Fundamental Physics
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Related Articles from SNS
Tabletop experiment helps reconcile fundamental physics
Tabletop experiment helps reconcile fundamental physics Gaby Clark Scientific Editor Andrew Zinin Lead Editor Assistant Professor Haocun Yu is something of a scientific diplomat. In a recent Physical Review Letters publication, she and her colleagues show how a tabletop experiment can bring together two bedrock physics theories that have never been fully reconciled. More than a century ago, Albert Einstein gave us the theory of general relativity, describing gravity in relation to space and...
Quantitative Nonequilibrium Pathway from Fundamental Physics to the Emergence and Persistence of Exoplanetary Biospheres
arXiv:2606.02648v1 Announce Type: new Abstract: We present a physics-based framework that runs from fundamental interactions and constants to biospheres, using a sequence of quantitative nonequilibrium thresholds ("gates"). Each gate is an inequality in measurable variables-free-energy flux, reaction-transport rates, replication fidelity, coding capacity, ecological closure, and climate feedback gains. Crucially, the gate vector is anchored in fundamental physics: dimensionless constants,...
Relativity from the Perspectives of Observers
Announce Type: new Abstract: This paper reviews the role of observers in the development of relativity theory, from special relativity to general relativity, emphasizing that observer-dependent descriptions are as fundamental as the covariance of physical laws. This paper reviews the role of observers in the development of relativity theory, from special relativity to general relativity, emphasizing that observer-dependent descriptions are as fundamental as the covariance of physical laws....
Vision Language Models Cannot Reason About Physical Transformation
Announce Type: replace Abstract: Understanding physical transformations is fundamental for reasoning in dynamic environments. While Vision Language Models (VLMs) show promise in embodied applications, whether they genuinely understand physical transformations remains unclear. We introduce ConservationBench evaluating conservation -- whether physical quantities remain invariant under transformations.
Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
Announce Type: cross Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and...
Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and...
Creating and Probing Spin-Squeezed States of Molecules
Announce Type: new Abstract: Polar molecules are a promising platform for quantum-enhanced sensing and precision tests of fundamental physics, owing to their strong long-range dipolar interactions, broad sensitivity to electromagnetic fields, and sensitivity to potential physics beyond the Standard Model. However, the creation of metrologically useful entangled states in molecular systems has remained elusive. Here, we report the first observation of a class of metrologically useful...
Physics-Informed Deep Learning for Entropy Prediction in Heterogeneous Systems: Thermodynamic and Information-Theoretic Case Studies
arXiv:2606.01179v1 Announce Type: new Abstract: Entropy production governs irreversibility and uncertainty in both physical and information-theoretic systems. While Physics-Informed Neural Networks (PINNs) successfully solve differential equations, current architectures remain inherently domain-specific. The extraction of domain-invariant entropy representations across fundamentally different physical laws remains unexplored.
Intrinsic Nonlocality of Spin- and Polarization-Resolved Probabilities in Strong-Field Quantum Electrodynamics
arXiv:2603.11148v2 Announce Type: replace-cross Abstract: Spin and polarization are central to precision tests of fundamental physics and for interpreting radiation from astrophysical sources and ultraintense laser-matter experiments. Here, focusing on the fundamental process of nonlinear Compton scattering, we demonstrate that a key assumption underlying current strong-field quantum electrodynamics (SFQED) models, i.e., that emission can be treated as an instantaneous random event sampled...
First results of a high sensitivity and transportable Ring Laser Gyroscope
arXiv:2606.02594v1 Announce Type: new Abstract: Within the frame of the GINGER project, aimed at installing an array of large frame ring laser gyroscopes for fundamental physics tests and as part of a geophysics observatory located in the underground laboratory at Gran Sasso, Italy (LNGS-INFN), we are developing a ring laser gyroscope design to reduce spurious rotation of instrumental origin and the ability to extend the cavity perimeter from 1.5 up to 5m, thanks to the implementation of...