arXiv subjects

Brian Le

Publications and source records attributed to Brian Le.

4 recordsLinked to original sources

arXiv2024

Flat panel laser displays enabled by large-scale visible photonic integrated circuits

Laser-based displays are highly sought after for their superior brightness and color performance, especially in advanced applications like augmented reality (AR). However, their broader adoption has been hindered by bulky projector designs and complex optical module assemblies. Here, we introduce a new laser display architecture enabled by large-scale visible photonic integrated circuits (PICs) to address these challenges. Unlike previous projector-style laser displays, this architecture features an ultra-thin, flat-panel form factor, replacing bulky free-space illumination modules with a single, high-performance photonic chip. Centimeter-scale PIC devices, which integrate thousands of distinct optical components on-chip, are carefully tailored to achieve high display uniformity, contrast, and efficiency. We demonstrate a 2 mm-thick flat-panel laser display combining the PIC with a liquid-crystal-on-silicon (LCoS) panel, achieving 211% of the color gamut and more than 80% volume reduction compared to traditional LCoS displays. We further showcase its application in a see-through AR system. Our work represents a major advancement in the integration of nanophotonics with display technology, enabling a range of new display concepts, from high-performance immersive displays to slim-panel 3D holography.

physics.optics↗

arXiv2024

Reconstructing short-lived particles using hypergraph representation learning

In collider experiments, the kinematic reconstruction of heavy, short-lived particles is vital for precision tests of the Standard Model and in searches for physics beyond it. Performing kinematic reconstruction in collider events with many final-state jets, such as the all-hadronic decay of top-antitop quark pairs, is challenging. We present HyPER: Hypergraph for Particle Event Reconstruction, a novel architecture based on graph neural networks that uses hypergraph representation learning to build more powerful and efficient representations of collider events. HyPER is used to reconstruct parent particles from sets of final-state objects. Trained and tested on simulation, the HyPER model is shown to perform favorably when compared to existing state-of-the-art reconstruction techniques, while demonstrating superior parameter efficiency. The novel hypergraph approach allows the method to be applied to particle reconstruction in a multitude of different physics processes.

hep-ph↗

arXiv2017

Deep learning approach to the Higgs boson CP measurement in H to tau tau decay and associated systematics

The H to tau tau decays form the prime channel for the measurement of the Higgs boson state and tests of the CP invariance of Higgs boson couplings. A previous study has shown the viability of deep learning techniques for the measurement. In this paper, the study is expanded. Effects due to the partial modelling of experimental effects are discussed. Furthermore, systematics due to ? decay modelling for complex cascade decays to tau^pm to a_1^pm nu_tau to rho^0 pi^pm nu_tau to 3pi^\pm nu_tau are also addressed. Various parameterisations are considered using low-energy collision data.

hep-ph↗

arXiv2017

Understanding magnetotransport signatures in networks of connected permalloy nanowires

The change in electrical resistance associated with the application of an external magnetic field is known as the magnetoresistance (MR). The measured MR is quite complex in the class of connected networks of single-domain ferromagnetic nanowires, known as "artificial spin ice", due to the geometrically-induced collective behavior of the nanowire moments. We have conducted a thorough experimental study of the MR of a connected honeycomb artificial spin ice, and we present a simulation methodology for understanding the detailed behavior of this complex correlated magnetic system. Our results demonstrate that the behavior, even at low magnetic fields, can be well-described only by including significant contributions from the vertices at which the legs meet, opening the door to new geometrically-induced MR phenomena.

cond-mat.mes-hall↗