arXiv subjects

Paras Regmi

Publications and source records attributed to Paras Regmi.

4 recordsLinked to original sources

arXiv2026

DragonflyPol: Wide-Field Optical Linear Polarimetry with the Dragonfly Telephoto Array (Instrument Description and Commissioning)

We present DragonflyPol, a wide-field optical linear polarimetry capability implemented on the Dragonfly Telephoto Array. DragonflyPol leverages Dragonfly's modular, multi-lens architecture to obtain simultaneous measurements in four linear polarization orientations ($0^\circ$, $45^\circ$, $90^\circ$, and $135^\circ$) across a $\sim5\,deg^2$ field of view, distributed across 44 polarized lens--detector units. Four additional units serve as unpolarized reference channels. We describe a broad range of science goals enabled by this capability, including magnetic field mapping and tomography, dust grain properties, CMB foreground characterization, and the three-dimensional structure of diffuse interstellar clouds. We integrate Canon polarizers and Baader Sloan $r'$ bandpass filters into the drop-in filter holders of the Canon lenses, and conduct a three-phase laboratory characterization program to select optimal polarimetric components, measure contrast ratios and transmission efficiencies, and determine and mark the transmission axis of each polarizer with sub-degree repeatability. Laboratory measurements across all 44 deployed polarizers yield a mean noise-subtracted contrast ratio of $1228 \pm 104$ and a single-polarizer transmission efficiency of $\sim$33\% in the $r'$ band. On-sky commissioning, including twilight flat-field characterization and twilight-sky polarization measurements, confirms throughput stability across polarization groups and successful recovery of the expected Rayleigh scattering signal. DragonflyPol achieved first polarimetric light in September 2025.

astro-ph.IM↗

arXiv2024

Large Bandgap Observed on the Surfaces of EuZn2As2 Single Crystals

EuM2As2 (M = Zn, Cd, In, Sn etc.) is an excellent material system for studying topological properties, which can be easily tuned by magnetism involved. Theoretical calculations predict gapped and flat bands in EuZn2As2 but gapless structure in EuCd2As2. In this work, low-temperature (77 K) cleaved EuZn2As2 crystals are studied using scanning tunneling microscopy/spectroscopy (STM/S) and density functional theory (DFT) calculations. Defects-induced local density of states (LDOS) modification with a triangular shape helps identify the surface terminations: Eu versus AsZn surface. While large bandgaps (~1.5 eV at 77 K) are observed on both pristine surfaces, the bandgap width is found to be very sensitive to local heterogeneity, such as defects and step edges, with the tendency of reduction. Combining experimental data with DFT simulations, we conclude that the modified bandgap in the heterogeneous area arises from Zn vacancies and/or substitution by As atoms. Our investigation offers important information for reevaluating the electron topology of the EuM2As2 family.

cond-mat.mtrl-sci↗

arXiv2024

Multiscale structure-property discovery via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. The local structures are conventionally probed using spatially resolved studies and the property correlations are usually deciphered by a researcher based on sequential explorations and auxiliary information, thus limiting the throughput efficiency. Here we demonstrate a Bayesian deep learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This autonomous method is deployed on the low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn2As2, one of the promising candidates for studying the magnetism-driven topological properties. The framework employs a sparse sampling approach to efficiently construct the scalar-property space using a minimal number of measurements, about 1 - 10 % of the data required in standard hyperspectral imaging methods. We further demonstrate a target-property-guided active learning of structures within a multiscale framework. This is implemented across length scales in a hierarchical fashion for the autonomous discovery of structural origins for an observed material property. This framework offers the choice to select and derive a suitable scalar property from the spectroscopic data to steer exploration across the sample space. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

cond-mat.mtrl-sci↗

arXiv2022

Deep learning for enhanced free-space optical communications

Atmospheric effects, such as turbulence and background thermal noise, inhibit the propagation of coherent light used in ON-OFF keying free-space optical communication. Here we present and experimentally validate a convolutional neural network to reduce the bit error rate of free-space optical communication in post-processing that is significantly simpler and cheaper than existing solutions based on advanced optics. Our approach consists of two neural networks, the first determining the presence of coherent bit sequences in thermal noise and turbulence and the second demodulating the coherent bit sequences. All data used for training and testing our network is obtained experimentally by generating ON-OFF keying bit streams of coherent light, combining these with thermal light, and passing the resultant light through a turbulent water tank which we have verified mimics turbulence in the air to a high degree of accuracy. Our convolutional neural network improves detection accuracy over threshold classification schemes and has the capability to be integrated with current demodulation and error correction schemes.

cs.LG↗