###### Abstract  
The popularity of Machine Learning (ML) has been increasing in recent decades in almost every area, with the commercial and scientific fields being the most notorious ones. In particle physics, ML has been proven a useful resource to make the most of projects such as the Large Hadron Collider (LHC). The main advantage provided by ML is a reduction in the time and effort required for the measurements carried out by experiments, and improvements in the performance. With this work we aim to encourage scientists working with particle colliders to use ML and to try the different alternatives that are available, focusing on the separation of signal and background. We assess some of the most-used libraries in the field, such as Toolkit for Multivariate Data Analysis with ROOT, and also newer and more sophisticated options such as PyTorch and Keras. We also assess the suitability of some of the most common algorithms for signal-background discrimination, such as Boosted Decision Trees, and propose the use of others, namely Neural Networks. We compare the overall performance of different algorithms and libraries in simulated LHC data and produce some guidelines to help analysts deal with different situations. Examples include the use of low or high-level features from particle detectors or the amount of statistics that are available for training the algorithms. Our main conclusion is that the algorithms and libraries used more frequently at LHC collaborations might not always be those that provide the best results for the classification of signal candidates, and fully connected Neural Networks trained with Keras can improve the performance scores in most of the cases we formulate. high-energy physics; machine learning; LHC; LHCb +
Footnote †: journal: Medical  
to collide against each other, allowing for the study of the smallest pieces of matter. These collisions happen at an incredibly high rate (\(\sim\)40 million times per second), giving rise to hundreds of other particles whose interaction is then recorded by detectors (such as LHCb), which act as gigantic photographic cameras that collect 90 petabytes of data per year [4]. From these vast datasets, scientists try to extract interesting and rare phenomena, such as undiscovered particles or very infrequent particle decays, whose characteristics help to understand the properties of the smallest pieces of matter. The usual pipeline in analyses at a LHC experiment is as follows. Particles produced by collisions leave different types of electronic signals at the different components of the detectors (or subdetectors). These subdetectors are specialized in different aspects, from tracking to particle identification (PID). Their final goal is to provide as realistic a picture as possible of what happened after the collision. This picture involves knowing the energy, identity and position of the particles produced, and is usually referred to as \"reconstruction\". To reconstruct each collision, different algorithms help to interpret the electronic signals grouping the particle \"hits\" into tracks or understanding which type of particle left those hits. Another type of object that is often reconstructed at particle colliders are jets, which are groups of particles flying very close to each other and usually originating from a single energetic particle of interest. Determining the nature of this particle from the properties of jets (known as tagging) is also a frequent task in the reconstruction of collisions. Although they are not the goal of this study, all these aspects have recently led to surge in the use of ML. Recent examples are the use of graph neural networks (aspects of some of these ML algorithms are discussed later in the paper) for PID [5], reinforcement learning for jet reconstruction [6] or deep learning for track reconstruction [7] or tagging of jets [8]. We refer one more time to Reference [1] for a more complete compendium of examples.