector positions. The position of the red points is calculated using formula~\ref{eq:lamb} and the blue are the possible positions of detector which does not break the minimum detector distance (MDD) given on top of each plot.}202 Figure~\ref{lin12} shows examples of detector positions. The position of the red points is calculated using formula~\ref{eq:lamb} and blue are possible detector positions which don't break the minimum detector distance (MDD) given on top of each plot.} 202 203 \begin{figure}[htbp] 203 204 \centering … … 209 210 \end{figure} 210 211 211 Figure~\ref{biglin} shows a comparison of the performances achieved with such positioning for different MDD. In each case the triple sine sampling (Ts) is better than the linear sampling (Ls) and close from the maximum linear sampling (Lsmx).212 Figure~\ref{biglin} shows a comparison of the performances achieved with such positioning for different MDD. In each case the triple sine sampling (Ts) is better than the linear sampling (Ls) and close from the maximum number of detectors with linear sampling (Lsmx). 212 213 %As the Lsmx configuration is physically impossible, 213 214 So the Ts configuration is favored and will be used in the rest of this paper. The comparison between Ts1, Ts5 and Ts10 shows that reconstruction performances are limited by the MDD. … … 217 218 \includegraphics*[width=90mm]{plots/51.eps} \\ 218 219 \includegraphics*[width=90mm]{plots/52.eps} 219 \caption{Comparison of different sampling with number of MDD with $\chi^2$ criterium (top) and $\Delta_{FWHM}$ (bottom). Ls is linear sampling with $1^o,5^o,10^0$ MDD and Ts is Triple sine sampling; mx mean the reconstruction use the maximum number of detectors (blue and red dots on figure \ref{lin12}).}%VH add picture220 \caption{Comparison of different sampling with number of MDD with $\chi^2$ criterium (top) and $\Delta_{FWHM}$ (bottom). Ls is linear sampling with $1^o,5^o,10^0$ MDD and Ts is Triple sine sampling; mx mean the reconstruction use the maximum number of detectors (blue and red dots in figure \ref{lin12}).}%VH add picture 220 221 \label{biglin} 221 222 \end{figure} … … 223 224 224 225 %This section has been moved from elsewhere 225 The choice of 33 frequencies for the sampling of the spectrum was made to match the current layout used on E-203. However it is important to check if there is an optimum value. To perform this check we used the same simulations and the same simulated spectrum but sampled with 3 to 140 points. The effect of changing the sampling frequencies on the $\chi^2$ is shown on figure~\ref{sampling_chi2}. This study uses Triple sine sampling with 1000 profiles for each point and both reconstruction method.226 The choice of 33 frequencies for the sampling of the spectrum was made to match the current layout used on E-203. However it is important to check if there is an optimum value. To perform this check we used the same simulations and the same simulated spectrum but sampled with 3 to 140 points. The effect of changing the sampling frequencies on the $\chi^2$ is shown in figure~\ref{sampling_chi2}. This study uses Triple sine sampling with 1000 profiles for each point and both reconstruction method. 226 227 227 228 … … 276 277 \end{equation} 277 278 278 Conditions for the constants A, B and C are the same. Comparison of different LF extrapolation can be found on figure~\ref{lf} and the performances of these methods on figure~\ref{lf2}.279 Conditions for the constants A, B and C are the same. Comparison of different LF extrapolation can be found in figure~\ref{lf} and the performances of these methods in figure~\ref{lf2}. 279 280 280 281 \begin{figure}[htbp] … … 283 284 \includegraphics*[width=65mm]{plots/72.eps}\\ 284 285 285 \caption{Comparison of different LF extrapolation: example of spectrum (top) and profile (bottom) and histogram with mean $\chi^2$ for each method (bottom). Gaussian and Taylorian methods are described in the text. "Real LF spectrum" means that the real LF spectrum is used. For this simulation was used the Hilbert method of phase recovery and $A\omega^B$ high frequency extrapolation.}286 \caption{Comparison of different LF extrapolation: example of spectrum (top) and profile (bottom) and histogram with mean $\chi^2$ for each method (bottom). Gaussian and Taylorian methods are described in the text. "Real LF spectrum" means that the real LF spectrum is used. For this simulation the Hilbert method of phase recovery and $A\omega^B$ high frequency extrapolation were used.} 286 287 \label{lf} 287 288 \end{figure} … … 300 301 301 302 302 Several high frequency (HF) extrapolation method were also tested. The most common~\cite{VBthesis,DESYthesis} is :303 Several high frequency (HF) extrapolation methods were also tested. The most common~\cite{VBthesis,DESYthesis} is : 303 304 \begin{equation} 304 305 \rho _ {HF} (\omega)=A\omega^{-4}, … … 306 307 where $\rho_ {HF} (\omega)$ is the extrapolated spectrum at high frequency and $A=\rho_f \omega_f^{4} $, where $\rho_f$ is spectrum value of final point $\omega_f$. 307 308 308 The second method use the same consideration as in Lai and Sievers~\cite{LaiS}:309 The second method uses the same consideration as in Lai and Sievers~\cite{LaiS}: 309 310 Assuming that the bunch size is finite with two end points at $z=0$ and at $z=\sigma_z$ then the longitudinal charge distribution ($S$) must follow $S(0)=S(\sigma_z)=0$. 310 311 An integration by parts gives~: … … 324 325 \rho_{HF}(\omega)=A\omega^{-2}+B\omega^{-3} 325 326 \end{equation} 326 or extrapolation with the exponentas free parameter:327 or extrapolation with degree of frequency as free parameter: 327 328 \begin{equation} 328 329 \rho_{HF}(\omega)=A\omega^B … … 330 331 331 332 332 where the A and B coefficientare calculated from the last data samples and the boundary conditions as follow:333 where the A and B -- coefficients which are calculated from the last data samples and the boundary conditions as follow: 333 334 \begin{itemize} 334 335 \item $B={\rho_{HF}'(\omega_{fmax}) \omega_{fmax}}/{\rho(\omega_{fmax})}$ … … 337 338 338 339 The requirement of finite bunch size requires $B\le2$, so in the case where the fit gives $B>-2$ we use $B=-2$. 339 Two other extrapolation methods have alsobeen investigated:340 Two other extrapolation methods also have been investigated: 340 341 \begin{itemize} 341 342 \item $\rho_{HF}(\omega_f)= 0 $ for $ \omega_f > \omega_{fmax}$ … … 343 344 \end{itemize} 344 345 345 These HF extrapolation methods are compared on figure~\ref{hf} and~\ref{hf2}.\par346 Thus, by virtue of the above arguments and simulations, It's naturally to choose the high-frequency extrapolation by power function.346 These HF extrapolation methods are compared in figure~\ref{hf} and~\ref{hf2}.\par 347 Thus, by virtue of the above arguments and simulations, it's naturally to choose the high-frequency extrapolation by power function. 347 348 348 349 … … 352 353 \includegraphics*[width=65mm]{plots2/92.eps}\\ 353 354 354 \caption{Comparison of different HF extrapolation ~: example of spectrum (top) and profile (bottom). For these simulation was used the Hilbert reconstruction method of phase recovery and Gaussian LF extrapolations.}355 \caption{Comparison of different HF extrapolations~: example of spectrum (top) and profile (bottom). For these simulations the Hilbert reconstruction method of phase recovery and Gaussian LF extrapolations were used.} 355 356 \label{hf} 356 357 \end{figure} … … 360 361 \includegraphics*[width=65mm]{plots/101.eps}\\ 361 362 362 \caption{Comparison of different HF extrapolation for Gaussian~: histogram with mean $\chi^2$ (top) and $\Delta_{FWHM}$ (bottom).}363 \caption{Comparison of different HF extrapolation for Gaussian~: histogram with mean $\chi^2$ (top) and $\Delta_{FWHM}$ (bottom).} 363 364 \label{hf2} 364 365 \end{figure} … … 382 383 \section{Study of the reconstruction performance} 383 384 384 After applying extrapolation and interpolation, the spectrum recovery is completed. Then we used different reconstruction techniques to reconstruct the original profile. For each reconstruction method some profiles are very well reconstructed whereas some other are not so well reconstructed. Examples of well reconstructed profiles are shown on figure~\ref{good_profiles} and examples of poorly reconstructed profile are shown on figure~\ref{bad_profiles}.385 After applying extrapolation and interpolation, the spectrum recovery is completed. Then we used different reconstruction techniques to reconstruct the original profile. For each reconstruction method some profiles are very well reconstructed whereas some other are not so well reconstructed. Examples of well reconstructed profiles are shown in figure~\ref{good_profiles} and examples of poorly reconstructed profile are shown in figure~\ref{bad_profiles}. 385 386 386 387 \begin{figure}[htbp] … … 405 406 406 407 407 The $\Delta_{FWXM}$ and $\chi^2$ distribution of the 1000 simulations which were made and then reconstructed using the Hilbert transform method and Kramers-Kornig reconstruction are shown in on figure~\ref{profiles_stats_hilbert}. There is a good agreement in FWHM between the two methods indicating that they are both good at finding the bunch length. However, the Hilbert method gives lower $\chi^2$ indicating that this method is better at reconstructionthe bunch profile.408 The $\Delta_{FWXM}$ and $\chi^2$ distribution of the 1000 simulations which were made and then reconstructed using the Hilbert transform method and Kramers-Kornig reconstruction are shown in figure~\ref{profiles_stats_hilbert}. There is a good concordance in FWHM between two methods indicating that they are both good at finding the bunch length. However, the Hilbert method gives lower $\chi^2$ indicating that this method is better at reconstruction of the bunch profile. 408 409 409 410 … … 420 421 \centering 421 422 \includegraphics*[width=100mm]{plots/14.eps} 422 \caption{Example of reconstructed profile with zooms on the peak and tails. One can see that the profile reconstructed using the Kramers-Kronig method has a negative component. This will dominate the final $\chi^2$ and explains why the $\chi^2$ obtained by this method is higher as shown on figure~\ref{profiles_stats_hilbert}.}% VH change name of picture and unite with other423 \caption{Example of reconstructed profile with zooms on the peak and tails. One can see that the profile reconstructed using the Kramers-Kronig method has a negative component. This will dominate the final $\chi^2$ and explains why the $\chi^2$ obtained by this method is higher as shown in figure~\ref{profiles_stats_hilbert}.}% VH change name of picture and unite with other 423 424 \label{expKK} 424 425 \end{figure} 425 426 % VH add this block 426 427 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% 427 Figure~\ref{fwxm} shows the different values the FWXH for different values of X. This shows that at different height of the profiles the quality of reconstruction varies: There is a better agreement in the tails (X=10\%) than at the top of the profile (X=90\%).428 Figure~\ref{fwxm} shows the different FWXH values for different values of X. This shows that at different height of the profiles the quality of reconstruction varies: there is a better agreement in the tails (X=10\%) than at the top of the profile (X=90\%). 428 429 Figure~\ref{mod} shows the modulus of the difference between the original and reconstructed profiles. One can see oscillations in the difference between the original and reconstructed profile. 429 430 … … 447 448 448 449 449 While doing this work we also became aware of the discussion in~\cite{Pelliccia:2014vba} where it is argued that these reconstruction method have more difficulties with lorentzian profiles than gaussian profiles. Therefore we simulated 1000 lorenzian profiles and performed a similar study. This is shown on figure~\ref{lorenz}. Although the $\chi^2$ is slightly worse in that case than in the case of gaussian profiles there still a good agreement between the original and reconstructed profiles.450 While doing this work we also became aware of the discussion in~\cite{Pelliccia:2014vba} where it is argued that these reconstruction method have more difficulties with lorentzian profiles than gaussian profiles. Therefore we simulated 1000 Lorenzian profiles and performed a similar study. This is shown in figure~\ref{lorenz}. Although the $\chi^2$ is slightly worse in that case than in the case of gaussian profiles there still a good agreement between the original and reconstructed profiles. 450 451 451 452 \begin{figure}[htbp] … … 454 455 \includegraphics*[width=70mm]{plot1510/172.eps} \\ 455 456 % \includegraphics*[width=75mm]{THPME088f10.eps} 456 \caption{Distribution of the $\chi^2$ in the case of a lorenzian distribution. }457 \caption{Distribution of the $\chi^2$ in the case of a Lorenzian distribution. } 457 458 \label{lorenz} 458 459 \end{figure} … … 479 480 \section{Discussion} 480 481 481 We performed extensive simulation to estimate the performance of two phase recovery methods in the case of multi-gaussian and lorenzian profiles. In both cases we found that when the sampling frequencies are chosen correctly we obtained a good agreement between the original and reconstructed profiles (in most cases $\Delta_{FWXM} < 10\%$; $\chi^2 \sim 10^{-6}$). This confirms that such methods are suitable to reconstruct the longitudinal profiles measured at particle accelerators using radiative methods.482 We performed extensive simulation to estimate the performance of two phase recovery methods in the case of multi-gaussian and Lorenzian profiles. In both cases we found that when the sampling frequencies are chosen correctly we obtained a good agreement between the original and reconstructed profiles (in most cases $\Delta_{FWXM} < 10\%$; $\chi^2 \sim 10^{-6}$). This confirms that such methods are suitable to reconstruct the longitudinal profiles measured at particle accelerators using radiative methods. 482 483 483 484
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