We demonstrate that the quenched average genuine multipartite entanglement (GME) can approach its maximum value in the ergodic phase of a disordered quantum spin model. In contrast, GME vanishes in the many-body localized (MBL) phase, both in equilibrium and in the long-time dynamical steady state, indicating a lack of useful entanglement in the localized regime. To establish this, we analyze the disordered Heisenberg spin chain subjected to a random magnetic field and incorporating two- and three-body Dzyaloshinskii-Moriya (DM) interactions. We exhibit that the behavior of GME, in both static eigenstates and in dynamically evolved states from an initial Neel configuration, serves as a reliable indicator of the critical disorder strength required for the ergodic-to-MBL transition. The identified transition point aligns well with standard indicators such as the gap ratio and correlation length. Moreover, we find that the presence of DM interactions, particularly the three-body one, significantly stabilizes the thermal phase and delays the onset of localization. This shift in the transition point is consistently reflected in both static and dynamical analyses, reinforcing GME as a robust probe for MBL transitions.

The precise measurement of cosmic-ray antinuclei serves as an important means for identifying the nature of dark matter and other new astrophysical phenomena, and could be used with other cosmic-ray species to understand cosmic-ray production and propagation in the Galaxy. For instance, low-energy antideuterons would provide a "smoking gun" signature of dark matter annihilation or decay, essentially free of astrophysical background. Studies in recent years have emphasized that models for cosmic-ray antideuterons must be considered together with the abundant cosmic antiprotons and any potential observation of antihelium. Therefore, a second dedicated Antideuteron Workshop was organized at UCLA in March 2019, bringing together a community of theorists and experimentalists to review the status of current observations of cosmic-ray antinuclei, the theoretical work towards understanding these signatures, and the potential of upcoming measurements to illuminate ongoing controversies. This review aims to synthesize this recent work and present implications for the upcoming decade of antinuclei observations and searches. This includes discussion of a possible dark matter signature in the AMS-02 antiproton spectrum, the most recent limits from BESS Polar-II on the cosmic antideuteron flux, and reports of candidate antihelium events by AMS-02; recent collider and cosmic-ray measurements relevant for antinuclei production models; the state of cosmic-ray transport models in light of AMS-02 and Voyager data; and the prospects for upcoming experiments, such as GAPS. This provides a roadmap for progress on cosmic antinuclei signatures of dark matter in the coming years.

The Facility for Antiproton and Ion Research (FAIR) will be the accelerator-based flagship research facility in many basic sciences and their applications in Europe for the coming decades. FAIR will open up unprecedented research opportunities in hadron and nuclear physics, in atomic physics and nuclear astrophysics as well as in applied sciences like materials research, plasma physics and radiation biophysics with applications towards novel medical treatments and space science. FAIR is currently under construction as an international facility at the campus of the GSI Helmholtzzentrum for Heavy-Ion Research in Darmstadt, Germany. While the full science potential of FAIR can only be harvested once the new suite of accelerators and storage rings is completed and operational, some of the experimental detectors and instrumentation are already available and will be used starting in summer 2018 in a dedicated research program at GSI, exploiting also the significantly upgraded GSI accelerator chain. The current manuscript summarizes how FAIR will advance our knowledge in various research fields ranging from a deeper understanding of the fundamental interactions and symmetries in Nature to a better understanding of the evolution of the Universe and the objects within.

This paper focuses on the construction of a general parametric model that can be implemented executing multiple swap tests over few qubits and applying a suitable measurement protocol. The model turns out to be equivalent to a two-layer feedforward neural network which can be realized combining small quantum modules. The advantages and the perspectives of the proposed quantum method are discussed.

After decades of observations the physical mechanisms that generate short gamma-ray bursts (SGRBs) still remain unclear. Observational evidence provides support to the idea that SGRBs originate from the merger of compact binaries, consisting of two neutron stars (NSs) or a NS and a black hole (BH). Theoretical models and numerical simulations seem to converge to an explanation in which the central engine of SGRBs is given by a spinning BH surrounded by a hot accretion torus. Such a BH-torus system can be formed in compact binary mergers and is able to launch a relativistic jet, which can then produce the SGRB. This basic scenario, however, has recently been challenged by Swift satellite observations, which have revealed long-lasting X-ray afterglows in association with a large fraction of SGRB events. The long durations of these afterglows (from minutes to several hours) cannot be explained by the $\sim\text{s}$ accretion timescale of the torus onto the BH, and, instead, suggest a long-lived NS as the persistent source of radiation. Yet, if the merger results in a massive NS the conditions to generate a relativistic jet and thus the prompt SGRB emission are hardly met. Here we consider an alternative scenario that can reconcile the two aspects and account for both the prompt and the X-ray afterglow emission. Implications for future observations, multi-messenger astronomy and for constraining NS properties are discussed, as well as potential challenges for the model.

This paper presents the measurements of the angular differential cross sections for the forward production of He, Li, Be, B, C and N nuclei in the fragmentation process of a 400$\text{MeV/nucleon}$ $^{16}\text{O}$ beam interacting with a graphite target. Due to the limited data available in this energy regime, these measurements of nuclear fragmentation cross sections are relevant to improve nuclear interaction models for Particle Therapy and space radioprotection applications. The data analyzed in this paper were collected during a measurement campaign carried out at the GSI Helmholtz Center for Heavy Ion Research facility in Darmstadt (Germany) by the FOOT collaboration. The results are compared with similar results found in the literature and with a previous FOOT measurement of the same process, using the same setup, from a previous pilot run performed at GSI. The pilot run data, however, had limited statistics and only allowed for the measurement of elemental fragmentation cross sections integrated in the setup acceptance. This data set, with statistics more than 100 times larger compared to the data collected in the previous run, enabled the measurement of angular differential cross sections, fully exploiting the granularity of the FOOT $\Delta \text{E}$-TOF system. Furthermore, a better comprehension of the FOOT apparatus allowed to improve the analysis techniques, leading to a reduction in the final systematic uncertainties.

A simulation tool based on GEMC framework to describe the MRPC telescope of the Extreme Energy Events (EEE) Project is presented. The EEE experiment is mainly devoted to the study of the secondary cosmic muons by using MRPC telescope distributed in high schools and research centres in Italy and at CERN. This takes into account the muon interactions with EEE telescopes and the structures surrounding the experimental apparata; it consists of a dedicated event generator producing realistic muon distribution and a detailed geometry description of the detector. Microscopic behaviour of MRPCs has been included to produce experimental-like data. A method to estimate the chamber effciency directly from data has been implemented and tested by comparing the experimental and simulated polar angle distribution of muons.

We present a systematic numerical-relativity study of the dynamical ejecta, winds and nucleosynthesis in neutron star merger remnants. Binaries with the chirp mass compatible with GW170817, different mass ratios, and five microphysical equations of state (EOS) are simulated with an approximate neutrino transport and a subgrid model for magnetohydrodynamics turbulence up to 100 milliseconds postmerger. Spiral density waves propagating from the neutron star remnant to the disk trigger a wind with mass flux ${\sim}0.1{-}0.5\,{\rm M_\odot/s}$ persisting for the entire simulation as long as the remnant does not collapse to black hole. This wind has average electron fraction $\gtrsim 0.3$ and average velocity ${\sim}0.1-0.17\,$c and thus is a site for the production of weak $r$-process elements (mass number A<195). Disks around long-lived remnants have masses ${\sim}0.1{-}0.2\,{\rm M_\odot}$, temperatures peaking at $\lesssim10\,$MeV near the inner edge, and a characteristic double-peak distribution in entropy resulting from shocks propagating through the disk. The dynamical and spiral-wave ejecta computed in our targeted simulations are not compatible with those inferred from AT2017gfo using two-components kilonova models. Rather, they indicate that multi-component kilonova models including disk winds are necessary to interpret AT2017gfo. The nucleosynthesis in the combined dynamical ejecta and spiral-wave wind in the comparable-mass long-lived mergers robustly accounts for all the $r$-process peaks, from mass number ${\sim}75$ to actinides in terms of solar abundances. Total abundandes are weakly dependent on the EOS, while the mass ratio affect the production of first peak elements.

This paper describes a new technique for determining the optimal period of a pulsar and consequently its light curve. The implemented technique makes use of the Principal Component Analysis (PCA) applied to the so-called waterfall diagram, which is a bidimensional representation of the pulsar acquired data. In this context we have developed the python package pywpf to easily retrieve the period with the presented method. We applied this technique to sets of data of the brightest pulsars in visible light that we obtained with the fast photon counter Iqueye. Our results are compared with those obtained by different and more classical analyses (e.g., epoch folding), showing that the periods so determined agree within the errors, and that the errors associated to the waterfall-PCA folding technique are slightly smaller than those obtained by the $\chi^2$ epoch folding technique. We also simulated extremely noisy situations, showing that by means of a new merit function associated to the waterfall-PCA folding it is possible to get more confidence on the determined period with respect to the $\chi^2$ epoch folding technique.

Entanglement is a well-known resource in quantum information, in particular it can be exploited for quantum key distribution (QKD). In this paper we define a two-way QKD scheme employing GHZ-type states of three qubits obtaining an extension of the standard E91 protocol with a significant increasing of the number of shared bits. Eavesdropping attacks can be detected measuring violation of the CHSH inequality and the secret key rate can be estimated in a device-independent scenario.

In this work we theoretically investigate the false vacuum decay process in a ferromagnetic quantum spin-1/2 chain. We develop a many-body theory describing the nucleation and the coherent dynamics of true-vacuum bubbles that is analytically tractable and agrees with numerical matrix product state calculations in all parameter regimes up to intermediate times. This bosonic theory allows us to identify different regimes in the parameter space and unravel the underlying physical mechanisms. In particular, regimes that closely correspond to the cosmological false vacuum decay picture are highlighted and characterized in terms of observable quantities.

The progresses of the AEgIS collaboration on its way towards directly measuring the gravitational free-fall of neutral antimatter atoms are reviewed. The experiment recently developed the first pulsed cold antihydrogen source and entered in its second phase, aiming at the first proof-of-concept gravitational measurement. Several major upgrades were deployed, including an upgraded antihydrogen production scheme and a fully-redesigned antiproton trap. AEgIS re-started its operation on the new CERN ELENA decelerator in late 2021, capturing its first antiprotons and commissioning its new antiproton energy degrading system and hardware/software control systems.

SiPM-based readouts are becoming the standard for light detection in particle detectors given their superior resolution and ease of use with respect to vacuum tube photo-multipliers. However, the contributions of detection noise such as the dark rate, cross-talk, and after-pulsing may impact significantly their performance. In this work, we present the development of highly reflective single-phase argon chambers capable of light yields up to 32 photo-electrons per keV, with roughly 12 being primary photo-electrons generated by the argon scintillation, while the rest are accounted by optical cross-talk. Furthermore, the presence of compound processes results in a generalized Fano factor larger than 2 already at an over-voltage of 5 V. Finally, we present a parametrization of the optical cross-talk for the FBK NUV-HD-Cryo SiPMs at 87 K that can be extended to future detectors with tailored optical simulations.

The joint detection of the gravitational wave GW170817, of the short $\gamma$-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star merger observed on August 17, 2017, is a milestone in multimessenger astronomy and provides new constraints on the neutron star equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multi-components models that also account for non-spherical ejecta. Observational data favor anisotropic geometries to spherically symmetric profiles, with a log-Bayes' factor of ${\sim}10^{4}$, and favor multi-component models against single-component ones. The best fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical-relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q<1.54 and the reduced tidal parameter to 120<\tilde\Lambda<1110. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a neutron star of $1.4~{\rm M}_\odot$ to $12.2\pm0.5~{\rm km}$ ($1\sigma$ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

In the current era, quantum resources are extremely limited, and this makes difficult the usage of quantum machine learning (QML) models. Concerning the supervised tasks, a viable approach is the introduction of a quantum locality technique, which allows the models to focus only on the neighborhood of the considered element. A well-known locality technique is the k-nearest neighbors (k-NN) algorithm, of which several quantum variants have been proposed. Nevertheless, they have not been employed yet as a preliminary step of other QML models, whereas the classical counterpart has already proven successful. In this paper, we present (i) an implementation in Python of a QML pipeline for local classification, and (ii) its extensive empirical evaluation. Specifically, the quantum pipeline, developed using Qiskit, consists of a quantum k-NN and a quantum binary classifier. The results have shown the quantum pipeline's equivalence (in terms of accuracy) to its classical counterpart in the ideal case, the validity of locality's application to the QML realm, but also the strong sensitivity of the chosen quantum k-NN to probability fluctuations and the better performance of classical baseline methods like the random forest.

Ensemble methods in machine learning aim to improve prediction accuracy by combining multiple models. This is achieved by ensuring diversity among predictors to capture different data aspects. Homogeneous ensembles use identical models, achieving diversity through different data subsets, and weighted-average ensembles assign higher influence to more accurate models through a weight learning procedure. We propose a method to achieve a weighted homogeneous quantum ensemble using quantum classifiers with indexing registers for data encoding. This approach leverages instance-based quantum classifiers, enabling feature and training point subsampling through superposition and controlled unitaries, and allowing for a quantum-parallel execution of diverse internal classifiers with different data compositions in superposition. The method integrates a learning process involving circuit execution and classical weight optimization, for a trained ensemble execution with weights encoded in the circuit at test-time. Empirical evaluation demonstrate the effectiveness of the proposed method, offering insights into its performance.

We present a Bayesian method for estimating spectral quantities in multivariate Gaussian time series. The approach, based on periodograms and Wishart statistics, yields closed-form expressions at any given frequency for the marginal posterior distributions of the individual power spectral densities, the pairwise coherence, and the multiple coherence, as well as for the joint posterior distribution of the full cross-spectral density matrix. In the context of noise projection - where one series is modeled as a linear combination of filtered versions of the others, plus a background component - the method also provides closed-form posteriors for both the susceptibilities, i.e., the filter transfer functions, and the power spectral density of the background. Originally developed for the analysis of the data from the European Space Agency's LISA Pathfinder mission, the method is particularly well-suited to very-low-frequency data, where long observation times preclude averaging over large sets of periodograms, which would otherwise allow these to be treated as approximately normally distributed.

This paper summarizes some of the relevant features exhibited by bi-nary mixtures of Bose-Einstein condensates in the presence of coherent coupling at zero temperature. The coupling, which is experimentally produced by proper photon transitions, can either involve negligible momentum transfer from the electromagnetic radiation (Rabi coupling) or large momentum transfer (Raman coupling) associated with spin-orbit effects.The nature of the quantum phases exhibited by coherently coupled mixtures is discussed in detail, including their paramagnetic, ferromagnetic, and, in the case of spin-orbit coupling, supersolid phases.The behavior of the corresponding elementary excitations is discussed, with explicit emphasis on the novel features caused by the spin-like degree of freedom. Focus is further given to the topological excitations (solitons, vortices) as well as to the superfluid properties. The paper also points out relevant open questions which deserve more systematic theoretical and experimental investigations.