Difference: IntroductionToSearches (2 vs. 3)

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META TOPICPARENT name="ThiagoTomei"

Introduction to Searches

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 In one way or another, you have to estimate the SM background in the signal region. Purely data-driven methods are the most reliable, but they may not always be possible. The next best thing is a properly validated SM simulation. The less you rely on simulation, the better; ratios of simulated distributions, for instance, are more reliable than the distributions themselves.

In our case, we don't have a purely-data driven method. However, we are going to take the ratio of the dilepton-jet mass distributions in the signal region and in the control region, for the SM background. We expect the simulation to not get this ratio that wrong. We multiply this ratio by the same distribution for the data in the background region, and we use that product as the estimate of the SM background in the signal region.

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Optimisation of signal region

Usually, your first guess of the definition of the signal region and control regions is going to be suboptimal. Any analysis should go through an optimisation step. where the variable ranges whichd define the regions are scanned in order to achieve an optimal signal / background separation. This optimisation is usually guided by the computation and maximisation of a figure of merit; many choices are available for that, from the simple signal-background ratio (S/B) to more sophisticated ones - Punzi significance is defined as Eff / sqrt(1.0 + B), where Eff is the signal selection efficiency. An alternative way is to optimise for the best expected limit.

Notice that this step should always be done before looking at the data in the signal region. This is the main principle of a so-called blind analysis; the analysis is done in this way as to prevent researcher bias, like tuning cuts in order to get better data/simulation agreement.

 

Comparison of SM background and data in signal region

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After the analysis is fully optimised, and all the experimental techniques have been deemed correct by both thorough scrutinization and comparison of the data to simulaiton in the control regions, one can look at the data in the signal region. Since we are doing a search for a new resonance in the example, we are going to look at the M(llj) distribution and see if the observed data agrees with the SM expectation, or if there is a localised excess (bump) somewhere. In the former case, we have found no evidence of New Physics, and should proceed to set limits on the cross-section of a physical process which would have produced such a bump.

The latter case is more interesting - after all, we may be on the verge of discovering a new particle! The first thing one should do is characterise the significance of the excess - in other words, how improbable is it to see such a bump, given that we know our background with a given uncertainty. If the excess is not significant enough, it is probably the result of a statistical fluctuation, and again limits should be set - the excess just means that your limits will not be as good. Notice that "significant enough" is rather subjective, so we have some objective criteria: we call a three-sigma global significance "evidence" for a new particle, while a five-sigma global significance would call for the announcement of a "discovery".

In our exemple, we search for a bump in M(llj) at a given mass, and set a limit for that mass. This allows us to make a curve of limit vs mass. There are automated tools in CMS to make this kind of curve.

  -- Main.trtomei - 2014-06-24 \ No newline at end of file
 
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