| 1. |
- Clarke, Leonardo, et al.
(författare)
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An Old Stellar Population or Diffuse Nebular Continuum Emission Discovered in Green Pea Galaxies
- 2021
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Ingår i: Astrophysical Journal Letters. - : American Astronomical Society. - 2041-8205 .- 2041-8213. ; 912:2
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Tidskriftsartikel (refereegranskat)abstract
- We use new Hubble Space Telescope (HST) images of nine Green Pea galaxies (GPGs) to study their resolved structure and color. The choice of filters, F555W and F850LP, together with the redshift of the galaxies (z ∼ 0.25), minimizes the contribution of the nebular [O iii] and Hα emission lines to the broadband images. While these galaxies are typically very blue in color, our analysis reveals that it is only the dominant stellar clusters that are blue. Each GPG does clearly show the presence of at least one bright and compact star-forming region, but these are invariably superimposed on a more extended and lower surface brightness emission. Moreover, the colors of the star-forming regions are on average bluer than those of the diffuse emission, reaching up to 0.6 magnitudes bluer. Assuming that the diffuse and compact components have constant and single-burst star formation histories, respectively, the observed colors imply that the diffuse components (possibly the host galaxy of the star formation episode) have, on average, old stellar ages (>1 Gyr), while the star clusters are younger than 500 Myr. While a redder stellar component is perhaps the most plausible explanation for these results, the limitations of our current data set lead us to examine possible alternative mechanisms, particularly recombination emission processes, which are unusually prominent in systems with such strong line emission. With the available data, however, it is not possible to distinguish between these two interpretations. A substantial presence of old stars would indicate that the mechanisms allowing large escape fractions in these local galaxies may be different from those at play during the reionization epoch.
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| 2. |
- Lin, Yu-Heng, et al.
(författare)
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Low-metallicity Galaxies from the Dark Energy Survey
- 2023
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Ingår i: Astrophysical Journal. - 0004-637X .- 1538-4357. ; 951:2
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Tidskriftsartikel (refereegranskat)abstract
- We present a new selection of 358 blue compact dwarf galaxies (BCDs) from 5000 square degrees in the Dark Energy Survey, and the spectroscopic follow-up of a subsample of 68 objects. For the subsample of 34 objects with deep spectra, we measure the metallicity via the direct Te method using the auroral [O iii]λ 4363 emission line. These BCDs have an average oxygen abundance of 12+log(O/H) = 7.8, with stellar masses between 107 and 108M⊙ and specific star-formation rates between ∼10−9 and 10−7 yr−1. We compare the position of our BCDs with the mass–metallicity (M–Z) and luminosity–metallicity (L–Z) relation derived from the Local Volume Legacy sample. We find the scatter about the M–Z relation is smaller than the scatter about the L–Z relation. We identify a correlation between the offsets from the M–Z and L–Z relation that we suggest is due to the contribution of metal-poor inflows. Finally, we explore the validity of the mass–metallicity–SFR fundamental plane in the mass range probed by our galaxies. We find that BCDs with stellar masses smaller than 108M⊙ do not follow the extrapolation of the fundamental plane. This result suggests that mechanisms other than the balance between inflows and outflows may be at play in regulating the position of low-mass galaxies in the M–Z–SFR space.
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| 3. |
- Mahabal, Ashish, et al.
(författare)
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Machine Learning for the Zwicky Transient Facility
- 2019
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Ingår i: Publications of the Astronomical Society of the Pacific. - : IOP Publishing. - 0004-6280 .- 1538-3873. ; 131:997
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Tidskriftsartikel (refereegranskat)abstract
- The Zwicky Transient Facility is a large optical survey in multiple filters producing hundreds of thousands of transient alerts per night. We describe here various machine learning (ML) implementations and plans to make the maximal use of the large data set by taking advantage of the temporal nature of the data, and further combining it with other data sets. We start with the initial steps of separating bogus candidates from real ones, separating stars and galaxies, and go on to the classification of real objects into various classes. Besides the usual methods (e.g., based on features extracted from light curves) we also describe early plans for alternate methods including the use of domain adaptation, and deep learning. In a similar fashion we describe efforts to detect fast moving asteroids. We also describe the use of the Zooniverse platform for helping with classifications through the creation of training samples, and active learning. Finally we mention the synergistic aspects of ZTF and LSST from the ML perspective.
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