## 1 Introduction  
During the last decade, machine learning has become a cornerstone in numerous scientific and industrial domains, providing powerful tools for fast and efficient data processing applications. To name a few examples, these applications include highly accurate skin cancer recognition [1], complex protein folding predictions [2], and advanced financial strategies like deep hedging, which uses machine learning to optimize hedging strategies under realistic market conditions [3]. In recent years, machine learning has also become instrumental in particle physics experiments [4, 5, 6]. These experiments, typically conducted at particle accelerators, involve colliding heavy atomic nuclei--such as gold or lead--at nearly the speed of light, creating an extremely hot or dense fireball to study the properties of nuclear matter under extreme conditions [7, 8, 9]. To search for rare probes, some of these experiments have trended toward higher collision rates [10, 11], driving the need for sophisticated data processing techniques capable of handling massive datasets on high-performance computing resources. Machinelearning methods are increasingly used to address challenges such as particle identification and event classification in these data-intensive environments. The future heavy-ion experiment Compressed Baryonic Matter (CBM) at the Facility for Antiproton and Ion Research (FAIR) [12] in Darmstadt, Germany, is such an experiment, targeting a collision rate of up to 10 MHz [13]. This unprecedented rate of collisions will generate vast volumes of about 1 TByte of data per second [14], demanding real-time event reconstruction and analysis capabilities that go beyond traditional methods, as it is simply not feasible to store all the data produced [15]. To meet these requirements, CBM will rely on high-performance computing infrastructure combined with highly efficient algorithms for tasks such as online event reconstruction [16] and event selection [17; 18], identifying events of particular scientific interest to retain for further analysis. Newly developed machine learning applications will complement the existing algorithm packages by increasing reconstruction efficiency and improving the quality of physics measurements [19; 20; 21], enabling more precise insights into the conditions created in heavy-ion collisions. One of the core packages of CBM, the Kalman Filter (KF) Particle Finder [22], was developed for the online reconstruction and selection of short-lived particles. Due to their instability, these particles decay before or in the tracking system and, therefore, cannot be directly detected or reconstructed [23]. To reconstruct them, their decay products, known as daughter particles, are analyzed to infer the original particle, referred to as the mother particle. During the reconstruction process, when a decay is identified, all potential mother particles are created to ensure that no real particle signal is inadvertently discarded due to an incorrect hypothesis. However, this method leads to noise, the so-called background, which contains both incorrectly reconstructed particles (referred to as combinatorial background or ghosts) and particles that have been finally assigned to the wrong particle type (physical background). The KF Particle Finder is part of the First-Level Event Selection (FLES) package [17], which selects the most interesting events for the current research online. Previous work has investigated machine learning techniques to enhance particle reconstruction quality and background suppression. Boosted Decision Trees have been employed to automatically optimize cut parameters for \(\Lambda\) particle selection [24], while neural networks have been integrated into the KF Particle Finder's particle competition to reduce the mutual background between short-lived particle candidates through particle classification [25], demonstrating promising improvements in reconstruction performance.