Engineering topics

Kuropatkin, Nikolay

Publications and source records attributed to Kuropatkin, Nikolay.

5 records

DOE OSTI2022

SOAR/Goodman Spectroscopic Assessment of Candidate Counterparts of the LIGO/Virgo Event GW190814*

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 counterpart 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.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

DELVE-ing into the Jet: A Thin Stellar Stream on a Retrograde Orbit at 30 kpc

We perform a detailed photometric and astrometric analysis of stars in the Jet stream using data from the first data release of the DECam Local Volume Exploration Survey DR1 and Gaia EDR3. We discover that the stream extends over ∼ 29° on the sky (increasing the known length by 18°), which is comparable to the kinematically cold Phoenix, ATLAS, and GD-1 streams. Using blue horizontal branch stars, we resolve a distance gradient along the Jet stream of 0.2 kpc deg$^{−1}$, with distances ranging from D $_{⊙}$ ∼ 27–34 kpc. We use natural splines to simultaneously fit the stream track, width, and intensity to quantitatively characterize density variations in the Jet stream, including a large gap, and identify substructure off the main track of the stream. Furthermore, we report the first measurement of the proper motion of the Jet stream and find that it is well aligned with the stream track, suggesting the stream has likely not been significantly perturbed perpendicular to the line of sight. Finally, we fit the stream with a dynamical model and find that it is on a retrograde orbit, and is well fit by a gravitational potential including the Milky Way and Large Magellanic Cloud. These results indicate the Jet stream is an excellent candidate for future studies with deeper photometry, astrometry, and spectroscopy to study the potential of the Milky Way and probe perturbations from baryonic and dark matter substructure.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Candidate Periodically Variable Quasars from the Dark Energy Survey and the Sloan Digital Sky Survey

Periodically variable quasars have been suggested as close binary supermassive black holes. We present a systematic search for periodic light curves in 625 spectroscopically confirmed quasars with a median redshift of 1.8 in a 4.6 deg 2 overlapping region of the Dark Energy Survey Supernova (DES-SN) fields and the Sloan Digital Sky Survey Stripe 82 (SDSS-S82). Our sample has a unique 20-year long multi-color ( griz ) light curve enabled by combining DES-SN Y6 observations with archival SDSS-S82 data. The deep imaging allows us to search for periodic light curves in less luminous quasars (down to r ~23.5 mag) powered by less massive black holes (with masses ≳ 10 8.5 M ⊙ ) at high redshift for the first time. We find five candidates with significant (at >99.74% single-frequency significance in at least two bands with a global p-value of ~7 × 10 -4 –3× 10 -3 accounting for the look-elsewhere effect) periodicity with observed periods of ~3–5 years (i.e., 1–2 years in rest frame) having ~4–6 cycles spanned by the observations. If all five candidates are periodically variable quasars, this translates into a detection rate of |${\sim }0.8^{+0.5}_{-0.3}$| % or |${\sim }1.1^{+0.7}_{-0.5}$| quasar per deg 2 . Our detection rate is 4–80 times larger than those found by previous searches using shallower surveys over larger areas. This discrepancy is likely caused by differences in the quasar populations probed and the survey data qualities. Furthermore, we discuss implications on the future direct detection of low-frequency gravitational waves. Additionally, continued photometric monitoring will further assess the robustness and characteristics of these candidate periodic quasars to determine their physical origins.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Dust Reverberation Mapping in Distant Quasars from Optical and Mid-infrared Imaging Surveys

The size of the dust torus in active galactic nuclei (AGNs) and their high-luminosity counterparts, quasars, can be inferred from the time delay between UV/optical accretion disk continuum variability and the response in the mid-infrared (MIR) torus emission. This dust reverberation mapping (RM) technique has been successfully applied to ~70 z ≲ 0.3 AGNs and quasars. Here we present first results of our dust RM program for distant quasars covered in the Sloan Digital Sky Survey Stripe 82 region combining ~20 yr ground-based optical light curves with 10 yr MIR light curves from the WISE satellite. Here, we measure a high-fidelity lag between W1 band (3.4 μm) and g band for 587 quasars over 0.3 ≲ z ≲ 2 () and two orders of magnitude in quasar luminosity. They tightly follow (intrinsic scatter ~0.17 dex in lag) the IR lag–luminosity relation observed for z < 0.3 AGNs, revealing a remarkable size–luminosity relation for the dust torus over more than four decades in AGN luminosity, with little dependence on additional quasar properties such as Eddington ratio and variability amplitude. This study motivates further investigations in the utility of dust RM for cosmology and strongly endorses a compelling science case for the combined 10 yr Vera C. Rubin Observatory Legacy Survey of Space and Time (optical) and 5 yr Nancy Grace Roman Space Telescope 2 μm light curves in a deep survey for low-redshift AGN dust RM with much lower luminosities and shorter, measurable IR lags. The compiled optical and MIR light curves for 7384 quasars in our parent sample are made public with this work.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

79 ASTRONOMY AND ASTROPHYSICS↗