- Timestamp:
- 11/10/04 22:17:06 (20 years ago)
- Location:
- trunk/MagicSoft/TDAS-Extractor
- Files:
-
- 2 edited
Legend:
- Unmodified
- Added
- Removed
-
trunk/MagicSoft/TDAS-Extractor/Algorithms.tex
r5315 r5376 232 232 \end{equation} 233 233 234 In the case of an uncorrelated noise with zero mean the noise autocorrelation matrix is: 235 236 \begin{equation} 237 \boldsymbol{B}_{ij}= \langle b_i b_j \rangle \delta_{ij} = \sigma^2(b_i) \ , 238 \end{equation} 239 240 where $\sigma(b_i)$ is the standard deviation of the noise of the discrete measurements. Equation (\ref{of_noise}) than becomes: 241 242 243 \begin{equation} 244 \frac{\sigma^2(b_i)}{\sigma_E^2} = \sum_{i=1}^{n}{g_i^2} - \frac{\sum_{i=1}^{n}{g_i \dot{g}_i}}{\sum_{i=1}^{n}{\dot{g}_i^2}} \ . 245 \end{equation} 234 The expected contribution of the noise to the estimated timing ,$\sigma_{\tau}$, is: 235 236 \begin{equation}\label{of_noise_time} 237 E^2 \cdot \sigma_{\tau}^2=\frac{{\boldsymbol{g}}^T\boldsymbol{B}^{-1}{\boldsymbol{g}}}{(\boldsymbol{g}^T \boldsymbol{B}^{-1} \boldsymbol{g})(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\dot{\boldsymbol{g}}) -(\dot{\boldsymbol{g}}^T\boldsymbol{B}^{-1}\boldsymbol{g})^2} \ . 238 \end{equation} 239 240 For the MAGIC signals, as implemented in the MC simulations, a pedestal RMS of a single FADC slice of 4 FADC counts introduces an error in the reconstructed signal and time of: 241 242 \begin{equation}\label{of_noise} 243 \sigma_E \approx 8.3 \ \mathrm{FADC\ counts} \qquad \sigma_{\tau} \approx \frac{6.5\ \Delta T_{\mathrm{FADC}}}{E\ /\ \mathrm{FADC\ counts}} \ , 244 \end{equation} 245 246 where $\Delta T_{\mathrm{FADC}} = 3.33$ ns is the sampling intervall of the MAGIC FADCs. 247 248 In the current implementation a two step procedure is applied to reconstruct the signal. The weight functions $w_{\mathrm{amp}}(t)$ and $w_{\mathrm{time}}(t)$ are computed numerically with a resolution of $1/10$ of an FADC slice. In the first step the quantities $e_{i_0}$ and $e\tau_{i_0}$ are computed: 249 250 \begin{equation} 251 e_{i_0}=\sum_{i=i_0}^{i=i_0+5} w_{\mathrm{amp}(t_i)}y(t_{i+i_0}) \qquad e\tau_{i_0}=\sum_{i=i_0}^{i=i_0+5} w(t_i)_{\mathrm{time}(t_i)}y(t_{i+i_0}) 252 \end{equation} 253 254 for all possible signal start samples $i_0$. Let $i_0^*$ be the signal start sample with the largest $e_{i_0}$. Then in a seconst step the timing offset $\tau$ is calculated: 255 256 \begin{equation} 257 \tau=\frac{e\tau_{i_0^*}}{e_{i_0^*}} 258 \end{equation} 259 260 and the weigths iterated: 261 262 \begin{equation} 263 E_{i_0^*}=\sum_{i=i_0^*}^{i=i_0^*+5} w_{\mathrm{amp}(t_i - \tau)}y(t_{i+i_0^*}) \qquad E \tau_{i_0^*}=\sum_{i=i_0^*}^{i=i_0^*+5} w(t_i - \tau)_{\mathrm{time}(t_i)}y(t_{i+i_0^*}) \ . 264 \end{equation} 265 266 The reconstructed signal is then taken to be $E_{i_0^*}$ and the reconstructed arrival time $t_{\text{arrival}}$ is 267 268 \begin{equation} 269 t_{\text{arrival}} = i_0^* + \tau_{i_0^*} + \tau \ . 270 \end{equation} 271 272 273 274 % This does not apply for MAGIC as the LONs are giving always a correlated noise (in addition to the artificial shaping) 275 276 %In the case of an uncorrelated noise with zero mean the noise autocorrelation matrix is: 277 278 %\begin{equation} 279 %\boldsymbol{B}_{ij}= \langle b_i b_j \rangle \delta_{ij} = \sigma^2(b_i) \ , 280 %\end{equation} 281 282 %where $\sigma(b_i)$ is the standard deviation of the noise of the discrete measurements. Equation (\ref{of_noise}) than becomes: 283 284 285 %\begin{equation} 286 %\frac{\sigma^2(b_i)}{\sigma_E^2} = \sum_{i=1}^{n}{g_i^2} - \frac{\sum_{i=1}^{n}{g_i \dot{g}_i}}{\sum_{i=1}^{n}{\dot{g}_i^2}} \ . 287 %\end{equation} 246 288 247 289 -
trunk/MagicSoft/TDAS-Extractor/Changelog
r5370 r5376 19 19 20 20 -*-*- END OF LINE -*-*- 21 22 2004/11/10: Hendrik Bartko 23 * Algorithms.tex: added the theoretical error on the arrival time 24 determination, added information about the current implementation 25 of the digital filter 21 26 22 27 2004/11/10: Markus Gaug
Note:
See TracChangeset
for help on using the changeset viewer.