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talksposters:transientx [2024/07/15 11:31] lspitler |
talksposters:transientx [2024/08/30 11:01] (current) lspitler |
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- Clustering of candidates | - Clustering of candidates | ||
- Plotting | - Plotting | ||
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+ | ===== General parameters ===== | ||
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+ | * Block size: This should be chosen such that one block corresponds to of order 10,000 time samples. This can, in principal, be optimized for the properties of the CPU being used. | ||
+ | * Overlap: RFI mitigation and DM-time plot generation are applied only on the data that are within a single block. Only during dedispersion is the block size padded by the duration of the DM sweep. If the burst lands on or near a block size boundary, it could be missed. Therefore, a small amount of overlap is warranted. | ||
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===== Skewness/kurtosis filtering ===== | ===== Skewness/kurtosis filtering ===== | ||
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===== Downsampling ===== | ===== Downsampling ===== | ||
- | Next, the filterbank is downsampled in time and frequency according to the values given by ''--td'' and ''--fd''. Be aware that the filterbank can be downsampled in addition to the values given in the ''ddplan''. Hence, the file might be downsampled multiple times! | + | Next, the filterbank is downsampled in time and frequency according to the values given by ''--td'' and ''--fd''. Be aware that the filterbank can be downsampled in addition to the values given in the ''ddplan''. Hence, the file might be downsampled multiple times! Also, RFI algorithms that take time and frequency downsampling factors are applied in addition to the global time and frequency. |
===== Normalization ===== | ===== Normalization ===== | ||
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===== Clustering of candidates ===== | ===== Clustering of candidates ===== | ||
- | To avoid seeing the same candidates at several DMs, transientX clusters them based on the DBSCAN algorithm. This algorithm searches for other candidates in a radius around a candidate in DM (difference in dispersive delay) and time. If the specified number of candidates is found, they are collected as a core point, i.e. summarized into the one with the highest S/N. The radius is controlled with ''-r'' and should be large enough so that the delay from the DM step size fits comfortably in the radius. | + | To avoid seeing the same candidates at several DMs and adjacent time bins, transientX clusters them based on the DBSCAN algorithm. This algorithm searches for other candidates in a radius around a candidate in DM (difference in dispersive delay) and time. If the specified number of candidates is found, they are collected as a core point, i.e. summarized into the one with the highest S/N. The radius is controlled with ''-r'' and should be large enough so that the delay from the DM step size fits comfortably in the radius. |
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+ | If the code is running sluggishly, it is mostly likely that the clustering step has a large number of candidates to group together. Check the parameters you are using in the search. In particular avoid searching with widths that correspond to a larger number of time samples; it is better to downsample in time in these cases. | ||
===== Plotting ===== | ===== Plotting ===== | ||
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====== replot_fil ====== | ====== replot_fil ====== | ||
Next to ''transientx_fil'', ''replot_fil'' is the second important tool when searching single pulses. The purpose of replot_fil is to do a finer search for the TOA, DM, and the width of the pulse candidates. If a candidate is RFI, the change is large and then the candidate is dropped. | Next to ''transientx_fil'', ''replot_fil'' is the second important tool when searching single pulses. The purpose of replot_fil is to do a finer search for the TOA, DM, and the width of the pulse candidates. If a candidate is RFI, the change is large and then the candidate is dropped. | ||
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+ | ===== Debugging ===== | ||
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+ | If you want to know how a candidate that is filtered out by ''replot_fil'' "looks like", you can run the software with the "no clean" option and it will return all of the ''TransientX'' candidates as they are reprocessed by ''replot_fil''. | ||
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