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Extracting non-Gaussian information from the next generation weak lensing surveys will require fast and accurate full-sky simulations. This is difficult to achieve in practice with existing simulation methods: ray-traced $N$-body simulations are computationally expensive, and approximate simulation methods (such as lognormal mocks) are not accurate enough. Here, we present GANSky, an interpretable machine learning method that uses Generative Adversarial Networks (GANs) to produce fast and accurate full-sky tomographic weak lensing maps. The input to our GAN are lognormal maps that approximately describe the late-time convergence field of the Universe. Starting from these lognormal maps, we use GANs to learn how to locally redistribute mass to achieve simulation-quality maps. This can be achieved using remarkably small networks ($\approx 10^3$ parameters). We validate the GAN maps by computing a number of summary statistics in both simulated and GANSky maps. We show that GANSky maps correctly reproduce both the mean and $\chi^2$ distribution for several statistics, specifically: the 2-pt function, 1-pt PDF, peak and void counts, and the equilateral, folded and squeezed bispectra. These successes makes GANSky an attractive tool to compute the covariances of these statistics. In addition to being useful for rapidly generating large ensembles of artificial data sets, our method can be used to extract non-Gaussian information from weak lensing data with field-level or simulation-based inference., Comment: 14 pages, 10 Figures, comments welcome

In this study, we investigate the spin dynamics of a frustrated Kagom\'e lattice system, focusing on the nonlinearity of spin oscillations induced by a harmonic magnetic field under varying strengths of Dzyaloshinskii-Moriya interaction (DMI), exchange field, and anisotropy energy. We have utilized Poincar\'e Surface Sections (PSS) and Power Spectra (PS) for different DMI and anisotropy energy to study the spin dynamics. Our findings reveal that when the DMI strength, external field, anisotropy, and applied magnetic field are weak, the oscillations are quasi-periodic, mostly dominated by the exchange field. With the increase in the DMI strength, the oscillation of the system becomes highly aperiodic. Strong anisotropy tends to induce periodic oscillations, but increasing DMI eventually leads to chaotic behaviour. Additionally, the external magnetic field destabilizes the periodicity of oscillations in systems with weak easy-axis anisotropy, but the systems with strong anisotropy, the oscillations remain unaffected by the external field's strength. Our analysis of magnon dispersion and magnetic resonance (MR) spectra reveals multiple resonance peaks at higher DMI strengths, indicating a complex interplay between spin wave excitation and system parameters. These results underscore the importance of understanding the inherent DMI and anisotropy in the Kagom\'e lattice during fabrication for various applications. Moreover, our comprehensive analysis of spin dynamics in a Kagom\'e lattice system demonstrates a clear transition from quasi-periodic to chaotic oscillations with the increase in DMI strength.

Validating modeling choices through simulated analyses and quantifying the impact of different systematic effects will form a major computational bottleneck in the preparation for 3$\times$2 analysis with Stage-IV surveys such as Vera Rubin Observatory's Legacy Survey of Space and Time (LSST). We can significantly reduce the computational requirements by using machine learning based emulators, which allow us to run fast inference while maintaining the full realism of the data analysis pipeline. In this paper, we use such an emulator to run simulated 3$\times$2 (cosmic shear, galaxy-galaxy lensing, and galaxy clustering) analyses for mock LSST-Y1/Y3/Y6/Y10 surveys and study the impact of various systematic effects (galaxy bias, intrinsic alignment, baryonic physics, shear calibration and photo-$z$ uncertainties). Closely following the DESC Science Requirement Document (with several updates) our main findings are: {\it a)} The largest contribution to the `systematic error budget' of LSST 3$\times$2 analysis comes from galaxy bias uncertainties, while the contribution of baryonic and shear calibration uncertainties are significantly less important. {\it b)} Tighter constraints on intrinsic alignment and photo-$z$ parameters can improve cosmological constraints noticeably, which illustrates synergies of LSST and spectroscopic surveys. {\it c)} The scale cuts adopted in the DESC SRD may be too conservative and pushing to smaller scales can increase cosmological information significantly. {\it d)} We investigate the impact of photo-$z$ outliers on 3$\times$2 pt analysis and find that we need to determine the outlier fraction to within $5-10\%$ accuracy to ensure robust cosmological analysis. We caution that these findings depend on analysis choices (parameterizations, priors, scale cuts) and can change for different settings., Comment: 16 pages, 10 Figures, To be submitted to PRD. Comments welcome

The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge to analyzing theoretical models of cosmology. Additionally, new mitigation techniques of baryonic effects, intrinsic alignment, and other systematic effects will inevitably introduce more parameters, slowing down the convergence of Bayesian analyses. In this scenario, machine-learning-based accelerators are a promising solution, capable of reducing the computational costs and execution time of such tools by order of thousands. Yet, they have not been able to provide accurate predictions over the wide prior ranges in parameter space adopted by Stage III/IV collaborations in studies employing real-space two-point correlation functions. This paper offers a leap in this direction by carefully investigating the modern transformer-based neural network (NN) architectures in realistic simulated Rubin Observatory year one cosmic shear $\Lambda$CDM inferences. Building on the framework introduced in Part I, we generalize the transformer block and incorporate additional layer types to develop a more versatile architecture. We present a scalable method to efficiently generate an extensive training dataset that significantly exceeds the scope of prior volumes considered in Part I, while still meeting strict accuracy standards. Through our meticulous architecture comparison and comprehensive hyperparameter optimization, we establish that the attention-based architecture performs an order of magnitude better in accuracy than widely adopted NN designs. Finally, we test and apply our emulators to calibrate tension metrics.

We update the field-level inference code KARMMA to enable tomographic forward-modelling of shear maps. Our code assumes a lognormal prior on the convergence field, and properly accounts for the cross-covariance in the lensing signal across tomographic source bins. We use mock weak lensing data from N-body simulations to validate our mass-mapping forward model by comparing our posterior maps to the input convergence fields. We find that KARMMA produces more accurate reconstructions than traditional mass-mapping algorithms. More-over, the KARMMA posteriors reproduce all statistical properties of the input density field we tested -- one- and two-point functions, and the peak and void number counts -- with $\lesssim~10\%$ accuracy. Our posteriors exhibit a small bias that increases with decreasing source redshift, but these biases are small compared to the statistical uncertainties of current (DES) cosmic shear surveys. Finally, we apply KARMMA to Dark Energy Survey Year 3 (DES-Y3) weak lensing data, and verify that the two point shear correlation function $\xi_+$ is well fit by the correlation function of the reconstructed convergence field. This is a non-trivial test that traditional mass mapping algorithms fail. The code is publicly available at https://github.com/Supranta/KaRMMa.git. KARMMA DES-Y3 mass maps are publicly available at https://zenodo.org/records/10672062., Comment: 14 pages, 13 figures, To be submitted to PRD, Comments welcome

We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its capabilities in forecasting models beyond the standard $\Lambda$CDM, we forecast how well LSST Year 1 data will be able to probe the consistency between geometry $\Omega^{\rm geo}_\mathrm{m}$ and growth $\Omega^{\rm growth}_\mathrm{m}$ dark matter densities in the so-called split $\Lambda$CDM parameterization. When trained with a few million samples, our emulator shows uniform accuracy across a wide range in an 18-dimensional parameter space. We provide a detailed comparison of three neural network designs, illustrating the importance of adopting state-of-the-art Transformer blocks. Our study also details their performance when computing Bayesian evidence for cosmic shear on three fiducial cosmologies. The transformers-based emulator is always accurate within PolyChord's precision. As an application, we use our emulator to study the degeneracies between dark energy models and growth geometry split parameterizations. We find that the growth-geometry split remains to be a meaningful test of the smooth dark energy assumption., Comment: 16 pages, 6 figures

In this work, we have studied the spin dynamics of a synthethic Antiferromagnet (SAFM)$|$Heavy Metal (HM)$|$Ferromagnet (FM) double barrier magnetic tunnel junction (MTJ) in presence of Ruderman-Kittel-Kasuya-Yoside interaction (RKKYI), interfacial Dzyaloshinskii-Moriya interaction (iDMI), N\'eel field and Spin-Orbit Coupling (SOC) with different Spin Transfer Torque (STT). We employ Landau-Lifshitz-Gilbert-Slonczewski (LLGS) equation to investigate the AFM dynamics of the proposed system. We found that the system exhibits a transition from regular to damped oscillations with the increase in strength of STT for systems with weaker iDMI than RKKYI while display sustained oscillatons for system having same order of iDMI and RKKYI. On the other hand the iDMI dominating system exhibits self-similar but aperiodic patterns in absence of N\'eel field. In the presence of N\'eel field, the RKKYI dominating systems exhibit chaotic oscillations for low STT but display sustained oscillation under moderate STT. Our results suggest that the decay time of oscillations can be controlled via SOC. The system can works as an oscillator for low SOC but display nonlinear characteristics with the rise in SOC for systems having weaker iDMI than RKKYI while an opposite characteristic are noticed for iDMI dominating systems. We found periodic oscillations under low external magnetic field in RKKYI dominating systems while moderate field are necessary for sustained oscillation in iDMI dominating systems. Moreover, the system exhibits saddle-node bifurcation and chaos under moderate N\'eel field and SOC with suitable iDMI and RKKYI. In addition, our results indicate that the magnon lifetime can be enhanced by increasing the strength of iDMI for both optical and acoustic modes.

Field-level inference is emerging as a promising technique for optimally extracting information from cosmological datasets. Indeed, previous analyses have shown field-based inference produces tighter parameter constraints than power spectrum analyses. However, estimates of the detailed quantitative gain in constraining power differ. Here, we demonstrate the gain in constraining power depends on the parameter space being constrained. As a specific example, we find that field-based analysis of an LSST Y1-like mock data set only marginally improves constraints relative to a 2-point function analysis in $\Lambda$CDM, yet it more than doubles the constraining power of the data in the context of $w$CDM models. This effect reconciles some, but not all, of the discrepant results found in the literature. Our results demonstrate the importance of using a full systematics model when quantifying the information gain for realistic field-level analyses of future data sets., Comment: 5 pages, 4 figures, comments welcome

In this work, we investigate the physical and electronic properties of $\beta_{12}$-borophene FET-based gas sensor using a theoretical quantum capacitance model based on tight-binding approach. We study the impact of adsorbed NH$_3$, NO, NO$_2$ and CO gas molecule on its density of states, carrier concentration, quantum capacitance and I-V characteristics. We found a remarkable variation in the energy band structure and the density of states (DOS) of the $\beta_{12}$-borophene in the presence of the adsorbed gas molecule. The appearance of non-identical Van-Hove singularities in the DOS in the presence of adsorbed gas molecules strongly indicates the high sensitivity of $\beta_{12}$-borophene. We found a significant increase in the carrier concentration for NH$_3$ gas while it decreases for all other gases. Moreover, a drastic change in quantum capacitance and current-voltage relation is also observed in adsorbed gases. The different properties of the given gas molecules are compared with the pristine borophene and found to exhibit distinct wrinkles in each case, thereby indicating the strong selectivity of our proposed gas sensor. Though $\beta_{12}$ - borophene is found to be highly sensitive for all studied gases, the NO gas is found to be most sensitive compared to the others.

We study the Andreev levels, supercurrent and tunnelling conductance in a clean Ising superconductor (ISC)/half metal (HM)/Ising superconductor (ISC) Josephson junction with spin-active interfaces using Bogoliubov-de Gennes equations. We theoretically demonstrate the effect of spin mixing, spin flipping processes and spin-orbit coupling (SOC) of the ISC on Andreev Bound States (ABS) spectra, current phase relation (CPR) and tunnelling conductance in transparent and opaque barrier limit. We witness an additional splitting of the Andreev levels due to SOC of the ISC and $0 - \pi$ transition for different barrier magnetic moments. Also, different $\phi$ - junctions can be achieved by tuning the strength of the barrier magnetic moment and spin mismatch angle. Moreover, a possible $0 - \pi$ transition can also be achieved for SOC stronger than the chemical potential of the ISC using suitable control parameters. The interplay of spin mixing and spin flipping processes with SOC can also host Majorana modes in the proposed system. The tunnelling conductance is found to be dependent on the spin mismatch angle. Also, a finite sub-gap conductance is observed, which indicates different probabilities of Andreev reflected electrons and holes in the presence of SOC. Furthermore, anomalous Andreev levels are observed for different HM length scales signifying its role in the tunability of the $\phi$ - phase Josephson junction.

In this paper, we have realized the left-right symmetric model with modular symmetry. We have used $\Gamma$(3) modular group which is isomorphic to non-abelian discrete symmetry group $A_4$. The advantage of using modular symmetry is the non-requirement for the use of extra particles called 'flavons'. In this model, the Yukawa couplings are expressed in terms of modular forms $(Y_1,Y_2,Y_3)$. In this work, we have studied minimal Left-Right Symmetric Model for both type-I and type-II dominances. Here, we have calculated the values for the Yukawa couplings and then plotted it against the sum of the neutrino masses. The results obtained are well within the experimental limits for the desired values of sum of neutrino masses. We have also briefly analyzed the effects of the implications of modular symmetry on neutrinoless double beta decay with the new physics contributions within Left-Right Symmetric Model., Comment: 35 pages, 42 figures