Engineering topics

Luo, Yufeng (ORCID:0000000246230683)

Publications and source records attributed to Luo, Yufeng (ORCID:0000000246230683).

4 records

DOE OSTI2026

ODIN: Spectroscopic Validation of Lyα-emitting Galaxy Samples with DESI

The One-hundred-deg$^{2}$ DECam Imaging in Narrowbands (ODIN) survey is conducting the widest-field deep narrowband (NB) imaging of the equatorial and southern skies. ODIN uses three custom-built NB filters that sample Lyα-emitting galaxies (LAEs) within thin cosmic slices centered at z = 2.4, 3.1, and 4.5. In this work, we utilize extensive DESI spectroscopy of ODIN-selected galaxies in the COSMOS and XMM-LSS fields to validate our LAE selection. Exposures of 2-4 hr with DESI yielded redshift confirmation of 3075 ODIN LAE candidates with NB magnitudes brighter than 26 mag. Restricting to objects that yield high-confidence redshifts, the confirmation rates are (93%, 96%, 92%) at z = (2.4, 3.1, 4.5). The primary contaminants consist of active galactic nuclei at the expected Lyα-redshift range and lower redshifts (C iv , C iii ]), with the remainder being star-forming galaxies ([O ii ] and [O iii ]). We find minimal contamination from [O ii ] emitters in our sample (≲1%), implying that our rest-frame equivalent width (REW) > 20 Å NB excess photometry requirement is sufficient to remove them.

Pinarski, Ethan [Purdue U., West Lafayette] (ORCID↗

DOE OSTI2026

A Semi-Supervised Learning Method for the Identification of Bad Exposures in Large Imaging Surveys

As the data volume of astronomical imaging surveys rapidly increases, traditional methods for image anomaly detection, such as visual inspection by human experts, are becoming impractical. We introduce a machine-learning-based approach to detect poor-quality exposures in large imaging surveys, with a focus on the DECam Legacy Survey (DECaLS) in regions of low extinction (i.e., E ( B − V ) < 0.04 ). Our semi-supervised pipeline integrates a vision transformer (ViT), trained via self-supervised learning (SSL), with a k-Nearest Neighbor (kNN) classifier. We train and validate our pipeline using a small set of labeled exposures observed by surveys with the Dark Energy Camera (DECam). A clustering-space analysis of where our pipeline places images labeled in good and bad categories suggests that our approach can efficiently and accurately determine the quality of exposures. Applied to new imaging being reduced for DECaLS Data Release 11, our pipeline identifies 780 problematic exposures, which we subsequently verify through visual inspection. Being highly efficient and adaptable, our method offers a scalable solution for quality control in other large imaging surveys.

Luo, Yufeng (ORCID:0000000246230683)↗

DOE OSTI2024

General Relativistic Stability and Gravitational Wave Content of Rotating Triaxial Neutron Stars

Triaxial neutron stars can be sources of continuous gravitational radiation detectable by ground-based interferometers. The amplitude of the emitted gravitational wave can be greatly affected by the state of the hydrodynamical fluid flow inside the neutron star. In this work, we examine the most triaxial models along two sequences of constant rest mass, confirming their dynamical stability. We also study the response of a triaxial figure of quasiequilibrium under a variety of perturbations that lead to different fluid flows. Starting from the general relativistic compressible analog of the Newtonian Jacobi ellipsoid, we perform simulations of Dedekind-type flows. We find that in some cases the triaxial neutron star resembles a Riemann-S-type ellipsoid with minor rotation and gravitational wave emission as it evolves towards axisymmetry. The present results highlight the importance of understanding the fluid flow in the interior of a neutron star in terms of its gravitational wave content.

Luo, Yufeng (ORCID:0000000246230683)↗

DOE OSTI2023

HPC-driven computational reproducibility in numerical relativity codes: a use case study with IllinoisGRMHD

Abstract Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produceconsistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using theIllinoisGRMHDcode that is part of an open-source community software for simulation in relativistic astrophysics, theEinstein Toolkit. We verify that numerical results of simulating a single isolated neutron star withIllinoisGRMHDcan be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find thatIllinoisGRMHDreproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version ofIllinoisGRMHDreproduces these results once we account for bug fixes which have occurred since the original publication.

Astronomy & Astrophysics↗