Engineering topics

Kim, Dae-Won

Publications and source records attributed to Kim, Dae-Won.

6 records

DOE OSTI2017

Interferometric Monitoring of Gamma-Ray Bright AGNs: S5 0716+714

We present the results of very long baseline interferometry (VLBI) observations of gamma-ray bright blazar S5 0716+714 using the Korean VLBI Network (KVN) at the 22, 43, 86, and 129 GHz bands, as part of the Interferometric Monitoring of Gamma-ray Bright active galactic nuclei (iMOGABA) KVN key science program. Observations were conducted in 29 sessions from 2013 January 16 to 2016 March 1, with the source being detected and imaged at all available frequencies. In all epochs, the source was compact on the milliarcsecond scale, yielding a compact VLBI core dominating the synchrotron emission on these scales. Based on the multiwavelength data between 15 GHz (Owens Valley Radio Observatory) and 230 GHz (Submillimeter Array), we found that the source shows multiple prominent enhancements of the flux density at the centimeter (cm) and millimeter (mm) wavelengths, with mm enhancements leading cm enhancements by −16 ± 8 days. The turnover frequency was found to vary between 21 and 69 GHz during our observations. By assuming a synchrotron self-absorption model for the relativistic jet emission in S5 0716+714, we found the magnetic field strength in the mas emission region to be ≤5 mG during the observing period, yielding a weighted mean of 1.0 ± 0.6 mG for higher turnover frequencies (e.g., >45 GHz).

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DOE OSTI2016

INTERFEROMETRIC MONITORING OF GAMMA-RAY BRIGHT AGNs. I. THE RESULTS OF SINGLE-EPOCH MULTIFREQUENCY OBSERVATIONS

We present results of single-epoch very long baseline interferometry (VLBI) observations of gamma-ray bright active galactic nuclei (AGNs) using the Korean VLBI Network (KVN) at the 22, 43, 86, and 129 GHz bands, which are part of a KVN key science program, Interferometric Monitoring of Gamma-Ray Bright AGNs. We selected a total of 34 radio-loud AGNs of which 30 sources are gamma-ray bright AGNs with flux densities of >6 × 10{sup −10} ph cm{sup −2} s{sup −1}. Single-epoch multifrequency VLBI observations of the target sources were conducted during a 24 hr session on 2013 November 19 and 20. All observed sources were detected and imaged at all frequency bands, with or without a frequency phase transfer technique, which enabled the imaging of 12 faint sources at 129 GHz, except for one source. Many of the target sources are resolved on milliarcsecond scales, yielding a core-jet structure, with the VLBI core dominating the synchrotron emission on a milliarcsecond scale. CLEAN flux densities of the target sources are 0.43–28 Jy, 0.32–21 Jy, 0.18–11 Jy, and 0.35–8.0 Jy in the 22, 43, 86, and 129 GHz bands, respectively. Spectra of the target sources become steeper at higher frequency, with spectral index means of −0.40, −0.62, and −1.00 in the 22–43 GHz, 43–86 GHz and 86–129 GHz bands, respectively, implying that the target sources become optically thin at higher frequencies (e.g., 86–129 GHz).

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2014

Supervised detection of anomalous light curves in massive astronomical catalogs

The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here we present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information we have about known objects, our method is based on a supervised algorithm. In particular, we train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set. We then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set, we perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low joint probability. Our method is suitable for exploring massive data sets given that the training process is performed offline. We tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis, we divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts were principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and were consequently removed from our outlier list and added to the training set. After retraining, we selected about 4000 objects, which we passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates we identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them we identified three unknown variability types and a few individual outliers that will be followed up in order to perform a deeper analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

DETECTING VARIABILITY IN MASSIVE ASTRONOMICAL TIME-SERIES DATA. II. VARIABLE CANDIDATES IN THE NORTHERN SKY VARIABILITY SURVEY

We present variability analysis of data from the Northern Sky Variability Survey (NSVS). Using the clustering method, which defines variable candidates as outliers from large clusters, we cluster 16,189,040 light curves having data points at more than 15 epochs as variable and non-variable candidates in 638 NSVS fields. Variable candidates are selected depending on how strongly they are separated from the largest cluster and how rarely they are grouped together in eight-dimensional space spanned by variability indices. All NSVS light curves are also cross-correlated with IRAS, AKARI, Two Micron All Sky Survey, Sloan Digital Sky Survey (SDSS), and GALEX objects, as well as known objects in the SIMBAD database. The variability analysis and cross-correlation results are provided in a public online database, which can be used to select interesting objects for further investigation. Adopting conservative selection criteria for variable candidates, we find about 1.8 million light curves as possible variable candidates in the NSVS data, corresponding to about 10% of our entire NSVS sample. Multi-wavelength colors help us find specific types of variability among the variable candidates. Moreover, we also use morphological classification from other surveys such as SDSS to suppress spurious cases caused by blending objects or extended sources due to the low angular resolution of the NSVS.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2012

A REFINED QSO SELECTION METHOD USING DIAGNOSTICS TESTS: 663 QSO CANDIDATES IN THE LARGE MAGELLANIC CLOUD

We present 663 QSO candidates in the Large Magellanic Cloud (LMC) selected using multiple diagnostics. We started with a set of 2566 QSO candidates selected using the methodology presented in our previous work based on time variability of the MACHO LMC light curves. We then obtained additional information for the candidates by crossmatching them with the Spitzer SAGE, the Two Micron All Sky Survey, the Chandra, the XMM, and an LMC UBVI catalog. Using this information, we specified six diagnostic features based on mid-IR colors, photometric redshifts using spectral energy distribution template fitting, and X-ray luminosities in order to further discriminate high-confidence QSO candidates in the absence of spectra information. We then trained a one-class Support Vector Machine model using the diagnostics features of the confirmed 58 MACHO QSOs. We applied the trained model to the original candidates and finally selected 663 high-confidence QSO candidates. Furthermore, we crossmatched these 663 QSO candidates with the newly confirmed 151 QSOs and 275 non-QSOs in the LMC fields. On the basis of the counterpart analysis, we found that the false positive rate is less than 1%.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2011

QUASI-STELLAR OBJECT SELECTION ALGORITHM USING TIME VARIABILITY AND MACHINE LEARNING: SELECTION OF 1620 QUASI-STELLAR OBJECT CANDIDATES FROM MACHO LARGE MAGELLANIC CLOUD DATABASE

We present a new quasi-stellar object (QSO) selection algorithm using a Support Vector Machine, a supervised classification method, on a set of extracted time series features including period, amplitude, color, and autocorrelation value. We train a model that separates QSOs from variable stars, non-variable stars, and microlensing events using 58 known QSOs, 1629 variable stars, and 4288 non-variables in the MAssive Compact Halo Object (MACHO) database as a training set. To estimate the efficiency and the accuracy of the model, we perform a cross-validation test using the training set. The test shows that the model correctly identifies {approx}80% of known QSOs with a 25% false-positive rate. The majority of the false positives are Be stars. We applied the trained model to the MACHO Large Magellanic Cloud (LMC) data set, which consists of 40 million light curves, and found 1620 QSO candidates. During the selection none of the 33,242 known MACHO variables were misclassified as QSO candidates. In order to estimate the true false-positive rate, we crossmatched the candidates with astronomical catalogs including the Spitzer Surveying the Agents of a Galaxy's Evolution LMC catalog and a few X-ray catalogs. The results further suggest that the majority of the candidates, more than 70%, are QSOs.

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