arXiv subjects

Alexandru Nicolin-Żaczek

Publications and source records attributed to Alexandru Nicolin-Żaczek.

4 recordsLinked to original sources

arXiv2026

Pattern formation in driven condensates

The onset of pattern formation in a spatially homogeneous system subjected to external driving is an important topic in various scientific fields. A celebrated classical example is the Faraday instability, where a vertically oscillated fluid surface undergoes a parametric resonance, giving rise to standing waves that self-organize into regular spatial patterns. Bose-Einstein condensates (BECs) provide an ideal quantum-mechanical platform for studying pattern-forming mechanisms due to their exceptional degree of experimental control. As a compressible state of quantum matter, a condensate responds sensitively to external perturbations, including time-periodic modulation of interactions, trapping potentials, or external fields. These features make BECs particularly well suited for exploring driven nonequilibrium phases and pattern formation. In this chapter, we review the remarkable progress achieved in this field over the past two decades. We begin with the first theoretical proposal predicting parametric instabilities and emergent Faraday waves in driven condensates. We then discuss key experimental and theoretical breakthroughs that confirmed these predictions and refined the understanding of the underlying mechanisms. This line of research has culminated in the recent observation of a stabilized square lattice pattern in a periodically driven BEC confined in a two-dimensional geometry. This driven superfluid state with superposed density modulation was shown to exhibit some features of a supersolid state.

cond-mat.quant-gas↗

arXiv2025

Quantum Computing Tools for Fast Detection of Gravitational Waves in the Context of LISA Space Mission

The field of gravitational wave (GW) detection is progressing rapidly, with several next-generation observatories on the horizon, including LISA. GW data is challenging to analyze due to highly variable signals shaped by source properties and the presence of complex noise. These factors emphasize the need for robust, advanced analysis tools. In this context, we have initiated the development of a low-latency GW detection pipeline based on quantum neural networks (QNNs). Previously, we demonstrated that QNNs can recognize GWs simulated using post-Newtonian approximations in the Newtonian limit. We then extended this work using data from the LISA Consortium, training QNNs to distinguish between noisy GW signals and pure noise. Currently, we are evaluating performance on the Sangria LISA Data Challenge dataset and comparing it against classical methods. Our results show that QNNs can reliably distinguish GW signals embedded in noise, achieving classification accuracies above 98\%. Notably, our QNN identified 5 out of 6 mergers in the Sangria blind dataset. The remaining merger, characterized by the lowest amplitude, highlights an area for future improvement in model sensitivity. This can potentially be addressed using additional mock training datasets, which we are preparing, and by testing different QNN architectures and ansatzes.

gr-qc↗

arXiv2024

Waiting times for sea level variations in the Port of Trieste: a computational data-driven study

We report here a series of detailed statistical analyses on the sea level variations in the Port of Trieste using one of the largest existing catalogues that covers more than a century of measurements. We show that the distribution of waiting times, which are defined here akin to econophysics, namely the series of shortest time spans between a given sea level L and the next sea level of at least L + δ in the catalogue, exhibits a distinct scale-free character for small values of δ, while for larger values of δ the distribution is very similar to the exponential distribution. The distribution of waiting times for small values of δ is typical for complex systems exhibiting criticality and is reported abundantly in the literature, while the exponential-like distribution observed for large values of δ has been observed in contexts as diverse as magnetic systems and light sleep duration.

physics.comp-ph↗

arXiv2024

Motifs in earthquake networks: Romania, Italy, United States of America, and Japan

We present a detailed description of seismic activity in Romania, Italy, and Japan, as well as the California seismic zone in the United States of America, based on the statistical analysis of the underlying earthquake networks used to model the aforementioned zones. Our results on network connectivity and simple network motifs allow for a complex description of seismic zones, while at the same time reinforcing the current understanding of seismicity as a critical phenomenon. The reported distributions on node connectivity, three-, and four-event motifs are consistent with power-law, i.e., scale-free, distributions over large intervals and are robust across earthquake networks obtained from different discretizations of the seismic zones of interest. In our analysis of the distributions of node connectivity and simple motifs, we distinguish between the global distribution and the powerlaw part of it with the help of maximum likelihood estimation (MLE) method and complementary cumulative distribution functions (CCDF). The main message is that the distributions reported for the aforementioned seismic zones have large power-law components, extending over some orders of magnitude, independent of discretization. All the results were obtained using publicly-available databases and open-source software, as well as a new toolbox available on GitHub, specifically designed to automatically analyze earthquake databases.

physics.comp-ph↗