nils-herrmann/clean_scirepeval huggingface dataset:
Dense gas formation and destruction in a simulated Perseus-like galaxy cluster with spin-driven black hole feedback

Extended filamentary Hα emission nebulae are a striking feature of nearby galaxy clusters but the formation mechanism of the filaments, and the processes which shape their morphology remain unclear.
Aims. We conduct an investigation into the formation, evolution and destruction of dense gas in the centre of a simulated, Perseus-like, cluster under the influence of a spin-driven jet. The jet is powered by the supermassive black hole (SMBH) located in the cluster’s brightest cluster galaxy. We particularly study the role played by condensation of dense gas from the diffuse intracluster medium, and the impact of direct uplifting of existing dense gas by the jets, in determining the spatial distribution and kinematics of the dense gas.
Methods. We present a hydrodynamical simulation of an idealised Perseus-like cluster using the adaptive mesh refinement code RAMSES. Our simulation includes a SMBH that self-consistently tracks its spin evolution via its local accretion, and in turn drives a large-scale jet whose direction is based on the black hole’s spin evolution. The simulation also includes a live dark matter (DM) halo, a SMBH free to move in the DM potential, star formation and stellar feedback.
Results. We show that the formation and destruction of dense gas is closely linked to the SMBH’s feedback cycle, and that its morphology is highly variable throughout the simulation. While extended filamentary structures readily condense from the hot intra-cluster medium, they are easily shattered into an overly clumpy distribution of gas during their interaction with the jet driven outflows. Condensation occurs predominantly onto infalling gas located 5−15 kpc from the centre during quiescent phases of the central AGN, when the local ratio of the cooling time to free fall time falls below 20, i.e. when tcool/tff <  20.
Conclusions. We find evidence for both condensation and uplifting of dense gas, but caution that purely hydrodynamical simulations struggle to effectively regulate the cluster cooling cycle and produce overly clumpy distributions of dense gas morphologies, compared to observation.
Flexible, Fast and Accurate Densely-Sampled Light Field Reconstruction Network

The densely-sampled light field (LF) is highly desirable in various applications, such as 3-D reconstruction, post-capture refocusing and virtual reality. However, it is costly to acquire such data. Although many computational methods have been proposed to reconstruct a densely-sampled LF from a sparsely-sampled one, they still suffer from either low reconstruction quality, low computational efficiency, or the restriction on the regularity of the sampling pattern. To this end, we propose a novel learning-based method, which accepts sparsely-sampled LFs with irregular structures, and produces densely-sampled LFs with arbitrary angular resolution accurately and efficiently. Our proposed method, an end-to-end trainable network, reconstructs a densely-sampled LF in a coarse-to-fine manner. Specifically, the coarse sub-aperture image (SAI) synthesis module first explores the scene geometry from an unstructured sparsely-sampled LF and leverages it to independently synthesize novel SAIs, giving an intermediate densely-sampled LF. Then, the efficient LF refinement module learns the angular relations within the intermediate result to recover the LF parallax structure. Comprehensive experimental evaluations demonstrate the superiority of our method on both real-world and synthetic LF images when compared with state-of-the-art methods. In addition, we illustrate the benefits and advantages of the proposed approach when applied in various LF-based applications, including image-based rendering, depth estimation enhancement, and LF compression.
Building Small-Satellites to Live Through the Kessler Effect

The rapid advancement and miniaturization of spacecraft electronics, sensors, actuators, and power systems have resulted in growing proliferation of small-spacecraft. Coupled with this is the growing number of rocket launches, with left-over debris marking their trail. The space debris problem has also been compounded by test of several satellite killer missiles that have left large remnant debris fields. In this paper, we assume a future in which the Kessler Effect has taken hold and analyze the implications on the design of small-satellites and CubeSats. We use a multiprong approach of surveying the latest technologies, including the ability to sense space debris in orbit, perform obstacle avoidance, have sufficient shielding to take on small impacts and other techniques to mitigate the problem. Detecting and tracking space debris threats on-orbit is expected to be an important approach and we will analyze the latest vision algorithms to perform the detection, followed by quick reaction control systems to perform the avoidance. Alternately there may be scenarios where the debris is too small to track and avoid. In this case, the spacecraft will need passive mitigation measures to survive the impact. Based on these conditions, we develop a strawman design of a small spacecraft to mitigate these challenges. Based upon this study, we identify if there is sufficient present-day COTS technology to mitigate or shield satellites from the problem. We conclude by outlining technology pathways that need to be advanced now to best prepare ourselves for the worst-case eventuality of Kessler Effect taking hold in the upper altitudes of Low Earth Orbit.
Galaxy disc scaling relations: A tight linear galaxy–halo connection challenges abundance matching

In ΛCDM cosmology, to first order, galaxies form out of the cooling of baryons within the virial radius of their dark matter halo. The fractions of mass and angular momentum retained in the baryonic and stellar components of disc galaxies put strong constraints on our understanding of galaxy formation. In this work, we derive the fraction of angular momentum retained in the stellar component of spirals, fj, the global star formation efficiency fM, and the ratio of the asymptotic circular velocity (Vflat) to the virial velocity fV, and their scatter, by fitting simultaneously the observed stellar mass-velocity (Tully–Fisher), size–mass, and mass–angular momentum (Fall) relations. We compare the goodness of fit of three models: (i) where the logarithm of fj, fM, and fV vary linearly with the logarithm of the observable Vflat; (ii) where these values vary as a double power law; and (iii) where these values also vary as a double power law but with a prior imposed on fM such that it follows the expectations from widely used abundance matching models. We conclude that the scatter in these fractions is particularly small (∼0.07 dex) and that the linear model is by far statistically preferred to that with abundance matching priors. This indicates that the fundamental galaxy formation parameters are small-scatter single-slope monotonic functions of mass, instead of being complicated non-monotonic functions. This incidentally confirms that the most massive spiral galaxies should have turned nearly all the baryons associated with their haloes into stars. We call this the failed feedback problem.
The formation of ultradiffuse galaxies in clusters

We use the IllustrisTNG cosmological hydrodynamical simulation to study the formation of ultradiffuse galaxies (UDGs) in galaxy clusters. We supplement the simulations with a realistic mass–size relation for galaxies at the time of infall into the cluster, as well as an analytical model to describe the tidally induced evolution of their stellar mass, velocity dispersion, and size. The model assumes ‘cuspy’ NFW haloes and, contrary to recent claims, has no difficulty reproducing the observed number of UDGs in clusters. Our results further suggest that the UDG population consists of a mixture of ‘normal’ low surface brightness galaxies such as those found in the field (‘born’ UDGs, or B-UDGs), as well as a distinct population that owe their large size and low surface brightness to the effects of cluster tides (‘tidal’, or T-UDGs). The simulations indicate that T-UDGs entered the cluster earlier and should be more prevalent than B-UDGs near the cluster centres. T-UDGs should also have, at given stellar mass, lower velocity dispersion, higher metallicities, and lower dark matter content than B-UDGs. Our results suggest that systems like DF-44 are consistent with having been born as UDGs, while others such as DF2, DF4, and VLSB-D are possibly extreme T-UDG examples.
Properties of reionization-era galaxies from JWST luminosity functions and 21-cm interferometry

Upcoming observations will probe the first billion years of our Universe in unprecedented detail. Foremost among these are 21-cm interferometry with the Hydrogen Epoch of Reionization Arrays (HERA) and the Square Kilometre Array (SKA), and high-z galaxy observations with the James Webb Space Telescope (JWST). Here, we quantify how observations from these instruments can be used to constrain the astrophysics of high-z galaxies. We generate several mock JWST luminosity functions (LFs) and SKA1 21-cm power spectra, which are consistent with current observations, but assume different properties for the unseen, ultrafaint galaxies driving the epoch of reionization (EoR). Using only JWST data, we predict up to a factor of 2–3 improvement (compared with Hubble Space Telescope, HST) in the fractional uncertainty of the star formation rate to halo mass relation and the turnover magnitude. Most parameters regulating the ultraviolet (UV) galaxy properties can be constrained at the level of ∼10 per cent or better, if either (i) we are able to better characterize systematic lensing uncertainties than currently possible; or (ii) the intrinsic LFs peak at magnitudes brighter than MUV ≲ −13. Otherwise, improvement over HST-based inference is modest. When combining with upcoming 21-cm observations, we are able to significantly mitigate degeneracies, and constrain all of our astrophysical parameters, even for our most pessimistic assumptions about upcoming JWST LFs. The 21-cm observations also result in an order of magnitude improvement in constraints on the EoR history.
Thermodynamics of 4D dilatonic black holes and the weak gravity conjecture

Taking a thermodynamic perspective, we study the weak gravity conjecture in the context of 4D Einstein-Maxwell-dilaton theory. We find closed-form expressions for the corrected thermodynamic quantities in the presence of four-derivative terms in the action, and in particular the charge-to-mass ratio and entropy, for several families of solutions of special magnetic-to-electric charge ratio or dilaton coupling constant. Assuming that dyonic black holes themselves are the conjectured charged states, this places constraints on the Wilson coefficients of the theory which we show are satisfied under mild assumptions on the UV theory.
The Impact of Metallicity Evolution of the Universe on the Maximum Mass of LIGO Binary Black Holes

We can be biased against observing massive black holes (BHs) merging in the local universe as the bounds on the maximum BH mass ( ) depends on the assumptions regarding the metallicity evolution of the star-forming gas across the cosmic time. We investigate the bounds on the metallicity evolution, mass distribution, and delay times of the binary BH sources based on the 10 observed events by LIGO. We parametrize to be a function of metallicity that itself is modeled to evolve with redshift in either a modest or rapid fashion. Rapid metallicity evolution models predict a stringent bound of , while the bound on in the models with modest metallicity evolution is . Therefore, inferring from GW data depends on the assumed metal enrichment history of the universe that is not severely constrained at the moment.
Trouble on the Horizon: Forecasting the Derailment of Online Conversations as they Develop

Online discussions often derail into toxic exchanges between participants. Recent efforts mostly focused on detecting antisocial behavior after the fact, by analyzing single comments in isolation. To provide more timely notice to human moderators, a system needs to preemptively detect that a conversation is heading towards derailment before it actually turns toxic. This means modeling derailment as an emerging property of a conversation rather than as an isolated utterance-level event. Forecasting emerging conversational properties, however, poses several inherent modeling challenges. First, since conversations are dynamic, a forecasting model needs to capture the flow of the discussion, rather than properties of individual comments. Second, real conversations have an unknown horizon: they can end or derail at any time; thus a practical forecasting model needs to assess the risk in an online fashion, as the conversation develops. In this work we introduce a conversational forecasting model that learns an unsupervised representation of conversational dynamics and exploits it to predict future derailment as the conversation develops. By applying this model to two new diverse datasets of online conversations with labels for antisocial events, we show that it outperforms state-of-the-art systems at forecasting derailment.
Off the Baryonic Tully–Fisher Relation: A Population of Baryon-dominated Ultra-diffuse Galaxies

We study the gas kinematics traced by the 21 cm emission of a sample of six H i-rich low surface brightness galaxies classified as ultra-diffuse galaxies (UDGs). Using the 3D kinematic modeling code 3DBarolo we derive robust circular velocities, revealing a startling feature: H i-rich UDGs are clear outliers from the baryonic Tully–Fisher relation, with circular velocities much lower than galaxies with similar baryonic mass. Notably, the baryon fraction of our UDG sample is consistent with the cosmological value: these UDGs are compatible with having no “missing baryons” within their virial radii. Moreover, the gravitational potential provided by the baryons is sufficient to account for the amplitude of the rotation curve out to the outermost measured point, contrary to other galaxies with similar circular velocities. We speculate that any formation scenario for these objects will require very inefficient feedback and a broad diversity in their inner dark matter content.
A New Technique for Finding Galaxies Leaking Lyman-continuum Radiation: [S ii]-deficiency

The source responsible for the reionization of the universe is believed to be the population of star-forming galaxies at z ∼ 6 to 12. The biggest uncertainty concerns the fraction of Lyman-continuum photons that actually escape from the galaxies. In recent years, several relatively small samples of “leaky” galaxies have been discovered, and clues have begun to emerge as to both the indirect signposts of leakiness and of the conditions/processes that enable the escape of ionizing radiation. In this paper we present the results of a pilot program to test a new technique for finding leaky galaxies, using the weakness of the [S ii] nebular emission lines relative to typical star-forming galaxies as evidence that the interstellar medium (ISM) is optically thin to the Lyman continuum. We use the Cosmic Origins Spectrograph on the Hubble Space Telescope to detect significant emerging flux below the Lyman edge in two out of three [S ii]-weak star-forming galaxies at z ∼ 0.3. We show that these galaxies differ markedly in their properties from the class of leaky “Green-Pea” galaxies at similar redshifts: our sample galaxies are more massive, more metal-rich, and less extreme in terms of their stellar population and the ionization state of the ISM. Like the Green Peas, they have exceptionally high star formation rates per unit area. They also share some properties with the known leaky galaxies at z ∼ 3, but are significantly dustier. Our results validate a new method to identify local laboratories for exploring the processes that made it possible for galaxies to reionize the universe.
Cosmology from the two-dimensional renormalization group acting as the Ricci flow

The two-dimensional renormalization group acting as the Ricci flow $\Lambda\frac{\partial}{\partial\Lambda} g_{\mu\nu} = R_{\mu\nu}$ produces a specific 1+3 dimensional space-time metric which describes an expanding universe that starts with a big bang $a \sim t^{\scriptscriptstyle 1/\sqrt3}$ then decelerates until $z=0.2$ then accelerates until ending at $t_{\max}=1.6\,t_{H}$ with a big blowup $a \sim (t_{\max}-t)^{\scriptscriptstyle -1/\sqrt3}$. The only free parameters are the overall time scale and the value of the present time $t_{0}$. These are fixed by the Hubble constant $H_{0}=t_{H}^{-1}$ and the present deceleration parameter $q(t_{0})$. This crude calculation of cosmology omits all but the gravitational field. The only energy-momentum is purely gravitational dark matter and energy. This is a preliminary exploration towards a specific, comprehensive, testable calculation of cosmology from a fundamental theory in which physics is produced by a quantum version of the two-dimensional renormalization group.
Deep Equilibrium Models

We present a new approach to modeling sequential data: the deep equilibrium model (DEQ). Motivated by an observation that the hidden layers of many existing deep sequence models converge towards some fixed point, we propose the DEQ approach that directly finds these equilibrium points via root-finding. Such a method is equivalent to running an infinite depth (weight-tied) feedforward network, but has the notable advantage that we can analytically backpropagate through the equilibrium point using implicit differentiation. Using this approach, training and prediction in these networks require only constant memory, regardless of the effective “depth” of the network. We demonstrate how DEQs can be applied to two state-of-the-art deep sequence models: self-attention transformers and trellis networks. On large-scale language modeling tasks, such as the WikiText-103 benchmark, we show that DEQs 1) often improve performance over these state-of-the-art models (for similar parameter counts); 2) have similar computational requirements to existing models; and 3) vastly reduce memory consumption (often the bottleneck for training large sequence models), demonstrating an up-to 88% memory reduction in our experiments. The code is available at https://github.com/locuslab/deq.
The Bottom-up Evolution of Representations in the Transformer: A Study with Machine Translation and Language Modeling Objectives

We seek to understand how the representations of individual tokens and the structure of the learned feature space evolve between layers in deep neural networks under different learning objectives. We chose the Transformers for our analysis as they have been shown effective with various tasks, including machine translation (MT), standard left-to-right language models (LM) and masked language modeling (MLM). Previous work used black-box probing tasks to show that the representations learned by the Transformer differ significantly depending on the objective. In this work, we use canonical correlation analysis and mutual information estimators to study how information flows across Transformer layers and observe that the choice of the objective determines this process. For example, as you go from bottom to top layers, information about the past in left-to-right language models gets vanished and predictions about the future get formed. In contrast, for MLM, representations initially acquire information about the context around the token, partially forgetting the token identity and producing a more generalized token representation. The token identity then gets recreated at the top MLM layers.
Making Efficient Use of Demonstrations to Solve Hard Exploration Problems

This paper introduces R2D3, an agent that makes efficient use of demonstrations to solve hard exploration problems in partially observable environments with highly variable initial conditions. We also introduce a suite of eight tasks that combine these three properties, and show that R2D3 can solve several of the tasks where other state of the art methods (both with and without demonstrations) fail to see even a single successful trajectory after tens of billions of steps of exploration.
How to Build User Simulators to Train RL-based Dialog Systems

User simulators are essential for training reinforcement learning (RL) based dialog models. The performance of the simulator directly impacts the RL policy. However, building a good user simulator that models real user behaviors is challenging. We propose a method of standardizing user simulator building that can be used by the community to compare dialog system quality using the same set of user simulators fairly. We present implementations of six user simulators trained with different dialog planning and generation methods. We then calculate a set of automatic metrics to evaluate the quality of these simulators both directly and indirectly. We also ask human users to assess the simulators directly and indirectly by rating the simulated dialogs and interacting with the trained systems. This paper presents a comprehensive evaluation framework for user simulator study and provides a better understanding of the pros and cons of different user simulators, as well as their impacts on the trained systems.
Are we living in a bidirectional big bang / big crunch universe?

The interrelation of macroscopic classical and usually microscopic quantum physics is considered. Arguments for fixed two state vector quantum mechanics are outlined in a somewhat pedagogic way. An heuristic concept is developed how something like classical physics could emerge in an early epoch of a finite universe with a compact initial state and an extremely extended final one. The concept contains no intrinsic paradoxes. However it can not incorporate free agents which are considered essential. To allow for something like free agents the fixed final state is replaced by a matching state of maximum extend between an expanding and a contracting universe. How a bidirectional macroscopic world with possible free agents could emerge in such a big bang / big crunch universe is the central point of the paper.
Modelling the AGN broad-line region using single-epoch spectra − II. Nearby AGNs

The structure of the broad-line region (BLR) is an essential ingredient in the determination of active galactic nucleus (AGN) virial black hole masses, which in turn are important to study the role of black holes in galaxy evolution. Constraints on the BLR geometry and dynamics can be obtained from velocity-resolved studies using reverberation mapping data (i.e. monitoring data). However, monitoring data are observationally expensive and only available for a limited sample of AGNs, mostly confined to the local Universe. Here, we explore a new version of a Bayesian inference, physical model of the BLR that uses an individual spectrum and prior information on the BLR size from the radius–luminosity relation, to model the AGN BLR geometry and dynamics. We apply our model to a sample of 11 AGNs, which have been previously modelled using monitoring data. Our single-epoch BLR model is able to constrain some of the BLR parameters with inferred parameter values that agree within the uncertainties with those determined from the modelling of monitoring data. We find that our model is able to derive stronger constraints on the BLR for AGNs with broad emission lines that qualitatively have more substructure and more asymmetry, presumably as they contain more information to constrain the physical model. The performance of this model makes it a practical and cost-effective tool to determine some of the BLR properties of a large sample of low- and high-redshift AGNs, for which monitoring data are not available.