We introduce a new clustering algorithm, MulGuisin (MGS), that can identify distinct galaxy over-densities using topological information from the galaxy distribution. This algorithm was first introduced in an LHC experiment as a Jet Finder software, which looks for particles that clump together in close proximity. The algorithm preferentially considers particles with high energies and merges them only when they are closer than a certain distance to create a jet. MGS shares some similarities with the minimum spanning tree (MST) since it provides both clustering and network-based topology information. Also, similar to the density-based spatial clustering of applications with noise (DBSCAN), MGS uses the ranking or the local density of each particle to construct clustering. In this paper, we compare the performances of clustering algorithms using controlled data and some realistic simulation data as well as the SDSS observation data, and we demonstrate that our new algorithm finds networks most correctly and defines galaxy networks in a way that most closely resembles human vision.
The Event Horizon Telescope (EHT) has successfully revealed the shadow of the supermassive black hole, M87∗, with an unprecedented angular resolution of approximately 20 μas at 230 GHz. However, because of limited short baseline lengths, the EHT has been constrained in its ability to recover larger-scale jet structures. The extended Korean VLBI Network (eKVN) is committed to joining the EHT from 2024 that can improve short baseline coverage. This study evaluates the impact of the participation of eKVN in the EHT on the recovery of the M87∗ jet. Synthetic data, derived from a simulated M87∗ model, were observed using both the EHT and the combined EHT+eKVN arrays, followed by image reconstructions from both configurations. The results indicate that the inclusion of eKVN significantly improves the recovery of jet structures by reducing residual noise. Furthermore, jackknife tests, in which one or two EHT telescopes were omitted—simulating potential data loss due to poor weather—demonstrate that eKVN effectively compensates for these missing telescopes, particularly in short baseline coverage. Multi-frequency synthesis imaging at 86–230 GHz shows that the EHT+eKVN array enhances the recovered spectral index distribution compared to the EHT alone and improves image reconstruction at each frequency over single-frequency imaging. As the EHT continues to expand its array configuration and observing capabilities to probe black hole physics more in depth, the integration of eKVN into the EHT will significantly enhance the stability of observational results and improve image fidelity. This advancement will be particularly valuable for future regular monitoring observations, where consistent data quality is essential.
We present optical observations of a nearby Type Ia supernova (SN Ia) 2018kp on January 24 2018, +1.4 days after the estimated first light time. Its host galaxy, NGC 3367, has been under high-cadence monitoring (≲1 day) with the purpose of providing valuable early light curves of supernovae as a primary target of the Intensive Monitoring Survey of Nearby Galaxies (IMSNG; Im et al. 2019). SN 2018kp exhibits the characteristics of a normal SN Ia, with a peak luminosity of MB = −19.0 ± 0.4 mag and Δm15(B) = 1.19 ± 0.03 mag, derived from our long-term light curve analysis. We estimate the host extinction to be high [E(B − V )host = 0.697 ± 0.028 mag], contrasting with its sibling, SN 1986A. We estimate the mass of 56Ni synthesized in the explosion to beMNi = 0.55±0.14M⊙. A single power-law model (tα) describes the rising behavior of the early light curve well, with little evidence of the shock-heated cooling emission. We place upper limits on the radii of the progenitor (Rp ≤ 1.8 R⊙) and the companion star (Rc ≤ 1.9 R⊙ at the optimal or Rc ≤ 19.2 R⊙ at the common viewing angle, respectively) ruling out a large companion such as a red giant. Based on our data, we derive a distance to the host galaxy of 41.38 ± 2.20 Mpc assuming that SN 2018kp follows the Phillips relation.
Galaxy evolution studies require the measurement of the physical properties of galaxies at different redshifts. In this work, we build supervised machine learning models to predict the redshift and physical properties (gas-phase metallicity, stellar mass, and star formation rate) of star-forming galaxies from the broad-band and medium-band photometry covering optical to near-infrared wavelengths, and present an evaluation of the model performance. Using 55 magnitudes and colors as input features, the optimized model can predict the galaxy redshift with an accuracy of σ(Δz/1+z) = 0.008 for a redshift range of z < 0.4. The gas-phase metallicity [12 + log(O/H)], stellar mass [log(Mstar)], and star formation rate [log(SFR)] can be predicted with the accuracies of σNMAD = 0.081, 0.068, and 0.19 dex, respectively. When magnitude errors are included, the scatter in the predicted values increases, and the range of predicted values decreases, leading to biased predictions. Near-infrared magnitudes and colors (H, K, and H −K), along with optical colors in the blue wavelengths (m425–m450), are found to play important roles in the parameter prediction. Additionally, the number of input features is critical for ensuring good performance of the machine learning model. These results align with the underlying scaling relations between physical parameters for star-forming galaxies, demonstrating the potential of using medium-band surveys to study galaxy scaling relations with large sample of galaxies.
In this study, we examine the relationships between the National Oceanic and Atmospheric Administration (NOAA) space weather scale frequencies and the maximum monthly sunspot number in each solar cycle: 1975 to 2020 for radio blackouts (R scales) and solar radiation storms (S scales), 1932 to 2020 for geomagnetic storms (G scales). Our main results are as follows. First, we find that NOAA space weather scale frequencies have strong solar cycle dependencies. Second, we propose new linear relationships between the frequency of certain scales (R1 to R4, and G1 to G4) and the maximum monthly sunspot number. T-test results show that R1 to R3 and G1 to G4 relationships are statistically meaningful, but marginal for R4. Third, our results significantly reduce the root-mean-square error (RMSE) between observed and suggested frequencies compared to the NOAA’s current frequencies. For example, in the case of solar cycle 24, our new prediction (74) for R3 scale is much more consistent with the observational frequency (74) than the NOAA prediction (175), and our prediction (85) for G3 scale is much closer to the observation (40) than the NOAA prediction (200). Our work may provide a useful guideline for advancing the space weather scales.
This paper introduces a simple and reliable photometric calibration method to extract Hα line flux from narrowband images. The equivalent width of the Hα line (EWHα) is derived using two- and simplified three-filter methods. Synthetic photometry of CALSPEC stars demonstrates the dependency of EWHα on the V − R color, described by a skewed Gaussian function within −0.1 < V − R < 0.7. Systematic errors of the two- and three-filter methods are analyzed under 0%–10% R-band flux contamination. Although the three-filter method underestimates EWHα by 10%, it exhibits less scatter compared to the two-filter method. The simplified three-filter method was validated with the Landolt SA 107 field and surpasses the two-filter method in terms of precision and accuracy. Additionally, applying our method to V960 Mon yields EWHα consistent with high-resolution spectroscopic results.
Scaling relations are fundamental tools for exploring the morphological properties of galaxies and understanding their formation and evolution. Typically, galaxies follow a scaling relation between mass and size, measured by effective radius. However, a compact class of galaxies exists as outliers from this relation, and the origin of these compact galaxies in the local universe remains unclear. In this study, we investigate the compact dwarf galaxy SDSS J134313.15+364457.5 (J1343+3644), which is the result of a merger. Our analysis reveals that J1343+3644 has a half-light radius of 482 pc, significantly smaller than typical galaxies with the same brightness (Mr = −19.17 mag). With a high star-formation rate (SFR) of 0.87 M⊙ year−1, J1343+3644 is expected to evolve into a compact elliptical galaxy in a few million years. J1343+3644 could, therefore, be a progenitor of a compact elliptical galaxy. The phenomenon happened in early universe, where compact galaxies were common.
Photometric and spectroscopic observations of GV Leo were performed from 2017 to 2024. The light curves show a flat bottom at the primary eclipse and the conventional O’Connell effect. The echelle spectra reveal that the effective temperature and rotation velocity of the more massive secondary are Teff,2 = 5220 ± 120 K and v2 sin i = 223 ± 40 kms−1, respectively. Our binary modeling indicates that the program target is a W-subclass contact binary with a mass ratio of q = 5.48, an inclination angle of i = 81.◦68, a temperature difference of (Teff,1 − Teff,2) = 154 K, and a filling factor of f = 36%. The light asymmetries were reasonably modeled by a dark starspot on the secondary’s photosphere. Including our 26 minimum epochs, 84 times of minimum light were used to investigate the orbital period of the system. We found that the eclipse times of GV Leo have varied by a sinusoid with a period of 14.9 years and a semi-amplitude of 0.0076 days superimposed on a downward parabola. The periodic modulation is interpreted as a light time effect produced by an unseen outer tertiary with a minimum mass of 0.26 M⊙, while the parabolic component is thought to be a combination of mass transfer (secondary to primary) and angular momentum loss driven by magnetic braking. The circumbinary tertiary would have caused the eclipsing pair of GV Leo to evolve into its current short-period contact state by removing angular momentum from the primordial widish binary.