Aims. We aim to generate a catalogue of merging galaxies within the 5.4 sq. deg. North Ecliptic Pole over the redshift range 0.0 < z < 0.3. To do this, imaging data from the Hyper Suprime-Cam are used along with morphological parameters derived from these same data. Methods. The catalogue was generated using a hybrid approach. Two neural networks were trained to perform binary merger non-merger classifications: one for galaxies with z < 0.15 and another for 0.15 ≤ z < 0.30. Each network used the image and morphological parameters of a galaxy as input. The galaxies that were identified as merger candidates by the network were then visually checked by experts. The resulting mergers will be used to calculate the merger fraction as a function of redshift and compared with literature results. Results. We found that 86.3% of galaxy mergers at z < 0.15 and 79.0% of mergers at 0.15 ≤ z < 0.30 are expected to be correctly identified by the networks. Of the 34 264 galaxies classified by the neural networks, 10 195 were found to be merger candidates. Of these, 2109 were visually identified to be merging galaxies. We find that the merger fraction increases with redshift, consistent with literature results from observations and simulations, and that there is a mild star-formation rate enhancement in the merger population of a factor of 1.102 ± 0.084.

The aim of this work is to create a new catalog of reliable AGN candidates selected from the AKARI NEP-Deep field. Selection of the AGN candidates was done by applying a fuzzy SVM algorithm, which allows to incorporate measurement uncertainties into the classification process. The training dataset was based on the spectroscopic data available for selected objects in the NEP-Deep and NEP-Wide fields. The generalization sample was based on the AKARI NEP-Deep field data including objects without optical counterparts and making use of the infrared information only. A high quality catalog of previously unclassified 275 AGN candidates was prepared.

To understand the cosmic accretion history of supermassive black holes, separating the radiation from active galactic nuclei (AGNs) and star-forming galaxies (SFGs) is critical. However, a reliable solution on photometrically recognizing AGNs still remains unsolved. In this work, we present a novel AGN recognition method based on Deep Neural Network (Neural Net; NN). The main goals of this work are (i) to test if the AGN recognition problem in the North Ecliptic Pole Wide (NEPW) field could be solved by NN; (ii) to show that NN exhibits an improvement in the performance compared with the traditional, standard spectral energy distribution (SED) fitting method in our testing samples; and (iii) to publicly release a reliable AGN/SFG catalogue to the astronomical community using the best available NEPW data, and propose a better method that helps future researchers plan an advanced NEPW data base. Finally, according to our experimental result, the NN recognition accuracy is around 80.29 per cent–85.15 per cent, with AGN completeness around 85.42 per cent–88.53 per cent and SFG completeness around 81.17 per cent–85.09 per cent.

Measuring the redshift of active galactic nuclei (AGNs) requires the use of time-consuming and expensive spectroscopic analysis. However, obtaining redshift measurements of AGNs is crucial as it can enable AGN population studies, provide insight into the star formation rate, the luminosity function, and the density rate evolution. Hence, there is a requirement for alternative redshift measurement techniques. In this project, we aim to use the Fermi Gamma-ray Space Telescope’s 4LAC Data Release 2 catalog to train a machine-learning (ML) model capable of predicting the redshift reliably. In addition, this project aims at improving and extending with the new 4LAC Catalog the predictive capabilities of the ML methodology published in Dainotti et al. Furthermore, we implement feature engineering to expand the parameter space and a bias correction technique to our final results. This study uses additional ML techniques inside the ensemble method, the SuperLearner, previously used in Dainotti et al. Additionally, we also test a novel ML model called Sorted L-One Penalized Estimation. Using these methods, we provide a catalog of estimated redshift values for those AGNs that do not have a spectroscopic redshift measurement. These estimates can serve as a redshift reference for the community to verify as updated Fermi catalogs are released with more redshift measurements.

How does the environment affect active galactic nucleus (AGN) activity? We investigated this question in an extinction-free way, by selecting 1120 infrared galaxies in the AKARI North Ecliptic Pole Wide field at redshift z ≤ 1.2. A unique feature of the AKARI satellite is its continuous 9-band infrared (IR) filter coverage, providing us with an unprecedentedly large sample of IR spectral energy distributions (SEDs) of galaxies. By taking advantage of this, for the first time, we explored the AGN activity derived from SED modelling as a function of redshift, luminosity, and environment. We quantified AGN activity in two ways: AGN contribution fraction (ratio of AGN luminosity to the total IR luminosity), and AGN number fraction (ratio of number of AGNs to the total galaxy sample). We found that galaxy environment (normalised local density) does not greatly affect either definitions of AGN activity of our IRG/LIRG samples (log LTIR ≤ 12). However, we found a different behavior for ULIRGs (log LTIR &gt; 12). At our highest redshift bin (0.7 ≲ z ≲ 1.2), AGN activity increases with denser environments, but at the intermediate redshift bin (0.3 ≲ z ≲ 0.7), the opposite is observed. These results may hint at a different physical mechanism for ULIRGs. The trends are not statistically significant (p ≥ 0.060 at the intermediate redshift bin, and p ≥ 0.139 at the highest redshift bin). Possible different behavior of ULIRGs is a key direction to explore further with future space missions (e.g., JWST, Euclid, SPHEREx).