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Marco Rossi

Publications and source records attributed to Marco Rossi.

At least 19 recordsLinked to original sources

From classical Lax ODEs to quantum integrable theories: the moduli

The general idea of this paper is to start from a classical integrable (partial differential) equation which arises as a compatibility condition for a matrix linear differential problem. For definitiveness' sake, a generalised sinh-Gordon equation depending on $2N-1$ complex coefficients or moduli is considered. Then, the connexion coefficients (Wronskians) of different solutions to this problem satisfy, in the spirit of the Ordinary Differential Equation/Integrable Model correspondence, functional relations, which can be considered, -- if supplemented by suitable asymptotic behaviours --, as identifying a specific state of a quantum integrable model: in fact they are the eigenvalues of extensions of Baxter operators $Q$ and $T$, the transfer matrix. Moreover, Y-system and (implementing the asymptotic conditions) thermodynamic Bethe Ansatz equations originate from them, without any passage through the scattering theory, and providing an invariant parametrisation of the monodromy space. The crucial novelty is the modification of all the relations because of their dependence on the moduli. For zero momentum, they fully describe physically the quantum homogeneous sine-Gordon model, {\it i.e.} scattering amplitudes of gauge fields in $\mathcal{N} = 4$ SYM at strong coupling or their dual null polygonal light-like Wilson loops in $AdS_3$. As a direct consequence of the correspondence, two Zamolodchikov's conjectures, based on previous results, are also proven.

hep-th

Multiscale X-ray computed tomography of standard optical fibers

Optical fiber technologies enable high-speed communication, medical imaging, and advanced sensing. Among the techniques for the characterization of optical fibers, Xray computed tomography has recently emerged as a versatile non-destructive tool for mapping their refractive index variations in 3D. In this study, we present a multiscale characterization of standard optical fibers. We carry out an intercomparison of three tomography setups: classical computed microtomography, X-ray microscopy, and nanotomography. In each method, our analysis highlights the trade-offs between resolution, field of view, and segmentation efficiency. Additionally, we integrate deep learning segmentation thresholding to improve the image analysis process. Thanks to its large field of view, microtomography with classical sources is ideal for the analysis of relatively long fiber spans, where a low spatial resolution is acceptable. The other way around, nanotomography has the highest spatial resolution, but it is limited to very small fiber samples, e.g., fiber tapers and nanofibers, which have diameters of the order of a few microns. Finally, X-ray microscopy provides a good compromise between the sample size fitting the device's field of view and the spatial resolution needed for properly imaging the inner features of the fiber. Specifically, thanks to its practicality in terms of costs and cumbersomeness, we foresee that the latter will provide the most suitable choice for the quality control of fiber drawing in real-time, e.g., using the "One-Minute Tomographies with Fast Acquisition Scanning Technology" developed by Zeiss. In this regard, the combination of X-ray computed tomography and artificial intelligence-driven enhancements is poised to revolutionize fiber characterization, by enabling precise monitoring and adaptive control in fiber manufacturing.

physics.app-ph

Broad nonlocal spectrum in the Pb-InSb hybrid three terminals for potential realization of Kitaev chains

Hybrid superconductor-semiconductor(SC-SM) nanowires remain one of the foremost platforms for engineering topological superconductivity and Majorana zero modes(MZMs) towards fault-tolerant topological qubits, especially with the rapid development of artificial Kitaev chains. In contrast to the widely used aluminum(Al)-based hybrids, lead(Pb) offers a bulk superconducting gap of ~1.4meV and a critical temperature of ~7.2K, giving rise to a proximity-induced gap that is roughly five times larger than that obtained with Al. Here we present the first three-terminal Pb-hybrid devices and perform nonlocal differential-conductance spectroscopy on this platform. The nonlocal measurement simultaneously resolves a dual-gap feature of the parent Pb gap and the large, hard, gate-tunable induced superconducting gap, distinguished by a switch between electron- and hole-like dissipation processes. Within the induced gap we observe several types of Andreev bound states(ABSs) that undergo singlet-doublet transitions. Moreover, by tuning gate voltages we achieve gate-controlled resonating sign reversals of the nonlocal conductance, identifying three distinct regimes that correspond to different configurations of quantum-dot(QD) resonances(single-resonance, double-resonance, and series-resonance). Finally, the coupling between ABSs and QDs also present and can be modulated from the weak- to strong-coupling limit, indicating the feasibility of realizing the artificial Kitaev chains. Crucially, the robust nonlocal signatures persist up to temperatures(~1K) far above the operating temperature of Al-based devices thanks to the unusually large induced gap, thereby widening the accessible parameter space greatly and underscoring the suitability of Pb-based hybrids for implementing warm temperature artificial Kitaev chains and the topological quantum devices protected by a substantially larger topological gap.

quant-ph

Regular and Floquet bases for gauge and gravity theories: a non perturbative approach

The main topic of the paper is represented by the change of basis, in Heun-type equations, from the one of decaying (at two singular points) solutions to that of Floquet solutions. Crucial in the connection relations is the phase acquired by the Floquet solutions by going from a (ir)regular singularity to another. The new 'kink method' is exploited to compute the quantum momentum of the Floquet solutions as convergent series, explicitly at all orders. Hence, upon integrating it term by term, the acquired phase can be derived explicitly as a similar series. Since Heun equation and its confluences describe ${\cal N}=2$ SYM theories in the NS background as quantisation of Seiberg-Witten differentials and also appear in perturbations of gravity solutions, tests and predictions in both domains can be made. A very encouraging test is the matching of the acquired phase with the dual gauge period, $A_D$, as given by the Nekrasov instanton function. Actually, the procedure can be considered an alternative to instanton computations. On the gravity side, results for the wave functions are at leading order compatible with analogous expressions found by studying the Teukolsky (or others, like Regge-Wheeler) equation. Besides, the whole construction can represent an useful approach to these equations at all orders, shedding also light on non-perturbative contributions, which should reveal very interesting for the consequences in gravity perturbations.

hep-th

Enhanced and modulable induced superconducting gap and effective Land\'e g-factor in Pb-InSb hybrid devices

The hybrid system of a conventional superconductor (SC) on a semiconductor (SM) nanowire with strong spin-orbit coupling (SOC) represents a promising platform for achieving topological superconductivity and Majorana zero modes (MZMs) towards topological quantum computation. While aluminum (Al)-based hybrid nanowire devices have been widely utilized, their limited superconducting gap and intrinsic weak SOC as well as small Land\'e g-factor may hinder future experimental advancements. In contrast, we demonstrate that lead (Pb)-based hybrid quantum devices exhibit a remarkably large and hard proximity-induced superconducting gap, exceeding that of Al by an order of magnitude. By exploiting electrostatic gating to modulate wavefunction distribution and SC-SM interfacial coupling, this gap can be continuously tuned from its maximum value (~1.4 meV, matching the bulk Pb gap) down to nearly zero while maintaining the hardness. Furthermore, magnetic-field-dependent measurements reveal a radial evolution of the gap structure with anti-crossing feature, indicative of strong SOC and huge effective g-factors up to 76. These findings underscore the superior functionality of Pb-based hybrid systems, significantly advancing their potential for realizing and stabilizing MZMs and the further scalable topological quantum architectures.

cond-mat.mes-hall

Rapid diagnostics of reconfigurable intelligent surfaces using space-time-coding modulation

Reconfigurable intelligent surfaces (RISs) have emerged as a key technology for shaping smart wireless environments in next-generation wireless communication systems. To support the large-scale deployment of RISs, a reliable and efficient diagnostic method is essential to ensure optimal performance. In this work, a robust and efficient approach for RIS diagnostics is proposed using a space-time coding strategy with orthogonal codes. The method encodes the reflected signals from individual RIS elements into distinct code channels, enabling the recovery of channel power at the receiving terminals for fault identification. Theoretical analysis shows that the normally functioning elements generate high power in their respective code channels, whereas the faulty elements exhibit significantly lower power. This distinction enables rapid and accurate diagnostics of elements' operational states through simple signal processing techniques. Simulation results validate the effectiveness of the proposed method, even under high fault ratios and varying reception angles. Proof-of-principle experiments on two RIS prototypes are conducted, implementing two coding strategies: direct and segmented. Experimental results in a realistic scenario confirm the reliability of the diagnostic method, demonstrating its potential for large-scale RIS deployment in future wireless communication systems and radar applications.

physics.optics

Few-Mode and Anisotropic Quantum Transport in InSb Nanoribbons Using an All-van der Waals Material-Based Gate

High-quality electrostatic gating is a fundamental ingredient for successful semiconducting device physics, and a key element of realizing clean quantum transport. Inspired by the widespread improvement of transport quality when two-dimensional van der Waals (vdW) materials are gated exclusively by other vdW materials, we have developed a method for gating non-vdW materials with an all-vdW gate stack, consisting of a hexagonal boron nitride dielectric layer and a few-layer graphite gate electrode. We demonstrate this gating approach on MOVPE-grown InSb nanoribbons (NRs), a novel variant of the InSb nanowire, with a flattened cross-section. In our all-vdW gated NR devices we observe conductance features that are reproducible and have low- to near-zero gate hysteresis. We also report quantized conductance, which persists to lower magnetic fields and longer channel lengths than typical InSb nanowire devices reported to date. Additionally, we observe level splitting that is highly anisotropic in an applied magnetic field, which we attribute to the ribbon cross-section. The performance of our devices is consistent with the reduced disorder expected from the all-vdW gating scheme, and marks the first report of ballistic, few-modes quantum transport in a non-vdW material with an all-vdW gate. Our results establish all-vdW gating as a promising approach for high-quality gating of non-vdW materials for quantum transport, which is in principle applicable generically, beyond InSb systems. In addition, the work showcases the specific potential of all-vdW gate/InSb NR devices for enabling clean quantum devices that may be relevant for spintronics and topological superconductivity studies.

cond-mat.mes-hall

From Painlev\'e equations to ${\cal N}=2$ susy gauge theories: prolegomena

We study the linear problems in $z,t$ (time) associated to the Painlev\'e III$_3$ and III$_1$ equations when the Painlev\'e solution $q(t)$ approaches a pole or a zero. In this limit the problem in $z$ for the Painlev\'e III$_3$ reduces to the modified Mathieu equation, while that for the Painlev\'e III$_1$ to the Doubly Confluent Heun Equation. These equations appear as Nekrasov-Shatashvili quantisations/deformations of Seiberg-Witten differentials for $SU(2)$ ${\cal N}=2$ super Yang-Mills gauge theory with number of flavours $N_f=0$ and $N_f=2$, respectively. These results allow us to conjecture that this link holds for any Painlev\'e equation relating each of them to a different matter theory, which is actually the same as in the well-established Painlev\'e gauge correspondence, but {\it with another deformation ($\Omega$-background)}. An explicit expression for the dual gauge period (and then prepotential) is also found. As a by-product, a new solution to the connexion problem is illustrated.

hep-th

Versatile Method of Engineering the Band Alignment and the Electron Wavefunction Hybridization of Hybrid Quantum Devices

With the development of quantum technology, hybrid devices that combine superconductors (S) and semiconductors (Sm) have attracted great attention due to the possibility of engineering structures that benefit from the integration of the properties of both materials. However, until now, none of the experiments have reported good control of band alignment at the interface, which determines the strength of S-Sm coupling and the proximitized superconducting gap. Here, we fabricate hybrid devices in a generic way with argon milling to modify the interface while maintaining its high quality. First, after the milling the atomically connected S-Sm interfaces appear, resulting in a large induced gap, as well as the ballistic transport revealed by the multiple Andreev reflections and quantized above-gap conductance plateaus. Second, by comparing transport measurement with Schr\"odinger-Poisson (SP) calculations, we demonstrate that argon milling is capable of varying the band bending strength in the semiconducting wire as the electrons tend to accumulate on the etched surface for longer milling time. Finally, we perform nonlocal measurements on advanced devices to demonstrate the coexistence and tunability of crossed Andreev reflection (CAR) and elastic co-tunneling (ECT) -- key ingredients for building the prototype setup for realization of Kitaev chain and quantum entanglement probing. Such a versatile method, compatible with the standard fabrication process and accompanied by the well-controlled modification of the interface, will definitely boost the creation of more sophisticated hybrid devices for exploring physics in solid-state systems.

cond-mat.mes-hall

Diffusive and Ballistic Transport in Ultra-thin InSb Nanowire Devices Using a Few-layer-Graphene-AlOx Gate

Quantum devices based on InSb nanowires (NWs) are a prime candidate system for realizing and exploring topologically-protected quantum states and for electrically-controlled spin-based qubits. The influence of disorder on achieving reliable topological regimes has been studied theoretically, highlighting the importance of optimizing both growth and nanofabrication. In this work we investigate both aspects. We developed InSb nanowires with ultra-thin diameters, as well as a new gating approach, involving few-layer graphene (FLG) and Atomic Layer Deposition (ALD)-grown AlOx. Low-temperature electronic transport measurements of these devices reveal conductance plateaus and Fabry-P\'erot interference, evidencing phase-coherent transport in the regime of few quantum modes. The approaches developed in this work could help mitigate the role of material and fabrication-induced disorder in semiconductor-based quantum devices.

cond-mat.mes-hall

Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors

The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms. Machine learning (ML) techniques give their best in these types of classification problems. In this paper, we evaluate their performance against conventional (deterministic) algorithms. We demonstrate that both Convolutional Neural Networks (CNN) and Transformer-Encoder methods outperform deterministic algorithms in one of the most challenging classification problems of low-energy physics (single- versus double-beta events). We discuss the advantages and pitfalls of Transformer-Encoder methods versus CNN and employ these methods to optimize the detector parameters, with an emphasis on the DUNE Phase II detectors ("Module of Opportunity").

physics.ins-det

Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks

Extent of resection after surgery is one of the main prognostic factors for patients diagnosed with glioblastoma. To achieve this, accurate segmentation and classification of residual tumor from post-operative MR images is essential. The current standard method for estimating it is subject to high inter- and intra-rater variability, and an automated method for segmentation of residual tumor in early post-operative MRI could lead to a more accurate estimation of extent of resection. In this study, two state-of-the-art neural network architectures for pre-operative segmentation were trained for the task. The models were extensively validated on a multicenter dataset with nearly 1000 patients, from 12 hospitals in Europe and the United States. The best performance achieved was a 61\% Dice score, and the best classification performance was about 80\% balanced accuracy, with a demonstrated ability to generalize across hospitals. In addition, the segmentation performance of the best models was on par with human expert raters. The predicted segmentations can be used to accurately classify the patients into those with residual tumor, and those with gross total resection.

eess.IV

Understanding Traffic Jams Using Lindblad Superoperators

We propose a model to simulate different traffic-flow conditions in terms of quantum graphs hosting an (N+1)-level dot at each site. Our model allows us to keep track of the type and of the destination of each vehicle. The traffic flow inside the system is encoded in a proper set of Lindbladian local dissipators that describe the time evolution of the system density matrix. Taking advantage of the invariance of the Lindblad master equation under inhomogeneous transformations we derive the quantum Hamiltonian for the bulk dynamics in a proper experimental setup.

cond-mat.stat-mech

Deep Learning Applications to Particle Physics: from Monte Carlo simulation acceleration to ProtoDUNE reconstruction

The thesis arises in the context of deep learning applications to particle physics. The dissertation follows two main parallel streams: the development of hardware-accelerated tools for event simulation in high-energy collider physics, and the optimization of deep learning models for reconstruction algorithms at neutrino detectors. Event generation is a central concept in high-energy physics phenomenology studies. The state-of-the-art software dedicated to Monte Carlo simulation is often written for general-purpose computing architectures (CPUs), which allow great flexibility but are not compatible with specialized accelerating devices, GPUs. We present two original tools, PDFFlow and MadFlow, that manage to combine these two aspects in Python. PDFFlow, is a Parton Distribution Functions interpolator, while MadFlow aims at building a complete tool suite to accelerate the whole event generation framework. The reconstruction pipeline at neutrino detectors is comprised of many different algorithms that work in synergy to extract a high-level representation of detector data. All the most important experiments in neutrino physics are developing software to automatically process and extract this information. This work describes the implementation of deep learning techniques to improve neutrino reconstruction efficiency at the ProtoDUNE-SP detector. Two original contributions are presented concerning raw data denoising and a hit-clustering procedure named "slicing". Both denoising and slicing involve the implementation and the training of novel neural network architectures, based on state-of-the-art models in machine learning, such as feed-forward, convolutional and graph neural networks. They represent a proof of concept that these models are indeed capable of providing an important impact on signal reconstruction at neutrino detectors.

hep-ph

Fusion of two stable elastic structures resulting in an unstable system

It is shown that a compound elastic structure, which displays a dynamic instability, may be designed as the union (or 'fusion') of two structures which are stable when separately analyzed. The compound elastic structure has two degrees of freedom and is made up of a rigid rod connected with two springs to a smooth support, which evidences a jump in the curvature at the equilibrium configuration. Instability is proven in a linearized context and is related to the application of a non-conservative load of the follower type, so that the instability disappears under dead loads. In the fully nonlinear range, the instability is also confirmed through numerical simulations. The obtained results may be useful in the design of new mechanical sensors, or devices for energy harvesting, or architected materials. In addition, our findings have conceptual implications on piecewise-linear theories of mechanics such as for instance plasticity or frictional contact.

physics.class-ph

Traffic models and traffic-jam transition in quantum ($N$+1)-level systems

We propose a model to implement and simulate different traffic-flow conditions in terms of quantum graphs hosting an ($N$+1)-level dot at each site, which allows us to keep track of the type and of the destination of each vehicle. By implementing proper Lindbladian local dissipators, we derive the master equations that describe the traffic flow in our system. To show the versatility and the reliability of our technique, we employ it to model different types of traffic flow (the symmetric three-way roundabout and the three-road intersection). Eventually, we successfully compare our predictions with results from classical models.

cond-mat.stat-mech

Out of equilibrium charge transport in molecular electronic devices

Using the Lindblad equation approach, we study the nonequilibrium stationary state of a benzene ring connected to two reservoirs in the large bias regime, a prototype of a generic molecular electronic device. We show the emergence of an optimal working point (corresponding to a change in the monotonicity of the stationary current, as a function of the applied bias) and its robustness against chemical potential and bond disorder.

cond-mat.str-el

Deep Learning strategies for ProtoDUNE raw data denoising

In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment. The ProtoDUNE detector is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a forthcoming experiment in neutrino physics. The reconstruction workchain consists of converting digital detector signals into physical high-level quantities. We address the first step in reconstruction, namely raw data denoising, leveraging deep learning algorithms. We design two architectures based on graph neural networks, aiming to enhance the receptive field of basic convolutional neural networks. We benchmark this approach against traditional algorithms implemented by the DUNE collaboration. We test the capabilities of graph neural network hardware accelerator setups to speed up training and inference processes.

hep-ph