The detection of starlight from the host galaxies of quasars during the reionization epoch (z > 6) has been elusive, even with deep HST observations1,2. The current highest redshift quasar host detected3, at z = 4.5, required the magnifying effect of a foreground lensing galaxy. Low-luminosity quasars4,5,6 from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP)7 mitigate the challenge of detecting their underlying, previously-undetected host galaxies. Here we report rest-frame optical images and spectroscopy of two HSC-SSP quasars at z > 6 with JWST. Using NIRCam imaging at 3.6μm and 1.5μm and subtracting the light from the unresolved quasars, we find that the host galaxies are massive (stellar masses of 13 × and 3.4 × 1010 M⊙, respectively), compact, and disk-like. NIRSpec medium-resolution spectroscopy shows stellar absorption lines in the more massive quasar, confirming the detection of the host. Velocity-broadened gas in the vicinity of these quasars enables measurements of their black hole masses (1.4 × 109 and 2.0 × 108 M⊙, respectively). Their location in the black hole mass - stellar mass plane is consistent with the distribution at low redshift, suggesting that the relation between black holes and their host galaxies was already in place less than a billion years after the Big Bang.
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Where’s Swimmy?: Mining unique color features buried in galaxies by deep anomaly detection using Subaru Hyper Suprime-Cam data
We present the Swimmy (Subaru WIde-field Machine-learning anoMalY) survey program, a deep-learning-based search for unique sources using multicolored (grizy) imaging data from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). This program aims to detect unexpected, novel, and rare populations and phenomena, by utilizing the deep imaging data acquired from the wide-field coverage of the HSC-SSP. This article, as the first paper in the Swimmy series, describes an anomaly detection technique to select unique populations as “outliers” from the data-set. The model was tested with known extreme emission-line galaxies (XELGs) and quasars, which consequently confirmed that the proposed method successfully selected $\sim\!\! 60\%$–$70\%$ of the quasars and $60\%$ of the XELGs without labeled training data. In reference to the spectral information of local galaxies at z = 0.05–0.2 obtained from the Sloan Digital Sky Survey, we investigated the physical properties of the selected anomalies and compared them based on the significance of their outlier values. The results revealed that XELGs constitute notable fractions of the most anomalous galaxies, and certain galaxies manifest unique morphological features. In summary, deep anomaly detection is an effective tool that can search rare objects, and, ultimately, unknown unknowns with large data-sets. Further development of the proposed model and selection process can promote the practical applications required to achieve specific scientific goals.
more » « less- NSF-PAR ID:
- 10308506
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- Publications of the Astronomical Society of Japan
- Volume:
- 74
- Issue:
- 1
- ISSN:
- 0004-6264
- Format(s):
- Medium: X Size: p. 1-23
- Size(s):
- ["p. 1-23"]
- Sponsoring Org:
- National Science Foundation
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