Data - Monte Carlo Correction techninques in Particle physics It is said that the data does not always match with the Monte Carlo simulations in particle physics.(I guess even in the Higgs to gamma gamma channel, the peak in real data was at about 127GeV and thus it was corrected) .Thus,I wished to know what are the different ways in which the differences in Data and MC simulations are corrected, if possible, for the case of jets like bjets.
 A: You have to understand that a Monte Carlo simulation is a way of integrating the predictions of a model.
Theoretical models are often calculable, and can be inserted with "simple" generators. i.e. every event is generated with the probability given by the quantum mechanical calculation.
This is not enough to allow comparison of data with  theory. The data itself follows theoretical models, that describe particle interactions through the detector, with errors coming from statistical probability functions and estimates of these functions. All these errors need to be generated with the appropriate statistical probability width. The summation of events is the total integrals giving crossections for generating events in the experiment.
Thus a Monte Carlo program gives one event with the mathematically correctly combined probability of being observed in the detector. A simulation of one real event's probability.
When the Monte Carlo data do not fit the data, then two things are possible:
1) there is a programming error in the numerous generators  or the code, which has to be found
2) there is a new discovery  and everybody rejoices.
For example, back in the days when quantum chromodynamics was not even well formulated as a theory deep inelastic scattering on protons was thought to be an elementary interaction, and was modeled in monte carlos accordingly, with the appropriate theoretical  scattering amplitude in the generator. The data showed deviation in the high momentum transfer region, not reproduced in the monte carlo, which led the way to verifying  the complex quark internal structure of the proton.
A: If certain distributions of variables don't agree, then you can reweight the MC sample to match data (typically either using a nice clean control sample or s-weighted signal). There are a variety of reweighting techniques e.g. histograms, kernel-density estimators, boosted decision trees.
If resolution doesn't agree, you can apply 'smearing', whereby a variable is shifted by some random amount on an event-by-event basis according to a Gaussian distribution (or similar).
