IntroductionGalaxy cluster-scale strong gravitational lensing systems are rare yet valuable tools for investigating dark matter and dark energy, as well as providing the opportunity to study the distant universe at flux levels and spatial resolutions that would otherwise be unavailable. Large-scale imaging surveys present unprecedented opportunities to expand the sample of cluster lenses.MethodsIn this study, we adopt a deep learning-based approach to identify cluster lenses from the DESI Legacy Imaging Surveys, utilizing the catalog of galaxy cluster candidates identified by Zou et al. (2021). Our lens-finder employs a ResNet-18 architecture, trained with mock images of cluster lenses as positives and observational images of cluster scale non-lenses as negatives. We do an iterative operation to increase the completeness of our work, namely adding the found true positive samples back to the training set and training again for several times. Human inspection is conducted to further refine the candidates, categorizing them into grades (A, B, C) according to the significance of the strongly lensed arcs.ResultsReviewing all 540,432 objects in Zou’s catalog, we discover 485 high-confidence cluster lens candidates with a cluster M500 range of 1013.67∼14.97M⊙ and a Brightest Central Galaxy (BCG) redshift range of 0.04∼0.89. After excluding the lens candidates listed in previous studies, we identify 247 newly discovered cluster lens candidates, including 16 grade A, 90 grade B, and 141 grade C.DiscussionThis catalog of cluster lens candidates is publicly available online, and follow-up observations are encouraged to confirm and conduct thorough investigations of these systems.
Venkatesan Selvaraj, Saradhambal Ramachandran Singarasubramania, Parthasarathy Pandu
et al.
Abstract This study examines the presence of heavy metals (HMs) and their environmental impacts in samples collected from the surface sediments of Karaikkal Beach, located on the southeastern coast of India. To assess the textural attributes and heavy metal content in the area, 26 sediment samples were collected and analyzed using atomic absorption spectroscopy (AAS). The sediments are composed primarily of sand (98.56%), followed by silt (1.2%), clay (0.41%), and calcium carbonate, which ranges from 3.19% to 6.71%, with a mean value of 4.77% present at a significant level. Organic inputs from riverine sources were also observed to influence sediment composition, and the average organic matter concentration is 0.52%, with values ranging from 0.26% to 0.75%. The HM concentrations followed the descending order: Fe (22,434.42–36,525.69 µg/g) > Mn (230.15–395.49 µg/g) > Cr (114.33–244.63 µg/g) > Ni (13.60–24.22 µg/g) > Pb (29.89–55.96 µg/g) > Cu (22.47–36.52 µg/g) > Zn (24.14–40.69 µg/g) > Co (12.00–20.32 µg/g). Fe and Mn concentrations were primarily controlled by fluvial inputs and terrestrial influences. The derived indices such as enrichment factor (EF), geo-accumulation index (Igeo), contamination factor (CF), pollution load index (PLI), sediment pollution index (SPI), and potential ecological risk index (PERI) reveal that the coastal sediments mostly fall within the unpolluted to slightly polluted categories, indicating a low ecological threat. The origin of metal enrichment in the sediment fractions is attributed to natural geogenic sources. The sources of HMs and their inter-element associations were interpreted using principal component analysis (PCA) and a correlation matrix. These baseline data underscore the importance of continuous environmental monitoring to identify emerging pollution patterns and guide sustainable coastal management.
The China Space Station Telescope (CSST) is the next-generation Stage IV survey telescope. It can simultaneously perform multi-band imaging and slitless spectroscopic wide- and deep-field surveys in ten years and an ultra-deep field (UDF) survey in two years, which are suitable for cosmological studies. Here we review several CSST cosmological probes, such as weak gravita- tional lensing, two-dimensional (2D) and three-dimensional (3D) galaxy clustering, galaxy cluster abundance, cosmic void, Type Ia supernovae (SNe Ia), and baryonic acoustic oscillations (BAO), and explore their capabilities and prospects in discovering new physics and opportunities in cosmology. We find that CSST will measure the matter distribution from small to large scales and the expansion history of the Universe with extremely high accuracy, which can provide percent-level stringent constraints on the property of dark energy and dark matter and precisely test the theories of gravity.
Cosmic birefringence (CB) is a promising probe of parity-violating physics beyond the Standard Model, characterized by the rotation of the linear polarization plane of cosmic microwave background (CMB) photons. This effect, quantified by the birefringence angle β, generates non-zero EB and TB correlations that are otherwise absent in standard cosmology. However, instrumental miscalibration angles α can mimic this signal, necessitating a joint estimation approach. In this work, we forecast the sensitivity of the AliCPT experiment, combined with Planck HFI data, on constraining the isotropic CB angle using a semi-analytical maximum-likelihood method. We simulate observations under various foreground complexities, rotation angles, and scanning strategies, and demonstrate that AliCPT can achieve an uncertainty of σ(β) = 0.09∘ with one-year data, which will improve to 0.026∘ after four years' observations. We also find that neglecting or mismodeling the foreground EB correlation will introduce significant biases, which can be alleviated under a clean but small sky patch.
Yaoteng Zhang, Shuaipeng Wang, Yanlong Chen
et al.
To maintain marine ecosystem health, effective algae monitoring is essential. Traditional threshold-based methods and standard machine learning techniques often fall short in accurately and automatically distinguishing algae types. This study presents Algae-Mamba, an advanced network for algae extraction that builds upon the visual state-space (VSS) model. The Algae-Mamba unified VSS model and the Kolmogorov–Arnold network proposed the Kolmogorov–Arnold visual state space (KVSS) model. KVSS block combines VSS for comprehensive global feature extraction with a small-kernel convolution module to capture local spatial and channel-specific information, supporting multiscale data processing and improving model generalization. The KVSS represents high-dimensional features using orthogonal polynomial combinations through Gram polynomials and leverages an attention mechanism to index interactions between target algae and their features, enabling the model to learn distinct characteristics of sargassum and ulva effectively and enhance extraction precision. To address the common misclassification between sargassum and ulva under limited spectral data, Algae-Mamba incorporates the normalized difference water index (NDWI) to enhance semantic richness. Furthermore, the model addresses class imbalances by employing a hybrid cross-entropy and Lovász-Softmax loss function, ensuring balanced and robust training. Unlike other methods that depend on extensive spectral information, Algae-Mamba achieves precise differentiation of sargassum and ulva with just 4-band spectral imagery, offering a powerful tool for monitoring marine ecological security. Testing on the GF-1 algae dataset demonstrates that Algae-Mamba surpasses other deep learning approaches in accurately extracting sargassum and ulva.
This paper presents a novel theoretical framework establishing quantum black holes (QBHs) as mediators of a fifth fundamental force that manifests through a filamentary spacetime network analogous to neural connectivity patterns. The Quantum Black Holes Force Cosmic Expansion (QBHFCE) theory provides a mathematically consistent explanation for diverse phenomena across 18 orders of magnitude, from quantum scales to cosmic horizons. By developing an enhanced 8D tensor bundle formalism with explicit projection mechanisms to 4D physics, we demonstrate how QBH dynamics naturally generate cosmic acceleration without requiring a cosmological constant. The filamentary structure through which QBHs mediate forces creates geometric patterns that mirror information-processing networks, suggesting a fundamental connection between spacetime geometry and neural organization. The QBH filament network’s similarity to neural connectomes suggests spacetime geometry intrinsically encodes information-processing principles observable from cosmic to cellular scales. This work offers a unified explanation for the Great Attractor mechanism, Hubble tension, and other cosmological anomalies through the consistent application of quantum gravitational principles across all scales. We propose that this framework provides a natural bridge between quantum cosmology and neuroscience, with specific falsifiable predictions testable through upcoming JWST observations, LHC experiments, and studies of neutron star physics.
To many people in our present era of mankind it appears already as a blasphemy to only aim at a purely physics-based explanation of the phenomenon of the "whole universe". This appears, as if already the attempt of a purely physical interpretation of this huge real entity of creation would be equivalent to the complete loss of its creational beauty, independence, and wonderfullness, rather degrading the cosmic creation to something like a simple, trivial-mechanic clockwork. But, to the contrary, is not in a first line just the human interpretation of the present cosmic world a merely wonderfull sign that this cosmic world - as the "completely transcendental phenomenon" with respect to the human consciusness talks to our consciousness -, thereby even entering into an authentic interaction? - Mankind understands the universe, - and the universe thereby becomes an ontic entity understood by the human ratio
Traditional cosmic ray simulations make use of the Montecarlo method in a very naive way to randomise energy and direction for each simulated particle. The flux of cosmic rays is modelled as a rain coming from a plane above the object of interest (detectors in particle physics applications, planes in dosimetry studies, etc.) with an experimental angular and energy distributions. This strategy is very inefficient because many of the particles never touch the detector. Here a refined way of implementing the Montecarlo method is proposed in order to generate a sample of events that hit the target volume whose angular distribution coincides with the one from the naive implementation. It is based on the projection of a sphere containing the target volume onto a plane tangent to it with a fixed angle, we call it the secant method. This configuration allows to compute the probability of a cosmic particle hitting the sphere with this incoming angle as proportional to the area of the corresponding section of a cylinder. The performance of this method is faster in terms of computing time and identical physical results are achieved. It has been implemented in REST-for-Physics framework and it is tested with the geometry of a real detector, the IAXO-D0 Micromegas X-ray detector for the future axion helioscope BabyIAXO. Our method is 37 times more efficient than the traditional Montecarlo schema for the same accuracy, being more useful when the target volume departs from spherical shape
Physics-based closures such as eddy-viscosity and backscattering models are widely used for large-eddy simulation (LES) of geophysical turbulence for applications including weather and climate prediction. However, these closures have parameters that are often chosen empirically. Here, for the first time, we semi-analytically derive the parameters of the Leith and Smagorinsky eddy-viscosity closures and the Jansen-Held backscattering closure for 2D geophysical turbulence. The semi-analytical derivation provides these parameters up to a constant that can be estimated from the turbulent kinetic energy spectrum of a few snapshots of direct numerical simulation (DNS) or other high-fidelity (eddy resolving) simulations, or even obtained from earlier analytical work based on renormalization group. The semi-analytically estimated closure parameters agree with those obtained from online (a-posteriori) learning in several setups of 2D geophysical turbulence in our earlier work. LES with closures that use these parameters can correctly reproduce the key statistics of DNS, including those of the extreme events and interscale energy and enstrophy transfers, and outperform the baselines (dynamic Leith and Smagorinsky and the latter with standard parameter).
In a recent paper, the NANOGrav collaboration studied new physics explanations of the observed pulsar timing residuals consistent with a stochastic gravitational wave background (SGWB) [1], including cosmic strings in the Nambu-Goto (NG) approximation. Analysing one of current models for the loop distribution, it was found that the cosmic string model is disfavored compared to other sources, for example, super massive black hole binaries (SMBHBs). When both SMBHB and cosmic string models are included in the analysis, an upper bound on a string tension Gμ≲ 10-10 was derived. However, the analysis did not accommodate results from cosmic string simulations in an underlying field theory, which indicate that at most a small fraction of string loops survive long enough to emit GW. Following and extending our previous study [2], we suppose that a fraction f NG of string loops follow NG dynamics and emit only GWs, and study the three different models of the loop distribution discussed in the LIGO-Virgo-KAGRA (LVK) collaboration analyses. We re-analyse the NANOGrav 15yrs data with our signal models by using the NANOGrav ENTERPRISE analysis code via the wrapper PTArcade. We find that loop distributions similar to LVK Model B and C yield higher Bayes factor than Model A analysed in the NANOGrav paper, as they can more easily accommodate a blue-tilted spectrum of the observed amplitude. Furthermore, because of the degeneracy of Gμ and f NG in determining the signal amplitude, our posterior distribution extends to higher values of Gμ, and in some cases the uppermost value of credible intervals is close to the Cosmic Microwave Background limit Gμ≲ 10-7. Hence, in addition to the pulsar timing array data, further information about the fraction of long-lived loops in a cosmic string network is required to constrain the string tension.
Cosmic (super)strings offer promising ways to test ideas about the early universe and physics at high energies. While in field theory constructions their tension is usually assumed to be constant (or at most slowly-varying), this is often not the case in the context of String Theory. Indeed, the tensions of both fundamental and field theory strings within a string compactification depend on the expectation values of the moduli, which in turn can vary with time. We discuss how the evolution of a cosmic string network changes with a time-dependent tension, both for long-strings and closed loops, by providing an appropriate generalisation of the Velocity One Scale (VOS) model and its implications. The resulting phenomenology is very rich, exhibiting novel features such as growing loops, percolation and a radiation-like behaviour of the long string network. We conclude with a few remarks on the impact for gravitational wave emission.
Tentative observations of cosmic-ray antihelium by the AMS-02 collaboration have re-energized the quest to use antinuclei to search for physics beyond the standard model. However, our transition to a data-driven era requires more accurate models of the expected astrophysical antinuclei fluxes. We use a state-of-the-art cosmic-ray propagation model, fit to high-precision antiproton and cosmic-ray nuclei (B, Be, Li) data, to constrain the antinuclei flux from both astrophysical and dark matter annihilation models. We show that astrophysical sources are capable of producing 𝒪(1) antideuteron events and 𝒪(0.1) antihelium-3 events over 15 years of AMS-02 observations. Standard dark matter models could potentially produce higher levels of these antinuclei, but showing a different energy-dependence. Given the uncertainties in these models, dark matter annihilation is still the most promising candidate to explain preliminary AMS-02 results. Meanwhile, any robust detection of antihelium-4 events would require more novel dark matter model building or a new astrophysical production mechanism.
The Amaterasu cosmic ray particle appears to have come from the direction of the local cosmic void. We take this as evidence that it is a magnetic monopole rather than a proton or nucleus. This in turn strongly suggests physics at high energy is described by a quiver gauge theory.
The cosmic distance duality relation (DDR), which links the angular-diameter and luminosity distances, is a cornerstone in modern cosmology. Any deviation from DDR may indicate new physics beyond the standard cosmological model. In this study, we used four high-precision time-delayed strong gravitational lensing (SGL) systems provided by H0LiCOW to test the validity of DDR. To this end, we directly compared the angular-diameter distances from these SGL systems with the luminosity distances from the latest Pantheon+ compilation of SNe Ia. To reduce the statistical errors arising from redshift matching, a Gaussian process method was applied to reconstruct the distance-redshift relation from the Pantheon+ dataset. We parameterized the possible violation of DDR in three different models. All results confirm the validity of DDR at confidence level. Additionally, Monte Carlo simulations based on the future LSST survey indicated that the precision of DDR could reach the level with 100 SGL systems.
D. B. Thomas, Theodore Anton, Timothy Clifton
et al.
The Parameterised Post-Newtonian (PPN) approach is the default framework for performing precision tests of gravity in nearby astrophysical systems. In recent works we have extended this approach for cosmological applications, and in this paper we use observations of the anisotropies in the Cosmic Microwave Background to constrain the time variation of the PPN parameters α and γ between last scattering and the present day. We find their time-averages over cosmological history should be within ∼ 20% of their values in GR, with α̅= 0.89+0.08 -0.09 and γ̅ = 0.90+0.07 -0.08 at the 68% confidence level. We also constrain the time derivatives of these parameters, and find that their present-day values should be within a factor of two of the best Solar System constraints. Many of these results have no counter-part from Solar System observations, and are entirely new constraints on the gravitational interaction. In all cases, we find that the data strongly prefer α̅ ≃ γ̅, meaning that observers would typically find local gravitational physics to be compatible with GR, despite considerable variation of α and γ being allowed over cosmic history. This study lays the groundwork for future precision tests of gravity that combine observations made over all cosmological and astrophysical scales of length and time.
Abstract Drylands are critical in regulating global carbon sequestration, but the resiliency of these semi‐arid shrub, grassland and forest systems is under threat from global warming and intensifying water stress. We used synergistic satellite optical‐Infrared (IR) and microwave remote sensing observations to quantify plant‐to‐stand level vegetation water potentials and seasonal changes in dryland water stress in the southwestern U.S. Machine‐learning was employed to re‐construct global satellite microwave vegetation optical depth (VOD) retrievals to 500‐m resolution. The re‐constructed results were able to delineate diverse vegetation conditions undetectable from the original 25‐km VOD record, and showed overall favorable correspondence with in situ plant water potential measurements (R from 0.60 to 0.78). The VOD water potential estimates effectively tracked plant water storage changes from hydro‐climate variability over diverse sub‐regions. The re‐constructed VOD record improves satellite capabilities for monitoring the storage and movement of water across the soil‐vegetation‐atmosphere continuum in heterogeneous drylands.