Difference: AtlasDataAnalysis (79 vs. 80)

Revision 802011-02-08 - AlistairGemmell

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Computentp, Neural Nets and MCLIMITS

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The <sequence> parameter (in this case '0') is there so that you can specify the parameters for a given error for multiple channels, without falling foul of the uniqueness requirement for <tag>:<sequence>. We have chosen it so that it equals my_Eventtype for that process. 'Channel' is present just in case you're considering multiple channels. We're only considering the one channel in this case (SemiLeptonic). The final parameter (Process) is not actually used - the second parameter tells the ANN which errors are which, but this isn't very easily read by you, so feel free to add it in to help you keep track of the various errors! These final few parameters can be placed in any order, so long as they are separated by semicolons.

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Passing preselection and sensible states

Many of the generated events in our samples will not pass the preselection cuts we would use in our final analysis. Sometimes to pass preselection requires some mistakes on the part of the reconstruction (e.g. tt + 0j), othertimes to fail preselection requires either the final state particles to be inherently unsuitable for our reconstruction, or to be mis-reconstructed. However, even if an event passes preselection it is possible that the events as reconstructed give a nonsensical final state - for example, the the light jets might not be able to be combined in such a way as to give a reasonable value of the W mass. Based on a few simple mass cuts, an event passing preselection can be determined to have a sensible state or not.

Currently, the type of event you are looking at is determined by looking at my_failEvent. States failing preselection have this equal to 0, passing preselection but not having a sensible final state equal 1 and passing preselection and having a sensible final state equal 3. These numbers are the basis of a number of bitwise tests - thus when setting your own my_failEvents, consider which bits in a binary string you want to represent various things, and then convert those to decimal.

Current samples in use

Input data and cross-sections

These cross-sections are for the overall process, at √s = 7 TeV. The 'final effective cross-section' is the cross-section for the sample we're looking at in in its entirety. For processing in the ANN this is further modified after an initial run by the ANN production code, based on how many events of the total cross-section produce sensible states - the ANN trains only on sensible states, so we must use the cross-section relevant to those in our final analysis.

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The ttH sample cross-sections are provided for the overall process - the MC is divided into two samples with W+ and W- independent of one another. These two samples are merged before being put through the ANN.

Sample	Dataset numbers	Cross-section (pb)	Branching Ratios	Filter Efficiency	What the multiplicative factors are	Effective cross-section (pb)	Sources
ttH	109840, 109841	0.09756	0.6760.2162*0.675	0.8355	Overall	0.01607	Initial cross-section: https://twiki.cern.ch/twiki/bin/view/LHCPhysics/CERNYellowReportPageAt7TeV
			0.676		W → hadrons		Branching ratios: 2008 PDG Booklet

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Number of events surviving preselection, weights and TrainWeights

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This table will be completed with all the relevant weights and TrainWeights at a later date - these values are to be compared to the output from Computentp to ensure everything is working as intended, and are calculated for the sensible cross-sections/events. (A quick check of the TrainWeight is to multiply the number so events of each background by their TrainWeight and sum them - by design, this should equal the number of entries in the ttH sample.)

Sample	Dataset Number	Pileup?		Number of events			Cross-section (fb)
			Total	Passing Preselection	Sensible States	Total	Passing Preselection	Sensible States
ttH (W+ sample)	109840	Yes	29968	2685	1936
		No		2497	1761
ttH (W- sample)	109841	Yes	29980	2764	2020
		No		2600	1879
ttH (total)		Yes	59948	5449	3956	16.07	1.460	1.060
		No		5097	3640		1.366	0.976
tt + 0j	105894	No	25487	6	5	24251	5.709	4.758
	116102	Yes	66911	149	123	1643	3.658	3.020
		No		78	66		1.915	1.620
tt + 1j	105895	No	26980	21	18	24233	18.862	16.167
	116103	Yes	211254	960	787	5191	23.588	19.337
		No		638	517		15.676	12.703
tt + 2j	105896	No	17487	69	53	14481	57.138	43.889
	116104	Yes	265166	2478	1957	6519	60.923	48.114
		No		2026	1548		49.810	38.058
tt + 3j	105896	No	10990	96	77	10102	88.240	70.776
	116105	Yes	241235	3946	3022	5920	96.829	74.155
		No		3469	2619		85.124	64.266
gg → ttbb (QCD)	116101	Yes	89887	3550	2560	483	19.070	13.752
qq → ttbb (QCD)	116106	Yes	19985	496	366	76.09	1.888	1.393
gg → ttbb (EWK)	116100	Yes	19987	981	706	47.02	2.308	1.661

Setting Systematic Uncertainties

The fitting code can take into account two different types of systematic uncertainty - rate and shape. The basic method to obtain both these uncertainties is that you should make your input samples for both your nominal sample, and for the two bounds of a given error (e.g. Initial State Radiation, ISR). Repeat this for all of the errors you wish to consider. The rate systematic uncertainty is simply how the number of events change that pass your preselection cuts etc. (you can only consider this, if you like). To obtain the shape uncertainty, you should pass each of the resulting datasets through the ANN (up to and including the templating, so that you have ANN results for both the nominal results, and as a result of varying each background). These ANN outputs can then be used to produce the rate uncertainties based on their integrals, before being normalised to the nominal cross-section so as to find the shape uncertainty - a measure of the percentage change in the bin-by-bin distribution for each error.

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