arXiv subjects

Matteo Manzi

Publications and source records attributed to Matteo Manzi.

4 recordsLinked to original sources

arXiv2026

X-ray driven displacive excitation of coherent phonons

Modulating electron-phonon coupling offers a route to control structural displacements and tune material functionality. Valence-to-conduction band transitions, however, provide limited leverage over the driving force. Here, we demonstrate coherent lattice dynamics in trigonal tellurium using free-electron laser pulses tuned to the Te N4,5-edge. Over a broad fluence range, the oscillation amplitude obeys the displacive excitation of coherent phonons framework, extended to core resonance with twice the driving efficiency of a visible pump. Ab initio calculations decompose the force into competing multiband contributions, inaccessible to optical excitation, whose balance shifts as carriers relax. Tunable extreme-ultraviolet and X-ray pulses thus open a regime in which the displacive response is set by band-dependent coupling to the lattice, not by the number and temperature of the photocarriers alone.

cond-mat.mtrl-sci↗

arXiv2025

skfolio: Portfolio Optimization in Python

Portfolio optimization is a fundamental challenge in quantitative finance, requiring robust computational tools that integrate statistical rigor with practical implementation. We present skfolio, an open-source Python library for portfolio construction and risk management that seamlessly integrates with the scikit-learn ecosystem. skfolio provides a unified framework for diverse allocation strategies, from classical mean-variance optimization to modern clustering-based methods, state-of-the-art financial estimators with native interfaces, and advanced cross-validation techniques tailored for financial time series. By adhering to scikit-learn's fit-predict-transform paradigm, the library enables researchers and practitioners to leverage machine learning workflows for portfolio optimization, promoting reproducibility and transparency in quantitative finance.

cs.LG↗

arXiv2023

Polynomial Stochastic Dynamical Indicators

This paper introduces three types of dynamical indicators that capture the effect of uncertainty on the time evolution of dynamical systems. Two indicators are derived from the definition of Finite Time Lyapunov Exponents while a third indicator directly exploits the property of the polynomial expansion of the dynamics with respect to the uncertain quantities. The paper presents the derivation of the indicators and a number of numerical experiments that illustrates the use of these indicators to depict a cartography of the phase space under parametric uncertainty and to identify robust initial conditions and regions of practical stability in the restricted three-body problem.

math-ph↗

arXiv2023

Why Topological Data Analysis Detects Financial Bubbles?

We present a heuristic argument for the propensity of Topological Data Analysis (TDA) to detect early warning signals of critical transitions in financial time series. Our argument is based on the Log-Periodic Power Law Singularity (LPPLS) model, which characterizes financial bubbles as super-exponential growth (or decay) of an asset price superimposed with oscillations increasing in frequency and decreasing in amplitude when approaching a critical transition (tipping point). We show that whenever the LPPLS model is fitting with the data, TDA generates early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin historical price.

q-fin.ST↗