## 2 Main MI Algorithms for Signal and Background Discrimination and Their Use at Particle Colliders  
When tackling signal-background discrimination by means of ML, most analysts in LHC experiments automatically limit themselves to one library, TMVA [20], and one algorithm, BDTs with AdaBoost. In this section, we describe both of these and discuss other convenient options on the market, in terms of libraries and methods. The Toolkit for Multivariate Data Analysis (TMVA) used with ROOT [21], is an open-source data analysis framework that provides all the necessary functionalities for processing large volumes of data, statistic analysis, visualization and information storage. ROOT was created at CERN and, although it was designed for HEP, it is currently used in many other areas such as biology, chemistry, astronomy, etc. As ROOT is specifically designed for HEP, the library is very popular and well-known for LHC experiments. The same is true for TVMA. As both ROOT and TMVA were developed some time ago, some of the latest techniques and innovations concerning data analysis are not yet available using just these tools. In this regard, several different TMVA interfaces have been recently created to solve this issue, with many framework options developed to integrate TMVA with more sophisticated libraries, such as Keras and PyTorch (described below). Thanks to this expansion, the use of ML for particle physicists is becoming easier, wider and more common. TMVA can be convenient for Python users, providing easy interfaces to examine data correlations, overtraining checks and a simpler event weighting management. Even though these characteristics are available in other Python libraries, TMVA offers the possibility of integrating every step of the process without the need for additional ones. Moreover, it offers suitable pre-processing possibilities for the data before feeding them into any of the classifiers. Thiese data must be given in form of either ROOT TTrees or ASCII text files. The first is the usual format in which scientists at CERN deal with their datasets. The analyses in this paper are based on ROOT v6.22/08. Sklearn [22] is a native open-source library for Python, and is currently an essential tool for modern data analysis. It includes a large range of tools and algorithms, which allow for the appropriate statistical modeling of systems. It incorporates algorithms for classification, regression, clustering, and dimensionality reduction, and supports a wide variety of algorithms [23] such as KNNs, boosted decision trees, random forests and SVMs among others. Furthermore, it is compatible with other Python libraries such as matplotlib [24], pandas [25], SciPy [26] or NumPy [27]. Contrary to TMVA, Sklearn was originally designed for Python. This means that the library provides a variety of modules and algorithms that facilitate the learning and work of the data scientist in the early stages of its development. For TMVA users, Sklearn can provide a versatile and simple interface for ML, with very simple applications for the trained classifier in new datasets. In this paper, we use Sklearn version 0.20.4. Other popular types of libraries were designed to better deal with Neural Networks (NNs), which are introduced below. PyTorch [28] created by [PERSON], is object-oriented, which allows for dealing with NNs in a natural and convenient way. An NN in PyTorch is an object, so each NN is an instance of the class through which the network is defined, all of which are inherited from the torch NNs Module. The torch NNs Module provides the main tools for creating, training, testing and deploying an NN. In our analyses, we use PyTorch 1.8.0. Keras [29] is an open-source software library that provides a Python interface for artificial NNs. It is capable of running on top of Tensorflow [30], Theano [31] and CNTK [32]. Keras has wide compatibility between platforms for the developed models and excellent support for multiple GPUs. For this paper, we use Keras Release 2.6.0.