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Creators/Authors contains: "Kuropatkin, N."

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  1. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal Letters)
    Free, publicly-accessible full text available October 1, 2022
  2. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
    Abstract On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star–black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera on the 4 m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on eight separate nights to observe 11 candidates using the 4.1 m Southern Astrophysical Research (SOAR) telescope’s Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpartmore »of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond.« less
    Free, publicly-accessible full text available April 1, 2023
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Monthly Notices of the Royal Astronomical Society)
    Abstract We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs), and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97% accuracy tomore »mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼ 87% and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ε) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.« less
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  5. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal Letters)
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  6. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal Supplement Series)
    Free, publicly-accessible full text available August 16, 2022
  7. ; ; ; ; ; ; ; ; ; ; et al ( , The Planetary Science Journal)
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  8. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal)
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  9. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal Supplement Series)
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review Letters)
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