arXiv subjects

Jason P. Terry

Publications and source records attributed to Jason P. Terry.

6 recordsLinked to original sources

arXiv2026

Denoising Interferometric Observations Using Visibilities-Informed Neural Networks

The upcoming observations from the Square Kilometer Array Observatory will provide the astronomical community with a wealth of observations of important objects at long wavelengths. Full analysis of these outputs will necessitate specialized methods and software. Using synthetic observations of protoplanetary discs as an example, we present a machine learning-based visibilities-informed reconstruction for enhanced observations (VIREO) method for denoising data. This method explicitly provides a denoising U-Net with the interferometric observation's point spread function as both an additional input and term in the model's loss function. VIREO outperforms traditional cleaning methods and PSF-ignorant denoising models by producing data that is quantitatively cleaner and more conducive to analysis of the planets within the disc. Applying VIREO to archival ALMA data creates images with significantly less background noise, while maintaining, and in some cases enhancing, the substructure. By demonstrating the general utility of visibility-informed models, our results suggest that VIREO is generally applicable across the interferometric observatories.

astro-ph.IM↗

arXiv2025

Excitonic Landscapes in Monolayer Lateral Heterostructures Revealed by Unsupervised Machine Learning

Two-dimensional (2D) in-plane heterostructures including compositionally graded alloys and lateral heterostructures with defined interfaces display rich optoelectronic properties and offer versatile platforms to explore one-dimensional interface physics and many-body interaction effects. Graded \(\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2\) alloys show smooth spatial variations in composition and strain that continuously tune excitonic emission, while \(\mathrm{MoS}_2\)--\(\mathrm{WS}_2\) lateral heterostructures contain atomically sharp interfaces supporting one-dimensional excitonic phenomena. These single-layer systems combine tunable optical and electronic properties with potential for stable, high-performance optoelectronic devices. Hyperspectral and nano-resolved photoluminescence (PL) imaging enable spatial mapping of optical features along with local variations in composition, strain, and defects, but manual interpretation of such large datasets is slow and subjective. Here, we introduce a fast and scalable unsupervised machine-learning (ML) framework to extract quantitative and interpretable information from hyperspectral PL datasets of graded \(\mathrm{Mo}_x\mathrm{W}_{1-x}\mathrm{S}_2\) alloys and \(\mathrm{MoS}_2\)--\(\mathrm{WS}_2\) heterostructures. Combining principal-component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and density-based spatial clustering (DBSCAN), we uncover spectrally distinct domains associated with composition, strain, and defect variations. Decomposition of representative spectra reveals multiple emission species, including band-edge excitons and defect-related transitions, demonstrating that ML-driven analysis provides a robust and automated route to interpret rich optical properties of 2D materials.

cond-mat.mtrl-sci↗

arXiv2024

A Machine Learning Approach to Detecting Albedo Anomalies on the Lunar Surface

This study introduces a data-driven approach using machine learning (ML) techniques to explore and predict albedo anomalies on the Moon's surface. The research leverages diverse planetary datasets, including high-spatial-resolution albedo maps and element maps (LPFe, LPK, LPTh, LPTi) derived from laser and gamma-ray measurements. The primary objective is to identify relationships between chemical elements and albedo, thereby expanding our understanding of planetary surfaces and offering predictive capabilities for areas with incomplete datasets. To bridge the gap in resolution between the albedo and element maps, we employ Gaussian blurring techniques, including an innovative adaptive Gaussian blur. Our methodology culminates in the deployment of an Extreme Gradient Boosting Regression Model, optimized to predict full albedo based on elemental composition. Furthermore, we present an interactive analytical tool to visualize prediction errors, delineating their spatial and chemical characteristics. The findings not only pave the way for a more comprehensive understanding of the Moon's surface but also provide a framework for similar studies on other celestial bodies.

astro-ph.EP↗

arXiv2019

Quantum Isomer Search

Isomer search or molecule enumeration refers to the problem of finding all the isomers for a given molecule. Many classical search methods have been developed in order to tackle this problem. However, the availability of quantum computing architectures has given us the opportunity to address this problem with new (quantum) techniques. This paper describes a quantum isomer search procedure for determining all the structural isomers of alkanes. We first formulate the structural isomer search problem as a quadratic unconstrained binary optimization (QUBO) problem. The QUBO formulation is for general use on either annealing or gate-based quantum computers. We use the D-Wave quantum annealer to enumerate all structural isomers of all alkanes with fewer carbon atoms (n < 10) than Decane (C10H22). The number of isomer solutions increases with the number of carbon atoms. We find that the sampling time needed to identify all solutions scales linearly with the number of carbon atoms in the alkane. We probe the problem further by employing reverse annealing as well as a perturbed QUBO Hamiltonian and find that the combination of these two methods significantly reduces the number of samples required to find all isomers.

quant-ph↗

arXiv2018

Integral Field Spectroscopy of Supernova Remnant 1E0102-7219 Reveals Fast-moving Hydrogen and Sulfur-rich Ejecta

We study the optical emission from heavy element ejecta in the oxygen-rich young supernova remnant (SNR) 1E 0102.2-7219 (1E 0102) in the Small Magellanic Cloud. We have used the Multi-Unit Spectroscopic Explorer (MUSE) optical integral field spectrograph at the Very Large Telescope (VLT) on Cerro Paranal and the wide field spectrograph (WiFeS) at the ANU 2.3 m telescope at Siding Spring Observatory to obtain deep observations of 1E 0102. Our observations cover the entire extent of the remnant from below 3500Å to 9350Å. Our observations unambiguously reveal the presence of fast-moving ejecta emitting in [S II], [S III], [Ar III], and [Cl II]. The sulfur-rich ejecta appear more asymmetrically distributed compared to oxygen or neon, a product of carbon-burning. In addition to the forbidden line emission from products of oxygen burning (S, Ar, Cl), we have also discovered Hα and Hβ emission from several knots of low surface brightness, fast-moving ejecta. The presence of fast-moving hydrogen points towards a progenitor that had not entirely shed its hydrogen envelope prior to the supernova. The explosion that gave rise to 1E 0102 is therefore commensurate with a Type IIb supernova.

astro-ph.GA↗

arXiv2017

MUSE Integral Field Observations of the Oxygen-rich SNR 1E 0102.2-7219

We have observed the oxygen-rich SNR 1E 0102.2-7219 with the integral field spectrograph WiFeS at Siding Spring Observatory and discovered sulfur-rich ejecta for the first time. Follow-up deep DDT observations with MUSE on the VLT (8100 s on source) have led to the additional discovery of fast- moving hydrogen as well as argon-rich and chlorine-rich material. The detection of fast-moving hydrogen knots challenges the interpretation that the progenitor of 1E 0102 was a compact core of a Wolf-Rayet star that had shed its entire envelope. In addition to the detection of hydrogen and the products of oxygen-burning, this unprecedented sharp (0.2" spaxel size at ~0.7" seeing) and deep MUSE view of an oxygen-rich SNR in the Magellanic Clouds reveals further exciting discoveries, including [Fe xiv]λ5303 and [Fe xi]λ7892 emission, which we associate with the forward shock. We present this exciting data set and discuss some of its implications for the explosion mechanism and nucleosynthesis of the associated supernova.

astro-ph.GA↗