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Title: Enhancing Ly α Emitter Identification in HETDEX with a Convolutional Neural Network
Abstract We present a deep learning framework to enhance the identification of Lyαemitters (LAEs) in the Hobby–Eberly Telescope Dark Energy Experiment (HETDEX), an untargeted spectroscopic survey of LAEs at 1.9 < z < 3.5 without imaging preselection. We primarily address the low signal-to-noise ratio (S/N) regime (4.8 ≤ S/N ≤ 5.5), where LAE candidates suffer from substantial noise contamination. To distinguish LAE candidates from artifacts and sky residuals, we employ a convolutional neural network (CNN) trained on two-dimensional spectral images of single emission lines. The training sample is constructed from the HETDEX COSMOS catalog, with external validation from ancillary observations and our participatory science project, Dark Energy Explorers. For small-format, low-resolution spectroscopic data, the model achieves a balanced accuracy, precision, and recall of 94.1%, 97.5%, and 97.5%, respectively, in the high-S/N regime (S/N > 5.5), and 85.1%, 78.2%, and 84.4% in the low-S/N regime. Using HETDEX LAEs independently identified by Dark Energy Spectroscopic Instrument (DESI) spectroscopy, the model recovers 99% and 93% of the high- and low-S/N LAEs, respectively. Visual attribution indicates that the CNN attends to smooth, spatially extended central emission in true positives and to irregular or noisy features in true negatives. Applied to the full HETDEX catalog, the CNN enables an S/N threshold down to 4.8 by suppressing spurious spikes acrossz ∼ 1.9–2.5 in the redshift distribution. Our approach facilitates HETDEX cosmological analyses by mitigating false positives in galaxy clustering and highlights the value of domain-specific deep learning for refining low-S/N spectroscopic identification in untargeted surveys.  more » « less
Award ID(s):
2408637 2307354 2408358 2511145 2219212
PAR ID:
10709579
Author(s) / Creator(s):
; ; ; ; ; ; ; ; ; ; ;
Publisher / Repository:
AJ
Date Published:
Journal Name:
The Astronomical Journal
Volume:
171
Issue:
5
ISSN:
0004-6256
Page Range / eLocation ID:
318
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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