We extend field-level inference to jointly constrain the cosmological parameters $\{A,\omega_{\rm cdm},H_0\}$, in both real and redshift space. Our analyses are based on mock data generated using a perturbative forward model, with noise drawn from a Gaussian distribution with a constant power spectrum. This idealized setting, where the field-level likelihood is exactly Gaussian, allows us to precisely quantify the information content in the nonlinear field on large scales. We find that field-level inference accurately recovers all cosmological parameters in both real and redshift space, with uncertainties consistent with perturbation theory expectations. We show that these error bars are comparable to those obtained from a joint power spectrum and bispectrum analysis using the same perturbative model. Finally, we perform several tests using the Gaussian field-level likelihood to fit the mock data where the true noise model is non-Gaussian, and find significant biases in the inferred cosmological parameters. These results highlight that the success of field-level inference critically depends on using the correct likelihood, which may be the primary challenge for applying this method to smaller scales even in the perturbative regime.

Deep subspace clustering (DSC) algorithms face several challenges that hinder their widespread adoption across variois application domains. First, clustering quality is typically assessed using only the encoder's output layer, disregarding valuable information present in the intermediate layers. Second, most DSC approaches treat representation learning and subspace clustering as independent tasks, limiting their effectiveness. Third, they assume the availability of a held-out dataset for hyperparameter tuning, which is often impractical in real-world scenarios. Fourth, learning termination is commonly based on clustering error monitoring, requiring external labels. Finally, their performance often depends on post-processing techniques that rely on labeled data. To address this limitations, we introduce a novel single-view DSC approach that: (i) minimizes a layer-wise self expression loss using a joint representation matrix; (ii) optimizes a subspace-structured norm to enhance clustering quality; (iii) employs a multi-stage sequential learning framework, consisting of pre-training and fine-tuning, enabling the use of multiple regularization terms without hyperparameter tuning; (iv) incorporates a relative error-based self-stopping mechanism to terminate training without labels; and (v) retains a fixed number of leading coefficients in the learned representation matrix based on prior knowledge. We evaluate the proposed method on six datasets representing faces, digits, and objects. The results show that our method outperforms most linear SC algorithms with careffulyl tuned hyperparameters while maintaining competitive performance with the best performing linear appoaches.

We investigate the connection between the full- and flat-sky angular power spectra. First, we revisit this connection established on the geometric and physical grounds, namely that the angular correlations on the sphere and in the plane (flat-sky approximation) correspond to each other in the limiting case of small angles and a distant observer. To establish the formal conditions for this limit, we first resort to a simplified shape of the 3D power spectrum, which allows us to obtain analytic results for both the full- and flat-sky angular power spectra. Using a saddle-point approximation, we find that the flat-sky results are obtained in the limit when the comoving distance and wave modes $\ell$ approach infinity at the same rate. This allows us to obtain an analogous asymptotic expansion of the full-sky angular power spectrum for general 3D power spectrum shapes, including the LCDM Universe. In this way, we find a robust limit of correspondence between the full- and flat-sky results. These results also establish a mathematical relation, i.e., an asymptotic expansion of the ordinary hypergeometric function of a particular choice of arguments that physically corresponds to the flat-sky approximation of a distant observer. This asymptotic form of the ordinary hypergeometric function is obtained in two ways: relying on our saddle-point approximation and using some of the known properties of the hypergeometric function.

We report the results of a search for long-period ($100

Modelling the cosmic large-scale structure can be done through numerical N-body simulations or by using perturbation theory. Here, we present an N-body approach that effectively implements a multi-step forward model based on Lagrangian Perturbation Theory (LPT) in a $\Lambda$CDM Universe. This is achieved by introducing the second-order accurate BullFrog integrator, which automatically performs 2LPT time steps to second order without requiring the explicit computation of 2LPT displacements. Importantly, we show that BullFrog trajectories rapidly converge to the exact solution as the number of time steps increases, at any moment in time, even though 2LPT becomes invalid after shell-crossing. As a validation test, we compare BullFrog against other N-body integrators and high-order LPT, both for a realistic $\Lambda$CDM cosmology and for simulations with a sharp UV cutoff in the initial conditions. The latter scenario enables controlled experiments against LPT and, in practice, is particularly relevant for modelling coarse-grained fluids arising in the context of effective field theory. We demonstrate that BullFrog significantly improves upon other LPT-inspired integrators, such as FastPM and COLA, without incurring any computational overhead compared to standard N-body integrators. Implementing BullFrog in any existing N-body code is straightforward, particularly if FastPM is already integrated.

Galaxy clusters are expected to be dark matter (DM) reservoirs and storage rooms for the cosmic-ray protons (CRp) that accumulate along the cluster's formation history. Accordingly, they are excellent targets to search for signals of DM annihilation and decay at gamma-ray energies and are predicted to be sources of large-scale gamma-ray emission due to hadronic interactions in the intracluster medium. We estimate the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. We perform a detailed spatial and spectral modelling of the expected signal for the DM and the CRp components. For each, we compute the expected CTA sensitivity. The observing strategy of Perseus is also discussed. In the absence of a diffuse signal (non-detection), CTA should constrain the CRp to thermal energy ratio within the radius $R_{500}$ down to about $X_{500}<3\times 10^{-3}$, for a spatial CRp distribution that follows the thermal gas and a CRp spectral index $\alpha_{\rm CRp}=2.3$. Under the optimistic assumption of a pure hadronic origin of the Perseus radio mini-halo and depending on the assumed magnetic field profile, CTA should measure $\alpha_{\rm CRp}$ down to about $\Delta\alpha_{\rm CRp}\simeq 0.1$ and the CRp spatial distribution with 10% precision. Regarding DM, CTA should improve the current ground-based gamma-ray DM limits from clusters observations on the velocity-averaged annihilation cross-section by a factor of up to $\sim 5$, depending on the modelling of DM halo substructure. In the case of decay of DM particles, CTA will explore a new region of the parameter space, reaching models with \tau_{\chi}&gt;10^{27}s for DM masses above 1 TeV. These constraints will provide unprecedented sensitivity to the physics of both CRp acceleration and transport at cluster scale and to TeV DM particle models, especially in the decay scenario.

The gravitational collapse of collisionless matter leads to shell-crossing singularities that challenge the applicability of standard perturbation theory. Here, we present the first fully perturbative approach in three dimensions by using Lagrangian coordinates that asymptotically captures the highly nonlinear nature of matter evolution after the first shell-crossing. This is made possible essentially thanks to two basic ingredients: (1) We employ high-order standard Lagrangian perturbation theory to evolve the system until shell-crossing, and (2) we exploit the fact that the density caustic structure near the first shell-crossing begins generically with pancake formation. The latter property allows us to exploit largely known one-dimensional results to determine perturbatively the gravitational backreaction after collapse, yielding accurate solutions within our post-collapse perturbation theory (PCPT) formalism. We validate the PCPT predictions against high-resolution Vlasov-Poisson simulations and demonstrate that PCPT provides a robust framework for describing the early stages of post-collapse dynamics.

The next generation of galaxy surveys will provide highly precise measurements of galaxy clustering, therefore requiring a corresponding accuracy. Current approaches, which rely on approximations and idealized assumptions, may fall short in capturing the level of detail required for high-precision observations. In order to increase the modeling accuracy, recently, unequal-time contributions to the galaxy power spectrum have been introduced in order to include the effects of radial correlations. We present a generalization of the formalism for the observed unequal-time power spectrum, that includes Doppler and local general relativistic corrections, plus local primordial non-Gaussianity. We find that unequal time corrections can potentially mimic an effective $f_{\mathrm{NL}}$ of order unity. We provide a first assessment of the significance of unequal-time corrections for future galaxy clustering experiments, estimating a Signal-to-Noise-Ratio of $\sim3$ for Stage IV-like surveys.

Recent decades have seen a rise in the use of physics methods to study different societal phenomena. This development has been due to physicists venturing outside of their traditional domains of interest, but also due to scientists from other disciplines taking from physics the methods that have proven so successful throughout the 19th and the 20th century. Here we dub this field 'social physics' and pay our respect to intellectual mavericks who nurtured it to maturity. We do so by reviewing the current state of the art. Starting with a set of topics that are at the heart of modern human societies, we review research dedicated to urban development and traffic, the functioning of financial markets, cooperation as the basis for our evolutionary success, the structure of social networks, and the integration of intelligent machines into these networks. We then shift our attention to a set of topics that explore potential threats to society. These include criminal behaviour, large-scale migrations, epidemics, environmental challenges, and climate change. We end the coverage of each topic with promising directions for future research. Based on this, we conclude that the future for social physics is bright. Physicists studying societal phenomena are no longer a curiosity, but rather a force to be reckoned with. Notwithstanding, it remains of the utmost importance that we continue to foster constructive dialogue and mutual respect at the interfaces of different scientific disciplines.

We present a geometric placement algorithm for constructing template banks. We specialize in the case of Gravitational Wave searches, and use autoencoders for non-linear compression of the space of waveforms after these have been represented by a finite number of basis functions using an SVD decomposition. To ensure that the autoencoder is suitable for geometric placement we try to find a coordinate system describing the manifold of SVD coefficients such that distances in the latent and embedding space are equal. We show that the curvature of the banks is negligible and that such a system can be found. We then show that a geometric placement algorithm via a uniform grid in the latent space combined with rejection of unphysical points using a normalizing flow results in templates that, while slightly less in number than the similar construction using random forests of Ref.~\cite{Wadekar:2023kym}, perform slightly better in the effectualness tests, especially for high-mass binary systems. We discuss briefly how these dimensionality reduction techniques might be used in the context of cosmology, and a simple toy example where the periodicity of a flat manifold slightly complicates finding a distance-preserving coordinate system.

We propose a new approach for computing tunneling rates in quantum or thermal field theory with multiple scalar fields. It is based on exact analytical solutions of piecewise linear potentials with many segments that describes any given potential to arbitrary precision. The method is first developed for the single field case in 3 and 4 space-time dimensions and demonstrated on examples of classical potentials as well as the calculation of quantum fluctuations. A systematic expansion of the potential beyond the linear order is considered, taking into account higher order corrections, which paves the way for multiple scalar fields. We thereby provide a fast semi-analytical tool for evaluating the bounce action for theories with an extended scalar sector.

The last few years have seen the emergence of a wide array of novel techniques for analyzing high-precision data from upcoming galaxy surveys, which aim to extend the statistical analysis of galaxy clustering data beyond the linear regime and the canonical two-point (2pt) statistics. We test and benchmark some of these new techniques in a community data challenge "Beyond-2pt", initiated during the Aspen 2022 Summer Program "Large-Scale Structure Cosmology beyond 2-Point Statistics," whose first round of results we present here. The challenge dataset consists of high-precision mock galaxy catalogs for clustering in real space, redshift space, and on a light cone. Participants in the challenge have developed end-to-end pipelines to analyze mock catalogs and extract unknown ("masked") cosmological parameters of the underlying $\Lambda$CDM models with their methods. The methods represented are density-split clustering, nearest neighbor statistics, BACCO power spectrum emulator, void statistics, LEFTfield field-level inference using effective field theory (EFT), and joint power spectrum and bispectrum analyses using both EFT and simulation-based inference. In this work, we review the results of the challenge, focusing on problems solved, lessons learned, and future research needed to perfect the emerging beyond-2pt approaches. The unbiased parameter recovery demonstrated in this challenge by multiple statistics and the associated modeling and inference frameworks supports the credibility of cosmology constraints from these methods. The challenge data set is publicly available and we welcome future submissions from methods that are not yet represented.

We test the regime of validity of the effective field theory (EFT) of intrinsic alignments (IA) at the one-loop level by comparing with 3D halo shape statistics in N-body simulations. This model is based on the effective field theory of large-scale structure (EFT of LSS) and thus a theoretically well-motivated extension of the familiar non-linear alignment (NLA) model and the tidal-alignment-tidal-torquing (TATT) model. It contains a total of $8$ free bias parameters. Specifically, we measure the dark matter halo shape-shape multipoles $P_{EE}^{(0)}(k), P_{EE}^{(2)}(k), P_{BB}^{(0)}(k), P_{BB}^{(2)}(k)$ as well as the matter-shape multipoles $P_{\delta E}^{(0)}(k), P_{\delta E}^{(2)}(k)$ from the simulations and perform a joint fit to determine the largest wavenumber $k_{\text{max}}$ up to which the theory predictions from the EFT of IA are consistent with the measurements. We find that the EFT of IA is able to describe intrinsic alignments of dark matter halos up to $k_\text{max}=0.30\,h/\text{Mpc}$ at $z=0$. This demonstrates a clear improvement over other existing alignment models like NLA and TATT, which are only accurate up to $k_\text{max}=0.05\,h/\text{Mpc}$ . We examine the posterior distributions of the higher-order bias parameters, and show that their inclusion is necessary to describe intrinsic alignments in the quasi-linear regime. Further, the EFT of IA is able to accurately describe the auto-spectrum of intrinsic alignment B-modes, in contrast to the other alignment models considered.

Massive particles leave imprints on primordial non-Gaussianity via couplings to the inflaton, even despite their exponential dilution during inflation: practically, the Universe acts as a Cosmological Collider. We present the first dedicated search for spin-zero particles using BOSS redshift-space galaxy power spectrum and bispectrum multipoles, as well as Planck CMB non-Gaussianity data. We demonstrate that some Cosmological Collider models are well approximated by the standard equilateral and orthogonal parametrization; assuming negligible inflaton self-interactions, this facilitates us translating Planck non-Gaussianity constraints into bounds on Collider models. Many models have signatures that are not degenerate with equilateral and orthogonal non-Gaussianity and thus require dedicated searches. Here, we constrain such models using BOSS three-dimensional redshift-space galaxy clustering data, focusing on spin-zero particles in the principal series (i.e. with mass $m\geq 3H/2$) and constraining their couplings to the inflaton at varying speed and mass, marginalizing over the unknown inflaton self-interactions. This is made possible through an improvement in Cosmological Bootstrap techniques and the combination of perturbation theory and halo occupation distribution models for galaxy clustering. Our work sets the standard for inflationary spectroscopy with cosmological observations, providing the ultimate link between physics on the largest and smallest scales.

We revisit the flat-sky approximation for evaluating the angular power spectra of projected random fields by retaining information about the correlations along the line of sight. With broad, overlapping radial window functions, these line-of-sight correlations are suppressed and are ignored in the Limber approximation. However, retaining the correlations is important for narrow window functions or unequal-time spectra but introduces significant computational difficulties due to the highly oscillatory nature of the integrands involved. We deal with the integral over line-of-sight wave-modes in the flat-sky approximation analytically, using the FFTlog expansion of the 3D power spectrum. This results in an efficient computational method, which is a substantial improvement compared to any full-sky approaches. We apply our results to galaxy clustering (with and without redshift-space distortions), CMB lensing and galaxy lensing observables. For clustering, we find excellent agreement with the full-sky results on large (percent-level agreement) and intermediate or small (subpercent agreement) scales, dramatically out-performing the Limber approximation for both wide and narrow window functions, and in equal- and unequal-time cases. In the case of lensing, we show on the full sky that the angular power spectrum of the convergence can be very well approximated by projecting the 3D Laplacian (rather than the correct angular Laplacian) of the gravitational potential, even on large scales. Combining this approximation with our flat-sky techniques provides an efficient and accurate evaluation of the CMB lensing angular power spectrum on all scales.

Coastal water quality management is a public health concern, as poor coastal water quality can harbor pathogens that are dangerous to human health. Tourism-oriented countries need to actively monitor the condition of coastal water at tourist popular sites during the summer season. In this study, routine monitoring data of $Escherichia\ Coli$ and enterococci across 15 public beaches in the city of Rijeka, Croatia, were used to build machine learning models for predicting their levels based on environmental parameters as well as to investigate their relationships with environmental stressors. Gradient Boosting (Catboost, Xgboost), Random Forests, Support Vector Regression and Artificial Neural Networks were trained with measurements from all sampling sites and used to predict $E.\ Coli$ and enterococci values based on environmental features. The evaluation of stability and generalizability with 10-fold cross validation analysis of the machine learning models, showed that the Catboost algorithm performed best with R$^2$ values of 0.71 and 0.68 for predicting $E.\ Coli$ and enterococci, respectively, compared to other evaluated ML algorithms including Xgboost, Random Forests, Support Vector Regression and Artificial Neural Networks. We also use the SHapley Additive exPlanations technique to identify and interpret which features have the most predictive power. The results show that site salinity measured is the most important feature for forecasting both $E.\ Coli$ and enterococci levels. Finally, the spatial and temporal accuracy of both ML models were examined at sites with the lowest coastal water quality. The spatial $E. Coli$ and enterococci models achieved strong R$^2$ values of 0.85 and 0.83, while the temporal models achieved R$^2$ values of 0.74 and 0.67. The temporal model also achieved moderate R$^2$ values of 0.44 and 0.46 at a site with high coastal water quality.

The presence of a global internal symmetry in a quantum many-body system is reflected in the fact that the entanglement between its subparts is endowed with an internal structure, namely it can be decomposed as sum of contributions associated to each symmetry sector. The symmetry resolution of entanglement measures provides a formidable tool to probe the out-of-equilibrium dynamics of quantum systems. Here, we study the time evolution of charge-imbalance-resolved negativity after a global quench in the context of free-fermion systems, complementing former works for the symmetry-resolved entanglement entropy. We find that the charge-imbalance-resolved logarithmic negativity shows an effective equipartition in the scaling limit of large times and system size, with a perfect equipartition for early and infinite times. We also derive and conjecture a formula for the dynamics of the charged R\'enyi logarithmic negativities. We argue that our results can be understood in the framework of the quasiparticle picture for the entanglement dynamics, and provide a conjecture that we expect to be valid for generic integrable models.

This review provides an overview of defects in silicon carbide (SiC) with potential applications as quantum qubits. It begins with a brief introduction to quantum qubits and existing qubit platforms, outlining the essential criteria a defect must meet to function as a viable qubit. The focus then shifts to the most promising defects in SiC, notably the silicon vacancy (VSi) and divacancy (VC-VSi). A key challenge in utilizing these defects for quantum applications is their precise and controllable creation. Various fabrication techniques, including irradiation, ion implantation, femtosecond laser processing, and focused ion beam methods, have been explored to create these defects. Designed as a beginner-friendly resource, this review aims to support early-career experimental researchers entering the field of SiC-related quantum qubits. Providing an introduction to defect-based qubits in SiC offers valuable insights into fabrication strategies, recent progress, and the challenges that lie ahead.

Fe-Cr binary alloys serve as simplified model systems to study irradiation damage relevant to fusion structural materials. Here, Fe-3%Cr and Fe-5%Cr samples were irradiated with 4 MeV Fe ions under a dose rate of 4x10^5 dpa/s across a linear thermal gradient (120C to 480C) in a single experiment, enabling direct comparison of temperature and Cr content effects under identical conditions. Depth-resolved Laue micro-diffraction (~10^4 strain sensitivity), nanoindentation, and AFM reveal non-monotonic evolution of lattice strain and hardness: both decrease with temperature up to ~300C, then increase beyond. This turning point reflects a shift from enhanced defect mobility and partial recovery to solute-defect clustering and cavity formation, which stabilize damage. Fe-3%Cr shows consistently higher strain and hardening than Fe-5%Cr, especially at lower temperatures. Minimal change in post-indentation pile-up indicates limited softening or localization. These results highlight how Cr content and temperature jointly affect irradiation response, offering new insights into defect evolution in fusion-relevant alloys.

Hyperspectral imaging (HSI) holds significant potential for transforming the field of computational pathology. However, there is currently a shortage of pixel-wise annotated HSI data necessary for training deep learning (DL) models. Additionally, the number of HSI-based research studies remains limited, and in many cases, the advantages of HSI over traditional RGB imaging have not been conclusively demonstrated, particularly for specimens collected intraoperatively. To address these challenges we present a database consisted of 27 HSIs of hematoxylin-eosin stained frozen sections, collected from 14 patients with colon adenocarcinoma metastasized to the liver. It is aimed to validate pixel-wise classification for intraoperative tumor resection. The HSIs were acquired in the spectral range of 450 to 800 nm, with a resolution of 1 nm, resulting in images of 1384x1035 pixels. Pixel-wise annotations were performed by three pathologists. To overcome challenges such as experimental variability and the lack of annotated data, we combined label-propagation-based semi-supervised learning (SSL) with spectral-spatial features extracted by: the multiscale principle of relevant information (MPRI) method and tensor singular spectrum analysis method. Using only 1% of labeled pixels per class the SSL-MPRI method achieved a micro balanced accuracy (BACC) of 0.9313 and a micro F1-score of 0.9235 on the HSI dataset. The performance on corresponding RGB images was lower, with a micro BACC of 0.8809 and a micro F1-score of 0.8688. These improvements are statistically significant. The SSL-MPRI approach outperformed six DL architectures trained with 63% of labeled pixels. Data and code are available at: this https URL.