nils-herrmann/clean_scirepeval huggingface dataset:
EDGE: The Origin of Scatter in Ultra-faint Dwarf Stellar Masses and Surface Brightnesses

We demonstrate how the least luminous galaxies in the universe, ultra-faint dwarf galaxies, are sensitive to their dynamical mass at the time of cosmic reionization. We select a low-mass ( ) dark matter halo from a cosmological volume, and perform zoom hydrodynamical simulations with multiple alternative histories using “genetically modified” initial conditions. Earlier-forming ultra-faints have higher stellar mass today, due to a longer period of star formation before their quenching by reionization. Our histories all converge to the same final dynamical mass, demonstrating the existence of extended scatter (≥1 dex) in stellar masses at fixed halo mass due to the diversity of possible histories. One of our variants builds less than 2% of its final dynamical mass before reionization, rapidly quenching in situ star formation. The bulk of its final stellar mass is later grown by dry mergers, depositing stars in the galaxy’s outskirts and hence expanding its effective radius. This mechanism constitutes a new formation scenario for highly diffuse ( , ), metal-poor ( ), ultra-faint ( ) dwarf galaxies within the reach of next-generation low surface brightness surveys.
Higgs phenomenology as a probe of sterile neutrinos

Physics beyond the Standard Model can manifest itself as both new light states and heavy degrees of freedom. In this paper, we assume that the former comprise only a sterile neutrino, $N$. Therefore, the most agnostic description of the new physics is given by an effective field theory built upon the Standard Model fields as well as $N$. We show that Higgs phenomenology provides a sensitive and potentially crucial tool to constrain effective gauge interactions of sterile neutrinos, not yet probed by current experiments. In parallel, this motivates a range of new Higgs decay channels with clean signatures as candidates for the next LHC runs, including $h\to \gamma+p_T^\text{miss}$ and $h\to \gamma\gamma+p_T^\text{miss}$.
The AREPO Public Code Release

We introduce the public version of the cosmological magnetohydrodynamical moving-mesh simulation code Arepo. This version contains a finite-volume magnetohydrodynamics algorithm on an unstructured, dynamic Voronoi tessellation coupled to a tree-particle-mesh algorithm for the Poisson equation either on a Newtonian or cosmologically expanding spacetime. Time integration is performed adopting local time step constraints for each cell individually, solving the fluxes only across active interfaces, and calculating gravitational forces only between active particles, using an operator-splitting approach. This allows simulations with high dynamic range to be performed efficiently. Arepo is a massively distributed-memory parallel code, using the message passing interface (MPI) communication standard and employing a dynamical workload and memory balancing scheme to allow for optimal use of multi-node parallel computers. The employed parallelization algorithms of Arepo are deterministic and produce binary-identical results when rerun on the same machine and with the same number of MPI ranks. A simple primordial cooling and star formation model is included as an example of sub-resolution models commonly used in simulations of galaxy formation. Arepo also contains a suite of computationally inexpensive test problems, ranging from idealized tests for automated code verification to scaled-down versions of cosmological galaxy formation simulations, and is extensively documented in order to assist adoption of the code by new scientific users.
Dynamical Masses for the Triple System HD 28363 in the Hyades Cluster

The star HD 28363 in the Hyades cluster has been known for over a century as a visual binary with a period of 40 yr. The secondary is, in turn, a single-lined spectroscopic binary with a 21 day period. Here we report extensive spectroscopic monitoring of this hierarchical triple system that reveals the spectral lines of the third star for the first time. Combined with astrometric information, this makes it possible to determine the dynamical masses of all three stars. Only six other binaries in the Hyades have had their individual component masses determined dynamically. We infer the properties of the system by combining our radial-velocity measurements with visual observations, lunar occultation measurements, and with proper motions from the Hipparcos and Gaia missions that provide a constraint on the astrometric acceleration. We derive a mass of for the visual primary, and 1.210 ± 0.021 and 0.781 ± 0.014 M☉ for the other two stars. These measurements along with those for the other six systems establish an empirical mass–luminosity relation in the Hyades that is in broad agreement with current models of stellar evolution for the known age and chemical composition of the cluster.
Accreted Globular Clusters in external galaxies: Why adaptive dynamics is not the solution

Many astrophysical and galaxy-scale cosmological problems require a well determined gravitational potential which is often modeled by observers under strong assumptions. Globular clusters (GCs) surrounding galaxies can be used as dynamical tracers of the luminous and dark matter distribution at large (kpc) scales. A natural assumption for modeling the gravitational potential is that GCs accreted in the same dwarf galaxy merger event move at the present time on similar orbits in the host galaxy and should therefore have similar actions. We investigate this idea in one realistic Milky Way like galaxy of the cosmological N-body simulation suite Auriga. We show how the actions of accreted stellar particles in the simulation evolve and that minimizing the standard deviation of GCs in action space, however, cannot constrain the true potential. This approach known as ‘adaptive dynamics’ does therefore not work for accreted GCs.
Barium Tagging with Selective, Dry-Functional, Single Molecule Sensitive On-Off Fluorophores for the NEXT Experiment

In the search for neutrinoless double beta decay, understanding and reducing backgrounds is crucial for success. An advance that could drive backgrounds to negligible levels would be the ability to efficiently detect the barium daughter in $^{136}$Xe to $^{136}$Ba double beta decay, since no conventional radioactive process can produce barium ions or atoms in xenon at significant rates. In xenon gas, the barium daughter most likely survives as a dication. An approach under development by the NEXT collaboration involves transporting this ion from the active medium onto a coated transparent plane supporting a barium-sensitive fluorescent dye, monitored via fluorescence microscopy. Upon exposure to a barium dication, the dye will begin fluorescing, which, when correlated with the detection of a double electron signal at the anode, would confirm double beta decay.Our results have shown that a single barium ion can be resolved via Single Molecule Fluorescent Imaging (SMFI). The next challenge is a realization of this technique within in a large volume of xenon gas. Significant advances have recently been made: custom barium-tagging molecules that fluoresce strongly in the dry state when exposed to barium have been demonstrated, and devices constructed that can observe fluorescence via in-vacuum or in-gas Total Internal Reflection Fluorescence Microscopy. We present the status of this technique and the outlook for barium tagging with On-Off switchable fluorophores, including new results with a Ba$^{2+}$-selective dye that functions under our desired conditions in the visible region and with single ion sensitivity.
The biggest splash

Using a large sample of bright nearby stars with accurate Gaia Data Release 2 astrometry and auxiliary spectroscopy we map out the properties of the principle Galactic components such as the ‘thin’ and ‘thick’ discs and the halo. We confirm previous claims that in the Solar neighbourhood, there exists a large population of metal-rich ([Fe/H] > −0.7) stars on highly eccentric orbits. By studying the evolution of elemental abundances, kinematics, and stellar ages in the plane of azimuthal velocity vϕ and metallicity [Fe/H], we demonstrate that this metal-rich halo-like component, which we dub the Splash, is linked to the α-rich (or ‘thick’) disc. Splash stars have little to no angular momentum and many are on retrograde orbits. They are predominantly old, but not as old as the stars deposited into the Milky Way (MW) in the last major merger. We argue, in agreement with several recent studies, that the Splash stars may have been born in the MW’s protodisc prior to the massive ancient accretion event which drastically altered their orbits. We cannot, however, rule out other (alternative) formation channels. Taking advantage of the causal connection between the merger and the Splash, we put constraints of the epoch of the last massive accretion event to have finished 9.5 Gyr ago. The link between the local metal-rich and metal-poor retrograde stars is confirmed using a large suite of cutting-edge numerical simulations of the MW’s formation.
On factorization and vector bundles of conformal blocks from vertex algebras

Modules over conformal vertex algebras give rise to sheaves of coinvariants and conformal blocks on moduli of stable pointed curves. Here we prove the factorization conjecture for these sheaves. Our results apply in arbitrary genus and for a large class of vertex algebras. As an application, sheaves defined by finitely generated admissible modules over vertex algebras satisfying natural hypotheses are shown to be vector bundles. Factorization is essential to a recursive formulation of invariants, like ranks and Chern classes, and to produce new constructions of rational conformal field theories and cohomological field theories.
Velocity substructure from 
Gaia
 and direct searches for dark matter

Data from the Gaia satellite show that the solar neighbourhood of the Milky Way's stellar halo is imprinted with substructure from several accretion events. Evidence of these events is found in "the Shards", stars clustering with high significance in both action space and metallicity. Stars in the Shards share a common origin, likely as ancient satellite galaxies of the Milky Way, so will be embedded in dark matter (DM) counterparts. These "Dark Shards" contain two substantial streams (S1 and S2), as well as several retrograde, prograde and lower energy objects. The retrograde stream S1 has a very high Earth-frame speed of $\sim 550$ km/s while S2 moves on a prograde, but highly polar orbit and enhances peak of the speed distribution at around $300$ km/s. The presence of the Dark Shards locally leads to modifications of many to the fundamental properties of experimental DM signals. The S2 stream in particular gives rise to an array of effects in searches for axions and in the time dependence of nuclear recoils: shifting the peak day, inducing non-sinusoidal distortions, and increasing the importance of the gravitational focusing of DM by the Sun. Dark Shards additionally bring new features for directional signals, while also enhancing the DM flux towards Cygnus.
Probing gaseous galactic halos through the rotational kinematic Sunyaev-Zeldovich effect

The rotational kinematic Sunyaev-Zeldovich (rkSZ) signal, imprinted on the cosmic microwave background (CMB) by the gaseous halos (spinning "atmospheres") of foreground galaxies, would be a novel probe of galaxy formation. Although the signal is too weak to detect in individual galaxies, we analyze the feasibility of its statistical detection via stacking CMB data on many galaxies for which the spin orientation can be estimated spectroscopically. We use an "optimistic" model, in which fully ionized atmospheres contain the cosmic baryon fraction and spin at the halo's circular velocity $v_{\rm circ}$, and a more realistic model, based on hydrodynamical simulations, with multi-phase atmospheres spinning at a fraction of $v_{\rm circ}$. We incorporate realistic noise estimates into our analysis. 
Using low-redshift galaxy properties from the MaNGA spectroscopic survey (with median halo mass of $6.6\times10^{11}\,M_{\odot}$), and CMB data quality from Planck, we find that a $3\sigma$ detection would require a few$\times 10^4$ galaxies, even in the optimistic model. This is too high for current surveys, but upcoming higher-angular resolution CMB experiments will significantly reduce the requirements: stacking CMB data on galaxy spins in a $\sim10$ deg$^2$ can rule out the optimistic models, and $\approx$350 deg$^2$ will suffice for a $3\sigma$ detection with ACT. 
As a proof-of-concept, we stacked Planck data on the position of $\approx2,000$ MaNGA galaxies, aligned with the galaxies' projected spin, and scaled to their halos' angular size. We rule out average temperature dipoles larger than $\approx1.9\,\mu$K around field spiral galaxies.
Sunny and Dark Outside?! Improving Answer Consistency in VQA through Entailed Question Generation

While models for Visual Question Answering (VQA) have steadily improved over the years, interacting with one quickly reveals that these models lack consistency. For instance, if a model answers “red” to “What color is the balloon?”, it might answer “no” if asked, “Is the balloon red?”. These responses violate simple notions of entailment and raise questions about how effectively VQA models ground language. In this work, we introduce a dataset, ConVQA, and metrics that enable quantitative evaluation of consistency in VQA. For a given observable fact in an image (e.g. the balloon’s color), we generate a set of logically consistent question-answer (QA) pairs (e.g. Is the balloon red?) and also collect a human-annotated set of common-sense based consistent QA pairs (e.g. Is the balloon the same color as tomato sauce?). Further, we propose a consistency-improving data augmentation module, a Consistency Teacher Module (CTM). CTM automatically generates entailed (or similar-intent) questions for a source QA pair and fine-tunes the VQA model if the VQA’s answer to the entailed question is consistent with the source QA pair. We demonstrate that our CTM-based training improves the consistency of VQA models on the Con-VQA datasets and is a strong baseline for further research.
When Single Event Upset Meets Deep Neural Networks: Observations, Explorations, and Remedies

Deep Neural Network has proved its potential in various perception tasks and hence become an appealing option for interpretation and data processing in security sensitive systems. However, security-sensitive systems demand not only high perception performance, but also design robustness under various circumstances. Unlike prior works that study network robustness from software level, we investigate from hardware perspective about the impact of Single Event Upset (SEU) induced parameter perturbation (SIPP) on neural networks. We systematically define the fault models of SEU and then provide the definition of sensitivity to SIPP as the robustness measure for the network. We are then able to analytically explore the weakness of a network and summarize the key findings for the impact of SIPP on different types of bits in a floating point parameter, layer-wise robustness within the same network and impact of network depth. Based on those findings, we propose two remedy solutions to protect DNNs from SIPPs, which can mitigate accuracy degradation from 28% to 0.27% for ResNet with merely 0.24-bit SRAM area overhead per parameter.
Neural Embedding Allocation: Distributed Representations of Topic Models

Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents. On the other hand, topic models provide latent representations of the documents' topical themes. To get the benefits of these representations simultaneously, we propose a unifying algorithm, called neural embedding allocation (NEA), which deconstructs topic models into interpretable vector-space embeddings of words, topics, documents, authors, and so on, by learning neural embeddings to mimic the topic models. We showcase NEA's effectiveness and generality on LDA, author-topic models and the recently proposed mixed membership skip gram topic model and achieve better performance with the embeddings compared to several state-of-the-art models. Furthermore, we demonstrate that using NEA to smooth out the topics improves coherence scores over the original topic models when the number of topics is large.
Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Stochastic parameterizations account for uncertainty in the representation of unresolved subgrid processes by sampling from the distribution of possible subgrid forcings. Some existing stochastic parameterizations utilize data‐driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and subgrid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate time scales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both time scales, and the networks closely reproduce the spatiotemporal correlations and regimes of the Lorenz '96 system. We also find that, in general, those models which produce skillful forecasts are also associated with the best climate simulations.
First Analysis of Local GD on Heterogeneous Data

We provide the first convergence analysis of local gradient descent for minimizing the average of smooth and convex but otherwise arbitrary functions. Problems of this form and local gradient descent as a solution method are of importance in federated learning, where each function is based on private data stored by a user on a mobile device, and the data of different users can be arbitrarily heterogeneous. We show that in a low accuracy regime, the method has the same communication complexity as gradient descent.
Gradient Descent with Compressed Iterates

We propose and analyze a new type of stochastic first order method: gradient descent with compressed iterates (GDCI). GDCI in each iteration first compresses the current iterate using a lossy randomized compression technique, and subsequently takes a gradient step. This method is a distillation of a key ingredient in the current practice of federated learning, where a model needs to be compressed by a mobile device before it is sent back to a server for aggregation. Our analysis provides a step towards closing the gap between the theory and practice of federated learning, and opens the possibility for many extensions.
Neural Belief Reasoner

This paper proposes a new generative model called neural belief reasoner (NBR). It differs from previous models in that it specifies a belief function rather than a probability distribution. Its implementation consists of neural networks, fuzzy-set operations and belief-function operations, and query-answering, sample-generation and training algorithms are presented. This paper studies NBR in two tasks. The first is a synthetic unsupervised-learning task, which demonstrates NBR's ability to perform multi-hop reasoning, reasoning with uncertainty and reasoning about conflicting information. The second is supervised learning: a robust MNIST classifier for 4 and 9, which is the most challenging pair of digits. This classifier needs no adversarial training, and it substantially exceeds the state of the art in adversarial robustness as measured by the L2 metric, while at the same time maintains 99.1% accuracy on natural images.
CalBehav: A Machine Learning based Personalized Calendar Behavioral Model using Time-Series Smartphone Data

The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual’s dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ‘CalBehav’. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.
Equivariant stable categories for incomplete systems of transfers

In this paper, we construct incomplete versions of the equivariant stable category; that is, equivariant stabilizations of the category of G ‐spaces with respect to incomplete systems of transfers encoded by an N∞ operad O . These categories are built from the categories of O ‐algebras in G ‐spaces. Using this operadic formulation, we establish incomplete versions of the usual structural properties of the equivariant stable category, notably the tom Dieck splitting. Our work is motivated in part by the examples arising from the equivariant units and Picard space functors.
Indirect effects of dark matter

Here, we briefly review possible indirect effects of dark matter (DM) of the universe. It includes effects in cosmic rays (CR): first of all, the positron excess at  We tell that the main and least model-dependent constraint on such possible interpretation of CR effects goes from gamma-ray background.   see text] mode of DM decay or annihilation produces prompt photons (FSR) so much that it leads to contradiction with data on cosmic gamma-rays. We present our attempts to possibly avoid gamma-ray constraint. They concern with peculiarities of both space distribution of DM and their physics. The latter involves complications of decay/annihilation modes of DM, modifications of Lagrangian of DM-ordinary matter interaction and inclusion of mode with identical fermions in final state. In this way, no possibilities to suppress were found except, possibly, the mode with identical fermions. While the case of spatial distribution variation allows achieving consistency between different data. Also, we consider stable form of DM which can interact with baryons. We show which constraint such DM candidate can get from the damping effect in plasma during large-scale structure (LSS) formation in comparison with other existing constraints.