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.

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 the merger-driven galaxy evolution scenario, dust-obscured quasars are considered to be an intermediate population between merger-driven star-forming galaxies and unobscured quasars; however, this scenario is still controversial. To verify this, it is necessary to investigate whether dust-obscured quasars have higher Eddington ratio (λEdd) values than those of unobscured quasars, as expected in the merger-driven galaxy evolution scenario. In this study, we derive black hole (BH) masses of 10 dust-obscured quasars at z ∼ 2, during the peak period of star-formation and BH growth in the Universe, using a newly derived mid-infrared (MIR) continuum luminosity (LMIR)-based estimator that is highly resistant to dust extinction. Then, we compare the λEdd values of these dust-obscured quasars to those of unobscured type-1 quasars at similar redshifts. We find that the measured log (λEdd) values of the dust-obscured quasars, −0.06 ± 0.10, are significantly higher than those of the unobscured quasars, −0.86 ± 0.01. This result remains consistent across the redshift range from 1.5 to 2.5. Our results show that the dust-obscured quasars are at their maximal growth, consistent with the expectation from the merger-driven galaxy evolution scenario at the epoch quasar activities were most prominent in the cosmic history.