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Computentp, Neural Nets and MCLIMITS |
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The tt samples were initially generated to produce the equivalent of 75fb-1 of data, based on the LO cross-sections. Taking into account the k-factor of 1.84, this means that now all samples simulate 40.8fb-1 of data. These samples have also had a generator-level filter applied - most events (especially for tt+0j) are of no interest to us, so we don't want to fill up disk-space with them, so we apply filters based on the numbers of jets etc. The Filter Efficiency is the fraction of events that pass from the general sample into the final simulated sample. To clarify how all the numbers hang together, consider the case of tt+0j. We have simulated 66,911 events - as said above, this corresponds to 40.8fb-1 of data. We have a Filter Efficiency of 0.06774, so the full number of events that a complete semi-leptonic event would be comes to 987,762 events in 40fb-1. Divide this by 40 to get the number of events in 1fb-1 (i.e. the cross-section), and you get 24,694 events per fb-1. Our starting point for our cross-section is 13.18, with a k-factor of 1.84, which gives a cross-section of 24.25 - so all the numbers compare with each other pretty favourably. This of course makes getting from the number of sensible state events to the number expected per fb-1 rather easy - simply divide by 40.8.... You'll notice that the cross-section includes all the branching ratios already, so we don't need to worry about that. |
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> | **IMPORTANT** The Filter Efficiency for these samples was calculated based on a no-pileup sample. The filter is generator level, and one of the things it will cut an event for is not enough jets. However, pileup adds jets, but these are added well after the filter. The net result is that a number of events that failed the filter would have passed, had the pileup been added earlier in the process. This means the filter efficiency (and thus the cross-sections) are incorrect, by a yet to determined amount.... |
| | For the other samples, however, we do need to worry about branching ratios - the quoted initial cross-section includes all final states, so we need to apply branching ratios to the cross-section to reduce it down, so that it reflects the sample we've generated. We then subsequently need to reduce the cross-section further so that it reflects the number of sensible states. |
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0.06744 |
Generator-level filter efficiency |
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0.2142 |
Generator-level filter efficiency |
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0.4502 |
Generator-level filter efficiency |
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0.5860 |
Generator-level filter efficiency |
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