###### Abstract
Once particle collisions are reconstructed, analysts are faced with a list of objects they must use to conduct measurements of interest (to perform what particle physicists denote \"analyses\"). Note that this reconstruction is not perfect, but instead limited by the resolution of the detectors. Although efforts can be made to improve this resolution, such as improving the calibration of detectors by means of convolutional neural networks [9], the reconstructed objects will always be different to their \"original\" counterparts, which limits what experiments can do with them. One of the main consequences of this is that, very often, it becomes very hard to distinguish the target signals that experiments look for from backgrounds composed of other particles with similar properties. For instance, if one looks for an exotic new particle decaying into a pair of hadrons, this could be faked by situations in which each hadron originates from a different standard particle, although they are located close to each other in space. This all fits very nicely with the use of ML algorithms, particularly with classifiers that are able to separate scarce signals hiding behind much larger amounts of background. Finding the best of these classifiers has been the subject of many studies in particle physics, using algorithms of very different kind, ranging from boosted decision trees [10] to deep learning [11]. Alternative involves unsupervised [12] or semi-supervised [13] algorithms, with the goal of optimizing searches for rare phenomena for which more traditional methods have failed to date. Despite the many efforts being conducted in this area of signal-background discrimination by means of ML, as we shall see, most analysts either still rely on more traditional approaches or do not use the latest, best-performing, tools available in terms of libraries and algorithms. Assessing this question in detail is one the main goals of this paper. To some extent, the LHCb experiment has pioneered the application of ML at the LHC. This involves, for instance, the use of Multi-Variate Analysis (MVA) classification libraries in some of their first analyses [14] or the introduction of ML in the online trigger system of the detector [15; 16], which runs automatically at every LHC proton collision. Even if LHCb is, in many ways, an example of good use of ML in particle physics, in this paper we first assess the extent to which ML is used in their analyses and then what kind of libraries and algorithms are currently being used in the experiment. We also compare these aspects to those of other experiments. We then contrast the performance of some of the most frequent ML libraries and those used in particle physics with simulated LHCb data, to find out the extent to which there is room for improvement in the signal-background discrimination achieved in standard analyses. Note this is a novelty, since many other comparisons of this kind rely on existing High-Energy Physics (HEP) datasets designed for benchmarking, but corresponding to other topologies, kinematic ranges and experiments [17, 18, 19]. Next, we extend these tests to other libraries and algorithms. We quantify the overall margin of improvement and provide guidelines on how to use the latest libraries available in ML to improve the sensitivity of particle physics experiments. The conclusions of this study can mostly be expanded to experiments beyond LHCb. Therefore, instead of testing a specific algorithm to solve a specific problem, we try to provide a more global perspective, based on the actual popularity of algorithms and libraries in experiments and their potential performance. In summary, our main goals are:  
* Finding out the popularity of different algorithms and libraries at LHC experiments. * Determining whether the most popular methods are those that provide the best performance. * Providing examples of potential alternatives, which might have a better performance.