Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic 26Al emission from galactic-scale superbubble simulations

Emission from the radioactive trace element 26Al has been observed throughout the Milky Way with the COMPTEL and INTEGRAL satellites. In particular, the Doppler shifts measured with INTEGRAL connect 26Al with superbubbles, which may guide 26Al flows off spiral arms in the direction of Galactic rotation. In order to test this paradigm, we have performed galaxy-scale simulations of superbubbles with 26Al injection in a Milky Way-type galaxy. We produce all-sky synthetic γ-ray emission maps of the simulated galaxies. We find that the 1809 keV emission from the radioactive decay of 26Al is highly variable with time and the observer’s position. This allows us to estimate an additional systematic variability of 0.2 dex for a star formation rate derived from 26Al for different times and measurement locations in Milky Way-type galaxies. High-latitude morphological features indicate nearby emission with correspondingly high-integrated γ-ray intensities. We demonstrate that the 26Al scale height from our simulated galaxies depends on the assumed halo gas density. We present the first synthetic 1809 keV longitude-velocity diagrams from 3D hydrodynamic simulations. The line-of-sight velocities for 26Al can be significantly different from the line-of-sight velocities associated with the cold gas. Over time, 26Al velocities consistent with the INTEGRAL observations, within uncertainties, appear at any given longitude, broadly supporting previous suggestions that 26Al injected into expanding superbubbles by massive stars may be responsible for the high velocities found in the INTEGRAL observations. We discuss the effect of systematically varying the location of the superbubbles relative to the spiral arms.
The New EXor Outburst of ESO-Hα 99 Observed by Gaia ATLAS and TESS

We report photometry and spectroscopy of the outburst of the young stellar object ESO-Hα99. The outburst was first noticed in Gaia alert Gaia18dvc and later by the Asteroid Terrestrial-impact Last Alert System (ATLAS). We have established the outburst light curve with archival ATLAS orange filter photometry, Gaia data, new V-band photometry, and J, H, and Ks photometry from the Infrared Imaging System (IRIS) and the United Kingdom Infrared Telescope (UKIRT). The brightness has fluctuated several times near the light curve maximum. The Transiting Exoplanet Survey Satellite (TESS) satellite observed ESO-Hα 99 with high cadence during one of these minor minima and found brightness fluctuations on timescales of days and hours. Imaging with UKIRT shows the outline of an outflow cavity, and we find one knot of emission, now named MHO 1520, on the symmetry axis of this nebula, indicating recent collimated outflow activity from ESO-Hα 99. Its pre-outburst SED shows a flat far-infrared spectrum, confirming its early evolutionary state and its similarity to other deeply embedded objects in the broader EXor class. The pre-outburst luminosity is 34 L⊙, a much higher luminosity than typical EXors, indicating that ESO-Hα 99 may be a star of intermediate mass. Infrared and optical spectroscopy show a rich emission-line spectrum, including H i lines, strong red Ca ii emission, as well as infrared CO bandhead emission, all characteristic EXors in the broadest sense. Comparison of the present spectra with an optical spectrum obtained in 1993, presumably in the quiescent state of the object, shows that during the present outburst the continuum component of the spectrum has increased notably more than the emission lines. The Hα equivalent width during the outburst is down to one-half of its 1993 level, and shock-excited emission lines are much less prominent.
On the types for supercuspidal representations of inner forms of $\mathrm{GL}_{N}$.

Let $F$ be a non-Archimedean local field, $A$ be a central simple $F$-algebra, and $G$ be the multiplicative group of $A$. It is known that for every irreducible supercuspidal representation $\pi$, there exists a $[G, \pi]_{G}$-type $(J, \lambda)$, called a (maximal) simple type. We will show that $[G, \pi]_{G}$-types defined over some maximal compact subgroup are unique up to $G$-conjugations under some unramifiedness assumption on a simple stratum.
Formal Developments for Lorentz-Violating Dirac Fermions and Neutrinos

The current paper is a technical work that is focused on Lorentz violation for Dirac fermions as well as neutrinos, described within the nonminimal Standard-Model Extension. We intend to derive two theoretical results. The first is the full propagator of the single-fermion Dirac theory modified by Lorentz violation. The second is the dispersion equation for a theory of N neutrino flavors that enables the description of both Dirac and Majorana neutrinos. As the matrix structure of the neutrino field operator is very involved for generic N, we will use sophisticated methods of linear algebra to achieve our objectives. Our main finding is that the neutrino dispersion equation has the same structure in terms of Lorentz-violating operators as that of a modified single-fermion Dirac theory. The results will be valuable for phenomenological studies of Lorentz-violating Dirac fermions and neutrinos.
Weak-lensing Analysis of X-Ray-selected XXL Galaxy Groups and Clusters with Subaru HSC Data

We present a weak-lensing analysis of X-ray galaxy groups and clusters selected from the XMM-XXL survey using the first-year data from the Hyper Suprime-Cam (HSC) Subaru Strategic Program. Our joint weak-lensing and X-ray analysis focuses on 136 spectroscopically confirmed X-ray-selected systems at 0.031 ≤ z ≤ 1.033 detected in the 25 deg2 XXL-N region, which largely overlaps with the HSC-XMM field. With high-quality HSC weak-lensing data, we characterize the mass distributions of individual clusters and establish the concentration–mass (c–M) relation for the XXL sample, by accounting for selection bias and statistical effects and marginalizing over the remaining mass calibration uncertainty. We find the mass-trend parameter of the c–M relation to be and the normalization to be at and z = 0.3. We find no statistical evidence for redshift evolution. Our weak-lensing results are in excellent agreement with dark-matter-only c–M relations calibrated for recent ΛCDM cosmologies. The level of intrinsic scatter in c200 is constrained as ( CL), which is smaller than predicted for the full population of ΛCDM halos. This is likely caused in part by the X-ray selection bias in terms of the cool-core or relaxation state. We determine the temperature–mass (TX–M500) relation for a subset of 105 XXL clusters that have both measured HSC lensing masses and X-ray temperatures. The resulting TX–M500 relation is consistent with the self-similar prediction. Our TX–M500 relation agrees with the XXL DR1 results at group scales but has a slightly steeper mass trend, implying a smaller mass scale in the cluster regime. The overall offset in the TX–M500 relation is at the ∼1. 5σ level, corresponding to a mean mass offset of . We also provide bias-corrected, weak-lensing-calibrated M200 and M500 mass estimates of individual XXL clusters based on their measured X-ray temperatures.
Quiescent Galaxies 1.5 Billion Years after the Big Bang and Their Progenitors

We report two secure ( ) and one tentative (z ≈ 3.767) spectroscopic confirmations of massive and quiescent galaxies through K-band observations with Keck/MOSFIRE and Very Large Telescope/X-Shooter. The stellar continuum emission, absence of strong nebular emission lines, and lack of significant far-infrared detections confirm the passive nature of these objects, disfavoring the alternative solution of low-redshift dusty star-forming interlopers. We derive stellar masses of log(M⋆/M⊙) ∼ 11 and ongoing star formation rates placing these galaxies ≳1–2 dex below the main sequence at their redshifts. The adopted parameterization of the star formation history suggests that these sources experienced a strong ( M⊙ yr−1) and short (∼50 Myr) burst of star formation, peaking ∼150–500 Myr before the time of observation, all properties reminiscent of the characteristics of submillimeter galaxies (SMGs) at z > 4. We investigate this connection by comparing the comoving number densities and the properties of these two populations. We find a fair agreement only with the deepest submillimeter surveys detecting not only the most extreme starbursts but also more normal galaxies. We support these findings by further exploring the Illustris TNG cosmological simulation, retrieving populations of both fully quenched massive galaxies at z ∼ 3–4 and SMGs at z ∼ 4−5, with number densities and properties in agreement with the observations at z ∼ 3 but in increasing tension at higher redshift. Nevertheless, as suggested by the observations, not all of the progenitors of quiescent galaxies at these redshifts shine as bright SMGs in their past, and, similarly, not all bright SMGs quench by z ∼ 3, both fractions depending on the threshold assumed to define the SMGs themselves.
Acceptable Planning: Influencing Individual Behavior to Reduce Transportation Energy Expenditure of a City

Our research aims at developing intelligent systems to reduce the transportation-related energy expenditure of a large city by influencing individual behavior. We introduce Copter - an intelligent travel assistant that evaluates multi-modal travel alternatives to find a plan that is acceptable to a person given their context and preferences. We propose a formulation for acceptable planning that brings together ideas from AI, machine learning, and economics. This formulation has been incorporated in Copter that produces acceptable plans in real-time. We adopt a novel empirical evaluation framework that combines human decision data with a high fidelity multi-modal transportation simulation to demonstrate a 4% energy reduction and 20% delay reduction in a realistic deployment scenario in Los Angeles, California, USA. This article is part of the special track on AI and Society.
Determination of Transport Coefficients by Coronal Seismology of Flare-induced Slow-mode Waves: Numerical Parametric Study of a 1D Loop Model

Recent studies of a flaring loop oscillation event on 2013 December 28 observed by the Atmospheric Imaging Assembly of the Solar Dynamics Observatory have revealed the suppression of thermal conduction and significant enhancement of compressive viscosity in hot (∼10 MK) plasma. In this study we aim at developing a new coronal seismology method for determining the transport coefficients based on a parametric study of wave properties using a 1D nonlinear magnetohydrodynamics (MHD) loop model in combination with the linear theory. The simulations suggest a two-step scheme: we first determine the effective thermal conduction coefficient from the observed phase shift between temperature and density perturbations as this physical parameter is insensitive to the unknown viscosity; then from the loop model with the obtained thermal conduction coefficient, we determine the effective viscosity coefficient from the observed decay time using the parametric modeling. With this new seismology technique we are able to quantify the suppression of thermal conductivity by a factor of about 3 and the enhancement of viscosity coefficient by a factor of 10 in the studied flaring loop. Using the loop model with these refined transport coefficients, we study the excitation of slow magnetoacoustic waves by launching a flow pulse from one footpoint. The simulation can self-consistently produce the fundamental standing wave on a timescale in agreement with the observation.
Interpretable Models of Human Interaction in Immersive Simulation Settings

Immersive simulations are increasingly used for teaching and training in many societally important arenas including healthcare, disaster response and science education. The interactions of participants in such settings lead to a complex array of emergent outcomes that present challenges for analysis. This paper studies a central element of such an analysis, namely the interpretability of models for inferring structure in time series data. This problem is explored in the context of modeling student interactions in an immersive ecological-system simulation. Unsupervised machine learning is applied to data on system dynamics with the aim of helping teachers determine the effects of students' actions on these dynamics. We address the question of choosing the optimal machine learning model, considering both statistical information criteria and interpretabilty quality. Our approach adapts two interpretability tests from the literature that measure the agreement between the model output and human judgment. The results of a user study show that the models that are the best understood by people are not those that optimize information theoretic criteria. In addition, a model using a fully Bayesian approach performed well on both statistical measures and on human-subject tests of interpretabilty, making it a good candidate for automated model selection that does not require human-in-the-loop evaluation. The results from this paper are already being used in the classroom and can inform the design of interpretable models for a broad range of socially relevant domains.
X-shooter observations of strong H2-bearing DLAs at high redshift

We present results from spectroscopic observations with X-shooter at the Very Large Telescope of seven H2-bearing damped Lyman-α systems (DLAs) at high redshifts (zabs ∼ 2.5–3). These DLAs were originally selected from the presence of strong H2 lines directly seen at the DLA redshift in low-resolution low signal-to-noise ratio SDSS spectra. We confirm the detection of molecular hydrogen in all of them. We measure the column densities of H i, H2 in various rotational levels, and metal species, and associated dust extinction. The metallicities, obtained from undepleted species, are in the range log Z = −0.8 to −0.2. We discuss the chemical enrichment in these clouds and compare their properties with that of other molecular-rich systems selected by other means. In particular, we show that three different methods of pre-selection of H2-bearing DLAs in the SDSS have their own biases but complement each other mostly in terms of chemical enrichment. We use the rotational excitation of H2 molecules together with the fine-structure energy levels of neutral carbon to constrain the physical conditions in the gas with the help of numerical modelling as well as analytical expressions for the surface density at which atomic to molecular conversion happens. We find that the H2-bearing medium revealed by the studied DLAs has typical values for the kinetic temperature, hydrogen density, and UV radiation field of T ∼ 100 K, nH ∼ 100 cm−3, and IUV , respectively, about twice the intensity of the Draine field. Detailed studies combining different selections should therefore bring important clues to understand the H i-H2 transition at high redshift.
First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

We study the distributed Linear Quadratic Gaussian (LQG) control problem in discrete-time and finite-horizon, where the controller depends linearly on the history of the outputs and it is required to lie in a given subspace, e.g. to possess a certain sparsity pattern. It is well-known that this problem can be solved with convex programming within the Youla domain if and only if a condition known as Quadratic Invariance (QI) holds. In this paper, we first show that given QI sparsity constraints, one can directly descend the gradient of the cost function within the domain of output-feedback controllers and converge to a global optimum. Note that convergence is guaranteed despite non-convexity of the cost function. Second, we characterize a class of Uniquely Stationary (US) problems, for which first-order methods are guaranteed to converge to a global optimum. We show that the class of US problems is strictly larger than that of strongly QI problems and that it is not included in that of QI problems. We refer to Figure 1 for details. Finally, we propose a tractable test for the US property.
Power Allocation in Cache-Aided NOMA Systems: Optimization and Deep Reinforcement Learning Approaches

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users’ collaboration, and thus, avoids many complicated issues such as users’ privacy and security, selfishness, etc. In order to optimize users’ quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived making this method simple and flexible to the system context. The second one is based on deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance.
Nuclear Norm and Spectral Norm of Tensor Product

We show that the nuclear norm of the tensor product of two tensors is not greater than the product of the nuclear norms of these two tensors. As an application, we give lower bounds for the nuclear norm of an arbitrary tensor. We show that the spectral norm of the tensor product of two tensors is not greater than the product of the spectral norm of one tensor, and the nuclear norm of another tensor. By this result, we present an alternative formula for the spectral norm of a tensor, and give some lower bounds for the product of the nuclear norm and the spectral norm of an arbitrary tensor. The first result also shows that the nuclear norm of the square matrix is a matrix norm. We then extend the concept of matrix norm to tensor norm. A real function defined for all real tensors is called a tensor norm if it is a norm for any tensor space with fixed dimensions, and the norm of the tensor product of two tensors is always not greater than the product of the norms of these two tensors. We show that the 1-norm, the Frobenius norm and the nuclear norm of tensors are tensor norms but the infinity norm and the spectral norm of tensors are not tensor norms.
Stellar Velocity Dispersion of a Massive Quenching Galaxy at z = 4.01

We present the first stellar velocity dispersion measurement of a massive quenching galaxy at z = 4. The galaxy is first identified as a massive z ≥ 4 galaxy with suppressed star formation from photometric redshifts based on deep multiband data. A follow-up spectroscopic observation with MOSFIRE on Keck revealed strong multiple absorption features, which are identified as Balmer lines, giving a secure redshift of z = 4.01. This is the most distant quiescent galaxy known to date. Thanks to the high S/N of the spectrum, we are able to estimate the stellar velocity dispersion, , making a significant leap from the previous highest redshift measurement at z = 2.8. Interestingly, we find that the velocity dispersion is consistent with that of massive galaxies today, implying no significant evolution in velocity dispersion over the last 12 Gyr. Based on a stringent upper limit on its physical size from deep optical images (reff < 1.3 kpc), we find that its dynamical mass is consistent with the stellar mass inferred from photometry. Furthermore, the galaxy is located on the mass fundamental plane extrapolated from lower redshift galaxies. The observed no strong evolution in σ suggests that the mass in the core of massive galaxies does not evolve significantly, while most of the mass growth occurs in the outskirts of the galaxies, which also increases the size. This picture is consistent with a two-phase formation scenario in which mass and size growth is due to accretion in the outskirts of galaxies via mergers. Our results imply that the first phase may be completed as early as z ∼ 4.
Analysis of nanometre sized aligned conical pores using SAXS

Small angle X-ray scattering (SAXS) was used to quantitatively study the morphology of aligned, mono-disperse conical etched ion tracks in thin films of amorphous SiO2 with aspect ratios of around 6:1, and in polycarbonate foils with aspect ratios of around 1000:1. This paper presents the measurement procedure and methods developed for the analysis of the scattering images and shows results obtained for the two material systems. To enable accurate parameter extraction from the data collected from conical scattering objects a model fitting the 2-D detector images was developed. The analysis involved fitting images from a sequence of measurements with different sample tilts to minimise errors which may have been introduced due to the experimental set-up. The model was validated by the exploitation of the geometric relationship between the sample tilt angle and the cone opening angle, to an angle observed in the features of the SAXS images. We also demonstrate that a fitting procedure for 1-D data extracted from the scattering images using a hard cylinder model can also be used to extract the cone size. The application of these techniques enables us to reconstruct the cone morphologies with unprecedented precision.
The column measure and Gradient-Free Gradient Boosting

Sparse model selection by structural risk minimization leads to a set of a few predictors, ideally a subset of the true predictors. This selection clearly depends on the underlying loss function $\tilde L$. For linear regression with square loss, the particular (functional) Gradient Boosting variant $L_2-$Boosting excels for its computational efficiency even for very large predictor sets, while still providing suitable estimation consistency. For more general loss functions, functional gradients are not always easily accessible or, like in the case of continuous ranking, need not even exist. To close this gap, starting from column selection frequencies obtained from $L_2-$Boosting, we introduce a loss-dependent ''column measure'' $\nu^{(\tilde L)}$ which mathematically describes variable selection. The fact that certain variables relevant for a particular loss $\tilde L$ never get selected by $L_2-$Boosting is reflected by a respective singular part of $\nu^{(\tilde L)}$ w.r. t. $\nu^{(L_2)}$. With this concept at hand, it amounts to a suitable change of measure (accounting for singular parts) to make $L_2-$Boosting select variables according to a different loss $\tilde L$. As a consequence, this opens the bridge to applications of simulational techniques such as various resampling techniques, or rejection sampling, to achieve this change of measure in an algorithmic way.