Constraining jet production scenarios by studies of Narrow-Line-Radio-Galaxies

We study a large sample of narrow-line radio galaxies (NLRGs) with extended radio structures. Using 1.4 GHz radio luminosities, $L_{1.4}$, narrow optical emission line luminosities, L_{\oiii} and $L_{H_{\alpha}}$, as well as black hole masses $M_{BH}$ derived from stellar velocity dispersions measured from the optical spectra obtained with the Sloan Digital Sky Survey, we find that: (i) NLRGs cover about 4 decades of the Eddington ratio, $\lambda \equiv L_{bol}/L_{Edd} \propto L_{line}/M_{BH}$; (ii) $L_{1.4}/M_{BH}$ strongly correlates with $\lambda$; (iii) radio-loudness, ${\cal R} \equiv L_{1.4}/L_{line}$, strongly anti-correlates with $\lambda$. A very broad range of the Eddington ratio indicates that the parent population of NLRGs includes both radio-loud quasars (RLQs) and broad-line radio galaxies (BLRGs). The correlations they obey and their high jet production efficiencies favor a jet production model which involves the so-called 'magnetically choked' accretion scenario. In this model, production of the jet is dominated by the Blandford-Znajek mechanism, and the magnetic fields in the vicinity of the central black hole are confined by the ram pressure of the accretion flow. Since large net magnetic flux accumulated in central regions of the accretion flow required by the model can take place only via geometrically thick accretion, we speculate that the massive, 'cold' accretion events associated with luminous emission-line AGN can be accompanied by an efficient jet production only if preceded by a hot, very sub-Eddington accretion phase.Comment: 24 pages, 6 figures, published in ApJ, moderate revisions to match the published versio

Rare quantum metastable states in the strongly dispersive Jaynes-Cummings oscillator

We present evidence of metastable rare quantum-fluctuation switching for the driven dissipative Jaynes-Cummings oscillator coupled to a zero-temperature bath in the strongly dispersive regime. We show that single-atom complex amplitude bistability is accompanied by the appearance of a low-amplitude long-lived transient state, hereinafter called `dark state', having a distribution with quasi-Poissonian statistics both for the coupled qubit and cavity mode. We find that the dark state is linked to a spontaneous flipping of the qubit state, detuning the cavity to a low-photon response. The appearance of the dark state is correlated with the participation of the two metastable states in the dispersive bistability, as evidenced by the solution of the Master Equation and single quantum trajectories.Comment: Extensively revised text, 18 revised figures (16 in main and 2 in appendix), 38(+1) references, appendi

Retired galaxies: not to be forgotten in the quest of the star formation -- AGN connection

We propose a fresh look at the Main Galaxy Sample of the Sloan Digital Sky Survey by packing the galaxies in stellar mass and redshift bins. We show how important it is to consider the emission-line equivalent widths, in addition to the commonly used emission-line ratios, to properly identify retired galaxies (i.e. galaxies that have stopped forming stars and are ionized by their old stellar populations) and not mistake them for galaxies with low-level nuclear activity. We find that the proportion of star-forming galaxies decreases with decreasing redshift in each mass bin, while that of retired galaxies increases. Galaxies with $M_\star > 10^{11.5} M_\odot$ have formed all their stars at redshift larger than 0.4. The population of AGN hosts is never dominant for galaxy masses larger than $10^{10} M_\odot$. We warn about the effects of stacking galaxy spectra to discuss galaxy properties. We estimate the lifetimes of active galactic nuclei (AGN) relying entirely on demographic arguments --- i.e. without any assumption on the AGN radiative properties. We find upper-limit lifetimes of about 1--5 Gyr for detectable AGN in galaxies with masses between $10^{10}$--$10^{12} M_\odot$. The lifetimes of the AGN-dominated phases are a few $10^8$ yr. Finally, we compare the star-formation histories of star-forming, AGN and retired galaxies as obtained by the spectral synthesis code STARLIGHT. Once the AGN is turned on it inhibits star formation for the next $\sim$ 0.1 Gyr in galaxies with masses around $10^{10} M_\odot$, $\sim$ 1 Gyr in galaxies with masses around $10^{11} M_\odot$.Comment: accepted for MNRAS figure resolution has been degraded with respect to what will be published in MNRA

Person Identification from Streaming Surveillance Video using Mid-Level Features from Joint Action-Pose Distribution

We propose a real time person identification algorithm for surveillance based scenarios from low-resolution streaming video, based on mid-level features extracted from the joint distribution of various types of human actions and human poses. The proposed algorithm uses the combination of an auto-encoder based action association framework which produces per-frame probability estimates of the action being performed, and a pose recognition framework which gives per-frame body part locations. The main focus in this manuscript is to effectively combine these per-frame action probability estimates and pose trajectories from a short temporal window to obtain mid-level features. We demonstrate that these mid-level features captures the variation in the action performed with respect to an individual and can be used to distinguish one person from the next. Preliminary analysis on the KTH action dataset where each sequence is annotated with a specific person and a specific action is provided and shows some interesting results which verify this concept

Volume Component Analysis for Classification of LiDAR Data

One of the most difficult challenges of working with LiDAR data is the large amount of data points that are produced. Analysing these large data sets is an extremely time consuming process. For this reason, automatic perception of LiDAR scenes is a growing area of research. Currently, most LiDAR feature extraction relies on geometrical features specific to the point cloud of interest. These geometrical features are scene-specific, and often rely on the scale and orientation of the object for classification. This paper proposes a robust method for reduced dimensionality feature extraction of 3D objects using a volume component analysis (VCA) approach. This VCA approach is based on principal component analysis (PCA). PCA is a method of reduced feature extraction that computes a covariance matrix from the original input vector. The eigenvectors corresponding to the largest eigenvalues of the covariance matrix are used to describe an image. Block-based PCA is an adapted method for feature extraction in facial images because PCA, when performed in local areas of the image, can extract more significant features than can be extracted when the entire image is considered. The image space is split into several of these blocks, and PCA is computed individually for each block. This VCA proposes that a LiDAR point cloud can be represented as a series of voxels whose values correspond to the point density within that relative location. From this voxelized space, block-based PCA is used to analyze sections of the space where the sections, when combined, will represent features of the entire 3-D object. These features are then used as the input to a support vector machine which is trained to identify four classes of objects, vegetation, vehicles, buildings and barriers with an overall accuracy of 93.8%