Significant advances in heavy ion experiment data analysis: CSHINE spectrometer’s silicon strip telescope achieves ~90% particle track recognition efficiency

To address critical research challenges in heavy-ion collisions at Fermi-energy regimes—specifically targeting the nuclear equation of state (nEoS), Femtoscopic interferometry of light nuclei, and isospin transport dynamics in fast fission processes—the Tsinghua University research team constructed a Compact Spectrometer for Heavy-IoN Experiments (CSHINE). The Silicon Strip Detector Telescopes (SSDTs), as the core detection subsystem of CSHINE, are tasked with precisely measuring key physical information of light-charged particles, such as the energy, momentum, and particle type. However, inherent complexities such as charge-sharing effects, multi-hit event interference, non-uniform silicon thickness, and the non-linear response of CsI(Tl) crystals have rendered SSDT data analysis workflows cumbersome and technically demanding. To resolve this technological impediment, Tsinghua University and Henan Normal University jointly developed an advanced ROOT-based analysis framework for CSHINE-SSDTs. Adopting a modular design, the framework systematically integrates complex data processing workflows into three core modules:

1. Data Format Standardization and Quality Verification,
2. Precision Detector Calibration and Efficient Particle Identification,
3. Hit-Pattern Recognition Algorithms and High-Accuracy Track Reconstruction.

This innovative framework enables high-precision, end-to-end analysis, handling information from raw signals to the extraction of physical observables. It boosts data processing efficiency and reliability for heavy-ion reaction experiments. Moreover, it offers a reusable, scalable solution for other SSDT-based detection systems, and promotes innovation in heavy-ion physics research.

Technical Highlights:

1. Modular Architecture Design:  Utilizing encapsulated C++ class functions, the framework integrates complex workflows—including detector calibration, particle identification, and track reconstruction—into a streamlined system, significantly simplifying data processing logic. This design not only enhances code reusability but also ensures robust extensibility, enabling flexible adaptation of functional modules to meet experimental requirements.
2. Innovative Track Reconstruction Technology: The framework employs an optimized algorithm with geometric constraints and energy correlations. This approach efficiently addresses the challenges of energy reconstruction in charge-sharing events and resolves the positional uncertainties in multi-hit events. Furthermore, through the dynamic adjustment of signal selection thresholds informed by reconstructed physical data, including effective physical event counts and energy spectra, the track recognition algorithm attains an outstanding track recognition efficiency of approximately 90%.

Empowering Frontiers in Nuclear Physics

With modular and open-source features, this framework will drive technological innovation in similar detection systems globally. By improving data analysis accuracy and efficiency, it supports nuclear physics experiments and paves the way for exploring the microscopic mysteries of nuclear matter. Its open-source nature fosters global collaboration, fueling fundamental research and enabling breakthroughs in understanding nuclear structure, nuclear matter properties and heavy ion reaction mechanisms.

The complete study is accessible via DOI: 10.1007/s41365-025-01743-y.

Nuclear Science and Techniques (NST) is a peer-reviewed international journal sponsored by the Shanghai Institute of Applied Physics, Chinese Academy of Sciences. The journal publishes high-quality research across a broad range of nuclear science disciplines, including nuclear physics, nuclear energy, accelerator physics, and nuclear electronics. Its Editor-in-Chief is the renowned physicist, Professor Yu-Gang Ma.