arXiv subjects

Olivier De Castro

Publications and source records attributed to Olivier De Castro.

3 recordsLinked to original sources

arXiv2026

Non-resonant laser-driven narrowing of particle velocity distributions

Stark acceleration and deceleration based techniques for generating particle ensembles with low velocity spread are useful in many experimental applications. For a given velocity distribution of a particle ensemble, these techniques accelerate or decelerate a small subset of the total population, with a low velocity uncertainty. However, narrowing the original velocity distribution by accelerating or decelerating the ensemble particles near the mean velocity is fundamentally limited and not yet explored. We present a numerical study of particle dynamics using neutral cesium atoms as an example. We investigate different interaction regimes, identify key limitations, and propose an interaction regime in which optical Stark deceleration can be used to narrow the velocity distribution of a propagating ensemble about its mean velocity. These findings have potential implications for optical manipulation and control, controlled collisions, and matter interferometry.

physics.optics↗

arXiv2023

Roadmap for focused ion beam technologies

The focused ion beam (FIB) is a powerful tool for the fabrication, modification and characterization of materials down to the nanoscale. Starting with the gallium FIB, which was originally intended for photomask repair in the semiconductor industry, there are now many different types of FIB that are commercially available. These instruments use a range of ion species and are applied broadly in materials science, physics, chemistry, biology, medicine, and even archaeology. The goal of this roadmap is to provide an overview of FIB instrumentation, theory, techniques and applications. By viewing FIB developments through the lens of the various research communities, we aim to identify future pathways for ion source and instrumentation development as well as emerging applications, and the scope for improved understanding of the complex interplay of ion-solid interactions. We intend to provide a guide for all scientists in the field that identifies common research interests and will support future fruitful interactions connecting tool development, experiment and theory. While a comprehensive overview of the field is sought, it is not possible to cover all research related to FIB technologies in detail. We give examples of specific projects within the broader context, referencing original works and previous review articles throughout.

physics.ins-det↗

arXiv2020

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

Nanoparticles occur in various environments as a consequence of man-made processes, which raises concerns about their impact on the environment and human health. To allow for proper risk assessment, a precise and statistically relevant analysis of particle characteristics (such as e.g. size, shape and composition) is required that would greatly benefit from automated image analysis procedures. While deep learning shows impressive results in object detection tasks, its applicability is limited by the amount of representative, experimentally collected and manually annotated training data. Here, we present an elegant, flexible and versatile method to bypass this costly and tedious data acquisition process. We show that using a rendering software allows to generate realistic, synthetic training data to train a state-of-the art deep neural network. Using this approach, we derive a segmentation accuracy that is comparable to man-made annotations for toxicologically relevant metal-oxide nanoparticle ensembles which we chose as examples. Our study paves the way towards the use of deep learning for automated, high-throughput particle detection in a variety of imaging techniques such as microscopies and spectroscopies, for a wide variety of studies and applications, including the detection of plastic micro- and nanoparticles.

cs.LG↗