CMS ALGORITHMS FOR ANALYTICS AND DARK MATTER DETECTION

Quantum Physics II — Data collection, modelling, and analysis at CMS CERN

In this second part of the previous introductory article, we’ll tackle the more in-depth description of data collection, object modelling, and data analysis at CMS. The general workflow behind these kinds of experiments is complex, but I’ll try to give a brief description of each part so you can get a general idea of the whole process.

At the interaction vertex, these particles collide and decay products flow through the different parts of the detector, constructed so that measures are taken with high precision and allow for the (mostly) unequivocal identification of particles. However, not everything is as straightforward as it seems, and several events need to be taken into account; I’ll briefly explain them below.

In a collision between two protons, or more accurately between proton quarks, the immediate thought is that only the constituent quarks can interact, so only up and down quarks would interact. However, a measure of the mass of the proton and these quarks shows that most of the mass doesn’t come from its constituents but from internal bounding energy. This means that this excess energy, if the collider energy is enough, can produce other quarks such as quarks bottom and top, both several times the mass of quarks u and d, and these quarks may be the ones that interact at the collision point; an example of a collision may be seen in the image below.

Once meaningful data has been retrieved and objects are recreated, it’s time to check this data against previously known results for the SM. The way CERN takes care of this comparison is by employing data simulations with MonteCarlo samples. These simulations include all data related to processes’ cross-sections (defined as the probability of decay related to all possible decays), decays, and detector components (to the point of knowing the location of each pixel!) so that the uncertainty of these controllable events is minimised and meaningful conclusions can be made; if we want to measure a cross-section for dark matter, which may be very low, these uncertainties could be either the defining point of discovery or just statistical variations.

Now that we have collected real collision data and have data simulations, the next step is to define the process we want to study, like a certain decay of particles producing dark matter. To check if dark matter production is possible in this model, the investigator must include in its data all the possible backgrounds; in this context, a background is a decay that leaves the same traces in the detector as the main process we want to study.

It’s mandatory at this state to include blinding to the data, meaning that we shouldn’t include real data until the end of the study; this prevents the investigator from being biased.

Finally, the goal of these discovery projects can be summarised in one sentence: after including the simulation data, the investigator selects measures like the number of jets, tagged b quarks, missing transverse energy, etc. or defines new ones that they think could potentially be used to discriminate the signal (the studied process) against the backgrounds; this means, for example, selecting variable intervals where the dark matter processes are abundant while background processes are not.

Afterwards, the investigator includes the real data and checks if the results are in agreement with the SM. The way this is done is by a hypothesis test of:

The goal of this article is to show the reader the workflow of a CMS investigator researching a certain process, either a search for new particles like dark matter or already studied processes. From the detector components to the data collection, simulation and analysis, I hope you have acquired a general understanding of these concepts, albeit very superficial. In that regard, the literature regarding this subject is written by CERN physicists, so assumptions about all these steps are regularly made, and the average reader will be lost in the concepts and common slang employed.

I hope this article has helped you get a better understanding of this workflow, and that it’ll maybe spark some interest in particle physics, helping you understand further research done and news stories about it.

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