Physical Symmetry Research Articles

One-dimensional photonic crystal with tilted termination and its angular filtering properties for radiative cooling.

Photonic crystals are known for their band-gap structures. Due to their band-gaps, they can act as filters in both temporal and spatial domains. However, in most cases, due to their physical symmetry, their angular responses are symmetrical. Here, a structure based on a 1D photonic crystal is introduced and analyzed, which has an asymmetric angular selectivity. The structure is analyzed using the plane wave expansion method. The properties of the structure are expressed and verified by a commercial full-wave simulator software. Based on the analysis and its results, some simple design rules are derived. By using the extracted rules and some approximations, the potential of the structure to be used in radiative coolers, which are not completely toward the sky, is introduced. It is shown that if the structure is used as windows in buildings, it can save up to tens of watts per square meter in energy consumption for air conditioning. Finally, the whole structure including the radiative cooler is simulated, and the results support the calculations and approximations.

Fundamental symmetry in particle physics: Rules that tell the most when broken

Fundamental symmetry in particle physics: Rules that tell the most when broken

Manipulation of directional acoustic spin angular momentum density based on gradient-structured waveguides

In recent years, the discovery of the transverse spin of acoustic wave in a structural acoustic field and acoustic structural surface wave has expanded our knowledge of the basic characteristics of acoustic waves and opened up new avenues for their manipulation. On the structured surface, however, the distribution of acoustic surface waves often presents a uniform distribution, which restricts the local modification of acoustic spin angular momentum and particle manipulation capabilities. In this study, we develop some acoustic waveguides with gradients that are flat, up-convex, and down-concave in order to manipulate the lateral spin distributions of acoustic surface waves. We verify the direction-locking near-field acoustic spin-momentum, explore the pressure field distribution and the spin angular momentum density distribution of a spin acoustic source excited in each of the three gradient structures, and we also show how to manipulate the spin intensity distributions of acoustic surface waves in the gradient waveguides through theoretical analysis and numerical simulation. The numerical calculation results show that when the acoustic surface wave is excited by a clockwise rotating spin source and propagates along the left side of the waveguide, the spin angular momentum density is positive on the upper surface of the structured waveguide and negative on the lower surface. The spin angular momentum distribution and the direction of propagation of acoustic wave are entirely changed when the spin source is rotated counterclockwise. Specifically, an unequal distribution of acoustic spin angular momentum is produced by the upper convex-type waveguide and bottom concave-type waveguide when we convert the flat-type acoustic structure waveguide into a gradient-type waveguide. According to the computation results, the down-concave type waveguide exhibits a stronger density of acoustic spin angular momentum at the end and the acoustic surface waves gather at the end of the constructed waveguide. On the other hand, the waveguide collects acoustic waves close to the structure center when it is an up-convex structural waveguide. The findings can open up new avenues for manipulating particles using acoustic waves, by providing a means for controlling the acoustic spin angular momentum density and improving our understanding of symmetry in acoustic near-field physics.

The applicability of the concept of the field of rationality in the explanation of the fundamental role of symmetries in physics

The introduction of the concept of the field of rationality and its correlates (the field of potentiality and the formal field) by Józef Życiński and Michał (Michael) Heller opened up space for the philosophical explanation of the unreasonable effectiveness of mathematics in capturing regularities built into the physical reality. The presented study is a response to the clear incentive of these authors towards the development of the understanding and applicability of these concepts. It is argued that identifying symmetries within the field of rationality not only helps to articulate the fundamental role of symmetries in physics but also provides a better grasp on the issue of potentialities for the emergence of complexity in the Universe. Additionally, some global properties of this field can be more deeply comprehended. By indicating the drawbacks and limitations of this approach, perspectives for further inquiry into the meaning and usefulness are suggested.

Self-Testing in Prepare-and-Measure Scenarios and a Robust Version of Wigner's Theorem.

We consider communication scenarios where one party sends quantum states of known dimensionality D, prepared with an untrusted apparatus, to another, distant party, who probes them with uncharacterized measurement devices. We prove that, for any ensemble of reference pure quantum states, there exists one such prepare-and-measure scenario and a linear functional W on its observed measurement probabilities, such that W can only be maximized if the preparations coincide with the reference states, modulo a unitary or an antiunitary transformation. In other words, prepare-and-measure scenarios allow one to "self-test" arbitrary ensembles of pure quantum states. Arbitrary extreme D-dimensional quantum measurements, or sets thereof, can be similarly self-tested. Our results rely on a robust generalization of Wigner's theorem, a well-known result in particle physics that characterizes physical symmetries.

Accurate transition state generation with an object-aware equivariant elementary reaction diffusion model.

Transition state search is key in chemistry for elucidating reaction mechanisms and exploring reaction networks. The search for accurate 3D transition state structures, however, requires numerous computationally intensive quantum chemistry calculations due to the complexity of potential energy surfaces. Here we developed an object-aware SE(3) equivariant diffusion model that satisfies all physical symmetries and constraints for generating sets of structures-reactant, transition state and product-in an elementary reaction. Provided reactant and product, this model generates a transition state structure in seconds instead of hours, which is typically required when performing quantum-chemistry-based optimizations. The generated transition state structures achieve a median of 0.08 Å root mean square deviation compared to the true transition state. With a confidence scoring model for uncertainty quantification, we approach an accuracy required for reaction barrier estimation (2.6 kcal mol-1) by only performing quantum chemistry-based optimizations on 14% of the most challenging reactions. We envision usefulness for our approach in constructing large reaction networks with unknown mechanisms.

Developing Predictive Models under Controlled Conditions for the Selection of New Genotypes That Are Less Susceptible to Bactrocera oleae (Rossi) in Table Olive (Olea europaea L.) Breeding Programs

Bactrocera oleae (Rossi), the olive fly, represent an important biotic factor in olive groves (Olea europaea L.) causing significant production losses. Ensuring effective management of this pest is of paramount importance to safeguard and uphold the quality and quantity of table olive production. The University of Seville’s (US) table olive breeding program has focused its attention on finding new cultivars that exhibit reduced susceptibility to the olive fly. This study attempted to develop predictive models to enable the selection of new genotypes that are less susceptible to the olive fly based on fresh fruit traits. An extensive analysis of fruit physical (weight, size, symmetry, color, and texture) and chemical traits (moisture, oil content, and phenolic compounds) was conducted to evaluate the fly’s preference in oviposition bioassays (multiple choice and no choice) among four genotypes (US-06-1388, US-06-194, ‘Hojiblanca’, and ‘Kalamon’), under controlled conditions. The oviposition bioassays revealed the higher susceptibility of genotype US-06-194 and the lower susceptibility of ‘Kalamon’. The predictive models incorporated physical traits such as, fruit weight, longitudinal diameter, symmetry, CIELAB color attributes (L*, a*, and b*), and compression hardness, as well as chemical traits such as moisture, and the contents of demethyloleuropein, oleuropein, rutin, and verbascoside. These traits consistently predicted the preference of B. oleae for certain fruits.

Neutron stars in the Witten-Sakai-Sugimoto model

We utilize the top-down holographic QCD model, the Witten-Sakai-Sugimoto model, in a hybrid setting with the SLy4, soft chiral EFT and stiff chiral EFT equations of state to describe neutron stars with high precision. In particular, we employ a calibration that bootstraps the nuclear matter by fitting the Kaluza-Klein scale and the ’t Hooft coupling such that the physical saturation density and physical symmetry energy are achieved. We obtain static stable neutron star mass-radius data via the Tolman-Oppenheimer-Volkov equations that yield sufficiently large maximal masses of neutron stars to be compatible with the recently observed PSR-J0952-0607 data as well as all other known radius and tidal deformation constraints.

Irreducible representations of $\mathbb{Z}_2^2$-graded supersymmetry algebra and their applications

We give a brief review on recent developments of \mathbb{Z}_2^nℤ2n-graded symmetry in physics in which hidden \mathbb{Z}_2^nℤ2n-graded symmetries and \mathbb{Z}_2^nℤ2n-graded extensions of known systems are discussed. This elucidates physical relevance of the \mathbb{Z}_2^nℤ2n-graded algebras. As an example of physically interesting algebra, we take \mathbb{Z}_2^2ℤ22-graded supersymmetry (SUSY) algebras and consider their irreducible representations (irreps). A list of irreps for N = 1, 2N=1,2 algebras is presented and as an application of the irreps, \mathbb{Z}_2^2ℤ22-graded SUSY classical actions are constructed.

A neural network-based material cell for elastoplasticity and its performance in FE analyses of boundary value problems

This research focuses on evaluating the capacity and performance of a network-based material cell as a constitutive model for boundary-value problems. The proposed material cell aims to replicate constitutive relationships learned from datasets generated by random loading paths following a stochastic Gaussian process. The material cell demonstrates its effectiveness across three progressively complex constitutive models by incorporating physical extensions and symmetry constraint as prior knowledge. To address the challenge of magnitude gaps between strain increments in training sets and finite element simulations, an adaptive linear transformation is introduced to mitigate prediction errors. The material cell successfully reproduces constitutive relationships in finite element simulations, and its performance is comprehensively evaluated by comparing two different material cells: the sequentially trained gated recurrent unit (GRU)-based material cell and the one-to-one trained deep network-based material cell. The GRU-based material cell can be trained without explicit calibration of the internal variables. This enables us to directly derive the constitutive model using stress–strain data without consideration of the physics of internal variables.

Adversarial machine learning phases of matter

We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully crafted noises into the original legitimate examples will cause the classifiers to make incorrect predictions at a notably high confidence level. Through the lens of activation maps, we find that some important underlying physical principles and symmetries remain to be adequately captured for classifiers with even near-perfect performance. This explains why adversarial perturbations exist for fooling these classifiers. In addition, we find that, after adversarial training the classifiers will become more consistent with physical laws and consequently more robust to certain kinds of adversarial perturbations. Our results provide valuable guidance for both theoretical and experimental future studies on applying machine learning techniques to condensed matter physics.

Nuclear Shape-Phase Transitions and the Sextic Oscillator

This review delves into the utilization of a sextic oscillator within the β degree of freedom of the Bohr Hamiltonian to elucidate critical-point solutions in nuclei, with a specific emphasis on the critical point associated with the β shape variable, governing transitions from spherical to deformed nuclei. To commence, an overview is presented for critical-point solutions E(5), X(5), X(3), Z(5), and Z(4). These symmetries, encapsulated in simple models, all model the β degree of freedom using an infinite square-well (ISW) potential. They are particularly useful for dissecting phase transitions from spherical to deformed nuclear shapes. The distinguishing factor among these models lies in their treatment of the γ degree of freedom. These models are rooted in a geometrical context, employing the Bohr Hamiltonian. The review then continues with the analysis of the same critical solutions but with the adoption of a sextic potential in place of the ISW potential within the β degree of freedom. The sextic oscillator, being quasi-exactly solvable (QES), allows for the derivation of exact solutions for the lower part of the energy spectrum. The outcomes of this analysis are examined in detail. Additionally, various versions of the sextic potential, while not exactly solvable, can still be tackled numerically, offering a means to establish benchmarks for criticality in the transitional path from spherical to deformed shapes. This review extends its scope to encompass related papers published in the field in the past 20 years, contributing to a comprehensive understanding of critical-point symmetries in nuclear physics. To facilitate this understanding, a map depicting the different regions of the nuclide chart where these models have been applied is provided, serving as a concise summary of their applications and implications in the realm of nuclear structure.

Are models our tools not our masters?

It is often claimed that one can avoid the kind of underdetermination that is a typical consequence of symmetries in physics by stipulating that symmetry-related models represent the same state of affairs (Leibniz Equivalence). But recent commentators (Dasgupta in Philos Perspect 25:115–160, 2011; Pooley in: Knox and Wilson (eds) The Routledge companion to the philosophy of physics, Routledge, Milton Park, 2021; Pooley and Read in Br J Philos Sci, 2021, https://doi.org/10.1086/718274; Teitel in J Philos 119:233–278, 2021) have responded that claims about the representational capacities of models are irrelevant to the issue of underdetermination, which concerns possible worlds themselves. In this paper I distinguish two versions of this objection: (1) that a theory’s formalism does not (fully) determine the space of physical possibilities, and (2) that the relevant notion of possibility is not physical possibility. I offer a refutation of each.

Gauge fixing and physical symmetries

Gauge fixing and physical symmetries

Time-symmetric quantization of relativistic fields. 2. Electroweak theory. Observable effects of TSQ

In the method of time-symmetric quantization (TSQ) of relativistic systems, based on the Stückelberg-Feynman interpretation, the creation-annihilation operators of quanta of complex fields and massless gauge fields, are automatically normally ordered, and there is no vacuum energy and charge (Z. Zakir 2023, article 1). In this second article the application of TSQ to massive bosonic fields of electroweak theory and the observational consequences of TSQ are considered. It is shown that the vacuum of these fields is free of zero-point energy and zero-point charge, and thus a contribution of these fields to the cosmological constant is absent. A direct observational consequence of TSQ is crossing symmetry in particle physics. The observable effects, which were attributed to the zero-point energy of the vacuum, are actually generated by the fields of real charges, and there is no evidence of the existence of zero-point energy of fundamental fields. This fact contradicts the prediction of the standard formulation of quantum field theory, but indirectly confirms TSQ.

A symmetry group based supervised learning method for solving partial differential equations

Deep neural network provides an effective way to solve partial differential equations (PDEs) where physics-informed neural network (PINN) is a typical representative of which the outputs are constrained to approximate the solution of PDEs. However, the PINN method still fails to learn some solutions of particular properties and the reasons of failure are still unclear. In this paper, we employ both the loss landscape of the final optimization parameters of network and the imbalance of back-propagation gradient during the training to analyze why the PINN method fails to learn large amplitude solution and high-frequency solution of the Klein–Gordon equation. Consequently, we find that the PDE-based soft constraints in the loss function take a major responsibility for the failures of the PINN method in predicting such types of solutions. To remedy the pathologies we remove the PDE-based soft constraints in the loss function and calibrate a supervised learning model for predicting such types of particular solutions for a class of PDEs which cannot be learned by the PINN method and the two weight-related improved PINN methods. Specifically, if the governing PDEs under study admit a Lie symmetry group and the unique solution of the corresponding initial boundary problem is obtained via the symmetry group, we utilize the symmetry group to generate labeled data in the interior domain of PDEs from the discrete points on the known initial and boundary conditions. Then, with the labeled data we leverage the supervised learning model to predict the large amplitude solution and the high-frequency solution of the Klein–Gordon equation on a rectangular domain by means of a space–time translation group and a singular solution of the heat equation on a moving boundary domain with a physical symmetry group. Numerical experiments confirm the good performances of the proposed supervised learning method.

Schrödinger Dynamics and Berry Phase of Undulatory Locomotion.

Spectral mode representations play an essential role in various areas of physics, from quantum mechanics to fluid turbulence, but they are not yet extensively used to characterize and describe the behavioral dynamics of living systems. Here, we show that mode-based linear models inferred from experimental live-imaging data can provide an accurate low-dimensional description of undulatory locomotion in worms, centipedes, robots, and snakes. By incorporating physical symmetries and known biological constraints into the dynamical model, we find that the shape dynamics are generically governed by Schrödinger equations in mode space. The eigenstates of the effective biophysical Hamiltonians and their adiabatic variations enable the efficient classification and differentiation of locomotion behaviors in natural, simulated, and robotic organisms using Grassmann distances and Berry phases. While our analysis focuses on a widely studied class of biophysical locomotion phenomena, the underlying approach generalizes to other physical or living systems that permit a mode representation subject to geometric shape constraints.

Classical phase space crystals in an open environment

It was recently discovered that a crystalline many-body state can exist in the phase space of a closed dynamical system. A phase space crystal can be an anomalous Chern insulator that supports chiral topological transport without breaking physical time-reversal symmetry [L. Guo et al., Phys. Rev. B 105, 094301 (2022)]. In this work, we further study the effects of an open dissipative environment with thermal noise and identify the existence condition of classical phase space crystals in realistic scenarios. By defining a crystal order parameter, we plot the phase diagram in the parameter space of dissipation rate, interaction, and temperature. Our present work paves the way to realize phase space crystals and explore anomalous chiral transport in experiments.

On the perturbative picture and the Chang–Refsdal lens approximation for planetary microlensing

ABSTRACT Under the perturbative picture of planetary microlensing, the planet is considered to act as a uniform-shear Chang–Refsdal lens on one of the two images produced by the host star that comes close to the angular Einstein radius of the planet, leaving the other image unaffected. However, this uniform-shear approximation is only valid for isolated planetary caustics and breaks down in the resonant regime. Recently, the planetary-caustic degeneracy arising from the above formalism is found to generalize to the regime of central and resonant caustics, indicating that the perturbative picture and Chang–Refsdal lens approximation may have been underexplored in the past. Here, I introduce a new variable-shear Chang–Refsdal lens approximation, which not only supports central and resonant caustics, but also enables full magnification maps to be calculated analytically. Moreover, I introduce the generalized perturbative picture, which relaxes the required proximity between the planet and the image being perturbed in the previous work. Specifically, the planet always perturbs the image in the same half of the lens plane as the planet itself, leaving the other image largely unaffected. It is demonstrated how this new framework results in the offset degeneracy as a consequence of physical symmetry. The generalized perturbative picture also points to an approach to solve the two-body lens equation semi-analytically. The analytic and semi-analytic microlensing solutions associated with this work may allow for substantially faster light-curve calculations and modelling of observed events. A Python implementation is provided.

Should physical symmetries guide metaphysics? Two reasons why they should maybe not

Symmetry-based inferences have permeated many discussions in philosophy of physics and metaphysics of science. It is claimed that symmetries in our physical theories would allow us to draw metaphysical conclusions about the world, a view that I call ‘symmetry inferentialism’. This paper is critical to this view. I claim that (a) it assumes a philosophically questionable characterization of the relevant validity domain of physical symmetries, and (b) it overlooks a distinction between two opposing ways through which relevant physical symmetries become established. My conclusion is that symmetry inferentialism loses persuasive force when these two points are taken into consideration.