Particles

The blast-wave model with Boltzmann–Gibbs statistics is used to examine the transverse momentum spectra of ${K\ast }^0$ mesons generated at the Relativistic High-Energy Collider (RHIC) Beam Energies with mid-rapidity ( ) in symmetric collisions. There is a clear correlation between the extracted kinetic freeze-out temperature ($T_0$) and transverse flow velocity (${\beta }_T$) in various collision centralities and center-of-mass energies ($\sqrt{s_{NN}}$). Since a larger initial energy density delays freeze-out and a shorter system lifetime limits cooling, $T_0$ is directly proportional to both $\sqrt{s_{NN}}$ and peripheral collisions. On the other hand, ${\beta }_T$ drops in peripheral symmetric collisions due to weaker collective expansion, while it rises with $\sqrt{s_{NN}}$ because of larger pressure gradients. The concurrence between the thermal and collective energy components in the expanding fireball is reflected in the obvious anti-correlation between $T_0$ and ${\beta }_T$. These findings support hydrodynamic predictions and offer important new information about QGP’s freeze-out behavior.

Next-generation space observatories for high-energy gamma-ray astrophysics will increase scientific return using onboard machine learning (ML). This is now possible thanks to today’s low-power, radiation-tolerant processors and artificial intelligence accelerators. This paper provides an overview of current and future ML applications in gamma-ray space missions focused on high-energy transient phenomena. We discuss onboard ML use cases that will be implemented in the future, including real-time event detection and classification (e.g., gamma-ray bursts), and autonomous decision-making, such as rapid repointing to transient events or optimising instrument configuration based on the scientific target or environmental conditions.

The study of the antimatter component in cosmic rays is essential for the understanding of their acceleration and propagation mechanisms, and is one of the most powerful tools for the indirect search of dark matter. Current methods rely on magnetic spectrometers for charge-sign discrimination, but these are not suitable for extending measurements to the TeV region within a short timeframe of a few decades. Since most of present and upcoming high-energy space experiments use large calorimeters, it is crucial to develop an alternative charge-sign discrimination technique that can be integrated with them. The Electron/Positron Space Instrument (EPSI) project, a two-year R&D initiative launched in 2023 with EU recovery funds, aims to address this challenge. The basic idea is to exploit the synchrotron radiation emitted by charged particles moving through Earth’s magnetic field. The simultaneous detection of an electron/positron with an electromagnetic calorimeter and synchrotron photons with an X-ray detector is enough to discriminate between the two particles at the event level. The main challenge is to develop an X-ray detector with a very large active area, high X-ray detection efficiency, and a low-energy detection threshold, compliant with space applications. In this paper, we give an overview of the EPSI project, with a focus on the general idea of the detection principle, the concept of the space instrument, and the design of the X-ray detector.