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.

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.

We explored the effect of galaxy-galaxy interaction on the FIR-radio correlation of star-forming galaxies by comparing the qFIR parameter distribution between interacting and non-interacting galaxies. Our sample galaxies were selected from the SDSS Stripe 82 region, where relatively deep optical images are available in addition to ancillary FIR and radio data. The qFIR values were 2.73±0.49 and 2.53±0.90 for interacting and non-interacting galaxies, respectively. The t-test results indicated that the difference in qFIR values between the two categories is not statistically significant. Our findings align with those of previous studies suggesting that either FIR excess or radio excess occurs only transiently during brief timescales in the merger stages, rather than persisting throughout the majority of merger events identified by features such as tidal tails or double nuclei.

The Spectro-Photometer for the History of the Universe, Epoch of Reionization and Ices Explorer (SPHEREx) will provide all-sky spectral survey data covering optical to mid-infrared wavelengths with a spatial resolution of 6.′′2, which can be widely used to study galaxy formation and evolution. We investigate the galaxy-galaxy blending in SPHEREx datasets using the mock galaxy catalogs generated from cosmological simulations and observational data. Only ∼0.7% of the galaxies will be blended with other galaxies in all-sky survey data with a limiting magnitude of 19 AB mag. However, the fraction of blended galaxies dramatically increases to ∼7–9% in the deep survey area around the ecliptic poles, where the depth reaches ∼22 AB mag. We examine the impact of the blending in the number count and luminosity function analyses using the SPHEREx data. We find that the number count can be overestimated by up to 10–20% in the deep regions due to the flux boosting, suggesting that the impact of galaxy-galaxy blending on the number count is moderate. However, galaxy-galaxy blending can marginally change the luminosity function by up to 50% over a wide range of redshifts. As we only employ the magnitude limit at Ks-band for the source detection, the blending fractions determined in this study should be regarded as lower limits.