ﻻ يوجد ملخص باللغة العربية
In studies of the connection between active galactic nuclei (AGN) and their host galaxies there is widespread disagreement on some key aspects stemming largely from a lack of understanding of the nature of the full underlying AGN population. Recent attempts to probe this connection utilize both observations and simulations to correct for a missed population, but presently are limited by intrinsic biases and complicated models. We take a simple simulation for galaxy evolution and add a new prescription for AGN activity to connect galaxy growth to dark matter halo properties and AGN activity to star formation. We explicitly model selection effects to produce an observed AGN population for comparison with observations and empirically motivated models of the local universe. This allows us to bypass the difficulties inherent in many models which attempt to infer the AGN population by inverting selection effects. We investigate the impact of selecting AGN based on thresholds in luminosity or Eddington ratio on the observed AGN population. By limiting our model AGN sample in luminosity, we are able to recreate the observed local AGN luminosity function and specific star formation-stellar mass distribution, and show that using an Eddington ratio threshold introduces less bias into the sample by selecting the full range of growing black holes, despite the challenge of selecting low mass black holes. We find that selecting AGN using these various thresholds yield samples with different AGN host galaxy properties.
Aerial cinematography is significantly expanding the capabilities of film-makers. Recent progress in autonomous unmanned aerial vehicles (UAVs) has further increased the potential impact of aerial cameras, with systems that can safely track actors in
EXplainable AI (XAI) methods have been proposed to interpret how a deep neural network predicts inputs through model saliency explanations that highlight the parts of the inputs deemed important to arrive a decision at a specific target. However, it
We report the discovery of a new changing-look quasar, SDSS J101152.98+544206.4, through repeat spectroscopy from the Time Domain Spectroscopic Survey. This is an addition to a small but growing set of quasars whose blue continua and broad optical em
Learning from datasets without interaction with environments (Offline Learning) is an essential step to apply Reinforcement Learning (RL) algorithms in real-world scenarios. However, compared with the single-agent counterpart, offline multi-agent RL
We show that the recent NANOGrav result can be interpreted as a stochastic gravitational wave signal associated to formation of primordial black holes from high-amplitude curvature perturbations. The indicated amplitude and power of the gravitational