## 1 Introduction  
Over the last few years, the remarkable growth of machine learning and neural networks has had a profound impact across multiple scientific fields, redefining approaches to problem-solving and pushing the boundaries of scientific exploration [1]. The versatility and learning capacity of neural networks have positioned them as formidable tools in computational sciences, capable of addressing a broad spectrum of challenges across various domains. This includes, for instance, solving differential equations [2], and addressing forward and inverse problems involving nonlinear partial differential equations using physics-informed neural networks [3]. The predictive prowess of neural networks has resulted in models of outstanding accuracy. In fields like physics and computational science, these methodologies are offering new perspectives and solutions to longstanding challenges [4]. Particularly in particle physics, these methodologies permit applications at different stages of experiments, enabling entirely new approaches or complementing existing algorithms. This includes particle identification, trajectory reconstruction, event classification, and others [5, 6]. This powerful interplay between computational science and particle physics will be prominently featured in the future heavy-ion experiment, Compressed Baryonic Matter (CBM), at the Facility for Antiproton and Ion Research (FAIR) [7]. This experiment will leverage advancements in neural networks for improved data analysis and interpretation. CBM is planned as a fixed-target experiment using the particle accelerator at FAIR. It will provide scientists with the ability to explore states of matter in regions of high baryonicdensities at moderate temperatures [8] and will allow for the search for rare short-lived particles at unprecedented collision rates of up to 10 MHz [9]. In CBM, heavy ions (e.g., gold ions) will be accelerated to almost the speed of light to collide with a target (e.g., a thin plate of gold), to enforce a nucleus-nucleus collision that creates states of matter with extremely high density [10]. Under these conditions, a state known as Quark-Gluon Plasma can be produced. In this state, quarks and gluons are freed from their usual strong interactions within the plasma. After that phase, during hadronization, quarks combine into new particles that burst out of the central collision point (primary vertex) right into the detector setup. Some of these newly formed particles decay almost immediately, either due to instability or interaction with other particles. At the point of decay, particles are generally referred to as mother particles, whereas the particles created during the decay are so-called daughter particles. The specific mother particles that decay almost immediately are called short-lived particles, and they can only be measured indirectly by the reconstruction of decays or decay chains, since they rarely reach any detector before. Then, the daughter particles' trajectories (tracks) are used and extrapolated to find the point of decay (secondary vertex), whereas their properties help to identify the decayed particle. An efficient search for rare short-lived particles requires the already-mentioned high interaction rates of up to 10 MHz. This creates further computational challenges. At these interaction rates, it is not possible to store the data streams entirely, as they are produced with approximately 1 TB of data per second [11]. To reduce the amount of data that has to be stored, a reduction by approximately three orders of magnitude [12] is required, and can be achieved by selecting only collisions (events) of physicists' interest. However, in CBM, there is no simple criteria for event selection, as the search for rare short-lived particles requires a full event reconstruction [13], including the reconstruction of decays and decay chains.