The global influence of AGN-driven outflows remains uncertain, due to a lack of large samples with accurately-determined outflow properties. In the second paper of this series, we determine the mass and energetics of ionized outflows is 234 type II AGN, the largest such sample to date, by combining the infrared emission of the dust in the wind (paper I) with the emission line properties. We provide new general expressions for the properties of the outflowing gas, which depend on the ionization state of the gas. We also present a novel method to estimate the electron density in the outflow, based on optical line ratios and on the known location of the wind. The inferred electron densities, n e ∼ 10 4.5 cm −3 , are two orders of magnitude larger than typically found in most other cases of ionized outflows. We argue that the discrepancy is due to the fact that the commonly-used [SII]-based method underestimates the true density by a large factor. As a result, the inferred mass outflow rates and kinetic coupling efficiencies areṀ out ∼ 10 −2 (M /yr) and =Ė kin /L bol ∼ 10 −5 respectively, 1-2 orders of magnitude lower than previous estimates. Our analysis suggests the existence of a significant amount of neutral atomic gas at the back of the outflowing ionized gas clouds, with mass that is a factor of a few larger than the observed ionized gas mass. This has significant implications for the estimated mass and energetics of such flows.

We report on the determination of electron densities, and their impact on the outflow masses and rates, measured in the central few hundred parsecs of 11 local luminous active galaxies. We show that the peak of the integrated line emission in the AGN is significantly offset from the systemic velocity as traced by the stellar absorption features, indicating that the profiles are dominated by outflow. In contrast, matched inactive galaxies are characterised by a systemic peak and weaker outflow wing. We present three independent estimates of the electron density in these AGN, discussing the merits of the different methods. The electron density derived from the [SII] doublet is significantly lower than than that found with a method developed in the last decade using auroral and transauroral lines, as well as a recently introduced method based on the ionization parameter. The reason is that, for gas photoionized by an AGN, much of the [SII] emission arises in an extended partially ionized zone where the implicit assumption that the electron density traces the hydrogen density is invalid. We propose ways to deal with this situation and we derive the associated outflow rates for ionized gas, which are in the range 0.001–0.5 M⊙ yr−1 for our AGN sample. We compare these outflow rates to the relation between $\dot{M}_{\rm out}$ and LAGN in the literature, and argue that it may need to be modified and rescaled towards lower mass outflow rates.

How can we discover objects we did not know existed within the large datasets that now abound in astronomy? We present an outlier detection algorithm that we developed, based on an unsupervised Random Forest. We test the algorithm on more than two million galaxy spectra from the Sloan Digital Sky Survey and examine the 400 galaxies with the highest outlier score. We find objects which have extreme emission line ratios and abnormally strong absorption lines, objects with unusual continua, including extremely reddened galaxies. We find galaxy-galaxy gravitational lenses, double-peaked emission line galaxies, and close galaxy pairs. We find galaxies with high ionisation lines, galaxies which host supernovae, and galaxies with unusual gas kinematics. Only a fraction of the outliers we find were reported by previous studies that used specific and tailored algorithms to find a single class of unusual objects. Our algorithm is general and detects all of these classes, and many more, regardless of what makes them peculiar. It can be executed on imaging, time-series, and other spectroscopic data, operates well with thousands of features, is not sensitive to missing values, and is easily parallelisable.

Machine learning (ML) algorithms become increasingly important in the analysis of astronomical data. However, since most ML algorithms are not designed to take data uncertainties into account, ML based studies are mostly restricted to data with high signal-to-noise ratio. Astronomical datasets of such high-quality are uncommon. In this work we modify the long-established Random Forest (RF) algorithm to take into account uncertainties in the measurements (i.e., features) as well as in the assigned classes (i.e., labels). To do so, the Probabilistic Random Forest (PRF) algorithm treats the features and labels as probability distribution functions, rather than deterministic quantities. We perform a variety of experiments where we inject different types of noise to a dataset, and compare the accuracy of the PRF to that of RF. The PRF outperforms RF in all cases, with a moderate increase in running time. We find an improvement in classification accuracy of up to 10% in the case of noisy features, and up to 30% in the case of noisy labels. The PRF accuracy decreased by less then 5% for a dataset with as many as 45% misclassified objects, compared to a clean dataset. Apart from improving the prediction accuracy in noisy datasets, the PRF naturally copes with missing values in the data, and outperforms RF when applied to a dataset with different noise characteristics in the training and test sets, suggesting that it can be used for Transfer Learning.

The scaling relations between supermassive black holes and their host galaxy properties are of fundamental importance in the context black hole-host galaxy co-evolution throughout cosmic time. In this work, we use a novel algorithm that identifies smooth trends in complex datasets and apply it to a sample of 2 000 type I active galactic nuclei (AGN) spectra. We detect a sequence in emission line shapes and strengths which reveals a correlation between the narrow L([OIII])/L(Hβ) line ratio and the width of the broad Hα. This scaling relation ties the kinematics of the gas clouds in the broad line region to the ionisation state of the narrow line region, connecting the properties of gas clouds kiloparsecs away from the black hole to material gravitationally bound to it on sub-parsec scales. This relation can be used to estimate black hole masses from narrow emission lines only. It therefore enables black hole mass estimation for obscured type II AGN and allows us to explore the connection between black holes and host galaxy properties for thousands of objects, well beyond the local Universe. Using this technique, we present the M BH − σ and M BH − M * scaling relations for a sample of about 10 000 type II AGN from SDSS. These relations are remarkably consistent with those observed for type I AGN, suggesting that this new method may perform as reliably as the classical estimate used in non-obscured type I AGN. These findings open a new window for studies of black hole-host galaxy co-evolution throughout cosmic time.