arXiv subjects

Javier Gomez

Publications and source records attributed to Javier Gomez.

4 recordsLinked to original sources

arXiv2024

Magnetization reversal and direct observation of magnetic domains on FePt thin films

In ferromagnetic thin films, the presence of an out-of-plane component of the magnetic anisotropy may induce a transition from planar to stripe-like magnetic domains above a critical thickness, $t_c$. Because of the changes in the domain structure, important changes in the magnetization reversal mechanisms are observed. We present the analysis of the magnetization reversal in FePt thin films, where this phenomenon is observed, through the experimental observation of magnetic domains by magneto-optic Kerr effect microscopy. The observed reversal mechanisms are strongly dependent on the thickness of the sample. For $t t_c$, the stripe-domain structure determines the magnetization reversal.

cond-mat.mtrl-sci↗

arXiv2024

Asymptotic-state prediction for fast flavor transformation in neutron star mergers

Neutrino flavor instabilities appear to be omnipresent in dense astrophysical environments, thus presenting a challenge to large-scale simulations of core-collapse supernovae and neutron star mergers (NSMs). Subgrid models offer a path forward, but require an accurate determination of the local outcome of such conversion phenomena. Focusing on "fast" instabilities, related to the existence of a crossing between neutrino and antineutrino angular distributions, we consider a range of analytical mixing schemes, including a new, fully three-dimensional one, and also introduce a new machine learning (ML) model. We compare the accuracy of these models with the results of several thousands of local dynamical calculations of neutrino evolution from the conditions extracted from classical NSM simulations. Our ML model shows good overall performance, but struggles to generalize to conditions from a NSM simulation not used for training. The multidimensional analytic model performs and generalizes even better, while other analytic models (which assume axisymmetric neutrino distributions) do not have reliably high performances, as they notably fail as expected to account for effects resulting from strong anisotropies. The ML and analytic subgrid models extensively tested here are both promising, with different computational requirements and sources of systematic errors.

astro-ph.HE↗

arXiv2024

A Memory-Efficient Distributed Algorithm for Approximate Nearest Neighbour Search with Arbitrary Distances

Approximate nearest neighbour (ANN) search has become a central task in modern data-intensive applications, particularly when operating on large, heterogeneous, or high-dimensional datasets. However, many existing ANN methods struggle in such scenarios, either because they rely on metric assumptions or because their indexing strategies are not well suited to distributed environments or to settings with constrained memory resources. This work introduces PDASC (Parametrizable Distributed Approximate Similarity Search with Clustering), a distributed ANN search algorithm whose index design simultaneously supports arbitrary dissimilarity functions and efficient deployment in distributed, storage-aware environments. PDASC builds a distributed hierarchical index based on clustering mechanisms that are agnostic to distance properties, thereby accommodating non-metric and domain-specific similarities while naturally partitioning indexing and search across multiple computing nodes, with a compact per-node memory footprint. By preserving locally informative neighbourhood structure, the proposed index mitigates practical manifestations of the curse of dimensionality in high-dimensional spaces. We analyse how the index structural parameters govern the trade-offs among recall, computational cost, and memory usage. Experimental evaluation across multiple benchmark datasets and distance functions shows that PDASC achieves competitive accuracy-efficiency trade-offs while consistently requiring lower per-node memory compared to state-of-the-art ANN methods. By avoiding reliance on specialised hardware acceleration, PDASC enables scalable and energy-efficient similarity search in heterogeneous and distributed settings where memory efficiency and distance-function flexibility are first-class constraints.

cs.IR↗

arXiv2018

Topological structures are consistently overestimated in functional complex networks

Functional complex networks have meant a pivotal change in the way we understand complex systems, being the most outstanding one the human brain. These networks have classically been reconstructed using a frequentist approach that, while simple, completely disregards the uncertainty that derives from data finiteness. We here provide an alternative solution based on Bayesian inference, with link weights treated as random variables described by probability distributions, from which ensembles of networks are sampled. By using both statistical and topological considerations, we prove that the role played by links' uncertainty is equivalent to the introduction of a random rewiring, whose omission leads to a consistent overestimation of topological structures. We further show that this bias is enhanced in short time series, suggesting the existence of a theoretical time resolution limit for obtaining reliable structures. We also propose a simple sampling process for correcting topological values obtained in frequentist networks. We finally validate these concepts through synthetic and real network examples, the latter representing the brain electrical activity of a group of people during a cognitive task.

physics.data-an↗