A close quasar pair in a disk-disk galaxy merger at z = 2.17.

Galaxy mergers produce pairs of supermassive black holes (SMBHs), which may be witnessed as dual quasars if both SMBHs are rapidly accreting. The kiloparsec (kpc)-scale separation represents a physical regime sufficiently close for merger-induced effects to be important1 yet wide enough to be directly resolvable with the facilities currently available. Whereas many kpc-scale, dual active galactic nuclei-the low-luminosity counterparts of quasars-have been observed in low-redshift mergers2, no unambiguous dual quasar is known at cosmic noon (z ≈ 2), the peak of global star formation and quasar activity3,4. Here we report multiwavelength observations of Sloan Digital Sky Survey (SDSS) J0749 + 2255 as a kpc-scale, dual-quasar system hosted by a galaxy merger at cosmic noon (z = 2.17). We discover extended host galaxies associated with the much brighter compact quasar nuclei (separated by 0.46″ or 3.8 kpc) and low-surface-brightness tidal features as evidence for galactic interactions. Unlike its low-redshift and low-luminosity counterparts, SDSS J0749 + 2255 is hosted by massive compact disk-dominated galaxies. The apparent lack of stellar bulges and the fact that SDSS J0749 + 2255 already follows the local SMBH mass-host stellar mass relation, suggest that at least some SMBHs may have formed before their host stellar bulges. While still at kpc-scale separations where the host-galaxy gravitational potential dominates, the two SMBHs may evolve into a gravitationally bound binary system in around 0.22 Gyr.

Asymmetric Relatedness from Partial Correlation.

Relatedness is a key concept in economic complexity, since the assessment of the similarity between industrial sectors enables policymakers to design optimal development strategies. However, among the different ways to quantify relatedness, a measure that takes explicitly into account the time correlation structure of exports is still lacking. In this paper, we introduce an asymmetric definition of relatedness by using statistically significant partial correlations between the exports of economic sectors and we apply it to a recently introduced database that integrates the export of physical goods with the export of services. Our asymmetric relatedness is obtained by generalising a recently introduced correlation-filtering algorithm, the partial correlation planar graph, in order to allow its application on multi-sample and multi-variate datasets, and in particular, bipartite temporal networks. The result is a network of economic activities whose links represent the respective influence in terms of temporal correlations; we also compute the statistical confidence of the edges in the network via an adapted bootstrapping procedure. We find that the underlying influence structure of the system leads to the formation of intuitively-related clusters of economic sectors in the network, and to a relatively strong assortative mixing of sectors according to their complexity. Moreover, hub nodes tend to form more robust connections than those in the periphery.

AI-assisted superresolution cosmological simulations.

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to [Formula: see text] and the HR halo mass function to within [Formula: see text] down to [Formula: see text] We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

Complexity of Products: The Effect of Data Regularisation.

Among several developments, the field of Economic Complexity (EC) has notably seen the introduction of two new techniques. One is the Bootstrapped Selective Predictability Scheme (SPSb), which can provide quantitative forecasts of the Gross Domestic Product of countries. The other, Hidden Markov Model (HMM) regularisation, denoises the datasets typically employed in the literature. We contribute to EC along three different directions. First, we prove the convergence of the SPSb algorithm to a well-known statistical learning technique known as Nadaraya-Watson Kernel regression. The latter has significantly lower time complexity, produces deterministic results, and it is interchangeable with SPSb for the purpose of making predictions. Second, we study the effects of HMM regularization on the Product Complexity and logPRODY metrics, for which a model of time evolution has been recently proposed. We find confirmation for the original interpretation of the logPRODY model as describing the change in the global market structure of products with new insights allowing a new interpretation of the Complexity measure, for which we propose a modification. Third, we explore new effects of regularisation on the data. We find that it reduces noise, and observe for the first time that it increases nestedness in the export network adjacency matrix.

Energy input from quasars regulates the growth and activity of black holes and their host galaxies.

In the early Universe, while galaxies were still forming, black holes as massive as a billion solar masses powered quasars. Supermassive black holes are found at the centres of most galaxies today, where their masses are related to the velocity dispersions of stars in their host galaxies and hence to the mass of the central bulge of the galaxy. This suggests a link between the growth of the black holes and their host galaxies, which has indeed been assumed for a number of years. But the origin of the observed relation between black hole mass and stellar velocity dispersion, and its connection with the evolution of galaxies, have remained unclear. Here we report simulations that simultaneously follow star formation and the growth of black holes during galaxy-galaxy collisions. We find that, in addition to generating a burst of star formation, a merger leads to strong inflows that feed gas to the supermassive black hole and thereby power the quasar. The energy released by the quasar expels enough gas to quench both star formation and further black hole growth. This determines the lifetime of the quasar phase (approaching 100 million years) and explains the relationship between the black hole mass and the stellar velocity dispersion.