Realistic Simulation Research Articles

Size–mass relations for simulated low-mass galaxies: mock imaging versus intrinsic properties

ABSTRACT The observationally inferred size versus stellar–mass relationship (SMR) for low-mass galaxies provides an important test for galaxy formation models. However, the relationship relies on assumptions that relate observed luminosity profiles to underlying stellar mass profiles. Here we use the Feedback in Realistic Environments simulations of low-mass galaxies to explore how the predicted SMR changes depending on whether one uses star-particle counts directly or mock observations. We reproduce the SMR found in The Exploration of Local Volume Satellites survey remarkably well only when we infer stellar masses and sizes using mock observations. However, when we use star particles to directly infer stellar masses and half-mass radii, we find that our galaxies are too large and obey an SMR with too little scatter compared to observations. This discrepancy between the ‘true’ galaxy size and mass and those derived in the mock observation approach is twofold. First, our simulated galaxies have higher and more varied mass-to-light ratios (MLR) at a fixed colour than those commonly adopted, which tends to underestimate their stellar masses compared to their true, simulated values. Second, our galaxies have radially increasing MLR gradients therefore using a single MLR tends to underpredict the mass in the outer regions. Similarly, the true half-mass radius is larger than the half-light radius because the light is more concentrated than the mass. If our simulations are accurate representations of the real Universe, then the relationship between galaxy size and stellar mass is even tighter for low-mass galaxies than is commonly inferred from observed relations.

Simulation of coronary fractional flow reserve and whole-cycle flow based on optical coherence tomography in individual patients with coronary artery disease

Computer simulations of coronary fractional flow reserve (FFR) based on coronary imaging have emerged as an attractive alternative to invasive measurements. However, most methods are proprietary and employ non-physiological assumptions. Our aims were to develop and validate a physiologically realistic open-source simulation model for coronary flow, and to use this model to predict FFR based on intracoronary optical coherence tomography (OCT) data in individual patients. We included patients undergoing elective coronary angiography with angiographic borderline coronary stenosis. Invasive measurements of coronary hyperemic pressure and absolute flow and OCT imaging were performed. A computer model of coronary flow incorporating pulsatile flow and the effect of left ventricular contraction was developed and calibrated, and patient-specific flow simulation was performed. Forty-eight coronary arteries from 41 patients were included in the analysis. Average FFR was 0.79 ± 0.14, and 50% had FFR ≤ 0.80. Correlation between simulated and measured FFR was high (r = 0.83, p < 0.001). Average difference between simulated FFR and observed FFR in individual patients was − 0.009 ± 0.076. Overall diagnostic accuracy for simulated FFR ≤ 0.80 in predicting observed FFR ≤ 0.80 was 0.88 (0.75–0.95) with sensitivity 0.79 (0.58–0.93) and specificity 0.96 (0.79–1.00). The positive predictive value was 0.95 (0.75–1.00) and the negative predictive value was 0.82 (0.63–0.94). In conclusion, realistic simulations of whole-cycle coronary flow can be produced based on intracoronary OCT data with a new, computationally simple simulation model. Simulated FFR had moderate numerical agreement with observed FFR and a good diagnostic accuracy for predicting hemodynamic significance of coronary stenoses.

Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks

BackgroundImage-based crop growth modeling can substantially contribute to precision agriculture by revealing spatial crop development over time, which allows an early and location-specific estimation of relevant future plant traits, such as leaf area or biomass. A prerequisite for realistic and sharp crop image generation is the integration of multiple growth-influencing conditions in a model, such as an image of an initial growth stage, the associated growth time, and further information about the field treatment. While image-based models provide more flexibility for crop growth modeling than process-based models, there is still a significant research gap in the comprehensive integration of various growth-influencing conditions. Further exploration and investigation are needed to address this gap.MethodsWe present a two-stage framework consisting first of an image generation model and second of a growth estimation model, independently trained. The image generation model is a conditional Wasserstein generative adversarial network (CWGAN). In the generator of this model, conditional batch normalization (CBN) is used to integrate conditions of different types along with the input image. This allows the model to generate time-varying artificial images dependent on multiple influencing factors. These images are used by the second part of the framework for plant phenotyping by deriving plant-specific traits and comparing them with those of non-artificial (real) reference images. In addition, image quality is evaluated using multi-scale structural similarity (MS-SSIM), learned perceptual image patch similarity (LPIPS), and Fréchet inception distance (FID). During inference, the framework allows image generation for any combination of conditions used in training; we call this generation data-driven crop growth simulation.ResultsExperiments are performed on three datasets of different complexity. These datasets include the laboratory plant Arabidopsis thaliana (Arabidopsis) and crops grown under real field conditions, namely cauliflower (GrowliFlower) and crop mixtures consisting of faba bean and spring wheat (MixedCrop). In all cases, the framework allows realistic, sharp image generations with a slight loss of quality from short-term to long-term predictions. For MixedCrop grown under varying treatments (different cultivars, sowing densities), the results show that adding these treatment information increases the generation quality and phenotyping accuracy measured by the estimated biomass. Simulations of varying growth-influencing conditions performed with the trained framework provide valuable insights into how such factors relate to crop appearances, which is particularly useful in complex, less explored crop mixture systems. Further results show that adding process-based simulated biomass as a condition increases the accuracy of the derived phenotypic traits from the predicted images. This demonstrates the potential of our framework to serve as an interface between a data-driven and a process-based crop growth model.ConclusionThe realistic generation and simulation of future plant appearances is adequately feasible by multi-conditional CWGAN. The presented framework complements process-based models and overcomes their limitations, such as the reliance on assumptions and the low exact field-localization specificity, by realistic visualizations of the spatial crop development that directly lead to a high explainability of the model predictions.

Biases in ARG-Based Inference of Historical Population Size in Populations Experiencing Selection.

Inferring the demographic history of populations provides fundamental insights into species dynamics and is essential for developing a null model to accurately study selective processes. However, background selection and selective sweeps can produce genomic signatures at linked sites that mimic or mask signals associated with historical population size change. While the theoretical biases introduced by the linked effects of selection have been well established, it is unclear whether ancestral recombination graph (ARG)-based approaches to demographic inference in typical empirical analyses are susceptible to misinference due to these effects. To address this, we developed highly realistic forward simulations of human and Drosophila melanogaster populations, including empirically estimated variability of gene density, mutation rates, recombination rates, purifying, and positive selection, across different historical demographic scenarios, to broadly assess the impact of selection on demographic inference using a genealogy-based approach. Our results indicate that the linked effects of selection minimally impact demographic inference for human populations, although it could cause misinference in populations with similar genome architecture and population parameters experiencing more frequent recurrent sweeps. We found that accurate demographic inference of D. melanogaster populations by ARG-based methods is compromised by the presence of pervasive background selection alone, leading to spurious inferences of recent population expansion, which may be further worsened by recurrent sweeps, depending on the proportion and strength of beneficial mutations. Caution and additional testing with species-specific simulations are needed when inferring population history with non-human populations using ARG-based approaches to avoid misinference due to the linked effects of selection.

Behavioural tendencies of the last British–Irish Ice Sheet revealed by data–model comparison

ABSTRACTIntegrating ice‐sheet models with empirical data pertaining to palaeo‐ice sheets promotes advances in the models used in sea‐level predictions and can improve our understanding of past ice‐sheet behaviour. The large number of empirical constraints on the last British–Irish Ice Sheet make it ideal for model–data comparison experiments. Here, we present an ensemble of 600 model simulations, which are compared with data on former ice‐flow extent, flow geometry and deglaciation timing. Simulations which poorly recreate data were ruled out, allowing us to examine the remaining physically realistic simulations which capture the ice sheets' behavioural tendencies. Our results led to a novel reconstruction of behaviour in the data‐poor region of the North Sea, insights into the ice stream, potential ice‐shelf and readvance dynamics, and the potential locations of peripheral ice caps. We also propose that the asynchronous behaviour of the British–Irish Ice Sheet is a consequence of the geography of the British Isles and the merging and splitting of different bodies of ice through saddle merger and collapse. Furthermore, persistent model–data mismatches highlight the need for model development, especially regarding the physics of ice–ocean interactions. Thus, this work highlights the power of integrating models and data, a long‐held aim of palaeoglaciology.

Efficacy of climate forcings in transient CMIP6 simulations

For effective radiative forcing (ERF) to be an ideal metric for comparing the strength of different climate drivers (such as CO2 and aerosols), the ratio of radiative forcing to global-mean temperature change must be the same for each driver. Typically, this ratio is divided by the same ratio for CO2 and termed efficacy. Previously it has been shown that efficacy is close to unity in abrupt perturbation experiments for a range of climate drivers, but efficacy with respect to CO2 has not been investigated in transient realistic simulations. Here, we analyse transient simulations from CMIP6 experiments and show comparable results between transient and abrupt perturbation experiments. We demonstrate that aerosol efficacy is not significantly different from unity, however inter-model differences in aerosol experiments are notably large.

Applications of machine‐learning interatomic potentials for modeling ceramics, glass, and electrolytes: A review

AbstractThe emergence of artificial intelligence has provided efficient methodologies to pursue innovative findings in material science. Over the past two decades, machine‐learning potential (MLP) has emerged as an alternative technology to density functional theory (DFT) and classical molecular dynamics (CMD) simulations for computational modeling of materials and estimation of their properties. The MLP offers more efficient computation compared to DFT, while providing higher accuracy compared to CMD. This enables us to conduct more realistic simulations using models with more atoms and for longer simulation times. Indeed, the number of research studies utilizing MLPs has significantly increased since 2015, covering a broad range of materials and their structures, ranging from simple to complex, as well as various chemical and physical phenomena. As a result, there are high expectations for further applications of MLPs in the field of material science and industrial development. This review aims to summarize the applications, particularly in ceramics and glass science, and fundamental theories of MLPs to facilitate future progress and utilization. Finally, we provide a summary and discuss perspectives on the next challenges in the development and application of MLPs.

Leading-edge Metaverse in education: framework, applications, challenges, and future development

The incorporation of cutting-edge metaverse technologies into the field of education has emerged as a transformative paradigm, reshaping conventional learning environments and paving the way for novel immersive and interactive educational experiences. This paper undertakes a comprehensive examination of the framework, applications, challenges, and future development prospects of metaverse technologies within the educational domain, focusing on a diverse array of techniques that contribute to the continuous evolution of this innovative educational landscape. The framework analysis delves into the design and implementation strategies that form the foundation of metaverse-based educational platforms. Utilizing augmented reality (AR), virtual reality (VR), and mixed reality (MR), these frameworks strive to create dynamic and captivating learning environments that transcend geographical limitations. The paper explores how these technologies enable realistic simulations, collaborative problem-solving, and experiential learning, thereby fostering a more interactive and participatory educational experience. Case studies and examples illustrate their utilization across disciplines, spanning from science and mathematics to humanities and vocational training. The paper underscores the significance of artificial intelligence (AI) and machine learning (ML) algorithms in tailoring educational content to individual learning styles, thereby enhancing personalization and adaptability within metaverse-based educational systems. Nevertheless, the research also addresses the challenges associated with implementing metaverse technologies in education. Issues such as accessibility, digital equity, and data privacy are discussed, accompanied by considerations for overcoming these challenges to ensure a more inclusive and secure educational landscape. Looking ahead, the paper outlines potential developments in metaverse education, exploring emerging technologies and trends such as blockchain-based credentialing, haptic feedback integration, and the incorporation of the Internet of Things (IoT). The paper acknowledges the diverse techniques contributing to its ongoing evolution, emphasizing the dynamic nature of this transformative educational landscape.

A predictive mesoscale model for continuous dynamic recrystallization

Thermomechanical processing of titanium alloys often requires complex routes to achieve the desired final microstructure. Recent advances in modeling and simulation tools have facilitated the optimization of these processing routes. However, existing models often fail to accurately predict microstructural changes at large deformations. In this study, we refine the physical principles of an existing mean-field model and propose a calibration method that uses experimental results under isothermal conditions, accounting for the actual local deformation within the workpiece. This new approach improves the predictability of microstructural changes due to continuous dynamic recrystallization during torsion and compression experiments. Additionally, we integrate the model into the commercial FEM-based DEFORM™ 2D software to predict the local microstructure evolution within hot torsion specimens thermomechanically treated by resistive heating. Validation using non-isothermal deformation tests demonstrates that the model provides realistic simulations at high strain rates, where adiabatic heat modifies temperature, flow stress and microstructure. This study demonstrates the intrinsic correlation between microstructure, flow behavior, and workpiece geometry, considering the impact of deformation history in thermomechanical processes.

MRI-compatible abdomen phantom to mimic respiratory-triggered organ movement while performing needle-based interventions.

In vivo studies are often required to prove the functionality and safety of medical devices. Clinical trials are costly and complex, adding to ethical scrutiny of animal testing. Anthropomorphic phantoms with versatile functionalities can overcome these issues with regard to medical education or an effective development of assistance systems during image-guided interventions (e.g., robotics, navigation/registration algorithms). In this work, an MRI-compatible and customizable motion phantom is presented to mimic respiratory-triggered organ movement as well as human anatomy. For this purpose, polyvinyl alcohol cryogel (PVA-C) was the foundation for muscles, liver, kidneys, tumors, and remaining abdominal tissue in different sizes of the abdominal phantom body (APB) with the ability to mimic human tissue in various properties. In addition, a semi-flexible rib cage was 3D-printed. The motion unit (MU) with an electromagnetically shielded stepper motor and mechanical extensions simulated a respiration pattern to move the APB. Each compartment of the APB complied the relaxation times, dielectricity, and elasticity of human tissue. It showed resistance against mold and provided a resealable behavior after needle punctures. During long-term storage, the APB had a weight loss of 2.3%, followed by changes to relaxation times of 9.3% and elasticity up to 79%. The MU was able to physiologically appropriately mimic the organ displacement without reducing the MRI quality. This work presents a novel modularizable and low-cost PVA-C based APB to mimic fundamental organ motion. Beside a further organ motion analysis, an optimization of APB's chemical composition is needed to ensure a realistic motion simulation and reproducible long-term use. This phantom enhances diverse and varied training environments for prospective physicians as well as effective R&D of medical devices with the possibility to reduce in vivo experiments.

DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.

Complex diseases are often caused and characterized by misregulation of multiple biological pathways. Differential network analysis aims to detect significant rewiring of biological network structures under different conditions and has become an important tool for understanding the molecular etiology of disease progression and therapeutic response. With few exceptions, most existing differential network analysis tools perform differential tests on separately learned network structures that are computationally expensive and prone to collapse when grouped samples are limited or less consistent. We previously developed an accurate differential network analysis method-differential dependency networks (DDN), that enables joint learning of common and rewired network structures under different conditions. We now introduce the DDN3.0 tool that improves this framework with three new and highly efficient algorithms, namely, unbiased model estimation with a weighted error measure applicable to imbalance sample groups, multiple acceleration strategies to improve learning efficiency, and data-driven determination of proper hyperparameters. The comparative experimental results obtained from both realistic simulations and case studies show that DDN3.0 can help biologists more accurately identify, in a study-specific and often unknown conserved regulatory circuitry, a network of significantly rewired molecular players potentially responsible for phenotypic transitions. The Python package of DDN3.0 is freely available at https://github.com/cbil-vt/DDN3. A user's guide and a vignette are provided at https://ddn-30.readthedocs.io/.

The informativeness of [C ii] line-intensity mapping as a probe of the H i content and metallicity of galaxies at the end of reionization

ABSTRACT Line-intensity mapping (LIM) experiments coming online now will survey fluctuations in aggregate emission in the [C ii] ionized carbon line from galaxies at the end of reionization. Experimental progress must be matched by theoretical reassessments of approaches to modelling and the information content of the signal. We present a new model for the halo–[C ii] connection, building upon results from the Feedback In Realistic Environments simulations suggesting that gas mass and metallicity most directly determine [C ii] luminosity. Applying our new model to an ensemble of peak-patch halo light-cones, we generate new predictions for the [C ii] LIM signal at z ≳ 6. We expect a baseline 4000-h LIM survey from the Cerro Chajnantor Atacama Telescope facility to have the fundamental sensitivity to detect the [C ii] power spectrum at a significance of 5σ at z ∼ 6, with an extended or successor Stage 2 experiment improving significance to 48σ at z ∼ 6 and achieving 11σ at z ∼ 7.5. Cross-correlation through stacking, simulated against a mock narrow-band Lyman-break galaxy survey, would yield a strong detection of the radial profile of cosmological [C ii] emission surrounding star-forming galaxies. We also analyse the role of a few of our model’s parameters through the pointwise relative entropy (PRE) of the distribution of [C ii] intensities. While the PRE signature of different model parameters can become degenerate or diminished after factoring in observational distortions, various parameters do imprint themselves differently on the one-point statistics of the intrinsic signal. Further work can pave the way to access this information and distinguish different sources of non-Gaussianity in the [C ii] LIM observation.

Virtual reality as an engaging and enjoyable method for delivering emergency clinical simulation training: a prospective, interventional study of medical undergraduates

BackgroundIt is a requirement that medical students are educated in emergencies and feel well prepared for practice as a doctor, yet national surveys show that many students feel underprepared. Virtual reality (VR), combined with 360-degree filming, provides an immersive, realistic, and interactive simulation experience. Unlike conventional in-person simulation, it is scalable with reduced workforce demands. We sought to compare students’ engagement and enjoyment of VR simulation to desktop computer-based simulation.MethodsWe conducted a prospective, interventional, evaluation study. The study was carried out on final year medical students undertaking their Pre-Foundation Assistantship (n = 116) at Imperial College School of Medicine (ICSM) in London. We compared objective engagement, subjective engagement, and subjective enjoyment of VR simulation to desktop computer-based simulation using cardiac arrest and life-threatening asthma scenarios. Engagement was measured objectively using students’ physiological parameters, including heart rate and eye tracking, and facilitator observations using the validated ‘Behavioural Engagement Related to Instruction’ (BERI) protocol. Students’ subjective engagement and enjoyment levels were measured using a post-session survey.ResultsStudents’ maximum heart rates were significantly higher during VR simulation with a mean difference of 4.2 beats per minute (3.2 to 5.2, p < 0.001), and eye tracking showed they spent a significantly greater mean percentage of time of 6.4% (5.1 to 7.7, p < 0.001) focusing on the scenarios in VR compared to standard desktop. Qualitative data showed students enjoyed and felt engaged with the sessions, which provided a safe space for learning.ConclusionsOur study shows that students found VR simulations enjoyable and were more engaged compared to standard desktop simulation. This suggests that 360-degree VR simulation experiences provide students with immersive, realistic training, which is scalable, giving them the unique opportunity to manage emergencies and work within emergency teams, which would not typically occur during traditional training.

Advancements in Finite Element Modeling for Cardiac Device Leads and 3D Heart Models.

The human heart's remarkable vitality necessitates a deep understanding of its mechanics, particularly concerning cardiac device leads. This paper presents advancements in finite element modeling for cardiac leads and 3D heart models, leveraging computational simulations to assess lead behavior over time. Through detailed modeling and meshing techniques, we accurately captured the complex interactions between leads and heart tissue. Material properties were assigned based on ASTM (American Society for Testing and Materials) standards and in vivo exposure data, ensuring realistic simulations. Our results demonstrate close agreement between experimental and simulated data for silicone insulation in pacemaker leads, with a mean force tolerance of 19.6 N ± 3.6 N, an ultimate tensile strength (UTS) of 6.3 MPa ± 1.15 MPa, and a percentage elongation of 125% ± 18.8%, highlighting the effectiveness of simulation in predicting lead performance. Similarly, for polyurethane insulation in ICD leads, we found a mean force of 65.87 N ± 7.1 N, a UTS of 10.7 MPa ± 1.15 MPa, and a percentage elongation of 259.3% ± 21.4%. Additionally, for polyurethane insulation in CRT leads, we observed a mean force of 53.3 N ± 2.06 N, a UTS of 22.11 MPa ± 0.85 MPa, and a percentage elongation of 251.6% ± 13.2%. Correlation analysis revealed strong relationships between mechanical properties, further validating the simulation models. Classification models constructed using both experimental and simulated data exhibited high discriminative ability, underscoring the reliability of simulation in analyzing lead behavior. These findings contribute to the ongoing efforts to improve cardiac device lead design and optimize patient outcomes.

Ultrasound guided arterial access for combat medics: A blinded proof-of-concept study using echogenic needles.

Obtaining percutaneous vascular access in hemodynamically unstable patients with constricted vessels can be challenging. Training combat medics in this procedure is necessary for administration of fluid and blood products and introducing endovascular bleeding control tools in pre-hospital settings. Echogenic coated needles might provide better ultrasound visibility in invasive procedures and hereby lower complications. The primary aim was to evaluate the efficacy of a microteaching program for obtaining ultrasound-guided femoral artery access for ultrasound inexperienced combat medics. The secondary aim was to assess the additional value of innovative echogenic coated needles in ultrasound-guided vascular access. Combat medics participated in a four-step microteaching program. The program consisted of a theoretical and step-by-step practical part with three different models including live and dead tissue & a REBOA Access Task Trainer. During the final test, all participants had to obtain femoral artery access on a pressurized post-mortem human specimen model with both echogenic coated and conventional needles. Self-perceived and observed performance as well as procedure times were scored. All nine participants succeeded in blood vessel visualization and obtaining vascular access in the two models within 3 minutes and were significantly faster during the second attempt on the pressurized post-mortem human specimen model. Scoring comparison and usability preference by ultrasound inexperienced personnel showed a significant difference in favor of the echogenic coated needles. Microteaching may be an effective approach to train combat medics in obtaining ultrasound-guided percutaneous femoral artery access. The use of echogenic coatings on needles could be a valuable adjunct and provide advantage in obtaining vascular access. Future research should focus on realistic simulation of austere situations and further evaluation of the use of echogenic coated instruments for vascular access in these pre-hospital settings.

Inversion of Sea Surface Currents From Satellite‐Derived SST‐SSH Synergies With 4DVarNets

AbstractSatellite altimetry offers a unique approach for direct sea surface current observation, but it is limited to measuring the surface‐constrained geostrophic component. Ageostrophic dynamics, prevalent at horizontal scales below 100 km and time scales below 10 days, are often underestimated by ocean reanalyzes employing data assimilation schemes. To address this limitation, we introduce a novel deep learning scheme, rooted in a variational data assimilation formulation with trainable observations and a priori terms, that harnesses the synergies between satellite‐derived sea surface observations, namely sea surface height (SSH) and sea surface temperature (SST), to enhance sea surface current reconstruction. Numerical experiments, conducted using realistic simulations, in a case study area of the Gulf Stream, demonstrate the potential of the proposed scheme to capture ageostrophic dynamics at time scales of 2.5–3.0 days and horizontal scales of 0.5°–0.7°. The analysis of diverse observation configurations, encompassing nadir along‐track altimetry, wide‐swath SWOT (Surface Water and Ocean Topography) altimetry, and SST data, highlights the pivotal role of SST features in retrieving a significant portion of the ageostrophic dynamics (approximately 47%). These findings underscore the potential of deep learning and 4DVarNet schemes in improving ocean reanalyzes and enhancing our understanding of ocean dynamics.

Improved Representation of Vegetation Soil Moisture Coupling Enhances Soil Moisture Data Assimilation in Water‐Limited Regimes: A Case Study Over Texas

AbstractAssimilating remotely sensed surface soil moisture (SSM) into land surface models (LSM) is widely used to improve model representations of soil moisture (SM). However, the efficacy of SSM data assimilation (DA) has been found to be limited, particularly in resolving root‐zone soil moisture (RZSM). This study investigates how the representation of vegetation phenology, modulates the efficacy of SSM DA in enhancing the realism of RZSM simulations. To this end, two sets of climatological leaf area index (LAI) are implemented in Noah‐MP LSM over the state of Texas: (a) Noah‐MP default based on long‐term MODIS observations, (b) an alternative LAI adapted from AVHRR products. The former are found to exhibit conspicuous phase errors whereas the latter are more consistent with observed seasonal cycle. Two sets of DA experiments were performed accordingly, wherein SMAP L3 SSM is assimilated into Noah‐MP equipped with each LAI product from 2015 to 2019, Validation of the resulting products against in‐situ data reveals that (a) using the AVHRR‐based LAI, the Noah‐MP outperforms the baseline in reproducing the dynamics of RZSM, and the outperformance is particularly evident over the warm season and water‐stressed western Texas; (b) using the alternative LAI enhances the ability of DA to improve the accuracy of Noah‐MP RZSM, and to a lesser extent, SSM; and (c) gains in SM attained through improvement of LAI and application of DA is most pronounced over regions featuring tight vertical SM coupling. Additional model mechanistic limitations that need to be overcome to improve efficacy of DA are discussed.

On the detectability of the moving lens signal in CMB experiments

Upcoming cosmic microwave background (CMB) experiments are expected to detect new signals probing interaction of CMB photons with intervening large-scale structure. Among these the moving-lens effect, the CMB temperature anisotropy induced by cosmological structures moving transverse to our line of sight, is anticipated to be measured to high significance in the near future. In this paper, we investigate two possible strategies for the detection of this signal: pairwise transverse-velocity estimation and oriented stacking. We expand on previous studies by including in the analysis realistic simulations of competing signals and foregrounds. We confirm that the moving lens effect can be detected at ≥ 10σ level by a combination of CMB-S4 and LSST surveys. We show that the limiting factors in the detection depend on the strategy: for the stacking analysis, correlated extragalactic foregrounds, namely the cosmic infrared background and thermal Sunyaev Zel'dovich effect, play the most important role. The addition of foregrounds make the signal-to-noise ratio be most influenced by large and nearby objects. As for the pairwise detection, halo lensing and pair number counts are the main issues. In light of our findings, we elaborate on possible strategies to improve the analysis approach for the moving lens detection with upcoming experiments. We also deliver to the community all the simulations and tools we developed for this study.

LSPE-Strip on-sky calibration strategy using bright celestial sources

In this paper we describe the global on-sky calibration strategy of the LSPE-Strip instrument. Strip is a microwave telescope operating in the Q- and W-bands (central frequencies of 43 and 95 GHz respectively) from the Observatorio del Teide in Tenerife, with the goal to observe and characterise the polarised Galactic foreground emission, and complement the observations of the polarisation of the cosmic microwave background to be performed by the LSPE-SWIPE instrument and other similar experiments operating at higher frequencies to target the detection of the B-mode signal from the inflationary epoch of the Universe. Starting from basic assumptions on some of the instrument parameters (NET, 1/f noise knee frequency, beam properties, observing efficiency) we perform realistic simulations to study the level of accuracy that can be achieved through observations of bright celestial calibrators in the Strip footprint (sky fraction of 30%) on the determination and characterisation of the main instrument parameters: global and relative gain factors (in intensity and in polarisation), polarisation direction, polarisation efficiency, leakage from intensity to polarisation, beams, window functions and pointing model.

Eddy–Internal Wave Interactions: Stimulated Cascades in Cross-Scale Kinetic Energy and Enstrophy Fluxes

Abstract The interactions between oceanic mesoscale eddies, submesoscale currents, and internal gravity waves (IWs) are investigated in submesoscale-resolving realistic simulations in the North Atlantic Ocean. Using a novel analysis framework that couples the coarse-graining method in space with temporal filtering and a Helmholtz decomposition, we quantify the effects of the interactions on the cross-scale kinetic energy (KE) and enstrophy fluxes. By systematically comparing solutions with and without IW forcing, we show that externally forced IWs stimulate a reduction in the KE inverse cascade associated with mesoscale rotational motions and an enhancement in the KE forward cascade associated with divergent submesoscale currents, i.e., a “stimulated cascade” process. The corresponding IW effects on the enstrophy fluxes are seasonally dependent, with a stimulated reduction (enhancement) in the forward enstrophy cascade during summer (winter). Direct KE and enstrophy transfers from currents to IWs are also found, albeit with weaker magnitudes compared with the stimulated cascades. We further find that the forward KE and enstrophy fluxes associated with IW motions are almost entirely driven by the scattering of the waves by the rotational eddy field, rather than by wave–wave interactions. This process is investigated in detail in a companion manuscript. Finally, we demonstrate that the stimulated cascades are spatially localized in coherent structures. Specifically, the magnitude and direction of the bidirectional KE fluxes at submesoscales are highly correlated with, and inversely proportional to, divergence-dominated circulations, and the inverse KE fluxes at mesoscales are highly correlated with strain-dominated circulations. The predominantly forward enstrophy fluxes in both seasons are also correlated with strain-dominated flow structures.