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.

Even in an era where 8-meter class telescopes are common, small telescopes are considered very valuable research facilities since they are available for rapid follow-up or long term monitoring observations. To maximize the usefulness of small telescopes in Korea, we established the SomangNet, a network of 0.4{1.0 m class optical telescopes operated by Korean institutions, in 2020. Here, we give an overview of the project, describing the current participating telescopes, its scientic scope and operation mode, and the prospects for future activities. SomangNet currently includes 10 telescopes that are located in Australia, USA, and Chile as well as in Korea. The operation of many of these telescopes currently relies on operators, and we plan to upgrade them for remote or robotic operation. The latest SomangNet science projects include monitoring and follow-up observational studies of galaxies, supernovae, active galactic nuclei, symbiotic stars, solar system objects, neutrino/gravitational-wave sources, and exoplanets.

Intensive Monitoring Survey of Nearby Galaxies (IMSNG) is a high cadence observation program monitoring nearby galaxies with high probabilities of hosting supernovae (SNe). IMSNG aims to constrain the SN explosion mechanism by inferring sizes of SN progenitor systems through the detection of the shock-heated emission that lasts less than a few days after the SN explosion. To catch the signal, IMSNG utilizes a network of 0.5-m to 1-m class telescopes around the world and monitors the images of 60 nearby galaxies at distances D < 50 Mpc to a cadence as short as a few hours. The target galaxies are bright in near-ultraviolet (NUV) with MNUV < - 18.4 AB mag and have high probabilities of hosting SNe (0.06 SN yr-1 per galaxy). With this strategy, we expect to detect the early light curves of 3.4 SNe per year to a depth of R  19:5 mag, enabling us to detect the shock-heated emission from a progenitor star with a radius as small as 0.1 R. The accumulated data will be also useful for studying faint features around the target galaxies and other science projects. So far, 18 SNe have occurred in our target fields (16 in IMSNG galaxies) over 5 years, confirming our SN rate estimate of 0.06 SN yr-1 per galaxy.