Physics Information Research Articles

Infrared signatures of a quantum bounce in a minisuperspace analysis of Lemaître-Tolman-Bondi dust collapse

In the quantum treatment of collapsing matter, a viable mode analysis is supposed to give information regarding emission during the collapse. Nevertheless, partly owing to operator ordering ambiguities involved in a typical quantum gravity analysis, the availability of such well-defined modes is not guaranteed. We study the mode decomposition of the unitarily evolving wave packet constructed for the quantum model of spherically symmetric dust collapsing in a marginally bound Lema\^{\i}tre-Tolman-Bondi (LTB) model. We identify the observable depicting mode decomposition, and using the freedom of operator ordering ambiguity, we obtain the Hermitian extension of this operator alongside the Hermitian Hamiltonian. After identifying incoming and outgoing modes with this operator's eigenstates, we estimate their contributions to the radiation profile. The infrared sector of this process demonstrates some characteristic features which turn out to be highly sensitive to the near-bounce dynamics of the dust cloud. Near the epoch of classical singularity, there is a significant contribution from incoming/outgoing modes of small wavenumber in the expanding/collapsing phase of the dust cloud, which keeps on decreasing as one moves away from the singularity. The information of the bounce is carried over to the infrared modes through a flip from largely incoming to largely outgoing radiation as the evolution progresses from collapsing to expanding phase, much before the information of bounce comes about to any observer. In the infrared sector, the saturation value of the amplitude marks the bounce radius. Thus, we argue that the information of the short scale physics is essentially carried over to the longest wavelength in this quantum gravity model, which we argue is rather more prominent for low energy processes.

Model-independent versus model-dependent interpretation of the SDSS-III BOSS power spectrum: Bridging the divide

The traditional clustering analyses of galaxy redshift surveys compress the clustering data into a set of late-time physical variables in a model-independent way. This approach has recently been extended by an additional shape variable encoding early-time physics information. We apply this new technique, ShapeFit, to SDSS-III BOSS data and show that it matches the constraining power of alternative, model-dependent approaches, which directly constrain the model's parameters adopting a cosmological model ab-initio. ShapeFit is $\sim30$ times faster, model-independent, naturally splits early- and late-time variables, and enables a better control of observational systematics.

JUNO Oscillation Physics

The Jiangmen Underground Neutrino Observatory (JUNO) is a 20 kton multi-purpose liquid scintillator detector with an expected energy resolution, under construction in a 700 m underground laboratory in the south of China (Jiangmen city, Guangdong province). The exceptional energy resolution and the massive fiducial volume of the JUNO detector offer great opportunities for addressing many essential topics in neutrino and astroparticle physics. JUNO’s primary goals are to determine the neutrino mass ordering and precisely measure the related neutrino oscillation parameters. With six years of data taking with reactor antineutrinos, JUNO can determine the mass ordering at a 3-4σ significance and the neutrino oscillation parameters sin2 θ 12, , and to a precision of better than 0.6%. In addition, the atmospheric neutrino and solar neutrino measurement at JUNO can also provide complementary and important information for neutrino oscillation physics. This work focuses on the oscillation physics of JUNO, which includes measurement and analysis of the reactor neutrinos, the atmospheric neutrinos, and the solar neutrinos. With the delicate energy response calibration and event reconstruction potential, JUNO will make a world-leading measurement on the neutrino oscillation parameters and neutrino mass ordering in the near future.

The formation of M101-alike galaxies in the cold dark matter model

ABSTRACT The population of satellite galaxies in a host galaxy is a combination of the cumulative accretion of subhaloes and their associated star formation efficiencies; therefore, the luminosity distribution of satellites provides valuable information of both dark matter properties and star formation physics. Recently, the luminosity function of satellites in nearby Milky Way-mass galaxies has been well measured to satellites as faint as Leo I with MV ∼ −8. In addition to the finding of the diversity in the satellite luminosity functions, it has been noticed that there is a big gap among the magnitude of satellites in some host galaxies, such as M101, where the gap is around 5 in magnitude, noticeably larger than the prediction from the halo abundance matching method. The reason of this gap is still unknown. In this paper, we use a semi-analytical model of galaxy formation, combined with high-resolution N-body simulation, to investigate the probability and origin of such big gap in M101-alike galaxies. We found that, although M101 analogues are very rare with probability of $\sim 0.1\ \mathrm{ to}\ 0.2\,{{\rm per\, cent}}$ in the local universe, their formation is a natural outcome of the cold dark matter model. The gap in magnitude is mainly due to the mass of the accreted subhaloes, not from the stochastic star formation in them. We also found that the gap is correlated with the total satellite mass and host halo mass. By tracing the formation history of M101-type galaxies, we find that they were likely formed after z ∼ 1 due to the newly accreted bright satellites. The gap is not in a stable state, and it will disappear in 7 Gyr due to mergers of bright satellites with the central galaxy.

Laser cooling of antihydrogen atoms

The photon—the quantum excitation of the electromagnetic field—is massless but carries momentum. A photon can therefore exert a force on an object upon collision1. Slowing the translational motion of atoms and ions by application of such a force2,3, known as laser cooling, was first demonstrated 40 years ago4,5. It revolutionized atomic physics over the following decades6–8, and it is now a workhorse in many fields, including studies on quantum degenerate gases, quantum information, atomic clocks and tests of fundamental physics. However, this technique has not yet been applied to antimatter. Here we demonstrate laser cooling of antihydrogen9, the antimatter atom consisting of an antiproton and a positron. By exciting the 1S–2P transition in antihydrogen with pulsed, narrow-linewidth, Lyman-α laser radiation10,11, we Doppler-cool a sample of magnetically trapped antihydrogen. Although we apply laser cooling in only one dimension, the trap couples the longitudinal and transverse motions of the anti-atoms, leading to cooling in all three dimensions. We observe a reduction in the median transverse energy by more than an order of magnitude—with a substantial fraction of the anti-atoms attaining submicroelectronvolt transverse kinetic energies. We also report the observation of the laser-driven 1S–2S transition in samples of laser-cooled antihydrogen atoms. The observed spectral line is approximately four times narrower than that obtained without laser cooling. The demonstration of laser cooling and its immediate application has far-reaching implications for antimatter studies. A more localized, denser and colder sample of antihydrogen will drastically improve spectroscopic11–13 and gravitational14 studies of antihydrogen in ongoing experiments. Furthermore, the demonstrated ability to manipulate the motion of antimatter atoms by laser light will potentially provide ground-breaking opportunities for future experiments, such as anti-atomic fountains, anti-atom interferometry and the creation of antimatter molecules.

Deep learning of multibody minimal coordinates

AbstractMultibody systems [1] are the state‐of‐the‐art tool to model complex mechanical mechanisms. However, they typically include redundant coordinates plus constraints, leading to differential algebraic equations for the dynamics which require dedicated integration schemes and control/estimation algorithms.In the deep learning field, autoencoders are a particular neural network class with a symmetric structure to perform a nonlinear dimensionality reduction. In fact, they can be seen as a non‐linear extension of principal component analysis [2].In this work, it is proposed to use an autoencoder to reduce a general multibody model to minimal coordinates, eliminating the constraints. The obtained decoder function is used to perform a non‐linear projection of the multibody coordinates: urn:x-wiley:16177061:media:PAMM202000348:pamm202000348-math-0001 where are respectively the encoder and decoder neural network functions, while are respectively the obtained minimal coordinates and reconstructed full multibody coordinates.The novelty is that the autoencoder training includes the multibody physics information [3]. In this way, the autoencoder does not only perform a dimensionality reduction of the original coordinates but can be used for model order reduction. The obtained reduced‐order model consists of ordinary differential equations (with respect to the original differential algebraic equations) and can be used to perform dynamic simulations and easily coupled with standard estimation algorithms for state‐estimation.A typical application example is a mechanism with closed‐loops, such as a car suspension, for which no analytical expression is available to reduce the full multibody model to minimal coordinates. The introduced physics‐informed neural network allows to reduce the model and combine it with an augmented extended Kalman filtering scheme [4], in order to estimate difficult‐to‐measure inputs or parameters in the system.In a first application, the model in combination with measured strut displacement or acceleration, allows to estimate the strut force, in addition to the body full coordinates in position, velocity and acceleration. The rationale is reported in the figure below.rationale.pngThe Flanders Innovation & Entrepreneurship Agency within the IMPROVED project and the AI impulse program of the Flemish Government are gratefully acknowledged for their support.

The Importance of New Findings of Research on Consciousness for the Treatment of Depression in Elderly

The recent resumption of the discussion of Stuart Hameroff's and Roger Penrose's theory, which assume that theperception of qualia is realized by a quantum physics information processing in microtubules and coincidingpublication, at the same time of recent progress of the so called conscious electromagnetic information field (cemi)theory prompted the authors to the trial to integrate these theories with the earlier explanations of consciousness. Inorder to present the intuitive model of interacting components of consciousness, the authors provide short reviews ofthe 'Orch OR' and ‘cemi’ theories. This enables them to distinguish three distinct, cooperating processes underlyingthe phenomenon of consciousness, namely (a) perception, including the feeling of qualia, (b) the realization ofmental imagery and (c) the feeling of subjectivity, identity manifested by the ability to self-awareness. The ability tounderstand the essence of the self-awareness is important for health care professionals, because consciousnessdisorders are a frequent medical symptom in emergencies, anesthesia and depressive syndromes in elderly. Theauthors emphasize the usefulness of transcranial magnetic stimulation, as a method of intervention in brain'sendogenous electromagnetic field, or more precisely in its element caused by the 'default mode network'. Theyemphasize also that effectiveness of the use of mindfulness methods in the treatment of depressive syndromes alsoresults from reducing the activity of Default Mode Network”.

Combined Model for Short-term Wind Power Prediction Based on Deep Neural Network and Long Short-Term Memory

Wind power generation is affected by weather and historical wind power, which presents the characteristics of instability and high volatility. Most wind power prediction models ignore physics information. In this paper, a novel combined predicting model that simultaneously considers physics information and historical information is presented to address the drawbacks of existing models. First, the physical characteristics of wind speed, wind direction, and temperature are obtained by Deep Neural Network(DNN), and time-series characteristics from historical wind power are extracted by Long Short-Term Memory(LSTM). Then, the physical features and the time-series features are fully connected for feature fusion to obtain the final time-series physical features. Finally, the short-term wind power prediction is performed according to the obtained merged features. Experimental results demonstrate that the DNN-LSTM model proposed in this paper achieves high accuracy and stability, and provides technical support for wind power system dispatch.

Hilbert arithmetic as a Pythagorean arithmetic: arithmetic as transcendental

The paper considers a generalization of Peano arithmetic, Hilbert arithmetic as the basis of the world in a Pythagorean manner. Hilbert arithmetic unifies the foundations of mathematics (Peano arithmetic and set theory), foundations of physics (quantum mechanics and information), and philosophical transcendentalism (Husserl’s phenomenology) into a formal theory and mathematical structure literally following Husserl’s tracе of “philosophy as a rigorous science”. In the pathway to that objective, Hilbert arithmetic identifies by itself information related to finite sets and series and quantum information referring to infinite one as both appearing in three “hypostases”: correspondingly, mathematical, physical and ontological, each of which is able to generate a relevant science and area of cognition. Scientific transcendentalism is a falsifiable counterpart of philosophical transcendentalism. The underlying concept of the totality can be interpreted accordingly also mathematically, as consistent completeness, and physically, as the universe defined not empirically or experimentally, but as that ultimate wholeness containing its externality into itself.

Physics Information Aided Kriging using Stochastic Simulation Models

In this work, we propose a new Gaussian process regression (GPR) method: physics information aided Kriging (PhIK). In the standard data-driven Kriging, the unknown function of interest is usually t...

Decoding Photons: Physics in the Latent Space of a BIB-AE Generative Network

Given the increasing data collection capabilities and limited computing resources of future collider experiments, interest in using generative neural networks for the fast simulation of collider events is growing. In our previous study, the Bounded Information Bottleneck Autoencoder (BIB-AE) architecture for generating photon showers in a high-granularity calorimeter showed a high accuracy modeling of various global differential shower distributions. In this work, we investigate how the BIB-AE encodes this physics information in its latent space. Our understanding of this encoding allows us to propose methods to optimize the generation performance further, for example, by altering latent space sampling or by suggesting specific changes to hyperparameters. In particular, we improve the modeling of the shower shape along the particle incident axis.

Stress prediction and evaluation approach based on azimuthal amplitude‐versus‐offset inversion of unconventional reservoirs

ABSTRACTA novel approach of unconventional reservoir stress evaluation is proposed to enhance the accuracy of fracture development prediction. Differential horizontal stress ratio can reflect the stress characteristic of fractured reservoir. To calculate the differential horizontal stress ratio more intuitionistic and simpler, we re‐derive its formula with Poisson's ratio and fracture density. Meanwhile, a new azimuthal PP‐wave amplitude versus offset equation based on Poisson's ratio and fracture density was derived for inverting above elastic parameters directly. Therefore, two steps are needed to realize stress evaluation. First, amplitude‐versus‐azimuthal angle inversion in the Bayesian framework in constraint of prior information such as well log data or rock physics information is executed for elastic and fracture parameters using pre‐stack angle gathers of different azimuth angles. The second procedure is to estimate differential horizontal stress ratio with Poisson's ratio and fracture density. Finally, a real data set is studied to test the new approach and the result demonstrated that the estimated differential horizontal stress ratio can reflect the stress property and it agrees with geological law and the new drilled well interpretation. Therefore, we can conclude that the combination of newly derived differential horizontal stress ratio and azimuthal PP‐wave amplitude versus offset equation in this study provides an available method for estimating the differential horizontal stress ratio of unconventional reservoirs, and the new approach can offer reliable geophysical information for stress evaluation.

Characterization of disruption halo current between ‘W-Like’ graphite divertor and ‘ITER-Like’ divertor structure on EAST tokamak

The general characteristics of disruption halo currents, including current flowing paths between the components, are studied in the experiments of EAST tokamak with ‘W-Like’ graphite divertor and ‘ITER-like’ tungsten divertor. It is found that lower hybrid waves can efficiently reheat minor disruption plasma, this can drive plasma current for tens, or even hundreds, of milliseconds. The reheated plasma can lead to lower loop voltage and of course lower halo current. Additionally, it is confirmed that helium can not rapidly cool down plasmas with high thermal energy, but Argon can shut down plasma rapidly even with a little amount of gas. Until now no runaway electron plateau has been observed both in Argon and Helium mitigation experiments. The EAST disruption database shows that: (1) Larger loop voltage is driven in vertical displacement event disruptions than in major disruptions. (2) Halo currents decrease with the decrease of vertical displacement. (3) The boundary of is 0.72, less than the limit shown in IDDB. The experimental results described here can help to improve the design of the planned upgrade of the EAST lower divertor, and provide physics information for the ITER divertor.

Investigation of static and dynamic bulk moduli in a carbonate field

Bulk modulus which relates seismic attributes to rock and fluid properties is considered as an important parameter for seismic reservoir characterisation workflows, and can provide useful information for such studies. However, its measurement and modelling approaches are yet to be sufficiently addressed and discussed especially for carbonate rocks. In this research, in order to provide more information about bulk modulus, nine samples from a carbonate oilfield were subjected to static and dynamic investigations under dry and saturated conditions. Then, the dynamic modulus was calculated using well log data and ultrasonic measurements. In order to obtain static data, multi-stage triaxial compression tests were performed on the vertical plugs extracted from conventional cores taken at different selected depths. The results showed that static and dynamic bulk moduli follow similar trends, although the value of dynamic modulus is usually higher. Furthermore, several empirical relationships based on simple linear regression and forward and backward stepwise multiple linear regressions were developed to relate measured static and dynamic bulk moduli for the given field, then proposed equations were evaluated by ANOVA analysis. Besides, dry and saturated bulk moduli were modelled using Xu and Payne rock physics model. Here, saturated bulk modulus was modelled using either dynamic or static dry bulk moduli within the Gassmann’s theory. It is observed that Gassmann’s equation gives a more accurate result using dynamic data rather than static ones. The poor Gassmann’s equation prediction under static condition could be attributed to microcracks in the samples and the uncertainty associated with this theory for complex pore geometry. The outcomes of this study can, furthermore, provide the necessary information and relationships for rock physics (e.g. shear wave estimating and fluid substitution modelling) and geomechanical (e.g. CO2 injection and compaction forecast) studies for exploration and development programs.

Learning Physics-Based Reduced-Order Models for a Single-Injector Combustion Process

This paper presents a physics-based data-driven method to learn predictive reduced-order models (ROMs) from high-fidelity simulations and illustrates it in the challenging context of a single-injector combustion process. The method combines the perspectives of model reduction and machine learning. Model reduction brings in the physics of the problem, constraining the ROM predictions to lie on a subspace defined by the governing equations. This is achieved by defining the ROM in proper orthogonal decomposition (POD) coordinates, which embed the rich physics information contained in solution snapshots of a high-fidelity computational fluid dynamics model. The machine learning perspective brings the flexibility to use transformed physical variables to define the POD basis. This is in contrast to traditional model reduction approaches that are constrained to use the physical variables of the high-fidelity code. Combining the two perspectives, the approach identifies a set of transformed physical variables that expose quadratic structure in the combustion governing equations and learns a quadratic ROM from transformed snapshot data. This learning does not require access to the high-fidelity model implementation. Numerical experiments show that the ROM accurately predicts temperature, pressure, velocity, species concentrations, and the limit-cycle amplitude, with speedups of more than five orders of magnitude over high-fidelity models. Our ROM simulation is shown to be predictive 200% past the training interval. ROM-predicted pressure traces accurately match the phase of the pressure signal and yield good approximations of the limit-cycle amplitude.

Multifidelity Modeling by Polynomial Chaos-Based Cokriging to Enable Efficient Model-Based Reliability Analysis of NDT Systems

This work proposes a novel multifidelity metamodeling approach, the polynomial chaos-based Cokriging (PC-Cokriging). The proposed approach is used for fast uncertainty propagation in a reliability analysis of nondestructive testing systems using model-assisted probability of detection (MAPOD). In particular, PC-Cokriging is a multivariate version of polynomial chaos-based Kriging (PC-Kriging), which aims at combining the advantages of the regression-based polynomial chaos expansions and the interpolation-based Kriging metamodeling methods. Following a similar process as Cokriging, the PC-Cokriging advances PC-Kriging by enabling the incorporation of multifidelity physics information. The proposed PC-Cokriging is demonstrated on two analytical functions and three ultrasonic testing MAPOD cases. The results show that PC-Cokriging outperforms the state-of-the-art metamodeling approaches when providing the same number of training points. Specifically, PC-Cokriging reduces the high-fidelity training sample cost of the Kriging and PCE metamodels by over one order of magnitude, and the PC-Kriging and conventional Cokriging multifidelity metamodeling by up to $$50\%$$ to reach the same accuracy level (defined by the root mean squared error being no greater than $$1\%$$ of the standard deviation of the testing points). The accuracy and robustness of the proposed method of the key MAPOD metrics versus various detection thresholds are investigated and satisfactory results are obtained.

On echo intervals in gravitational wave echo analysis

Gravitational wave echoes, if they exist, could encode important information of new physics from the strong gravity regime. Current echo searches usually assume constant interval echoes (CIEs) a priori, although unequal interval echoes (UIEs) are also possible. Despite of its simplicity, the using of CIE templates need to be properly justified, especially given the high sensitivity of future gravitational wave detectors. In this paper, we assess the necessity of UIE templates in echo searches. By reconstructing injected UIE signals with both CIE and UIE templates, we show that the CIE template may significantly misinterpret the echo signals if the variation of the interval is greater than the statistical errors of the interval, which is further confirmed by a Bayesian analysis on model stelection. We also forecast the constraints on the echo intervals given by future GW detectors such as Advanced LIGO and Einstein Telescope.

Effects of a short classroom intervention on students’ identification of contradictions in an Internet forum text: Interaction with reading comprehension skills

Internet-based texts addressing science issues can contain inaccurate and contradictory information. However, prior research has shown that students often have difficulties in identifying contradictions in science texts. In a quasi-experimental study with treatment-control groups design with N = 129 high-school students from grades 10 and 11, we thus investigated whether a short classroom intervention could foster students' identification of contradictions in a physics text presented in a question & answer forum. Specifically, the intervention, which was provided in a regular physics lesson, was aimed at sensitizing students to the existence of inaccurate physics information on the Internet. Five weeks after the intervention, students of three intervention and three control classes were tasked to read a paper-printout of an Internet forum text on wind power plants that contained several contradictions. Subsequently, students performed a Conflict Verification Task (CVT) that measured their identification of the contradictions. The effectiveness of the intervention was moderated by students' reading comprehension skills. Students with higher reading comprehension skills benefitted from the intervention, with those in the intervention group achieving higher CVT scores than those in the control group. For students with lower reading comprehension skills, however, no differences between intervention and control group were obtained. In addition, prior domain knowledge was positively related to the identification of contradictions, independent of the intervention or students' reading comprehension skills. Theoretical and practical implications of these findings are discussed.

The Desargues and Pappus properties in finite quantum systems

The Desargues and Pappus properties in projective geometry, are studied in the context of Quantum Physics. The Desargues property implies that two experiments each of which involves two successive projective measurements, have selective correlations. The Pappus property in a quantum context, is also discussed. The work links Projective Geometry to Quantum Physics and Quantum Information.

Benchmark on the Dynamics of Liquid Draining Inside a Tank

Immense information and details observation of flow physics inside a draining tank can be achieved by adopting reliable numerical simulations. Yet the accuracy of numerical results has been always debatable and it is mainly affected by the grid convergence error and computational modeling approaches. Hence, this study is divided into two stages. In the first stage, this paper determines a systematic method of refining a computational grid for a liquid draining inside a tank using OpenFOAM software. The sensitivity of the computed flow field on different mesh resolutions is also examined. In order to study the effect of grid dependency, three different grid refinements are investigated: fine, medium and coarse grids. By using a form of Richardson extrapolation and Grid Convergence Index (GCI), the level of grid independence is attained. In this paper, a monotonic convergence criteria is reached when the fine grid has the GCI value below 10% for each parameter. In the second stage, different computational modeling approaches (DNS, RANS k-ε, RANS k-ω and LES turbulence models) are investigated using the finer grid from the first stage. The results for the draining time and flow visualization of the generation of an air-core are in a good agreement with the available published data. The Direct Numerical Simulation (DNS) seems most reasonably satisfactory for VOF studies relating air-core compared to other different turbulence modeling approaches.