The nature of dark matter and properties of neutrinos are among the most pressing issues in contemporary particle physics. The dual-phase xenon time-projection chamber is the leading technology to cover the available parameter space for weakly interacting massive particles, while featuring extensive sensitivity to many alternative dark matter candidates. These detectors can also study neutrinos through neutrinoless double-beta decay and through a variety of astrophysical sources. A next-generation xenon-based detector will therefore be a true multi-purpose observatory to significantly advance particle physics, nuclear physics, astrophysics, solar physics, and cosmology. This review article presents the science cases for such a detector.

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Terrestrial Gamma-ray Flashes (TGFs) are a prompt, high energy, very intense natural emission of gamma rays from Earth’s atmosphere. Consisting of an upward sub-millisecond bursts of gamma rays (energy up to one hundred MeV), TGFs are mostly generated in powerful thunderstorms by lightnings. Given their production mechanism, several TGF counterparts can be detected too (mostly radio waves, electron beams and neutrons from photo-production). To investigate the X- and gamma-ray components, the ideal experiment is a space-borne instrument, operating at Low Earth Orbit (LEO) and featuring a fast detector response, possibly with spectral abilities. The CubeSat space mission LIGHT-1, launched in December 21st, 2021 and deployed from the International Space Station (ISS) on February 3rd, 2022, has been tailored around such physics requirements and it represents the technological demonstrator of possible larger missions to detect and localize TGF events. LIGHT-1 will help in making advancements in the TGF current knowledge: TGF occurring rates, average ignition altitude, production mechanism and effects on daily life on Earth are yet to be fully modeled and understood. In this paper the main characteristics of LIGHT-1 mission and the first preliminary flight data are reported.

In this paper a microscopic, non-discrete, mathematical model based on stigmergy for predicting the nodal aggregation dynamics of decentralized, autonomous robotic swarms is proposed. The model departs from conventional applications of stigmergy in bioinspired path-finding optimization, serving as a dynamic aggregation algorithm for nodes with limited or no ability to perform discrete logical operations, aiding in agent miniaturization. Time-continuous simulations were developed and carried out where nodal aggregation efficiency was evaluated using the following metrics: time to aggregation equilibrium, agent spatial distribution within aggregate (including average inter-nodal distance, center of mass of aggregate deviation from target), and deviation from target agent number. The system was optimized using cost minimization of the above factors through generating a random set of cost datapoints with varying initial conditions (number of aggregates, agents, field dimensions, and other specific agent parameters) where the best-fit scalar field was obtained using a random forest ensemble learning strategy and polynomial regression. The scalar cost field global minimum was obtained through basin-hopping with L-BFGS-B local minimization on the scalar fields obtained through both methods. The proposed optimized model describes the physical properties that non-digital agents must possess so that the proposed aggregation behavior emerges, in order to avoid discrete state algorithms aiming towards developing agents independent of digital components aiding to their miniaturization.