arXiv subjects

Andrea Bulgarelli

Publications and source records attributed to Andrea Bulgarelli.

At least 19 recordsLinked to original sources

arXiv2026

Toward Hamiltonian simulations of Maxwell-Chern-Simons theory: constant modes and gauge field truncation

Maxwell-Chern-Simons (MCS) theory in $2+1$ dimensions provides a paradigmatic example of a topological gauge theory with both dynamical and topological degrees of freedom. Its Euclidean formulation suffers from a sign problem, making Hamiltonian numerical approaches particularly attractive. As a first step toward the non-perturbative Hamiltonian study of MCS theory, we investigate the constant mode sector on a spatial torus. Being analytically solvable in the continuum, it provides an ideal benchmark for understanding how the topological properties of the theory are encoded in a finite-dimensional lattice Hilbert space. We construct a finite-dimensional discretization of the torus of flat connections and show that the resulting lattice problem maps onto a generalized Harper-Hofstadter model with twisted boundary conditions. We identify the commensurability conditions under which the finite lattice exactly reproduces the magnetic translation algebra and the topological degeneracy of the continuum theory. A systematic analysis of gauge field truncation and its convergence toward the continuum limit is then presented.

hep-th↗

arXiv2026

Mapping twist fields to local operators via tensor networks

Twist fields are a powerful formal tool to compute R\'enyi entropies in quantum many-body systems, but their conventional formulation in tensor network states involves operations acting on virtual degrees of freedom, which are not directly accessible in experiments. In this work, we construct explicit local operators acting on the physical Hilbert space whose expectation values reproduce the action of twist fields in matrix product states. Our construction is exact in the injectivity limit and when the tensor is chosen at the center of orthogonality, and provides a direct operational method to evaluate R\'enyi entropies without accessing auxiliary tensor indices. We test our formulation numerically in the transverse-field Ising model, demonstrating rapid convergence to the exact entanglement entropy as the injectivity scale is reached. Furthermore, we show that twist operators determined from relatively small reference systems can be reliably transferred to larger systems, once the reference size exceeds a characteristic scale set by the correlation length. Since the resulting operators admit a decomposition in terms of a finite number of local observables, our results provide a scalable and experimentally accessible framework to probe entanglement in quantum simulators.

quant-ph↗

arXiv2026

AGILE detection of transient γ-ray emission from the region of the supergiant fast X-ray transient source IGR J17354-3255

Context. On April 14, 2009, the AGILE satellite detected a γ-ray flare from an unknown transient source. Subsequent X-ray follow-up observations with Swift and INTEGRAL identified the supergiant fast X-ray transient (SFXT) IGR J17354-3255 as the best candidate counterpart, based on positional coincidence and a similar temporal behaviour. Aside from this hint, no SFXT has been firmly detected at high energies to date. Overall, SFXTs comprise a subclass of high-mass X-ray binaries (HMXBs) that host a massive OB supergiant star as a companion donor. They tend to display the most extreme X-ray variability among HMXBs. These systems might be able to emit photons at MeV-TeV energies in the form of fast flares lasting from hours to a few days, with a low-duty cycle. Aims. In this work, we analyse archival AGILE data to search for γ-ray flares consistent with IGR J17354-3255 and evaluate their possible physical origin. Methods. We identified a transient source, AGL J1736-3250, which emitted 19 γ-ray flares and was seen to be positionally consistent with IGR J17354-3255. Most flares, detected on a 1 d timescale, concentrate most of their emission on two, four, and six hour timescales, resembling those observed in the X-ray band from IGR J17354-3255. Results. An orbital phase analysis revealed that approximately half of the γ-ray activity occurs around the apastron passage of the compact object hosted in the binary system. We also incorporated archival Swift and INTEGRAL observations to provide phase-folded light curves at lower energies. Our collected results strongly support a physical association between IGR J17354-3255 and AGL J1736-3250, offering evidence that SFXTs could constitute a new class of Galactic high-energy transient emitters.

astro-ph.HE↗

arXiv2026

Scaling flow-based approaches for topology sampling in $\mathrm{SU}(3)$ gauge theory

We develop a methodology based on out-of-equilibrium simulations to mitigate topological freezing when approaching the continuum limit of lattice gauge theories. We reduce the autocorrelation of the topological charge employing open boundary conditions, while removing exactly their unphysical effects using a non-equilibrium Monte Carlo approach in which periodic boundary conditions are gradually switched on. We perform a detailed analysis of the computational costs of this strategy in the case of the four-dimensional $\mathrm{SU}(3)$ Yang-Mills theory. After achieving full control of the scaling, we outline a clear strategy to sample topology efficiently in the continuum limit, which we check at lattice spacings as small as $0.045$ fm. We also generalize this approach by designing a customized Stochastic Normalizing Flow for evolutions in the boundary conditions, obtaining superior performances with respect to the purely stochastic non-equilibrium approach, and paving the way for more efficient future flow-based solutions.

hep-lat↗

arXiv2026

A Quantum Genetic Algorithm with application to Cosmological Parameters Estimation

An Amplitude-Encoded Quantum Genetic Algorithm (AEQGA) has been developed to minimize $χ^2$ functions of different cosmological probes (Supernovae Type Ia, Baryon Acoustic Oscillations, Cosmic Microwave Background Radiation), to find the best-fit value for two cosmological parameters, namely the Hubble Constant and the density matter content of the Universe today. Our main aim is to pave the way to testing the adoption of quantum optimization in the inference of the cosmological parameters that describe the universe evolution. AEQGA computes the merit function classically, and then uses a quantum circuit to entangle the population and perform crossover and mutation operations. The results show consistency with the isocontours of the objective functions. We then tested the general behavior of AEQGA as a function of its hyperparameters and compared it with a second quantum genetic algorithm found in the literature as well as with classical algorithms, finding consistent results.

astro-ph.CO↗

arXiv2026

A scalable flow-based approach to mitigate topological freezing

As lattice gauge theories with non-trivial topological features are driven towards the continuum limit, standard Markov Chain Monte Carlo simulations suffer for topological freezing, i.e., a dramatic growth of autocorrelations in topological observables. A widely used strategy is the adoption of Open Boundary Conditions (OBC), which restores ergodic sampling of topology but at the price of breaking translation invariance and introducing unphysical boundary artifacts. In this contribution we summarize a scalable, exact flow-based strategy to remove them by transporting configurations from a prior with a OBC defect to a fully periodic ensemble, and apply it to 4d SU(3) Yang--Mills theory. The method is based on a Stochastic Normalizing Flow (SNF) that alternates non-equilibrium Monte Carlo updates with localized, gauge-equivariant defect coupling layers implemented via masked parametric stout smearing. Training is performed by minimizing the average dissipated work, equivalent to a Kullback--Leibler divergence between forward and reverse non-equilibrium path measures, to achieve more reversible trajectories and improved efficiency. We discuss the scaling with the number of degrees of freedom affected by the defect and show that defect SNFs achieve better performances than purely stochastic non-equilibrium methods at comparable cost. Finally, we validate the approach by reproducing reference results for the topological susceptibility.

hep-lat↗

arXiv2026

Toward Scalable Normalizing Flows for the Hubbard Model

Normalizing flows have recently demonstrated the ability to learn the Boltzmann distribution of the Hubbard model, opening new avenues for generative modeling in condensed matter physics. In this work, we investigate the steps required to extend such simulations to larger lattice sizes and lower temperatures, with a focus on enhancing stability and efficiency. Additionally, we present the scaling behavior of stochastic normalizing flows and non-equilibrium Markov chain Monte Carlo methods for this fermionic system.

cond-mat.str-el↗

arXiv2025

Casimir effect in critical $\mathrm{O}(N)$ models from non-equilibrium Monte Carlo simulations

$\mathrm{O}(N)$ vector models in three dimensions, when defined in a geometry with a compact direction and tuned to criticality, exhibit long-range fluctuations which induce a Casimir effect. The strength of the resulting interaction is encoded in the excess free-energy density, which depends on a universal coefficient: the Casimir amplitude. We present a high-precision numerical calculation of the latter, by means of a novel non-equilibrium Monte Carlo algorithm, and compare our findings with results obtained from large-$N$ expansions and from the conformal bootstrap.

cond-mat.stat-mech↗

arXiv2025

Computing quantum entanglement with machine learning

Entanglement calculations in quantum field theories are extremely challenging and typically rely on the replica trick, where the problem is rephrased in a study of defects. We demonstrate that the use of deep generative models drastically outperforms standard Monte Carlo algorithms. Remarkably, such a machine-learning method enables high-precision estimates of Rényi entropies in three dimensions for very large lattices. Moreover, we propose a new paradigm for studying lattice defects with flow-based sampling.

hep-lat↗

arXiv2025

SAG-SCI: the Real-time, High-level Analysis Software for Array Control and Data Acquisition of the Cherenkov Telescope Array Observatory

The Cherenkov Telescope Array Observatory (CTAO) is going to be the leading observatory for very-high-energy gamma-rays over the next decades. Its unique sensitivity, wide field of view, and rapid slewing capability make the CTAO especially suited to study transient astrophysical phenomena. The CTAO will analyse its data in real-time, responding to external science alerts on transient events and issuing its own. The Science Alert Generation (SAG) automated pipeline, a component of the Array Control and Data Acquisition (ACADA) software, is designed to detect and issue candidate science alerts. In this work, we present the current development status of SAG-SCI, the SAG component responsible for the real-time, high-level analysis of CTAO data. The SAG-SCI pipelines receive gamma-ray data from multiple reconstruction lines, merge them, store them in a database, and trigger several parallel scientific analyses on the latest data. These analyses include estimating target significance and flux, producing sky maps and light curves, and conducting blind searches for sources within the field of view. We execute SAG-SCI on a set of simulated gamma-ray data, detecting the simulated sources and accurately reconstructing their flux and position. We also estimate the systematic errors introduced by the analysis and discuss the results in relation to the generation of candidate science alerts.

astro-ph.HE↗

arXiv2025

The new architecture design of the Science Alert Generation pipeline of the Cherenkov Telescope Array Observatory

The Cherenkov Telescope Array Observatory (CTAO) represents the next-generation gamma-ray observatory and will operate for several decades. It will be particularly suited to analyse transients and variable phenomena, which will trigger real-time scientific alerts. To support this, the Science Alert Generation (SAG) pipeline within the Array Control and Data Acquisition (ACADA) system will process data from telescope arrays in real time, using dedicated pipelines for data reconstruction (SAG-RECO), data quality monitoring (SAG-DQ) and science monitoring (SAG-SCI). The Supervisor (SAG-SUP) oversees the dynamic operations of SAG and its integration with other ACADA components. SAG is designed to issue candidate science alerts within 20 s of data availability, processing events on multiple time scales (seconds to hours) and handling trigger rates of tens of kHz. Meeting these requirements necessitates optimised software and hardware architectures. This work presents recent developments in SAG's architecture, aimed at two main challenges: (1) selecting data only from telescopes that have entered a stable tracking state, even when they begin tracking at different times during multi-telescope observations, and (2) incorporating environmental and system monitoring information to ensure high data quality. SAG-SUP can retrieve real-time telescope status and environmental conditions from telescope managers and the weather station through the ACADA Monitoring system, collect them in a database and then use them to filter out data from slewing phases or degraded conditions. These enhancements are crucial to ensure the reliability of science alerts and improve the overall performance and responsiveness of the CTAO real-time analysis framework.

astro-ph.IM↗

arXiv2025

Quantum Markov Chain Monte Carlo for Cosmological Functions

We present an implementation of Quantum Computing for a Markov Chain Monte Carlo method with an application to cosmological functions, to derive posterior distributions from cosmological probes. The algorithm proposes new steps in the parameter space via a quantum circuit whose resulting statevector provides the components of the shift vector. The proposed point is accepted or rejected via the classical Metropolis-Hastings acceptance method. The advantage of this hybrid quantum approach is that the step size and direction change in a way independent of the evolution of the chain, thus ideally avoiding the presence of local minima. The results are consistent with analyses performed with classical methods, both for a test function and real cosmological data. The final goal is to generalize this algorithm to test its application to complex cosmological computations.

astro-ph.CO↗

arXiv2025

Benchmarking of Geant4 simulations for the COSI Anticoincidence System

The Compton Spectrometer and Imager (COSI) is an upcoming NASA Small Explorer satellite mission, designed for all-sky observations in the soft gamma-ray domain with the use of germanium detectors (GeDs). An active Anticoincidence System (ACS) of BGO scintillators surrounds the GeDs to reduce the background and contribute to the detection of transient events. Accurately modeling the ACS performance requires simulating the intricate scintillation processes within the shields, which significantly increases the computational cost. We have encoded these effects into a correction matrix derived from dedicated Geant4 simulations with the inclusion of the optical physics. For this purpose, we use laboratory measurements for the energy and spatial response of the ACS lateral wall to benchmark the simulation and define instrument parameters, including the BGO absorption length and the electronic noise. We demonstrate that the simulations replicate the experimental energy resolution and light collection uniformity along the BGO crystal, with maximum discrepancies of 20% and 10%, respectively. The validated simulations are then used to develop the correction matrix for the lateral wall, accounting for the light collection efficiency and energy resolution based on the position within the crystal. The gamma-ray quantum detection efficiency is also position-dependent via the inclusion of the optical physics. It is enhanced by $\sim$8% close to the SiPMs and suppressed by $\sim$2% in the adjacent corners with respect to the average value. Finally, we explore the energy threshold and resolution of the bottom ACS, considering the impact of its smaller crystals compared with the lateral walls.

astro-ph.IM↗

arXiv2025

The Application of Quantum Fourier Transform in Cosmic Microwave Background Data Analysis

The Cosmic Microwave Background (CMB) data analysis and the map-making process rely heavily on the use of spherical harmonics. For suitable pixelizations of the sphere, the (forward and inverse) Fourier transform plays a crucial role in computing all-sky map from spherical harmonic expansion coefficients -- or from angular power spectrum -- and vice versa. While the Fast Fourier Transform (FFT) is traditionally employed in these computations, the Quantum Fourier Transform (QFT) offers a theoretical advantage in terms of computational efficiency and potential speedup. In this work, we study the potential advantage of using the QFT in this context by exploring the substitution of the FFT with the QFT within the \textit{healpy} package. Performance evaluations are conducted using the Aer simulator. Our results indicate that QFT exhibits potential advantages over FFT that are particularly relevant at high-resolution. However, classical-to-quantum data encoding overhead represents a limitation to current efficiency. In this work, we adopted amplitude encoding, due to its efficiency on encoding maximum data to minimum number of qubits. We identify data encoding as a potential significant bottleneck and discuss its impact on quantum speedup. Future improvements in quantum encoding strategies and algorithmic optimizations could further enhance the feasibility of QFT in CMB data analysis.

astro-ph.IM↗

arXiv2025

Scaling of Stochastic Normalizing Flows in $\mathrm{SU}(3)$ lattice gauge theory

Non-equilibrium Markov Chain Monte Carlo (NE-MCMC) simulations provide a well-understood framework based on Jarzynski's equality to sample from a target probability distribution. By driving a base probability distribution out of equilibrium, observables are computed without the need to thermalize. If the base distribution is characterized by mild autocorrelations, this approach provides a way to mitigate critical slowing down. Out-of-equilibrium evolutions share the same framework of flow-based approaches and they can be naturally combined into a novel architecture called Stochastic Normalizing Flows (SNFs). In this work we present the first implementation of SNFs for $\mathrm{SU}(3)$ lattice gauge theory in 4 dimensions, defined by introducing gauge-equivariant layers between out-of-equilibrium Monte Carlo updates. The core of our analysis is focused on the promising scaling properties of this architecture with the degrees of freedom of the system, which are directly inherited from NE-MCMC. Finally, we discuss how systematic improvements of this approach can realistically lead to a general and yet efficient sampling strategy at fine lattice spacings for observables affected by long autocorrelation times.

hep-lat↗

arXiv2025

Flow-Based Sampling for Entanglement Entropy and the Machine Learning of Defects

We introduce a novel technique to numerically calculate Rényi entanglement entropies in lattice quantum field theory using generative models. We describe how flow-based approaches can be combined with the replica trick using a custom neural-network architecture around a lattice defect connecting two replicas. Numerical tests for the $ϕ^4$ scalar field theory in two and three dimensions demonstrate that our technique outperforms state-of-the-art Monte Carlo calculations, and exhibit a promising scaling with the defect size.

quant-ph↗

arXiv2025

Numerical limits in the integration of Vlasov-Poisson equation for Cold Dark Matter

The Vlasov-Poisson systems of equations (VP) describes the evolution of a distribution of collisionless particles under the effect of a collective-field potential. VP is at the basis of the study of the gravitational instability of cosmological density perturbations in Dark-Matter (DM), but its range of application extends to other fields, such as plasma physics. In the case of Cold Dark Matter, a single velocity is associated with each fluid-element (or particle) , the initial condition presents a stiff discontinuity. This creates problems such as diffusion or negative distribution function when a grid based method is used to solve VP. In this work we want to highlight this problem, focusing on the technical aspects of this phenomenon. By comparing different finite volume methods and a spectral method we observe that, while all integration schemes preserve the invariants of the system (e.g, energy), the physical observable of interest, i.e., the density, is not correctly reproduced. We thus compare the density obtained with the different Eulerian integration schemes with the result obtained from a reference N-body method. We point out that the most suitable method to solve the VP system for a self-gravitating system is a spectral method.

physics.comp-ph↗

arXiv2025

Benchmarking Quantum Convolutional Neural Networks for Signal Classification in Simulated Gamma-Ray Burst Detection

This study evaluates the use of Quantum Convolutional Neural Networks (QCNNs) for identifying signals resembling Gamma-Ray Bursts (GRBs) within simulated astrophysical datasets in the form of light curves. The task addressed here focuses on distinguishing GRB-like signals from background noise in simulated Cherenkov Telescope Array Observatory (CTAO) data, the next-generation astrophysical observatory for very high-energy gamma-ray science. QCNNs, a quantum counterpart of classical Convolutional Neural Networks (CNNs), leverage quantum principles to process and analyze high-dimensional data efficiently. We implemented a hybrid quantum-classical machine learning technique using the Qiskit framework, with the QCNNs trained on a quantum simulator. Several QCNN architectures were tested, employing different encoding methods such as Data Reuploading and Amplitude encoding. Key findings include that QCNNs achieved accuracy comparable to classical CNNs, often surpassing 90\%, while using fewer parameters, potentially leading to more efficient models in terms of computational resources. A benchmark study further examined how hyperparameters like the number of qubits and encoding methods affected performance, with more qubits and advanced encoding methods generally enhancing accuracy but increasing complexity. QCNNs showed robust performance on time-series datasets, successfully detecting GRB signals with high precision. The research is a pioneering effort in applying QCNNs to astrophysics, offering insights into their potential and limitations. This work sets the stage for future investigations to fully realize the advantages of QCNNs in astrophysical data analysis.

astro-ph.HE↗