Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 x 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing

Advanced Techniques for Ground Penetrating Radar Imaging

Ground penetrating radar (GPR) has become one of the key technologies in subsurface sensing and, in general, in non-destructive testing (NDT), since it is able to detect both metallic and nonmetallic targets. GPR for NDT has been successfully introduced in a wide range of sectors, such as mining and geology, glaciology, civil engineering and civil works, archaeology, and security and defense. In recent decades, improvements in georeferencing and positioning systems have enabled the introduction of synthetic aperture radar (SAR) techniques in GPR systems, yielding GPR–SAR systems capable of providing high-resolution microwave images. In parallel, the radiofrequency front-end of GPR systems has been optimized in terms of compactness (e.g., smaller Tx/Rx antennas) and cost. These advances, combined with improvements in autonomous platforms, such as unmanned terrestrial and aerial vehicles, have fostered new fields of application for GPR, where fast and reliable detection capabilities are demanded. In addition, processing techniques have been improved, taking advantage of the research conducted in related fields like inverse scattering and imaging. As a result, novel and robust algorithms have been developed for clutter reduction, automatic target recognition, and efficient processing of large sets of measurements to enable real-time imaging, among others. This Special Issue provides an overview of the state of the art in GPR imaging, focusing on the latest advances from both hardware and software perspectives

Constructing networks of quantum channels for state preparation

Entangled possibly mixed states are an essential resource for quantum computation, communication, metrology, and the simulation of many-body systems. It is important to develop and improve preparation protocols for such states. One possible way to prepare states of interest is to design an open system that evolves only towards the desired states. A Markovian evolution of a quantum system can be generally described by a Lindbladian. Tensor networks provide a framework to construct physically relevant entangled states. In particular, matrix product density operators (MPDOs) form an important variational class of states. MPDOs generalize matrix product states to mixed states, can represent thermal states of local one-dimensional Hamiltonians at sufficiently large temperatures, describe systems that satisfy the area law of entanglement, and form the basis of powerful numerical methods. In this work we develop an algorithm that determines for a given linear subspace of MPDOs whether this subspace can be the stable space of some frustration free k-local Lindbladian and, if so, outputs an appropriate Lindbladian. We proceed by using machine learning with networks of quantum channels, also known as quantum neural networks (QNNs), to train denoising post-processing devices for quantum sources. First, we show that QNNs can be trained on imperfect devices even when part of the training data is corrupted. Second, we show that QNNs can be trained to extrapolate quantum states to, e.g., lower temperatures. Third, we show how to denoise quantum states in an unsupervised manner. We develop a novel quantum autoencoder that successfully denoises Greenberger-Horne-Zeilinger, W, Dicke, and cluster states subject to spin-flip, dephasing errors, and random unitary noise. Finally, we develop recurrent QNNs (RQNNs) for denoising that requires memory, such as combating drifts. RQNNs can be thought of as matrix product quantum channels with a quantum algorithm for training and are closely related to MPDOs. The proposed preparation and denoising protocols can be beneficial for various emergent quantum technologies and are within reach of present-day experiments

Identifying Structure Transitions Using Machine Learning Methods

Methodologies from data science and machine learning, both new and old, provide an exciting opportunity to investigate physical systems using extremely expressive statistical modeling techniques. Physical transitions are of particular interest, as they are accompanied by pattern changes in the configurations of the systems. Detecting and characterizing pattern changes in data happens to be a particular strength of statistical modeling in data science, especially with the highly expressive and flexible neural network models that have become increasingly computationally accessible in recent years through performance improvements in both hardware and algorithmic implementations. Conceptually, the machine learning approach can be regarded as one that employing algorithms that eschew explicit instructions in favor of strategies based around pattern extraction and inference driven by statistical analysis and large complex data sets. This allows for the investigation of physical systems using only raw configurational information to make inferences instead of relying on physical information obtained from a priori knowledge of the system. This work focuses on the extraction of useful compressed representations of physical configurations from systems of interest to automate phase classification tasks in addition to the identification of critical points and crossover regions

Modern Machine Learning for LHC Physicists

Modern machine learning is transforming particle physics, faster than we can follow, and bullying its way into our numerical tool box. For young researchers it is crucial to stay on top of this development, which means applying cutting-edge methods and tools to the full range of LHC physics problems. These lecture notes are meant to lead students with basic knowledge of particle physics and significant enthusiasm for machine learning to relevant applications as fast as possible. They start with an LHC-specific motivation and a non-standard introduction to neural networks and then cover classification, unsupervised classification, generative networks, and inverse problems. Two themes defining much of the discussion are well-defined loss functions reflecting the problem at hand and uncertainty-aware networks. As part of the applications, the notes include some aspects of theoretical LHC physics. All examples are chosen from particle physics publications of the last few years. Given that these notes will be outdated already at the time of submission, the week of ML4Jets 2022, they will be updated frequently.Comment: First version, we very much appreciate feedbac

W Boson Polarization Studies for Vector Boson Scattering at LHC: from Classical Approaches to Quantum Computing

The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN) has, in the recent years, delivered unprecedented high-energy proton-proton collisions that have been collected and studied by two multi-purpose experiments, ATLAS and CMS. In this thesis, we focus on one physics process in particular, the Vector Boson Scattering (VBS), which is one of the keys to probe the ElectroWeak sector of the Standard Model in the TeV regime and to shed light on the mechanism of ElectroWeak symmetry breaking. VBS measurement is extremely challenging, because of its low signal yields, complex final states and large backgrounds. Its understanding requires a coordinated effort of theorists and experimentalists, to explore all possible information about inclusive observables, kinematics and background isolation. The present work wants to contribute to Vector Boson Scattering studies by exploring the possibility to disentangle among W boson polarizations when analyzing a pure VBS sample. This work is organized as follows. In Chapter1, we overview the main concepts related to the Standard Model of particle physics. We introduce the VBS process from a theoretical perspective in Chapter2, underlying its role with respect to the known mechanism of ElectroWeak Symmetry Breaking. We emphasize the importance of regularizing the VBS amplitude by canceling divergences arising from longitudinally polarized vector bosons at high energy. In the same Chapter, we discuss strategies to explore how to identify the contribution of longitudinally polarized W bosons in the VBS process. We investigate the possibility to reconstruct the event kinematics and to thereby develop a technique that would efficiently discriminate between the longitudinal contribution and the rest of the participating processes in the VBS. In Chapter 3, we perform a Montecarlo generator comparison at different orders in perturbation theory, to explore the state-of-art of VBS Montecarlo programs and to provide suggestions and limits to the experimental community. In the last part of the same Chapter we provide an estimation of PDF uncertainty contribution to VBS observables. Chapter 4 introduces the phenomenological study of this work. We perform an extensive study on polarization fraction extraction and on reconstruction of the W boson reference frame. We first make use of traditional kinematic approaches, moving then to a Deep Learning strategy. Finally, in Chapter 5, we test a new technological paradigm, the Quantum Computer, to evaluate its potential in our case study and overall in the HEP sector. This work has been carried on in the framework of a PhD Executive project, in partnership between the University of Pavia and IBM Italia, and has therefore received supports from both the institutions. This work has been funded by the European Community via the COST Action VBSCan, created with the purpose of connecting all the main players involved in Vector Boson Scattering studies at hadron colliders, gathering a solid and multidisciplinary community and aiming at providing the worldwide phenomenological reference on this fundamental process