Gran Sasso Science Institute

Abstract Superconducting qubits can be sensitive to energy deposits caused by cosmic rays and ambient radioactivity. While previous studies have explored correlated effects in time and space due to cosmic ray interactions, we present the first direct comparison of a transmon qubit’s performance measured at two distinct sites: the above-ground SQMS facility (Fermilab, US) and the deep-underground Gran Sasso Laboratory (Italy). Despite the stark difference in radiation levels, we observe a similar average qubit relaxation time of approximately 80 microseconds at both locations. To investigate radiation-induced events, we employ a fast decay detection protocol, comparing the relative rates of events between the two environments. Although intrinsic noise remains the dominant source of errors in superconducting qubits, our analysis revealed a significant excess of radiation-induced events for high-coherence transmon qubits operated above-ground. Finally, using γ -ray sources with increasing activity levels, we evaluate the qubit response in a controlled low-background environment.

Abstract We study the propagation of energy in one-dimensional anharmonic chains subject to a periodic, localized forcing. For the purely harmonic case, forcing frequencies outside the linear spectrum produce exponentially localized responses, preventing equi-distribution of energy per degree of freedom. We extend this result to anharmonic perturbations with bounded second derivatives and boundary dissipation, proving that for small perturbations and non-resonant forcing, the dynamics converges to a periodic stationary state with energy exponentially localized uniformly in the system size. The perturbed periodic state is described by a convergent power type expansion in the strength of the anharmonicity. This excludes chaoticity induced by anharmonicity, independently of the size of the system. Our perturbative scheme can also be applied in higher dimensions.

Abstract Personal ethical preferences are key enablers for the design of autonomous systems that respect humans’ moral rights and values. This goes beyond embedding ethical and legal principles in the design of the system once and for all. It requires the ability to elicit personal soft ethical preferences, represent them in a digitally useable format, and link them to the individual for use when interacting with digital systems. The aim of this paper is to represent soft ethical preferences through dispositions. Dispositions are properties that are instantiated by any kind of entity and that may manifest if properly triggered; we will focus on moral dispositions of individuals. The dispositional properties in which we are interested are ethical and behavioural ones. We propose a general and formal model to elicit and handle individual moral preferences. The model allows for the examination of real-life situations involving moral dilemmas. Users engage with these scenarios, respond based on how they would act, and provide justifications for their choices. Their responses are then analysed to identify tendencies toward certain actions, which are represented as dispositions. These dispositions can subsequently serve as the foundation for disposition manifestation mechanisms.

Reconstructing chemical variations of magma during rifting is challenging due to the heterogeneous mantle sources of the melt and different evolution pathways that magma potentially takes. Therefore, how and when the magma generation process evolves to that typical of an oceanic ridge (MORB-like composition) is still unclear. The Afar depression is an ideal place for studying magma changes during rift evolution, with North Afar close to breakup and older rift products preserved across the region. To investigate magma sources, we applied clustering analyses to a vast geochemical dataset of more than 1000 samples from the Afar rift. Combinations of different clustering methods (K-means and hierarchical) and assessment of correlation between features (Pearson coefficient) show that both trace element and isotope clustering group the North Afar samples, identifying a mantle source containing residual MREE-bearing minerals and an enriched mantle component. This suggests that North Afar, where the rift is closest to breakup, has a stronger influence of the Afar plume and more extensive partial melting of metasomatized lithosphere than the rest of Afar. We show that geochemical variations during rifting do not always follow a progressive transition toward a MORB-like composition but, instead, plume-like magmatism can increase until the most advanced stages of rifting (i.e., North Afar), potentially because the mantle plume is focused towards regions of thinnest lithosphere.

We present a study on the melting dynamics of neighbouring ice bodies by means of idealised simulations, focusing on collective effects, with the goal of obtaining fundamental insight into how collective interactions influence the melting of ice. Two neighbouring (vertically or horizontally aligned), square-shaped and equally sized ice objects (size of the order of centimetres) are immersed in quiescent fresh water at a temperature of ${20}\,^\circ \textrm {C}$ . By performing two-dimensional direct numerical simulations, and using the phase-field method to model the phase change, the collective melting of these objects is studied. When the objects are horizontally aligned, no significant influence of the neighbouring object on the melting time is observed. On the other hand, when vertically aligned, although the melting of the upper object is mostly unaffected, the melting time and the morphology of the lower ice body strongly depends on the initial inter-object distance. We report that the melting of the bottom object can be enhanced by more than 10 %, or delayed more than 20 %, displaying a non-monotonic dependence on the initial object size. We show that this behaviour results from a non-trivial competition between layering of cold fluid, which lowers the heat transfer, and convective flows, which favour mixing and heat transfer. For this melting in mixed convection, we were able to collapse our data onto a single curve.

Crystal Eye is a space-based all-sky monitor optimized for the autonomous detection and localization of transients in the 10 keV to 30 MeV energy range, a region where extensive observations and monitoring of various astrophysical phenomena are required. By focusing on the operating environment and its impact on the observation process, we optimized the detector design and assessed its scientific potential. We explored the use of novel techniques to achieve the science goals of the experiment. We assumed the orbit of a potential future mission at approximately 550 km altitude near the equatorial region with a 20° inclination. In such an orbit, the main background contributions for this kind of detector are from different particles and radiation of cosmic origin and secondaries produced by their interaction in the Earth’s atmospheric and geomagnetic environment. We studied the response of Crystal Eye detector in this background environment, using the Geant4 Monte Carlo simulation toolkit. We also calculated other detector performance parameters to estimate its scientific capabilities. The effective area and efficiency of the detector are calculated for low energy γ -ray sources and used to estimate its sensitivity to short-duration transient sources. The calculation shows a better effective area and sensitivity by several factors compared to existing instruments of similar type. A method is also developed and discussed to estimate the online transient-localization performance of the detector, suggesting a better localization precision by about an order of magnitude than those typically reported by existing γ -ray monitors. We present here the simulation study and results of an innovative detector design concept that can make a significant contribution in the multi-messenger era. Moreover, this study can be useful as a technical reference for similar future experiments. • A novel and innovative design of space bound short-duration transient monitor in MeV. • All sky monitoring (¿ 2 π sr field-of-view) with prompt (sub-second) and precise localization ( ∼ a degree of uncertainty radius). • Autonomous prompt and precise alert enabling crucial input in the multimessenger astronomy.

We present a deep learning pipeline to perform a model-independent, likelihood-free search for anomalous (i.e., non-background) events in the proposed next-generation multi-ton scale liquid xenon-based direct detection experiment, DARWIN. We train an anomaly detector comprising a variational autoencoder (VAE) and a classifier on high-dimensional simulated detector response data and construct a 1D anomaly score to reject the background-only hypothesis in the presence of an excess of non-background-like events. We use simulated validation data to determine the power of the method to reject the background-only hypothesis in the presence of a WIMP dark matter signal, without any model-dependent assumption about the nature of the signal. We show that our neural networks learn relevant features of the events from low-level, high-dimensional detector outputs, avoiding lossy and computationally expensive compression into lower-dimensional observables. Our approach is complementary to the usual likelihood-based analysis, in that it reduces the reliance on many of the corrections and cuts that are traditionally part of the analysis chain, with the potential of achieving higher accuracy and significant reduction of analysis time. We envisage the methodology presented in this work augmenting or complementing likelihood-based and other data-driven methods currently utilized in the DARWIN (and in the future, XLZD) analysis pipeline.