We present a complete halo model for mixed dark matter composed of cold dark matter (CDM) and ultralight axion-like particles (ULAs). Our model treats ULAs as a biased tracer of CDM, in analogy to treatments of massive neutrinos and neutral hydrogen. The model accounts for clustering of ULAs around CDM host halos, and fully models the cross correlations of both components. The model inputs include the ULA Jeans scale, and soliton density profile. Our model can be used to predict the matter power spectrum, $P(k)$, on non-linear scales for sub-populations of ULAs across the mass range $10^{-33}\text{ eV}\leq m\leq 10^{-21}\text{ eV}$, and can be calibrated against future mixed DM simulations to improve its accuracy. The mixed DM halo model also allows us to assess the importance of various approximations.

The goal of this paper is to give confidence regions for the excursion set of a spatial function above a given threshold from repeated noisy observations on a fine grid of fixed locations. Given an asymptotically Gaussian estimator of the target function, a pair of data-dependent nested excursion sets are constructed that are sub- and super-sets of the true excursion set, respectively, with a desired confidence. Asymptotic coverage probabilities are determined via a multiplier bootstrap method, not requiring Gaussianity of the original data nor stationarity or smoothness of the limiting Gaussian field. The method is used to determine regions in North America where the mean summer and winter temperatures are expected to increase by mid 21st century by more than 2 degrees Celsius.

As a broader set of applications from simulations to data analysis and machine learning require more parallel computational capability, the demand for interactive and urgent high performance computing (HPC) continues to increase. This paper overviews the progress made so far and elucidates the challenges and opportunities for greater integration of interactive and urgent HPC policies, techniques, and technologies into HPC ecosystems.

In deep-inelastic positron-proton scattering, the lepton-jet azimuthal angular asymmetry is measured using data collected with the H1 detector at HERA. When the average transverse momentum of the lepton-jet system, $\lvert \vec{P}_\perp \rvert $, is much larger than the total transverse momentum of the system, $\lvert \vec{q}_\perp \rvert$, the asymmetry between parallel and antiparallel configurations, $\vec{P}_\perp$ and $\vec{q}_\perp$, is expected to be generated by initial and final state soft gluon radiation and can be predicted using perturbation theory. Quantifying the angular properties of the asymmetry therefore provides an additional test of the strong force. Studying the asymmetry is important for future measurements of intrinsic asymmetries generated by the proton's constituents through Transverse Momentum Dependent (TMD) Parton Distribution Functions (PDFs), where this asymmetry constitutes a dominant background. Moments of the azimuthal asymmetries are measured using a machine learning method for unfolding that does not require binning.

We present a same-level comparison of the most prominent inversion methods for the reconstruction of the matter density field in the quasi-linear regime from the Ly$\alpha$ forest flux. Moreover, we present a pathway for refining the reconstruction in the framework of numerical optimization. We apply this approach to construct a novel hybrid method. The methods which are used so far for matter reconstructions are the Richardson-Lucy algorithm, an iterative Gauss-Newton method and a statistical approach assuming a one-to-one correspondence between matter and flux. We study these methods for high spectral resolutions such that thermal broadening becomes relevant. The inversion methods are compared on synthetic data (generated with the lognormal approach) with respect to their performance, accuracy, their stability against noise, and their robustness against systematic uncertainties. We conclude that the iterative Gauss-Newton method offers the most accurate reconstruction, in particular at small S/N, but has also the largest numerical complexity and requires the strongest assumptions. The other two algorithms are faster, comparably precise at small noise-levels, and, in the case of the statistical approach, more robust against inaccurate assumptions on the thermal history of the intergalactic medium (IGM). We use these results to refine the statistical approach using regularization. Our new approach has low numerical complexity and makes few assumptions about the history of the IGM, and is shown to be the most accurate reconstruction at small S/N, even if the thermal history of the IGM is not known. Our code will be made publicly available under this https URL.

Manifold learning is a central task in modern statistics and data science. Many datasets (cells, documents, images, molecules) can be represented as point clouds embedded in a high dimensional ambient space, however the degrees of freedom intrinsic to the data are usually far fewer than the number of ambient dimensions. The task of detecting a latent manifold along which the data are embedded is a prerequisite for a wide family of downstream analyses. Real-world datasets are subject to noisy observations and sampling, so that distilling information about the underlying manifold is a major challenge. We propose a method for manifold learning that utilises a symmetric version of optimal transport with a quadratic regularisation that constructs a sparse and adaptive affinity matrix, that can be interpreted as a generalisation of the bistochastic kernel normalisation. We prove that the resulting kernel is consistent with a Laplace-type operator in the continuous limit, establish robustness to heteroskedastic noise and exhibit these results in numerical experiments. We identify a highly efficient computational scheme for computing this optimal transport for discrete data and demonstrate that it outperforms competing methods in a set of examples.

We introduce the MUSE gAlaxy Groups in COSMOS (MAGIC) survey, which was built to study the impact of environment on galaxy evolution over the last 8 Gyr. It consists of 17 MUSE fields targeting 14 massive structures at intermediate redshift ($0.321.5$. The spectroscopic redshift completeness is high: in the redshift range of [OII] emitters (0.25 \le z < 1.5), where most of the groups are found, it globally reaches a maximum of 80% down to $z_{app}^{++}=25.9$, and locally decreases from $\sim 100$% to $\sim50$% in magnitude bins from $z_{app}^{++}=23-24$ to $z_{app}^{++}=25.5$. We find that the fraction of quiescent galaxies increases with local density and with the time spent in groups. A morphological dichotomy is also found between bulge-dominated quiescent and disk-dominated star-forming galaxies. As environment gets denser, the peak of the stellar mass distribution shifts towards M_*>10^{10}~M_\odot, and the fraction of galaxies with M_*<10^9~M_\odot decreases significantly, even for star-forming galaxies. We also highlight peculiar features such as close groups, extended nebulae, and a gravitational arc. Our results suggest that galaxies are preprocessed in groups of increasing mass before entering rich groups and clusters. We publicly release two catalogs containing the properties of galaxies and groups, respectively.

We present a procedure to construct families of local, massive and interacting Haag-Kastler nets on the two-dimensional spacetime through an operator-algebraic method. An existence proof of local observable is given without relying on modular nuclearity. By a similar technique, another family of wedge-local nets is constructed using certain endomorphisms of conformal nets recently studied by Longo and Witten.

We present a new integral-field spectroscopic dataset of the central part of the Orion Nebula (M 42), observed with the MUSE instrument at the ESO VLT. We reduced the data with the public MUSE pipeline. The output products are two FITS cubes with a spatial size of ~5.9'x4.9' (corresponding to ~0.76 pc x 0.63 pc) and a contiguous wavelength coverage of 4595...9366 Angstrom, spatially sampled at 0.2". We provide two versions with a sampling of 1.25 Angstrom and 0.85 Angstrom in dispersion direction. Together with variance cubes these files have a size of 75 and 110 GiB on disk. They represent one of the largest integral field mosaics to date in terms of information content. We make them available for use in the community. To validate this dataset, we compare world coordinates, reconstructed magnitudes, velocities, and absolute and relative emission line fluxes to the literature and find excellent agreement. We derive a two-dimensional map of extinction and present de-reddened flux maps of several individual emission lines and of diagnostic line ratios. We estimate physical properties of the Orion Nebula, using the emission line ratios [N II] and [S III] (for the electron temperature $T_e$) and [S II] and [Cl III] (for the electron density $N_e$), and show two-dimensional images of the velocity measured from several bright emission lines.

We present a subgrid-scale model for the Multi-phase Interstellar medium, Star formation, and Turbulence (MIST) and explore its behaviour in high-resolution large-eddy simulations of isolated disc galaxies. MIST follows the evolution of a clumpy cold and a diffuse warm component of the gas within a volume element which exchange mass and energy via various cooling, heating and mixing processes. The star formation rate is dynamically computed from the state of the gas in the cold phase. An important feature of MIST is the treatment of unresolved turbulence in the two phases and its interaction with star formation and feedback by supernovae. This makes MIST a particularly suitable model for the interstellar medium in galaxy simulations. We carried out a suite of simulations varying fundamental parameters of our feedback implementation. Several observational properties of galactic star formation are reproduced in our simulations, such as an average star formation efficiency ~1%, a typical velocity dispersion around ~10 km/s in star-forming regions, and an almost linear relationship between the column densities of star formation and dense molecular gas.

We study cosmological structure formation with ultralight axion dark matter, or "fuzzy dark matter (FDM), using a particle-mesh scheme to account for the quantum pressure arising in the Madelung formulation of the Schr\"odinger-Poisson equations. Subpercent-level energy conservation and correct linear behavior are demonstrated. Whereas the code gives rise to the same core-halo profiles as direct simulations of the Schr\"odinger equation, it does not reproduce the detailed interference patterns. In cosmological simulations with FDM initial conditions, we find a maximum relative difference of O($10\%$) in the power spectrum near the quantum Jeans length compared to using a standard N-body code with identical initial conditions. This shows that the effect of quantum pressure during nonlinear structure formation cannot be neglected for precision constraints on a dark matter component consisting of ultralight axions.

Spatial modelling often uses Gaussian random fields to capture the stochastic nature of studied phenomena. However, this approach incurs significant computational burdens (O(n3)), primarily due to covariance matrix computations. In this study, we propose to use a low-rank approximation of a thin plate spline as a spatial random effect in Bayesian spatial models. We compare its statistical performance and computational efficiency with the approximated Gaussian random field (by the SPDE method). In this case, the dense matrix of the thin plate spline is approximated using a truncated spectral decomposition, resulting in computational complexity of O(kn2) operations, where k is the number of knots. Bayesian inference is conducted via the Hamiltonian Monte Carlo algorithm of the probabilistic software Stan, which allows us to evaluate performance and diagnostics for the proposed models. A simulation study reveals that both models accurately recover the parameters used to simulate data. However, models using a thin plate spline demonstrate superior execution time to achieve the convergence of chains compared to the models utilizing an approximated Gaussian random field. Furthermore, thin plate spline models exhibited better computational efficiency for simulated data coming from different spatial locations. In a real application, models using a thin plate spline as spatial random effect produced similar results in estimating a relative index of abundance for a benthic marine species when compared to models incorporating an approximated Gaussian random field. Although they were not the more computational efficient models, their simplicity in parametrization, execution time and predictive performance make them a valid alternative for spatial modelling under Bayesian inference.

We present a unified treatment of the abstract problem of finding the best approximation between a cone and spheres in the image of affine transformations. Prominent instances of this problem are phase retrieval and source localization. The common geometry binding these problems permits a generic application of algorithmic ideas and abstract convergence results for nonconvex optimization. We organize variational models for this problem into three different classes and derive the main algorithmic approaches within these classes (13 in all). We identify the central ideas underlying these methods and provide thorough numerical benchmarks comparing their performance on synthetic and laboratory data. The software and data of our experiments are all publicly accessible. We also introduce one new algorithm, a cyclic relaxed Douglas-Rachford algorithm, which outperforms all other algorithms by every measure: speed, stability and accuracy. The analysis of this algorithm remains open.

[Abbreviated] Rest-frame UV emission lines probe physical parameters of the emitting star-forming galaxies and their environments. The strongest main UV line, Ly$\alpha$, has been instrumental in advancing the general knowledge of galaxy formation in the early universe. However, observing Ly$\alpha$ emission becomes increasingly challenging at $z \gtrsim 6$ when the neutral hydrogen fraction of the CGM and IGM increases. Secondary weaker UV emission lines provide important alternative methods for studying galaxy properties at high redshift. We present a large sample of rest-frame UV emission line sources at intermediate redshift for calibrating and exploring the connection between secondary UV lines and the emitting galaxies' physical properties and their Ly$\alpha$ emission. The sample of 2052 emission line sources with $1.5 < z < 6.4$ was selected through untargeted source detection in three-dimensional MUSE data cubes. We searched optimally extracted 1D spectra of the full sample for UV emission features via emission line template matching, resulting in a sample of more than 100 rest-frame UV emission line detections. We show that the detection efficiency of (non-Ly$\alpha$) UV emission lines increases with survey depth, and that the UV emission line strength often correlate with the strength of Ciii]. We measured the velocity offsets of resonant emission lines with respect to systemic tracers as well as the electron density and the gas-phase abundance. Lastly, using "PhotoIonization Model Probability Density Functions" we find that the UV line emitters generally have ionization parameter log10(U) $\approx$ -2.5 and metal mass fractions that scatter around Z $\approx$ 10$^{-2}$, that is Z $\approx$ 0.66Z$\odot$. Value-added catalogs of the full sample of MUSE objects studied in this work and a collection of UV line emitters from the literature are provided with this paper.

In geometry processing, smoothness energies are commonly used to model scattered data interpolation, dense data denoising, and regularization during shape optimization. The squared Laplacian energy is a popular choice of energy and has a corresponding standard implementation: squaring the discrete Laplacian matrix. For compact domains, when values along the boundary are not known in advance, this construction bakes in low-order boundary conditions. This causes the geometric shape of the boundary to strongly bias the solution. For many applications, this is undesirable. Instead, we propose using the squared Frobenious norm of the Hessian as a smoothness energy. Unlike the squared Laplacian energy, this energy's natural boundary conditions (those that best minimize the energy) correspond to meaningful high-order boundary conditions. These boundary conditions model free boundaries where the shape of the boundary should not bias the solution locally. Our analysis begins in the smooth setting and concludes with discretizations using finite-differences on 2D grids or mixed finite elements for triangle meshes. We demonstrate the core behavior of the squared Hessian as a smoothness energy for various tasks.

Quantum weak energy inequalities have recently been extensively discussed as a condition on the dynamical stability of quantum field states, particularly on curved spacetimes. We formulate the notion of a quantum weak energy inequality for general dynamical systems on static background spacetimes and establish a connection between quantum weak energy inequalities and thermodynamic stability in the general setting. We show that the free scalar field in representations induced by quasifree Hadamard states provides an example system, and we indicate that (1) the microlocal spectrum condition, (2) quantum weak energy inequalities and (3) the existence of passive states (e.g., mixtures of ground- and thermal equilibrium states) are essentially equivalent, which is significant because each of these conditions becomes effective at a different length scale. [See title page of paper for the full abstract.]

The CARMENES radial velocity (RV) survey is observing 324 M dwarfs to search for any orbiting planets. In this paper, we present the survey sample by publishing one CARMENES spectrum for each M dwarf. These spectra cover the wavelength range 520--1710nm at a resolution of at least $R > 80,000$, and we measure its RV, H$\alpha$ emission, and projected rotation velocity. We present an atlas of high-resolution M-dwarf spectra and compare the spectra to atmospheric models. To quantify the RV precision that can be achieved in low-mass stars over the CARMENES wavelength range, we analyze our empirical information on the RV precision from more than 6500 observations. We compare our high-resolution M-dwarf spectra to atmospheric models where we determine the spectroscopic RV information content, $Q$, and signal-to-noise ratio. We find that for all M-type dwarfs, the highest RV precision can be reached in the wavelength range 700--900nm. Observations at longer wavelengths are equally precise only at the very latest spectral types (M8 and M9). We demonstrate that in this spectroscopic range, the large amount of absorption features compensates for the intrinsic faintness of an M7 star. To reach an RV precision of 1ms$^{-1}$ in very low mass M dwarfs at longer wavelengths likely requires the use of a 10m class telescope. For spectral types M6 and earlier, the combination of a red visual and a near-infrared spectrograph is ideal to search for low-mass planets and to distinguish between planets and stellar variability. At a 4m class telescope, an instrument like CARMENES has the potential to push the RV precision well below the typical jitter level of 3-4ms$^{-1}$.

An analytical framework is proposed to describe the elasticity, viscosity and fragility of metallic glasses in relation to their atomic-level structure and the effective interatomic interaction. The bottom-up approach starts with forming an effective Ashcroft-Born-Mayer interatomic potential based on Boltzmann inversion of the radial distribution function g(r) and on fitting the short-range part of $g(r)$ by means of a simple power-law approximation. The power exponent $\lambda$ represents a global repulsion steepness parameter. A scaling relation between atomic connectivity and packing fraction $Z \sim \phi^{1+\lambda}$ is derived. This relation is then implemented in a lattice-dynamical model for the high-frequency shear modulus where the attractive anharmonic part of the effective interaction is taken into account through the thermal expansion coefficient which maps the $\phi$-dependence into a $T$-dependence. The shear modulus as a function of temperature calculated in this way is then used within the cooperative shear model of the glass transition to yield the viscosity of the supercooled melt as a double-exponential function of $T$ across the entire Angell plot. The model, which has only one adjustable parameter (the characteristic atomic volume for high-frequency cage deformation) is tested against new experimental data of ZrCu alloys and provides an excellent one-parameter description of the viscosity down to the glass transition temperature.

The Easy Path Wavelet Transform is an adaptive transform for bivariate functions (in particular natural images) which has been proposed in [1]. It provides a sparse representation by finding a path in the domain of the function leveraging the local correlations of the function values. It then applies a one dimensional wavelet transform to the obtained vector, decimates the points and iterates the procedure. The main drawback of such method is the need to store, for each level of the transform, the path which vectorizes the two dimensional data. Here we propose a variation on the method which consists of firstly applying a segmentation procedure to the function domain, partitioning it into regions where the variation in the function values is low; in a second step, inside each such region, a path is found in some deterministic way, i.e. not data-dependent. This circumvents the need to store the paths at each level, while still obtaining good quality lossy compression. This method is particularly well suited to encode a Region of Interest in the image with different quality than the rest of the image. [1] Gerlind Plonka. The easy path wavelet transform: A new adaptive wavelet transform for sparse representation of two-dimensional data. Multiscale Modeling & Simulation, 7(3):1474$-$1496, 2008.