Active galactic nuclei (AGNs) are very powerful galaxies characterized by extremely bright emissions coming from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine their distance to investigate important astrophysical problems, such as the evolution of the early stars and their formation, along with the structure of early galaxies. The redshift determination is challenging because it requires detailed follow-up of multiwavelength observations, often involving various astronomical facilities. Here we employ machine-learning algorithms to estimate redshifts from the observed γ-ray properties and photometric data of γ-ray-loud AGNs from the Fourth Fermi-LAT Catalog. The prediction is obtained with the Superlearner algorithm using a LASSO-selected set of predictors. We obtain a tight correlation, with a Pearson correlation coefficient of 71.3% between the inferred and observed redshifts and an average Δz norm = 11.6 × 10−4. We stress that, notwithstanding the small sample of γ-ray-loud AGNs, we obtain a reliable predictive model using Superlearner, which is an ensemble of several machine-learning models.

We have conducted a 3-mm molecular-line survey towards 570 high-mass star-forming clumps, using the Mopra telescope. The sample is selected from the 10,000 clumps identified by the ATLASGAL survey and includes all of the most important embedded evolutionary stages associated with massive star formation, classified into five distinct categories (quiescent, protostellar, young stellar objects, HII regions and photo-dominated regions). The observations were performed in broadband mode with frequency coverage of 85.2 to 93.4 GHz and a velocity resolution of ∼0.9 km s −1 , detecting emission from 26 different transitions. We find significant evolutionary trends in the detection rates, integrated line intensities, and abundances of many of the transitions and also identify a couple of molecules that appear to be invariant to changes in the dust temperature and evolutionary stage (N 2 H + (1-0) and HN 13 C (1-0)). We use the K-ladders for CH 3 C 2 H (5-4) and CH 3 CH (5-4) to calculate the rotation temperatures and find ∼1/3 of the quiescent clumps have rotation temperatures that suggest the presence of an internal heating source. These sources may constitute a population of very young protostellar objects that are still dark at 70 µm and suggest that the fraction of truly quiescent clumps may only be a few per cent. We also identify a number of line ratios that show a strong correlation with the evolutionary stage of the embedded objects and discuss their utility as diagnostic probes of evolution.

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.

Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations and detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use of machine learning (ML) for this purpose has been growing over the last few years, primarily due to the availability of large-scale galactic surveys. However, due to observational errors, a significant fraction of these data sets often have missing entries, rendering that fraction unusable for ML regression applications. In this study, we demonstrate the performance of an imputation technique called Multivariate Imputation by Chained Equations (MICE), which rectifies the issue of missing data entries by imputing them using the available information in the catalog. We use the Fermi-LAT Fourth Data Release Catalog (4LAC) and impute 24% of the catalog. Subsequently, we follow the methodology described in Dainotti et al. (ApJ, 2021, 920, 118) and create an ML model for estimating the redshift of 4LAC AGNs. We present results which highlight positive impact of MICE imputation technique on the machine learning models performance and obtained redshift estimation accuracy.

Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations and detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use of machine learning (ML) for this purpose has been growing over the last few years, primarily due to the availability of largescale galactic surveys. However, due to observational errors, a significant fraction of these data sets often have missing entries, rendering that fraction unusable for ML regression applications. In this study, we demonstrate the performance of an imputation technique called Multivariate Imputation by Chained Equations (MICE), which rectifies the issue of missing data entries by imputing them using the available information in the catalog. We use the Fermi-LAT Fourth Data Release Catalog (4LAC) and impute 24% of the catalog. Subsequently, we follow the methodology described in Dainotti et al. (2021) and create an ML model for estimating the redshift of 4LAC AGNs. We present results which highlight positive impact of MICE imputation technique on the machine learning models performance and obtained redshift estimation accuracy.