Outputs (7)

One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics (2025)
Journal Article
Jones, W. S., & Farrow, D. J. (2025). One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics. Scientific reports, 15(1), Article 12157. https://doi.org/10.1038/s41598-025-94427-x

Machine learning (ML) models are increasingly being applied to diagnose and predict disease, but face technical challenges such as population drift, where the training and real-world deployed data distributions differ. This phenomenon can degrade mod... Read More about One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics.

Using Lyα Absorption to Measure the Intensity and Variability of z ∼ 2.4 Ultraviolet Background Light (2025)
Journal Article
Weiss, L. H., Gebhardt, K., Davis, D., Mentuch Cooper, E., Lujan Niemeyer, M., Qezlou, M., Mirza Khanlari, M., Ciardullo, R., Farrow, D., Gawiser, E., Gazagnes, S., Gronwall, C., Hill, G. J., & Schneider, D. P. (2025). Using Lyα Absorption to Measure the Intensity and Variability of z ∼ 2.4 Ultraviolet Background Light. The Astrophysical journal, 983(1), Article 72. https://doi.org/10.3847/1538-4357/adc0f9

We present measurements of z ∼ 2.4 ultraviolet (UV) background light using Lyα absorption from galaxies at z ∼ 2–3 in the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX) database. Thanks to the wide area of this survey, we also measure the var... Read More about Using Lyα Absorption to Measure the Intensity and Variability of z ∼ 2.4 Ultraviolet Background Light.

The Hobby–Eberly Telescope Dark Energy Experiment Survey (HETDEX) Active Galactic Nuclei Catalog: The Fourth Data Release (2025)
Journal Article
Liu, C., Gebhardt, K., Mentuch Cooper, E., Davis, D., Schneider, D. P., Jarvis, M. J., Farrow, D. J., Finkelstein, S. L., Chávez Ortiz, Ó. A., & The HETDEX Collaboration. (2025). The Hobby–Eberly Telescope Dark Energy Experiment Survey (HETDEX) Active Galactic Nuclei Catalog: The Fourth Data Release. Astrophysical Journal Supplement, 276(2), Article 72. https://doi.org/10.3847/1538-4365/ada4a5

We present the active galactic nuclei (AGN) catalog from the fourth data release (HDR4) of the Hobby–Eberly Telescope Dark Energy Experiment Survey (HETDEX). HETDEX is an untargeted spectroscopic survey. HDR4 contains 345,874 Integral Field Unit obse... Read More about The Hobby–Eberly Telescope Dark Energy Experiment Survey (HETDEX) Active Galactic Nuclei Catalog: The Fourth Data Release.

The Lyα Dependence on Nebular Properties from the HETDEX and MOSDEF Surveys (2024)
Journal Article
Chávez Ortiz, Ó. A., Leung, G. C. K., Finkelstein, S. L., Davis, D., Sutherland, R. S., Nicholls, D. C., Stephenson, M., Mentuch Cooper, E., Bagley, M., Gebhardt, K., House, L. R., Liu, C., Ciardullo, R., Gronwall, C., Hill, G. J., Farrow, D., & Schneider, D. P. (2024). The Lyα Dependence on Nebular Properties from the HETDEX and MOSDEF Surveys. The Astrophysical journal, 977(1), Article 107. https://doi.org/10.3847/1538-4357/ad7a00

Investigating the impact of galaxy properties on emergent Lyα emission is crucial for reionization studies, given the sensitivity of Lyα to neutral hydrogen. This study presents an analysis of the physical characteristics of 155 star-forming galaxies... Read More about The Lyα Dependence on Nebular Properties from the HETDEX and MOSDEF Surveys.

Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment (2024)
Journal Article
House, L. R., Gebhardt, K., Finkelstein, K., Mentuch Cooper, E., Davis, D., Farrow, D., & Schneider, D. P. (2024). Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment. The Astrophysical journal, 975(2), Article 172. https://doi.org/10.3847/1538-4357/ad782c

We are merging a large participatory science effort with machine learning to enhance the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX). Our overall goal is to remove false positives, allowing us to use lower signal-to-noise data and sources... Read More about Participatory Science and Machine Learning Applied to Millions of Sources in the Hobby-Eberly Telescope Dark Energy Experiment.

Do galaxy mergers prefer under-dense environments? (2024)
Journal Article
Sureshkumar, U., Durkalec, A., Pollo, A., Pearson, W., Farrow, D., Narayanan, A., Loveday, J., Taylor, E., & Suelves, L. (2024). Do galaxy mergers prefer under-dense environments?. Astronomy and Astrophysics, 686, Article A40. https://doi.org/10.1051/0004-6361/202347705

Context. Galaxy mergers play a crucial role in galaxy evolution. However, the correlation between mergers and the local environment of galaxies is not fully understood.
Aims. We aim to address the question of whether galaxy mergers prefer denser or... Read More about Do galaxy mergers prefer under-dense environments?.

Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions (2022)
Journal Article
Driver, S. P., Bellstedt, S., Robotham, A. S. G., Baldry, I. K., Davies, L. J., Liske, J., Obreschkow, D., Taylor, E. N., Wright, A. H., Alpaslan, M., Bamford, S. P., Bauer, A. E., Bland-Hawthorn, J., Bilicki, M., Bravo, M., Brough, S., Casura, S., Cluver, M. E., Colless, M., Conselice, C. J., …Wilkins, S. M. (2022). Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions. Monthly notices of the Royal Astronomical Society, 513(1), 439-467. https://doi.org/10.1093/mnras/stac472

In Galaxy And Mass Assembly Data Release 4 (GAMA DR4), we make available our full spectroscopic redshift sample. This includes 248 682 galaxy spectra, and, in combination with earlier surveys, results in 330 542 redshifts across five sky regions cove... Read More about Galaxy And Mass Assembly (GAMA): Data Release 4 and the z < 0.1 total and z < 0.08 morphological galaxy stellar mass functions.