# Machine Learning Applications for Jet Tagging in the CMS Experiment
## 1 Introduction  
The prime motivation of the Large Hadron Collider (LHC) [1] is to provide an explanation to the open issues of fundamental particle physics, among which one can mention: inclusion of gravity in a unique framework with electromagnetic, weak, and strong interactions; explanation of the dark sector; and discovery of the origin of matter-antimatter asymmetry. LHC activity started in 2008, and the data collected in the first years led to discovery of the Higgs boson in 2012 by the ATLAS and CMS Collaborations [2; 3]. The CMS Experiment studies the particles resulting from LHC proton-proton collisions in pursuit of understanding the laws that regulate their interactions. In particular, the CMS Detector [4] is a general purpose apparatus with cylindrical symmetry designed to trigger based on [5; 6] and to identify electrons, muons, photons, and hadrons [7; 8; 9; 10]. Particle reconstruction is performed via the \"particle-flow\" (PF) algorithm [11] that combines the information provided by the many subdetectors comprising the apparatus: the silicon inner tracker, the crystal electromagnetic, the brass-scintillator hadron calorimeters, the superconducting solenoid (capable of providing a 3.8 T magnetic field), and the gas-ionisation muon detectors interleaved with the solenoid return yoke. The PF algorithm is able to reconstruct leptons and hadrons as well as more complex objects, such as _jets_ of hadronic particles, and global features of the event such as the missing transverse momentum for the kinematic closure of the event [12; 13; 14]. In the last years, the performances of the LHC accelerator machine and the complexity of the experiments have grown largely. In the forthcoming Run III, proton beams will collide with a centre-of-mass of 14 TeV and with a mean numberof simultaneous interactions per each bunch crossing around 80 at a frequency of 40 MHz. For each collision, data collected by each subdetector must be stored for the so-called _offline_ reconstruction to be carried out at a later stage and each event provide information on the order of MBs. In order to reduce the event rate, the CMS detector implements a double-tier trigger system that reduces the bandwidth to be written to the disk by up to 100 MHz. The conditions to face are both the large number of events to analyse and the large size of each event given the number of particles produced. Such issues are tackled by reducing the dimensionality of the problems by grouping the information in step-by-step analysis and reconstruction by applying selection at each step. This task can also be reformulated in terms of a machine learning (ML) problem. Formally given a certain ensemble of observed data X, we would like to find a function \(f\) that returns an ensemble Y of reduced dimensionality by optimising some criteria. This metric \(L(y,f(x))\) is typically referred to as loss function and can be optimised with machine learning. In this sense, the conditions of the LHC environment are a perfect testing ground for the application of machine learning techniques. A complete discussion of the applicability of ML techniques for the LHC experiments is already provided in [15]. Many algorithms have been developed over the last years, and they are used for many tasks, ranging from physics object reconstruction to signal-to-background discrimination. A very detailed collection of the machine learning algorithms used in particle physics can be found in [16]. One of the most delicate tasks in event reconstruction and interpretation is, in fact, the treatment of signatures originating from outgoing quarks and gluons from the collision. These particles produce jets of hadrons, ultimately losing information on the nature of the originating particle and making it difficult, therefore, to discriminate a potential signature coming from a new physics signal with respect to a process from the Standard Model (SM). We present a review of some of the most interesting and advanced uses of ML algorithms for jet identification developed by the CMS Collaboration.