We investigate the coevolution of galaxies and hosted supermassive black holes throughout the history of the Universe by a statistical approach based on the continuity equation and the abundance matching technique. Specifically, we present analytical solutions of the continuity equation without source term to reconstruct the supermassive black hole (BH) mass function from the AGN luminosity functions. Such an approach includes physically-motivated AGN lightcurves tested on independent datasets, which describe the evolution of the Eddington ratio and radiative efficiency from slim-to thin-disc conditions. We nicely reproduce the local estimates of the BH mass function, the AGN duty cycle as a function of mass and redshift, along with the Eddington ratio function and the fraction of galaxies with given stellar mass hosting an AGN with given Eddington ratio. We exploit the same approach to reconstruct the observed stellar mass function at different redshift from the UV and far-IR luminosity functions associated to star formation in galaxies. These results imply that the buildup of stars and BHs in galaxies occurs via in-situ processes, with dry mergers playing a marginal role at least for stellar masses 3 × 10 11 M ⊙ and BH masses 10 9 M ⊙ , where the statistical data are more secure and less biased by systematic errors. In addition, we develop an improved abundance matching technique to link the stellar and BH content of galaxies to the gravitationally dominant dark matter component. The resulting relationships constitute a testbed for galaxy evolution models, highlighting the complementary role of stellar and AGN feedback in the star formation process. In addition, they may be operationally implemented in numerical simulations to populate dark matter halos or to gauge subgrid physics. Moreover, they may be exploited to investigate the galaxy/AGN clustering as a function of redshift, mass and/or luminosity. In fact, the clustering properties of BHs and galaxies are found to be in full agreement with current observations, so further validating our results from the continuity equation. Finally, our analysis highlights that (i) the fraction of AGNs observed in slim-disc regime, where anyway most of the BH mass is accreted, increases with redshift; (ii) already at z 6 substantial dust amount must have formed over timescales 10 8 yr in strongly starforming galaxies, making these sources well within the reach of ALMA surveys in (sub-)millimeter bands.

We exploit the recent, wide samples of far-infrared (FIR) selected galaxies followed-up in X rays and of X-ray/optically selected active galactic nuclei (AGNs) followed-up in the FIR band, along with the classic data on AGN and stellar luminosity functions at high redshift z 1.5, to probe different stages in the coevolution of supermassive black holes (BHs) and host galaxies. The results of our analysis indicate the following scenario: (i) the star formation in the host galaxy proceeds within a heavily dust-enshrouded medium at an almost constant rate over a timescale 0.5 − 1 Gyr, and then abruptly declines due to quasar feedback; over the same timescale, (ii) part of the interstellar medium loses angular momentum, reaches the circum-nuclear regions at a rate proportional to the star formation and is temporarily stored into a massive reservoir/proto-torus wherefrom it can be promptly accreted; (iii) the BH grows by accretion in a self-regulated regime with radiative power that can slightly exceed the Eddington limit L/L Edd 4, particularly at the highest redshifts; (iv) for massive BHs the ensuing energy feedback at its maximum exceeds the stellar one and removes the interstellar gas, thus stopping the star formation and the fueling of the reservoir; (v) afterwards, if the latter has retained enough gas, a phase of supply-limited accretion follows exponentially declining with a timescale of about 2 e-folding times. We also discuss how the detailed properties and the specific evolution of the reservoir can be investigated via coordinated, high-resolution observations of starforming, strongly-lensed galaxies in the (sub-)mm band with ALMA and in the X-ray band with Chandra and the next generation X-ray instruments.

In this paper we applied transfer learning techniques for image recognition, automatic categorization, and labeling of nanoscience images obtained by scanning electron microscope (SEM). Roughly 20,000 SEM images were manually classified into 10 categories to form a labeled training set, which can be used as a reference set for future applications of deep learning enhanced algorithms in the nanoscience domain. The categories chosen spanned the range of 0-Dimensional (0D) objects such as particles, 1D nanowires and fibres, 2D films and coated surfaces, and 3D patterned surfaces such as pillars. The training set was used to retrain on the SEM dataset and to compare many convolutional neural network models (Inception-v3, Inception-v4, ResNet). We obtained compatible results by performing a feature extraction of the different models on the same dataset. We performed additional analysis of the classifier on a second test set to further investigate the results both on particular cases and from a statistical point of view. Our algorithm was able to successfully classify around 90% of a test dataset consisting of SEM images, while reduced accuracy was found in the case of images at the boundary between two categories or containing elements of multiple categories. In these cases, the image classification did not identify a predominant category with a high score. We used the statistical outcomes from testing to deploy a semi-automatic workflow able to classify and label images generated by the SEM. Finally, a separate training was performed to determine the volume fraction of coherently aligned nanowires in SEM images. The results were compared with what was obtained using the Local Gradient Orientation method. This example demonstrates the versatility and the potential of transfer learning to address specific tasks of interest in nanoscience applications.

Far-infrared and submillimeter wavelength surveys have now established the important role of dusty, star-forming galaxies (DSFGs) in the assembly of stellar mass and the evolution of massive galaxies in the Universe. The brightest of these galaxies have infrared luminosities in excess of 10 13 L with implied star-formation rates of thousands of solar masses per year. They represent the most intense starbursts in the Universe, yet many are completely optically obscured. Their easy detection at submm wavelengths is due to dust heated by ultraviolet radiation of newly forming stars. When summed up, all of the dusty, star-forming galaxies in the Universe produce an infrared radiation field that has an equal energy density as the direct starlight emission from all galaxies visible at ultraviolet and optical wavelengths. The bulk of this infrared extragalactic background light emanates from galaxies as diverse as gas-rich disks to mergers of intense starbursting galaxies. Major advances in far-infrared instrumentation in recent years, both space-based and ground-based, has led to the detection of nearly a million DSFGs, yet our understanding of the underlying astrophysics that govern the start and end of the dusty starburst phase is still in nascent stage. This review is aimed at summarizing the current status of DSFG studies, focusing especially on the detailed characterization of the best-understood subset (submillimeter galaxies, who were summarized in the last review of this field over a decade ago, Blain et al. 2002), but also the selection and characterization of more recently discovered DSFG populations. We review DSFG population statistics, their physical properties including dust, gas and stellar contents, their environments, and current theoretical models related to the formation and evolution of these galaxies.