Gravitational-wave (GW) ringdown signals from black holes (BHs) encode crucial information about the gravitational dynamics in the strong-field regime, which offers unique insights into BH properties. In the future, the improving sensitivity of GW detectors is to enable the extraction of multiple quasi-normal modes (QNMs) from ringdown signals. However, incorporating multiple modes drastically enlarges the parameter space, posing computational challenges to data analysis. Inspired by the $F$-statistic method in the continuous GW searches, we develope an algorithm, dubbed as FIREFLY, for accelerating the ringdown signal analysis. FIREFLY analytically marginalizes the amplitude and phase parameters of QNMs to reduce the computational cost and speed up the full-parameter inference from hours to minutes, while achieving consistent posterior and evidence. The acceleration becomes more significant when more QNMs are considered. Rigorously based on the principle of Bayesian inference and importance sampling, our method is statistically interpretable, flexible in prior choice, and compatible with various advanced sampling techniques, providing a new perspective for accelerating future GW data analysis.

As physicists pursue precision neutrino measurements, complementary experiments covering varied oscillation landscapes have become essential for resolving current tensions in global fits. This thesis presents projected sensitivities and forecasted performance of two next-generation long-baseline experiments: DUNE and T2HK, through detailed simulations addressing fundamental questions including neutrino mass ordering, leptonic CP violation, and the octant of $\theta_{23}$. We demonstrate through simulated analyses that while each experiment alone faces inherent degeneracies, their complementary features enable breakthrough projected sensitivities in both standard oscillation parameter measurements and forecasted searches for new physics beyond the Standard Model. The combined simulation results reveal that DUNE-T2HK synergy will be crucial for achieving a comprehensive understanding of neutrino properties in the coming decade.

Deconfined quantum critical points (DQCPs) represent an unconventional class of quantum criticality beyond the Landau-Ginzburg-Wilson-Fisher paradigm. Nevertheless, both their theoretical identification and experimental realization remain challenging. Here we report compelling evidence of a DQCP in quantum Hall bilayers with half-filled $n=2$ Landau levels in each layer, based on large-scale variational uniform matrix product state (VUMPS) simulations and exact diagonalization (ED). By systematically analyzing the ground-state fidelity, low-lying energy spectra, exciton superfluid and stripe order parameters, and ground-state energy derivatives, we identify a direct and continuous quantum phase transition between two distinct symmetry-breaking phases by tuning the layer separation: an exciton superfluid phase with spontaneous $U(1)$ symmetry breaking at small separation, and a unidirectional charge density wave with broken translational symmetry at large separation. Our results highlight quantum Hall bilayers as an ideal platform for realizing and experimentally probing DQCPs under precisely tunable interactions.

It is well known that Kasner geometry with space-like singularity can be extended to bulk AdS-like geometry, furthermore one can study field theory on this Kasner space via its gravity dual. In this paper, we show that there exists a Kasner-like geometry with timelike singularity for which one can construct a dual gravity description. We then study various extremal surfaces including space-like geodesics in the dual gravity description. Finally, we compute correlators of highly massive operators in the boundary field theory with a geodesic approximation.

Inverse problems governed by partial differential equations (PDEs) play a crucial role in various fields, including computational science, image processing, and engineering. Particularly, Darcy flow equation is a fundamental equation in fluid mechanics, which plays a crucial role in understanding fluid flow through porous media. Bayesian methods provide an effective approach for solving PDEs inverse problems, while their numerical implementation requires numerous evaluations of computationally expensive forward solvers. Therefore, the adoption of surrogate models with lower computational costs is essential. However, constructing a globally accurate surrogate model for high-dimensional complex problems demands high model capacity and large amounts of data. To address this challenge, this study proposes an efficient locally accurate surrogate that focuses on the high-probability regions of the true likelihood in inverse problems, with relatively low model complexity and few training data requirements. Additionally, we introduce a sequential Bayesian design strategy to acquire the proposed surrogate since the high-probability region of the likelihood is unknown. The strategy treats the posterior evolution process of sequential Bayesian design as a Gaussian process, enabling algorithmic acceleration through one-step ahead prior. The complete algorithmic framework is referred to as Sequential Bayesian design for locally accurate surrogate (SBD-LAS). Finally, three experiments based the Darcy flow equation demonstrate the advantages of the proposed method in terms of both inversion accuracy and computational speed.

Determining crystal structures from X-ray diffraction data is fundamental across diverse scientific fields, yet remains a significant challenge when data is limited to low resolution. While recent deep learning models have made breakthroughs in solving the crystallographic phase problem, the resulting low-resolution electron density maps are often ambiguous and difficult to interpret. To overcome this critical bottleneck, we introduce XDXD, to our knowledge, the first end-to-end deep learning framework to determine a complete atomic model directly from low-resolution single-crystal X-ray diffraction data. Our diffusion-based generative model bypasses the need for manual map interpretation, producing chemically plausible crystal structures conditioned on the diffraction pattern. We demonstrate that XDXD achieves a 70.4\% match rate for structures with data limited to 2.0~Å resolution, with a root-mean-square error (RMSE) below 0.05. Evaluated on a benchmark of 24,000 experimental structures, our model proves to be robust and accurate. Furthermore, a case study on small peptides highlights the model's potential for extension to more complex systems, paving the way for automated structure solution in previously intractable cases.

Studies of entanglement dynamics in quantum many-body systems have focused largely on initial product states. Here, we investigate the far richer dynamics from initial entangled states, uncovering universal patterns across diverse systems ranging from many-body localization (MBL) to random quantum circuits. Our central finding is that the growth of entanglement entropy can exhibit a non-monotonic dependence on the initial entanglement in many non-ergodic systems, peaking for moderately entangled initial states. To understand this phenomenon, we introduce a conceptual framework that decomposes entanglement growth into two mechanisms: ``build'' and ``move''. The ``build'' mechanism creates new entanglement, while the ``move'' mechanism redistributes pre-existing entanglement throughout the system. We model a pure ``move'' dynamics with a random SWAP circuit, showing it uniformly distributes entanglement across all bipartitions. We find that MBL dynamics are ``move-dominated'', which naturally explains the observed non-monotonicity of the entanglement growth. This ``build-move'' framework offers a unified perspective for classifying diverse physical dynamics, deepening our understanding of entanglement propagation and information processing in quantum many-body systems.

Recently, multiple pulsar timing array collaborations have presented compelling evidence for a stochastic signal at nanohertz frequencies, potentially originating from cosmic strings. Cosmic strings are linear topological defects that can arise during phase transitions in the early Universe or as fundamental strings in superstring theory. This paper focuses on investigating the detection capabilities of Taiji, a planned space-based gravitational wave detector, for the gravitational wave background generated by cosmic strings. By analyzing simulated Taiji data and utilizing comprehensive Bayesian parameter estimation techniques, we demonstrate a significant improvement in precision compared to the NANOGrav 15-year data, surpassing it by an order of magnitude. This highlights the enhanced measurement capabilities of Taiji. Consequently, Taiji can serve as a valuable complementary tool to pulsar timing arrays in validating and exploring the physics of cosmic strings in the early Universe.

The cosmic distance duality relation (CDDR) is a fundamental and practical condition in observational cosmology that connects the luminosity distance and angular diameter distance. Testing its validity offers a powerful tool to probe new physics beyond the standard cosmological model. In this work, for the first time, we present a novel consistency test of CDDR by combining HII galaxy data with a comprehensive set of Baryon Acoustic Oscillations (BAO) measurements. The BAO measurements include two-dimensional (2D) BAO and three-dimensional (3D) BAO, as well as the latest 3D BAO data from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2). We adopt four different parameterizations of the CDDR parameter, $\eta(z)$, to investigate possible deviations and their evolution with cosmic time. To ensure accurate redshift matching across datasets, we reconstruct the distance measures through a model-independent Artificial Neural Network (ANN) approach. Our analysis uniquely examines two distinct approaches: $i)$ marginalization over the BAO sound horizon $r_d$, and $ii)$ fixing $r_d$ to specific values. We find no significant deviation from the CDDR (less than 68% confidence level) in either the marginalized $r_d$ or the $r_d=147.05$ Mpc scenario. However, a slight deviation at the 68% confidence level is found when applying 2D-BAO data with $r_d=139.5$ Mpc. Furthermore, our analysis shows that all BAO data considered in this work support the validity of the CDDR, where 3D-DESI BAO provides the tightest constraints. We find no tension between 2D and 3D BAO measurements, which confirms their mutual consistency. In addition, the treatment of the sound horizon $r_d$ significantly impacts $\eta(z)$ constraints, which proves its importance in CDDR tests. Finally, the consistency of our results supports the standard CDDR and demonstrates the robustness of our analytical approach.

Noncompact groups, similar to those that appeared in various supergravity theories in the 1970's, have been turning up in recent studies of string theory. First it was discovered that moduli spaces of toroidal compactification are given by noncompact groups modded out by their maximal compact subgroups and discrete duality groups. Then it was found that many other moduli spaces have analogous descriptions. More recently, noncompact group symmetries have turned up in effective actions used to study string cosmology and other classical configurations. This paper explores these noncompact groups in the case of toroidal compactification both from the viewpoint of low-energy effective field theory, using the method of dimensional reduction, and from the viewpoint of the string theory world sheet. The conclusion is that all these symmetries are intimately related. In particular, we find that Chern--Simons terms in the three-form field strength $H_{\mu\nu\rho}$ play a crucial role.

A false zero resistance behavior was observed during our study on the search of superconductivity in Ge-doped GaNb4Se8. This zero resistance was proved to be caused by open-circuit in multi-phase samples comprised of metals and insulators by measuring with four-probe method. The evidence strongly suggests that the reported superconductivity in hydrides should be carefully re-checked.

Strong gravitational lensing time-delay measurements, together with the distance sum rule (DSR), offer a model-independent approach to probe the geometry and expansion of the universe without relying on a fiducial cosmological model. In this work, we perform a cosmographic analysis by combining the latest Type Ia supernova datasets (PantheonPlus, DESY5, and Union3), baryon acoustic oscillation data from DESI-DR2, and updated time-delay distances from strong lensing systems. The analyses using SGL with individual SNIa datasets (SGL+PantheonPlus, SGL+DESY5, and SGL+Union3) indicate a preference for an open universe, though they remain consistent with spatially flat universe at the $95$ confidence level. When DESI-DR2 data is included in each combination, the constraints tighten and shift slightly toward a closed universe, while flatness remains supported at the $68$ confidence level. The best-fit values of $q_0$ and $j_0$ agree with $\Lambda$CDM expectations within $95$ or $99$ confidence depending on the dataset, whereas $s_0$ remains weakly constrained in all cases. This work is the first in a series of two companion papers on cosmography with DESI-DR2 and strong lensing.

We extend recent discussions about the effect of nonzero temperature on the induced electric charge, due to CP violation, of a Dirac or an 't Hooft-Polyakov monopole. In particular, we determine the fractional electric charge of a very small 't Hooft-Polyakov monopole coupled to light fermions at nonzero temperature. If dyons with fractional electric charge exist in the Weinberg-Salam model, as recently suggested in the literature, then their charge too should be temperature dependent.

Much research effort has been devoted to interfacial or two-dimensional (2D) superconductors, but the underlying pairing mechanisms and pairing symmetries are highly controversial in most cases. Here we propose an innovative approach to probe the pairing symmetry of 2D superconductors, based on a van der Waals heterostructure consisting of a prototypical 2D Ising superconductor coupled with a 2D hole gas through an insulating spacer. We first show that, by tuning the Coulomb attraction between the superconducting and hole layers, the gap of the corresponding indirect exciton insulators is tuned as well, resulting in contrasting manifestations of the distinct superconducting channels with spin-singlet (s-, extended s-, and d-wave) and spin-triplet (p- and f-wave) pairings. Strikingly, we find that the application of in-plane magnetic fields can suppress all other channels while selecting the spin-triplet p-wave channel to be the pure superconducting state, thus providing an ideal and practical platform for realizing highly desirable topological superconductivity. Such an approach can also be readily extended to other types of superconducting systems, offering unprecedented opportunities to probe the microscopic mechanisms of unconventional superconductivity.

Fiber-integrated nitrogen-vacancy (NV) magnetometers possess high sensitivity, integration, and flexibility, and thus have been explored extensively for industrial applications. While most studies have focused on the optimization of the quantum sensing head, less attention has been paid to the frequently employed professional, expensive, and bulky electronics, which hinder their practical applications. In this article, we fabricate a fiber-integrated NV magnetometer and develop a low-cost microcontroller-based software lock-in technique. In this technique, a microcontroller coordinates efficiently a microwave source chip and an analog-to-digital converter, and a program mimicking the lock-in mechanism realizes microwave frequency-modulated optically detected magnetic resonance of NV centers. As a result, with our setup and technique, we have realized the detection of weak magnetic field with a sensitivity of 93 nT/Hz^{1/2}, which is comparable to what obtained with bulky and professional devices. Furthermore, we demonstrated real-time magnetic field detection, achieving a standard deviation of 488 nT. Our work provides a novel and cost-effective technique for electronic miniaturization, thereby potentially accelerating the industrial application of NV magnetometers.

We derive the kinematic numerator factors for heavy-mass effective field theory from the field theory limit of the string theory vertex operator kinematic algebra introduced in arXiv:1806.09584. The kinematic numerators are derived as correlators of nested commutators of gluon vertex operators evaluated between massive tachyonic vertex operators. The resulting numerators are given by products of structure constants of the vertex operator algebra which yield gauge invariant expressions. The computation of the nested commutators leads to a natural organisation in the form of rooted trees, endowed with an order that facilitates the enumeration of the various contributions. This kinematic algebra gives a string theory understanding of the field theory fusion rules for constructing the heavy-mass effective field theory numerator of arXiv:2104.11206 and arXiv:2111.15649.

Quantum field theory (QFT) for interacting many-electron systems is fundamental to condensed matter physics, yet achieving accurate solutions confronts computational challenges in managing the combinatorial complexity of Feynman diagrams, implementing systematic renormalization, and evaluating high-dimensional integrals. We present a unifying framework that integrates QFT computational workflows with an AI-powered technology stack. A cornerstone of this framework is representing Feynman diagrams as computational graphs, which structures the inherent mathematical complexity and facilitates the application of optimized algorithms developed for machine learning and high-performance computing. Consequently, automatic differentiation, native to these graph representations, delivers efficient, fully automated, high-order field-theoretic renormalization procedures. This graph-centric approach also enables sophisticated numerical integration; our neural-network-enhanced Monte Carlo method, accelerated via massively parallel GPU implementation, efficiently evaluates challenging high-dimensional diagrammatic integrals. Applying this framework to the uniform electron gas, we determine the quasiparticle effective mass to a precision significantly surpassing current state-of-the-art simulations. Our work demonstrates the transformative potential of integrating AI-driven computational advances with QFT, opening systematic pathways for solving complex quantum many-body problems across disciplines.

Fast Radio Bursts (FRBs) are highly energetic millisecond-duration astrophysical phenomena typically categorized as repeaters or non-repeaters. However, observational limitations may result in misclassifications, potentially leading to a higher proportion of repeaters than currently identified. In this study, we leverage unsupervised machine learning techniques to classify FRBs using data from the CHIME/FRB catalogs, including both the first catalog and a recent repeater catalog. By employing Uniform Manifold Approximation and Projection for dimensionality reduction and clustering algorithms (k-means and Hierarchical Density-Based Spatial Clustering of Applications with Noise), we successfully segregate repeaters and non-repeaters into distinct clusters, identifying over 100 potential repeater candidates. Our analysis reveals several empirical relations within the clusters, including the ${\rm log \,}\Delta t_{sc}-{\rm log \,}\Delta t_{rw}$, ${\rm log \,}\Delta t_{sc}-{\rm log \,}T_B$, and $r - \gamma$ correlations, where ${\Delta t_{sc}, \Delta t_{rw}, T_B, r, \gamma}$ represent scattering time, rest-frame width, brightness temperature, spectral running, and spectral index, respectively. The Chow test results reveal that while some repeaters and non-repeaters share similar empirical relationships, the overall distinctions between the two groups remain significant, reinforcing the classification of FRBs into repeaters and non-repeaters. These findings provide new insights into the physical properties and emission mechanisms of FRBs. This study demonstrates the effectiveness of unsupervised learning in classifying FRBs and identifying potential repeaters, paving the way for more precise investigations into their origins and applications in cosmology. Future improvements in observational data and machine learning methodologies are expected to further enhance our understanding of FRBs.

In a previous paper arXiv:2507.21691, we have carried out one-loop renormalization of the type-I seesaw model in the modified minimal-subtraction ($\overline{\rm MS}$) scheme. In the present one, we continue to renormalize the type-I seesaw model in the on-shell scheme. Such an investigation is mainly motivated by the fact that the on-shell scheme has been widely adopted in the renormalization of the standard electroweak theory and implemented for its precision tests. We first specify the physical parameters in the on-shell scheme, and then fix the corresponding counterterms through on-shell renormalization conditions. In the presence of massive Majorana neutrinos, we propose a practical method to determine gauge-independent counterterms for the lepton flavor mixing matrix. With the explicit counterterms in both the $\overline{\rm MS}$ and on-shell schemes, we establish the matching relations of the electric charge, physical masses and flavor mixing matrix elements between these two schemes. Our results in the present and previous papers lay the foundation for precision calculations in the type-I seesaw model.