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In this paper, we employ the Density Dependent Relativistic Hadron (DDRH) field theoretical Model in a Bayesian analysis to investigate the equation of state (EOS) of dense matter featuring antikaon condensation for $K^-$ and $\bar{K}^0$ inside neutron stars. The vector coupling parameters within the kaonic sector are determined through the iso-spin counting rule and quark model. Our study integrates various constraints, including $\chi$EFT calculations, nuclear saturation properties, and astrophysical observations from pulsars PSR J0030+0451 and PSR J0740+66 and from the GW170817 event. We present posterior distributions of model parameters derived from these constraints, enabling us to explore the distributions of nuclear matter properties and neutron star (NS) characteristics such as radii, tidal deformabilities, central energy densities, and speed of sound. The antikaon potential at the 68(90)\% confidence intervals is determined to be $-129.36^{+12.53(+32.617)}_{-3.837(-5.696)}$ MeV. This aligns with several studies providing estimates within the range of $-120$ to $-150$ MeV. We find that the maximum neutron star mass is constrained to around 2M$_\odot$ due to the significant softening of the EOS caused by antikaon condensation. This softening results in a considerable decrease in the speed of sound. Although antikaon condensation for $K^-$ is not feasible inside the canonical neutron stars, it becomes feasible for higher NS masses. The condensation of both $K^-$ and $\bar{K}^0$ is probably present in the interior of neutron star with mass greater than 2M$_\odot$. We also discuss the interconnections among input variables, isoscalar and isovector aspects of the EOS, and specific NS properties in the context of antikaon condensation., Comment: 19 pages, 10 figures, Accepted for publication in Physical Review C (in press)

Multitask learning is a widely recognized technique in the field of computer vision and deep learning domain. However, it is still a research question in remote sensing, particularly for hyperspectral imaging. Moreover, most of the research in the remote sensing domain focuses on small and single-task-based annotated datasets, which limits the generalizability and scalability of the developed models to more diverse and complex real-world scenarios. Thus, in this study, we propose a multitask deep learning model designed to perform multiple classification and regression tasks simultaneously on hyperspectral images. We validated our approach on a large hyperspectral dataset called TAIGA, which contains 13 forest variables, including three categorical variables and ten continuous variables with different biophysical parameters. We design a sharing encoder and task-specific decoder network to streamline feature learning while allowing each task-specific decoder to focus on the unique aspects of its respective task. Additionally, a dense atrous pyramid pooling layer and attention network were integrated to extract multi-scale contextual information and enable selective information processing by prioritizing task-specific features. Further, we computed multitask loss and optimized its parameters for the proposed framework to improve the model performance and efficiency across diverse tasks. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods. We trained our model across 10 seeds/trials to ensure robustness. Our proposed model demonstrates higher mean performance while maintaining lower or equivalent variability. To make the work reproducible, the codes will be available at https://github.com/Koushikey4596/Multitask-Deep-Learning-Model-for-Taiga-datatset.

Because of their extreme densities and consequently, gravitational potential, compact objects such as neutron stars can prove to be excellent captors of dark matter particles. Considering purely gravitational interactions between dark and hadronic matter, we construct dark matter admixed stars composed of two-fluid matter subject to current astrophysical constraints of maximum mass and tidal deformability. We choose a wide range of parameters to construct the dark matter equation of state, and the DDME2 parameterization for the hadronic equation of state. We then examine the effect of dark matter on the stellar structure, tidal deformability and non-radial modes considering the relativistic Cowling approximation. We find the effect on $p$-modes is substantial, with frequencies decreasing up to the typical $f-$mode frequency range for most stars with a dark matter halo. The effects on the $f-$mode frequency are less extreme. Finally, we find the most probable and $1\sigma$ values of the dark matter parameters used in this study., Comment: 23 pages and 5 figures

Gravito-capillary waves at free-surfaces are ubiquitous in several natural and industrial processes involving quiescent liquid pools bounded by cylindrical walls. These waves emanate from the relaxation of initial interface distortions, which often take the form of a cavity (depression) centred on the symmetry axis of the container. These surface waves reflect from the container walls leading to a radially inward propagating wave-train converging (focussing) onto the symmetry axis. Under the inviscid approximation and for sufficiently shallow cavities, the relaxation is well-described by the linearised potential-flow equations. Naturally, adding viscosity to such a system introduces viscous dissipation that enervates energy and dampens the oscillations at the symmetry axis. However, for viscous liquids and deeper cavities, these equations are qualitatively inaccurate. In this study, we elucidate a modal approach to study the initial-value problem for concentric gravito-capillary waves generated on a free-surface for inviscid as well as viscous liquids. For a sufficiently deep cavity, the inward focusing of waves results in large interfacial oscillations at the axis, necessitating a second-order nonlinear theory. We demonstrate that this theory effectively models the interfacial behavior and highlights the crucial role of nonlinearity near the symmetry axis. Contrary to expectations, the addition of slight viscosity further intensifies the oscillations at the symmetry axis. This finding underscores the limitations of the potential flow model and suggests avenues for more accurate modelling of such complex free-surface flows.

High-spin states in the transitional $^{217}$Ac nucleus are established up to 3.8 MeV excitation energy and $I^{\pi} =$ 41/2$^+$ with the addition of around 20 new transitions. The structure of the yrast and near-yrast states below the 29/2$^+$ isomer is revisited. The inconsistencies in the level schemes reported earlier are resolved. The level structure above the 29/2$^+$ isomer is established for the first time. Large-basis shell-model calculations with the KHPE interaction are performed to compare the experimentally observed level energies with the theoretical predictions. A comparison with the systematics of the N = 128 isotones suggests that the yrast structures result from a weak coupling of the odd proton to the even-even 216Ra core, which is consistent with the shell-model configurations. Furthermore, alpha decay of the 29/2$^+$ isomer is revisited and the decay scheme established from this work is discussed in the framework of the shell model., Comment: 11 pages, 9 figures

Atmospheric neutrinos can act as a tool to probe the interior of Earth using weak interactions, and can provide information complementary to that obtained from gravitational and seismic measurements. While passing through Earth, multi-GeV neutrinos encounter Earth matter effects due to the coherent forward scattering with the ambient electrons, which alter the neutrino oscillation probabilities. These matter effects depend upon the density distribution of electrons inside Earth, and hence, can be used to determine the internal structure of Earth. In this work, we employ a five-layered model of Earth where the layer densities and radii are modified, keeping the mass and moment of inertia of Earth unchanged and respecting the hydrostatic equilibrium condition. We use the proposed INO-ICAL detector as an example of an atmospheric neutrino experiment that can distinguish between neutrinos and antineutrinos efficiently in the multi-GeV energy range. Our analysis demonstrates the role such an experiment can play in simultaneously constraining the density jumps inside Earth and the location of the core-mantle boundary., Comment: 34 pages, 10 figures, and 3 tables. Comments are welcome

The massive stars end their lives by supernova explosions leaving central compact objects that may evolve into neutron stars. Initially, after birth, the star remains hot and gradually cools down. We explore the matter and star properties during this initial stage of the compact stars considering the possibility of the appearance of deconfined quark matter in the core of the star. At the initial stage after the supernova explosion, the occurrence of non-radial oscillation in the newly born compact object is highly possible. Non-radial oscillations are an important source of GWs. There is a high chance for GWs from these oscillations, especially the nodeless fundamental (f-) mode to be detected by next-generation GW detectors. We study the evolution in frequencies of non-radial oscillation after birth considering phase transition and predicting the possible signature for different possibilities of theoretical compact star models., Comment: Accepted for publication in MNRAS

Experiments in the human brain reveal switching between different activity patterns and functional network organization over time. Recently, multilayer modeling has been employed across multiple neurobiological levels (from spiking networks to brain regions) to unveil novel insights into the emergence and time evolution of synchrony patterns. We consider two layers with the top layer directly coupled to the bottom layer. When isolated, the bottom layer would remain in a specific stable pattern. However, in the presence of the top layer, the network exhibits spatiotemporal switching. The top layer in combination with the inter-layer coupling acts as a symmetry breaker, governing the bottom layer and restricting the number of allowed symmetry-induced patterns. This structure allows us to demonstrate the existence and stability of pattern states on the bottom layer, but most remarkably, it enables a simple mechanism for switching between patterns based on the unique symmetry-breaking role of the governing layer. We demonstrate that the symmetry breaker prevents complete synchronization in the bottom layer, a situation that would not be desirable in a normal functioning brain. We illustrate our findings using two layers of Hindmarsh-Rose (HR) oscillators, employing the Master Stability function approach in small networks to investigate the switching between patterns., Comment: 15 Pages, 7 figures

The worm gearbox is a high-speed transmission system that plays a vital role in various industries. Therefore it becomes necessary to develop a robust fault diagnosis scheme for worm gearbox. Due to advancements in sensor technology, researchers from academia and industries prefer deep learning models for fault diagnosis purposes. The optimal selection of hyperparameters (HPs) of deep learning models plays a significant role in stable performance. Existing methods mainly focused on manual tunning of these parameters, which is a troublesome process and sometimes leads to inaccurate results. Thus, exploring more sophisticated methods to optimize the HPs automatically is important. In this work, a novel optimization, i.e. amended gorilla troop optimization (AGTO), has been proposed to make the convolutional neural network (CNN) adaptive for extracting the features to identify the worm gearbox defects. Initially, the vibration and acoustic signals are converted into 2D images by the Morlet wavelet function. Then, the initial model of CNN is developed by setting hyperparameters. Further, the search space of each Hp is identified and optimized by the developed AGTO algorithm. The classification accuracy has been evaluated by AGTO-CNN, which is further validated by the confusion matrix. The performance of the developed model has also been compared with other models. The AGTO algorithm is examined on twenty-three classical benchmark functions and the Wilcoxon test which demonstrates the effectiveness and dominance of the developed optimization algorithm. The results obtained suggested that the AGTO-CNN has the highest diagnostic accuracy and is stable and robust while diagnosing the worm gearbox.

Atmospheric neutrinos offer the possibility of exploring the internal structure of Earth. This information is complementary to the traditional probes of seismic and gravitational studies. While propagating through Earth, the multi-GeV neutrinos encounter the Earth's matter effects due to the coherent forward scattering with the ambient electrons, which alters the neutrino oscillation probabilities. We present how well an atmospheric neutrino oscillation experiment like the 50 kt Iron Calorimeter (ICAL) detector at India-based Neutrino Observatory would validate the presence of Earth's core, measure the location of the core-mantle boundary (CMB), and probe the dark matter (DM) inside the Earth in a unique way through Earth matter effects in neutrino oscillations. Owing to good angular resolution, ICAL can observe the core-passing neutrinos efficiently. Due to its magnetized setup, it would be able to observe neutrinos and antineutrinos separately. With 500 kt$\cdot$yr exposure, the presence of Earth's core can be independently confirmed at ICAL with a median $\Delta\chi^2$ of 7.45 (4.83) for normal (inverted) mass ordering. With 1000 kt$\cdot$yr exposure, ICAL would be able to locate the CMB with a precision of about $\pm$ 250 km at $1\sigma$. It would also be sensitive to the possible presence of dark matter with 3.5% of the mass of Earth at $1\sigma$. The charge identification capability of ICAL would play an important role in achieving these precisions., Comment: 4 pages, 2 figures; contribution to proceedings of The European Physical Society Conference on High Energy Physics (EPS-HEP2023)