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We investigate the black hole mass function at $z\sim5$ using XQz5, our recent sample of the most luminous quasars between the redshifts $4.5 < z < 5.3$. We include 72 quasars with black hole masses estimated from velocity-broadened emission-line measurements and single-epoch virial prescriptions in the footprint of a highly complete parent survey. The sample mean Eddington ratio and standard deviation is $\log\lambda \approx -0.20\pm0.24$. The completeness-corrected mass function is modelled as a double power-law, and we constrain its evolution across redshift assuming accretion-dominated mass growth. We estimate the evolution of the mass function from $z=5-4$, presenting joint constraints on accretion properties through a measured dimensionless e-folding parameter, $k_{\rm{ef}} \equiv \langle\lambda\rangle U (1-\epsilon)/\epsilon = 1.79\pm0.06$, where $\langle\lambda\rangle$ is the mean Eddington ratio, $U$ is the duty cycle, and $\epsilon$ is the radiative efficiency. If these supermassive black holes were to form from seeds smaller than $10^8\,M_{\odot}$, the growth rate must have been considerably faster at $z\gg5$ than observed from $z=5-4$. A growth rate exceeding $3\times$ the observed rate would reduce the initial heavy seed mass to $10^{5-6}\,M_{\odot}$, aligning with supermassive star and/or direct collapse seed masses. Stellar mass ($10^2\,M_{\odot}$) black hole seeds would require $\gtrsim4.5\times$ the observed growth rate at $z\gg5$ to reproduce the measured active black hole mass function. A possible pathway to produce the most extreme quasars is radiatively inefficient accretion flow, suggesting black holes with low angular momentum or photon trapping in supercritically accreting thick discs., Comment: 17 pages, 8 figures, 4 tables, accepted for publication by MNRAS

We consider impulse response inference in a locally misspecified vector autoregression (VAR) model. The conventional local projection (LP) confidence interval has correct coverage even when the misspecification is so large that it can be detected with probability approaching 1. This result follows from a "double robustness" property analogous to that of popular partially linear regression estimators. In contrast, the conventional VAR confidence interval with short-to-moderate lag length can severely undercover, even for misspecification that is small, economically plausible, and difficult to detect statistically. There is no free lunch: the VAR confidence interval has robust coverage only if the lag length is so large that the interval is as wide as the LP interval.

Around a million quasars have been catalogued in the Universe by probing deeper and using new methods for discovery. However, the hardest ones to find seem to be the rarest and brightest specimen. In this work, we study the properties of the most luminous of all quasars found so far. It has been overlooked until recently, which demonstrates that modern all-sky surveys have much to reveal. The black hole in this quasar accretes around one solar mass per day onto an existing mass of $\sim$17 billion solar masses. In this process its accretion disc alone releases a radiative energy of $2\times 10^{41}$ Watts. If the quasar is not strongly gravitationally lensed, then its broad line region (BLR) is expected to have the largest physical and angular diameter occurring in the Universe, and will allow the Very Large Telescope Interferometer to image its rotation and measure its black hole mass directly. This will be an important test for BLR size-luminosity relations, whose extrapolation has underpinned common black-hole mass estimates at high redshift., Comment: This version of the article has been accepted for publication after peer review and is subject to Springer Nature's AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at http://dx.doi.org/10.1038/s41550-024-02195-x

Bird's-eye view (BEV) maps are an important geometrically structured representation widely used in robotics, in particular self-driving vehicles and terrestrial robots. Existing algorithms either require depth information for the geometric projection, which is not always reliably available, or are trained end-to-end in a fully supervised way to map visual first-person observations to BEV representation, and are therefore restricted to the output modality they have been trained for. In contrast, we propose a new model capable of performing zero-shot projections of any modality available in a first person view to the corresponding BEV map. This is achieved by disentangling the geometric inverse perspective projection from the modality transformation, eg. RGB to occupancy. The method is general and we showcase experiments projecting to BEV three different modalities: semantic segmentation, motion vectors and object bounding boxes detected in first person. We experimentally show that the model outperforms competing methods, in particular the widely used baseline resorting to monocular depth estimation.

Successfully addressing a wide variety of tasks is a core ability of autonomous agents, requiring flexibly adapting the underlying decision-making strategies and, as we argue in this work, also adapting the perception modules. An analogical argument would be the human visual system, which uses top-down signals to focus attention determined by the current task. Similarly, we adapt pre-trained large vision models conditioned on specific downstream tasks in the context of multi-task policy learning. We introduce task-conditioned adapters that do not require finetuning any pre-trained weights, combined with a single policy trained with behavior cloning and capable of addressing multiple tasks. We condition the visual adapters on task embeddings, which can be selected at inference if the task is known, or alternatively inferred from a set of example demonstrations. To this end, we propose a new optimization-based estimator. We evaluate the method on a wide variety of tasks from the CortexBench benchmark and show that, compared to existing work, it can be addressed with a single policy. In particular, we demonstrate that adapting visual features is a key design choice and that the method generalizes to unseen tasks given a few demonstrations.

We present the fourth data release (DR4) of the SkyMapper Southern Survey (SMSS), the last major step in our hemispheric survey with six optical filters: u, v, g, r, i, z. SMSS DR4 covers 26,000 sq.deg from over 400,000 images acquired by the 1.3m SkyMapper telescope between 2014-03 and 2021-09. The 6-band sky coverage extends from the South Celestial Pole to Dec = +16deg, with some images reaching Dec ~ +28deg. In contrast to previous DRs, we include all good-quality images from the facility taken during that time span, not only those explicitly taken for the public Survey. From the image dataset, we produce a catalogue of nearly 13 billion detections made from ~700 million unique astrophysical objects. The typical 10sigma depths for each field range between 18.5 and 20.5 mag, depending on the filter, but certain sky regions include longer exposures that reach as deep as 22 mag in some filters. As with previous SMSS catalogues, we have cross-matched with a host of other imaging and spectroscopic datasets to facilitate additional science outcomes. SMSS DR4 is now available to the worldwide astronomical community., Comment: 32 pages. Submitted to the Publications of the Astronomical Society of Australia

In the context of autonomous navigation of terrestrial robots, the creation of realistic models for agent dynamics and sensing is a widespread habit in the robotics literature and in commercial applications, where they are used for model based control and/or for localization and mapping. The more recent Embodied AI literature, on the other hand, focuses on modular or end-to-end agents trained in simulators like Habitat or AI-Thor, where the emphasis is put on photo-realistic rendering and scene diversity, but high-fidelity robot motion is assigned a less privileged role. The resulting sim2real gap significantly impacts transfer of the trained models to real robotic platforms. In this work we explore end-to-end training of agents in simulation in settings which minimize the sim2real gap both, in sensing and in actuation. Our agent directly predicts (discretized) velocity commands, which are maintained through closed-loop control in the real robot. The behavior of the real robot (including the underlying low-level controller) is identified and simulated in a modified Habitat simulator. Noise models for odometry and localization further contribute in lowering the sim2real gap. We evaluate on real navigation scenarios, explore different localization and point goal calculation methods and report significant gains in performance and robustness compared to prior work.

Navigation has been classically solved in robotics through the combination of SLAM and planning. More recently, beyond waypoint planning, problems involving significant components of (visual) high-level reasoning have been explored in simulated environments, mostly addressed with large-scale machine learning, in particular RL, offline-RL or imitation learning. These methods require the agent to learn various skills like local planning, mapping objects and querying the learned spatial representations. In contrast to simpler tasks like waypoint planning (PointGoal), for these more complex tasks the current state-of-the-art models have been thoroughly evaluated in simulation but, to our best knowledge, not yet in real environments. In this work we focus on sim2real transfer. We target the challenging Multi-Object Navigation (Multi-ON) task and port it to a physical environment containing real replicas of the originally virtual Multi-ON objects. We introduce a hybrid navigation method, which decomposes the problem into two different skills: (1) waypoint navigation is addressed with classical SLAM combined with a symbolic planner, whereas (2) exploration, semantic mapping and goal retrieval are dealt with deep neural networks trained with a combination of supervised learning and RL. We show the advantages of this approach compared to end-to-end methods both in simulation and a real environment and outperform the SOTA for this task.

We report the discovery of a new Blue Large-Amplitude Pulsator, SMSS J184506-300804 (SMSS-BLAP-1) in Data Release 2 of the SkyMapper Southern Sky Survey. We conduct high-cadence photometric observations in the $u$ band to confirm a periodic modulation of the lightcurve. SMSS-BLAP-1 has a ~19-min pulsation period with an amplitude of 0.2 mag in $u$ band, and is similar to the classical BLAPs found by OGLE. From spectroscopic observations with the Wide-Field Spectrograph on the ANU 2.3m telescope, we confirm it as a low-gravity BLAP: best-fit parameters from the non-LTE Tlusty model are estimated as $T_\mathrm{eff}$ = 29,020$^{+193}_{-34}$ K, $\log g$ = 4.661$^{+0.008}_{-0.143}$ (cm s$^{-2}$), and $\log$ n(He)/n(H) = -2.722$^{+0.057}_{-0.074}$ dex. However, our BLAP exhibits a very He-deficient atmosphere compared to both low- and high-gravity BLAPs, which have $\log$ n(He)/n(H) in the range -0.41~-2.4., Comment: 9 pages, 8 figures, Accepted for publication in MNRAS

The inverse transfer of magnetic helicity is a fundamental process which may explain large scale magnetic structure formation and sustainement. Until very recently, direct numerical simulations (DNS) of the inverse transfer in magnetohydrodynamics (MHD) turbulence have been done in incompressible MHD or at low Mach numbers only. We review first results obtained through DNS of the isothermal MHD equations at Mach numbers ranging from subsonic to about 10. The spectral exponent of the magnetic helicity spectrum becomes flatter with increasing compressibility. When considering the Alfv\'en velocity in place of the magnetic field however, results found in incompressible MHD, including a dynamic balance between shear and twist, can be extended to supersonic MHD. In the global picture of an inverse transfer of magnetic helicity, three phenomena are at work: a local direct transfer mediated by the large scale velocity field, a local inverse transfer mediated by the intermediate scale velocity field and a nonlocal inverse transfer mediated by the small scale velocity field. The compressive part of the velocity field is geometrically favored in the local direct transfer and contributes to the nonlocal inverse transfer, but plays no role in the local inverse transfer., Comment: Chapter in Helicities in Geophysics, Astrophysics and Beyond (AGU Books, Wiley, 2023 or 2024)

Bright quasar samples at high redshift are useful for investigating active galactic nuclei evolution. In this study, we describe XQz5, a sample of 83 ultraluminous quasars in the redshift range $4.5 < z < 5.3$ with optical and near-infrared spectroscopic observations, with unprecendented completeness at the bright end of the quasar luminosity function. The sample is observed with the Southern Astrophysical Research Telescope, the Very Large Telescope, and the ANU 2.3m Telescope, resulting in a high-quality, moderate-resolution spectral atlas of the brightest known quasars within the redshift range. We use established virial mass relations to derive the black hole masses by measuring the observed Mg\,\textsc{ii}$\lambda$2799\AA\ emission-line and we estimate the bolometric luminosity with bolometric corrections to the UV continuum. Comparisons to literature samples show that XQz5 bridges the redshift gap between other X-shooter quasar samples, XQ-100 and XQR-30, and is a brighter sample than both. Luminosity-matched lower-redshift samples host more massive black holes, which indicate that quasars at high redshift are more active than their counterparts at lower-redshift, in concordance with recent literature., Comment: 20 pages, 8 figures, 3 tables, accepted for publication

In this paper we present an introduction to the area of computability in dynamical systems. This is a fairly new field which has received quite some attention in recent years. One of the central questions in this area is if relevant dynamical objects can be algorithmically presented by a Turing machine. After providing an overview of the relevant objects we discuss recent results concerning the computability of the entropy for symbolic systems and the computability of Julia sets as well as their Brolin-Lyubich measures., Comment: 13 pages, 4 figures

We study ergodic-theoretic properties of coded shift spaces. A coded shift space is defined as a closure of all bi-infinite concatenations of words from a fixed countable generating set. We derive sufficient conditions for the uniqueness of measures of maximal entropy and equilibrium states of Hoelder continuous potentials based on the partition of the coded shift into its concatenation set (sequences that are concatenations of generating words) and its residual set (sequences added under the closure). In this case we provide a simple explicit description of the measure of maximal entropy. We also obtain flexibility results for the entropy on the concatenation and residual sets. Finally, we prove a local structure theorem for intrinsically ergodic coded shift spaces which shows that our results apply to a larger class of coded shift spaces compared to previous works by Climenhaga, Climenhaga and Thompson, and Pavlov., Comment: 42 pages

A complete and pure sample of quasars with accurate redshifts is crucial for quasar studies and cosmology. In this paper, we present CatNorth, an improved Gaia DR3 quasar candidate catalog with more than 1.5 million sources in the 3$\pi$ sky built with data from Gaia, Pan-STARRS1, and CatWISE2020. The XGBoost algorithm is used to reclassify the original Gaia DR3 quasar candidates as stars, galaxies, and quasars. To construct training/validation datasets for the classification, we carefully built two different master stellar samples in addition to the spectroscopic galaxy and quasar samples. An ensemble classification model is obtained by averaging two XGBoost classifiers trained with different master stellar samples. Using a probability threshold of $p_{\mathrm{QSO\_mean}}>0.95$ in our ensemble classification model and an additional cut on the logarithmic probability density of zero proper motion, we retrieved 1,545,514 reliable quasar candidates from the parent Gaia DR3 quasar candidate catalog. We provide photometric redshifts for all candidates with an ensemble regression model. For a subset of 89,100 candidates, accurate spectroscopic redshifts are estimated with the Convolutional Neural Network from the Gaia BP/RP spectra. The CatNorth catalog has a high purity of ~ 90% while maintaining high completeness, which is an ideal sample to understand the quasar population and its statistical properties. The CatNorth catalog is used as the main source of input catalog for the LAMOST phase III quasar survey, which is expected to build a highly complete sample of bright quasars with $i < 19.5$., Comment: 27 pages, 15 figures, accepted by ApJS. Table 4 (The CatNorth quasar candidate catalog) is available at https://nadc.china-vo.org/res/r101313/

The black hole (BH) mass and luminosity are key factors in determining how a quasar interacts with its environment. In this study, we utilise data from the European Southern Observatory Large Programme XQ-100, a high-quality sample of 100 X-shooter spectra of the most luminous quasars in the redshift range $3.5 < z < 4.5$, and measure the properties of three prominent optical and ultraviolet broad emission-lines present in the wide wavelength coverage of X-shooter: CIV, MgII, and H$\beta$. The line properties of all three broad lines are used for virial estimates of the BH mass and their resulting mass estimates for this sample are tightly correlated. The BH mass range is $\log{(\rm{M_{BH}}/\rm{M_\odot})} = 8.6-10.3$ with bolometric luminosities estimated from the 3000A continuum in the range $\log{(\rm{L_{bol}}/\rm{erg\,s^{-1}})} = 46.7-48.0$. Robustly determined properties of these quasars enable a variety of follow-up research in quasar astrophysics, from chemical abundance and evolution in the broad-line region to radiatively driven quasar outflows., Comment: 18 pages, 9 figures, 4 tables

Most recent work in goal oriented visual navigation resorts to large-scale machine learning in simulated environments. The main challenge lies in learning compact representations generalizable to unseen environments and in learning high-capacity perception modules capable of reasoning on high-dimensional input. The latter is particularly difficult when the goal is not given as a category ("ObjectNav") but as an exemplar image ("ImageNav"), as the perception module needs to learn a comparison strategy requiring to solve an underlying visual correspondence problem. This has been shown to be difficult from reward alone or with standard auxiliary tasks. We address this problem through a sequence of two pretext tasks, which serve as a prior for what we argue is one of the main bottleneck in perception, extremely wide-baseline relative pose estimation and visibility prediction in complex scenes. The first pretext task, cross-view completion is a proxy for the underlying visual correspondence problem, while the second task addresses goal detection and finding directly. We propose a new dual encoder with a large-capacity binocular ViT model and show that correspondence solutions naturally emerge from the training signals. Experiments show significant improvements and SOTA performance on the two benchmarks, ImageNav and the Instance-ImageNav variant, where camera intrinsics and height differ between observation and goal.

Navigation of terrestrial robots is typically addressed either with localization and mapping (SLAM) followed by classical planning on the dynamically created maps, or by machine learning (ML), often through end-to-end training with reinforcement learning (RL) or imitation learning (IL). Recently, modular designs have achieved promising results, and hybrid algorithms that combine ML with classical planning have been proposed. Existing methods implement these combinations with hand-crafted functions, which cannot fully exploit the complementary nature of the policies and the complex regularities between scene structure and planning performance. Our work builds on the hypothesis that the strengths and weaknesses of neural planners and classical planners follow some regularities, which can be learned from training data, in particular from interactions. This is grounded on the assumption that, both, trained planners and the mapping algorithms underlying classical planning are subject to failure cases depending on the semantics of the scene and that this dependence is learnable: for instance, certain areas, objects or scene structures can be reconstructed easier than others. We propose a hierarchical method composed of a high-level planner dynamically switching between a classical and a neural planner. We fully train all neural policies in simulation and evaluate the method in both simulation and real experiments with a LoCoBot robot, showing significant gains in performance, in particular in the real environment. We also qualitatively conjecture on the nature of data regularities exploited by the high-level planner., Comment: 8 pages including references. International Conference on Intelligent Robots and Systems (IROS 2023)

We present precise photometric estimates of stellar parameters, including effective temperature, metallicity, luminosity classification, distance, and stellar age, for nearly 26 million stars using the methodology developed in the first paper of this series, based on the stellar colors from the Stellar Abundances and Galactic Evolution Survey (SAGES) DR1 and Gaia EDR3. The optimal design of stellar-parameter sensitive $uv$ filters by SAGES has enabled us to determine photometric-metallicity estimates down to $-3.5$, similar to our previous results with the SkyMapper Southern Survey (SMSS), yielding a large sample of over five million metal-poor (MP; [Fe/H]$\le -1.0$) stars and nearly one million very metal-poor (VMP; [Fe/H]$\le -2.0$) stars. The typical precision is around $0.1$ dex for both dwarf and giant stars with [Fe/H]$>-1.0$, and 0.15-0.25/0.3-0.4 dex for dwarf/giant stars with [Fe/H]$<-1.0$. Using the precise parallax measurements and stellar colors from Gaia, effective temperature, luminosity classification, distance and stellar age are further derived for our sample stars. This huge data set in the Northern sky from SAGES, together with similar data in the Southern sky from SMSS, will greatly advance our understanding of the Milky Way, in particular its formation and evolution., Comment: 14 pages, 14 figures, 3 tables, accepted by ApJ. arXiv admin note: text overlap with arXiv:2104.14154

We present an efficient dimension-by-dimension finite-volume method which solves the adiabatic magnetohydrodynamics equations at high discretization order, using the constrained-transport approach on Cartesian grids. Results are presented up to tenth order of accuracy. This method requires only one reconstructed value per face for each computational cell. A passage through high-order point values leads to a modest growth of computational cost with increasing discretization order. At a given resolution, these high-order schemes present significantly less numerical dissipation than commonly employed lower-order approaches. Thus, results of comparable accuracy are achievable at a substantially coarser resolution, yielding overall performance gains. We also present a way to include physical dissipative terms: viscosity, magnetic diffusivity and cooling functions, respecting the finite-volume and constrained-transport frameworks., Comment: Submitted to JCP. In version 1, the use of a global smoothness indicator: mass density+average of magnetic field components, suitable in supersonic flows, lead to difficulties in the convergence test (MHD vortex) and spurious oscillations for S6 and S8 in the hydro case. Version 2 uses individual smoothness indicators

Given the anisotropic emission from quasar accretion discs, their viewing angle affects estimates of the quasar luminosity, black-hole mass and Eddington ratio. Discs appear overluminous when viewed pole-on and underluminous when viewed at high inclination. In radio-quiet quasars, the viewing angle is usually unknown, although spectroscopic indicators have been proposed. Here, we use a recently discovered universality in the variability structure function (SF) of quasar light curves (LCs), where all quasars show the same SF when clocks run in units of orbital timescale. As an offset from the mean relation can be caused by incorrect orbital timescales and thus incorrect luminosities, we correlate these offsets with suggested inclination indicators. We derive SFs from NASA/ATLAS LCs spanning $\sim 6$ years of observation, using a sample of 183 luminous quasars with measured H$\beta$ lines as well as 753 quasars with CIV and MgII lines. Starting from the proposed orientation indicators, we expect quasars with narrower H$\beta$ lines and with more blueshifted CIV lines to be viewed more pole-on and thus appear overluminous. In contrast, our SF analysis finds that presumed pole-on discs appear underluminous, consistently for both line indicators. We discuss possible explanations for the behaviour of quasars with highly blueshifted CIV lines irrespective of inclination angle, including dusty outflows that might render the accretion disc underluminous and flatter disc temperature profiles with longer orbital timescales than in thin-disc models but reach no satisfying conclusion., Comment: 15 pages, 10 figures, Accepted for publication in MNRAS

Agents navigating in 3D environments require some form of memory, which should hold a compact and actionable representation of the history of observations useful for decision taking and planning. In most end-to-end learning approaches the representation is latent and usually does not have a clearly defined interpretation, whereas classical robotics addresses this with scene reconstruction resulting in some form of map, usually estimated with geometry and sensor models and/or learning. In this work we propose to learn an actionable representation of the scene independently of the targeted downstream task and without explicitly optimizing reconstruction. The learned representation is optimized by a blind auxiliary agent trained to navigate with it on multiple short sub episodes branching out from a waypoint and, most importantly, without any direct visual observation. We argue and show that the blindness property is important and forces the (trained) latent representation to be the only means for planning. With probing experiments we show that the learned representation optimizes navigability and not reconstruction. On downstream tasks we show that it is robust to changes in distribution, in particular the sim2real gap, which we evaluate with a real physical robot in a real office building, significantly improving performance.

Implicit representations such as Neural Radiance Fields (NeRF) have been shown to be very effective at novel view synthesis. However, these models typically require manual and careful human data collection for training. In this paper, we present AutoNeRF, a method to collect data required to train NeRFs using autonomous embodied agents. Our method allows an agent to explore an unseen environment efficiently and use the experience to build an implicit map representation autonomously. We compare the impact of different exploration strategies including handcrafted frontier-based exploration, end-to-end and modular approaches composed of trained high-level planners and classical low-level path followers. We train these models with different reward functions tailored to this problem and evaluate the quality of the learned representations on four different downstream tasks: classical viewpoint rendering, map reconstruction, planning, and pose refinement. Empirical results show that NeRFs can be trained on actively collected data using just a single episode of experience in an unseen environment, and can be used for several downstream robotic tasks, and that modular trained exploration models outperform other classical and end-to-end baselines. Finally, we show that AutoNeRF can reconstruct large-scale scenes, and is thus a useful tool to perform scene-specific adaptation as the produced 3D environment models can be loaded into a simulator to fine-tune a policy of interest.

The microscopic structure of two amorphous materials with extreme nonlinear optical properties has been studied. One of these materials exhibits second harmonic generation, while another material of similar molecular structure emits brilliant white light if being irradiated with a simple IR laser diode. Structural differences were investigated using X-ray scattering and EXAFS combined with molecular RMC. Transmission electron microscopy and scanning precession electron diffraction were used to understand specific structural differences on all length scales, from mesoscopic down to mutual molecular arrangements. Characteristic differences were found at all scales. Close core-core spacing between {SnS} clusters as well as characteristic cluster distortions appear to be characteristic features of the white light emitting material. In the other material, cores are undistorted and core distances are larger. There, the formation of nanocrystalline structures in the amorphous matrix could also be identified as reason for the WLG suppression., Comment: 12 pages, 8 figures

We develop an accretion disc (AD) fitting method, utilising thin and slim disc models and Bayesian inference with the Markov-Chain Monte-Carlo approach, testing it on the most luminous known quasar, SMSS J215728.21-360215.1, at redshift $z=4.692$. With a spectral energy distribution constructed from near-infrared spectra and broadband photometry, the AD models find a black hole mass of $\log(M_{\rm{AD}}/M_{\odot}) = 10.31^{+0.17}_{-0.14}$ with an anisotropy-corrected bolometric luminosity of $\log{(L_{\rm{bol}}/\rm{erg\,s^{-1}})} = 47.87 \pm 0.10$, and derive an Eddington ratio of $0.29^{+0.11}_{-0.10}$ as well as a radiative efficiency of $0.09^{+0.05}_{-0.03}$. Using the near-infrared spectra, we estimate the single-epoch virial black hole mass estimate to be $\log(M_{\rm{SE}}/M_{\odot}) = 10.33 \pm 0.08$, with a monochromatic luminosity at 3000\AA\ of $\log{(L(\rm{3000\text{\AA}})/\rm{erg\,s^{-1}})} = 47.66 \pm 0.01$. As an independent approach, AD fitting has the potential to complement the single-epoch virial mass method in obtaining stronger constraints on properties of massive quasar black holes across a wide range of redshifts., Comment: 18 pages, 7 figures, 3 tables

Estimating fluid dynamics is classically done through the simulation and integration of numerical models solving the Navier-Stokes equations, which is computationally complex and time-consuming even on high-end hardware. This is a notoriously hard problem to solve, which has recently been addressed with machine learning, in particular graph neural networks (GNN) and variants trained and evaluated on datasets of static objects in static scenes with fixed geometry. We attempt to go beyond existing work in complexity and introduce a new model, method and benchmark. We propose EAGLE, a large-scale dataset of 1.1 million 2D meshes resulting from simulations of unsteady fluid dynamics caused by a moving flow source interacting with nonlinear scene structure, comprised of 600 different scenes of three different types. To perform future forecasting of pressure and velocity on the challenging EAGLE dataset, we introduce a new mesh transformer. It leverages node clustering, graph pooling and global attention to learn long-range dependencies between spatially distant data points without needing a large number of iterations, as existing GNN methods do. We show that our transformer outperforms state-of-the-art performance on, both, existing synthetic and real datasets and on EAGLE. Finally, we highlight that our approach learns to attend to airflow, integrating complex information in a single iteration., Comment: Published as a conference paper at ICLR 2023

Rapidly growing black holes are surrounded by accretion disks that make them the brightest objects in the Universe. Their brightness is known to be variable, but the causes of this are not implied by simple disk models and still debated. Due to the small size of accretion disks and their great distance, there are no resolved images addressing the puzzle. In this work, we study the dependence of their variability on luminosity, wavelength and orbital/thermal timescale. We use over 5,000 of the most luminous such objects with light curves of almost nightly cadence from $>5$ years of observations by the NASA/ATLAS project, which provides 2 billion magnitude pairs for a structure function analysis. When time is expressed in units of orbital or thermal time scale in thin-disk models, we find a universal structure function, independent of luminosity and wavelength, supporting the model of magneto-rotational instabilities as a main cause. Over a $>1$~dex range in time, the fractional variability amplitude follows $\log (A/A_0) \simeq 1/2 \times \log (\Delta t/t_{\rm th})$. Deviations from the universality may hold clues on the structure and orientation of disks., Comment: 21 pages, 5 figures

Understanding and mapping a new environment are core abilities of any autonomously navigating agent. While classical robotics usually estimates maps in a stand-alone manner with SLAM variants, which maintain a topological or metric representation, end-to-end learning of navigation keeps some form of memory in a neural network. Networks are typically imbued with inductive biases, which can range from vectorial representations to birds-eye metric tensors or topological structures. In this work, we propose to structure neural networks with two neural implicit representations, which are learned dynamically during each episode and map the content of the scene: (i) the Semantic Finder predicts the position of a previously seen queried object; (ii) the Occupancy and Exploration Implicit Representation encapsulates information about explored area and obstacles, and is queried with a novel global read mechanism which directly maps from function space to a usable embedding space. Both representations are leveraged by an agent trained with Reinforcement Learning (RL) and learned online during each episode. We evaluate the agent on Multi-Object Navigation and show the high impact of using neural implicit representations as a memory source.

While the distribution patterns of cold-water corals, such as Paragorgia arborea, have received increasing attention in recent studies, little is known about their in situ activity patterns. In this paper, we examine polyp activity in P. arborea using machine learning techniques to analyze high-resolution time series data and photographs obtained from an autonomous lander cluster deployed in the Stjernsund, Norway. An interactive illustration of the models derived in this paper is provided online as supplementary material. We find that the best predictor of the degree of extension of the coral polyps is current direction with a lag of three hours. Other variables that are not directly associated with water currents, such as temperature and salinity, offer much less information concerning polyp activity. Interestingly, the degree of polyp extension can be predicted more reliably by sampling the laminar flows in the water column above the measurement site than by sampling the more turbulent flows in the direct vicinity of the corals. Our results show that the activity patterns of the P. arborea polyps are governed by the strong tidal current regime of the Stjernsund. It appears that P. arborea does not react to shorter changes in the ambient current regime but instead adjusts its behavior in accordance with the large-scale pattern of the tidal cycle itself in order to optimize nutrient uptake., Comment: 25 pages

We study the relationship between Galactic location ($R, Z$) and photometric activity for 3.6 million M dwarf stars within 1 kpc of the Sun. For this purpose, we identify 906 unique flare events as a proxy for magnetic activity from the SkyMapper Southern Survey DR3. We adopt vertical distance $|Z|$ from the Galactic disc as a proxy for age and confirm a strong trend of flaring fraction decreasing with growing stellar age. Among M dwarfs within 50 pc of the Sun, we find a flaring fraction of 1-in-1,500, independent of spectral type from M2 to M7, suggesting that these stars are all in a flare-saturated young evolutionary stage. We find a hint of a kink in the slope of the overall flare fraction near 100 pc from the plane, where a steep decline begins; this slope change is visible for mid-type M dwarfs (M3--M5), suggesting it is not an artefact of mixing spectral type. Together with SDSS H$\alpha$ emission, this trend is additional evidence that the activity fraction of M dwarfs depends on Galactic height and activity lifetime. While there is a hint of flattening of the overall activity fraction above $|Z|\approx$ 500 pc, our data do not constrain this further. Within $\sim$500 pc distance from the Sun, we find no sign of radial disk gradients in flare activity, which may only be revealed by samples covering a larger radial range., Comment: 13 pages, 11 figures, Accepted for publication in MNRAS

We describe the first results from the All-sky BRIght, Complete Quasar Survey (AllBRICQS), which aims to discover the last remaining optically bright quasars. We present 156 spectroscopically confirmed quasars (140 newly identified) having |b|>10deg. 152 of the quasars have Gaia DR3 magnitudes brighter than B_P=16.5 or R_P=16 mag, while four are slightly fainter. The quasars span a redshift range of z=0.07-3.93. In particular, we highlight the properties of J0529-4351 at z=3.93, which, if unlensed, is one of the most intrinsically luminous quasars in the Universe. The AllBRICQS sources have been selected by combining data from the Gaia and WISE all-sky satellite missions, and we successfully identify quasars not flagged as candidates by Gaia Data Release 3. We expect the completeness to be approximately 96% within our magnitude and latitude limits, while the preliminary results indicate a selection purity of approximately 96%. The optical spectroscopy used for source classification will also enable detailed quasar characterisation, including black hole mass measurements and identification of foreground absorption systems. The AllBRICQS sources will greatly enhance the number of quasars available for high-signal-to-noise follow-up with present and future facilities., Comment: 33 pages. Accepted for publication in PASA. Minor edits from previous version

The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will detect an unprecedentedly large sample of actively accreting supermassive black holes with typical accretion disk (AD) sizes of a few light days. This brings us to face challenges in the reverberation mapping (RM) measurement of AD sizes in active galactic nuclei (AGNs) using interband continuum delays. We examine the effect of LSST cadence strategies on AD RM using our metric AGNTimeLagMetric. It accounts for redshift, cadence, the magnitude limit, and magnitude corrections for dust extinction. Running our metric on different LSST cadence strategies, we produce an atlas of the performance estimations for LSST photometric RM measurements. We provide an upper limit on the estimated number of quasars for which the AD time lag can be computed within 01000 sources in each Deep Drilling field (DDF, 10 sq. deg) in any filter, with the redshift distribution of these sources peaking at z~1. We find the LSST observation strategies with a good cadence (~ 5 days) and a long cumulative season (~9 yr), as proposed for LSST DDF, are favored for the AD size measurement. We create synthetic LSST light curves for the most suitable DDF cadences and determine RM time lags to demonstrate the impact of the best cadences based on the proposed metric., Comment: accepted for publication in the Astrophysical Journal Focus Issue on Rubin LSST cadence and survey strategy

Binaries consisting of a hot subdwarf star and an accreting white dwarf (WD) are sources of gravitational wave radiation at low frequencies and possible progenitors of type Ia supernovae if the WD mass is large enough. Here, we report the discovery of the third binary known of this kind: it consists of a hot subdwarf O (sdO) star and a WD with an orbital period of 3.495 hours and an orbital shrinkage of 0.1 s in 6 yr. The sdO star overfills its Roche lobe and likely transfers mass to the WD via an accretion disk. From spectroscopy, we obtain an effective temperature of $T_{\mathrm{eff}}=54\,240\pm1\,840$ K and a surface gravity of $\log{g}=4.841\pm0.108$ for the sdO star. From the light curve analysis, we obtain a sdO mass of $M_{\mathrm{sdO}}=0.55$ ${\mathrm{M_{\odot}}}$ and a mass ratio of $q=M_{\mathrm{WD}}/M_{\mathrm{sdO}}=0.738\pm0.001$. Also, we estimate that the disk has a radius of $\sim 0.41R_\odot$ and a thickness of $\sim 0.18R_\odot$. The origin of this binary is probably a common envelope ejection channel, where the progenitor of the sdO star is either an RGB star or, more likely, an early AGB star; the sdO star will subsequently evolve into a WD and merge with its WD companion, likely resulting in an R CrB star. The outstanding feature in the spectrum of this object is strong Ca H&K lines, which are blueshifted by $\sim$200 km/s and likely originate from the recently ejected common envelope, and we estimated that the remnant CE material in the binary system has a density $\sim 6\times 10^{-10} {\rm g/cm^3}$., Comment: 13 pages, 15 figures, to be published in MNRAS