Large Parameter Space Research Articles

Constraints on Strong Phase Transitions in Neutron Stars

We study current bounds on strong first-order phase transitions (PTs) along the equation of state (EOS) of dense strongly interacting matter in neutron stars, under the simplifying assumption that on either side of the PT, the EOS can be approximated by a simple polytropic form. We construct a large ensemble of possible EOSs of this form, anchor them to chiral effective field theory calculations at nuclear density and perturbative Quantum Chromodynamics at high densities, and subject them to astrophysical constraints from high-mass pulsars and gravitational-wave observations. Within this setup, we find that a PT permits neutron-star solutions with larger radii, but only if the transition begins below twice nuclear saturation density. We also identify a large parameter space of allowed PTs currently unexplored by numerical-relativity studies. Additionally, we locate a small region of parameter space allowing twin-star solutions, though we find them to only marginally pass the current astrophysical constraints. Finally, we find that sizeable cores of high-density matter beyond the PT may be located in the centers of some stable neutron stars, primarily those with larger masses.

Tunable and Robust Mid‐Infrared Saturable Absorber Employing Tungsten Doping Cadmium Oxide

AbstractMid‐infrared (MIR) pulsed lasers operating at 2–5 µm have important applications in communication, sensing, and medicine. However, the lack of robust MIR saturable absorbers (SAs) remains a major obstacle. Here, a semiconductor material cadmium oxide (CdO) film with high laser‐induced damage threshold (800 nm, 134 mJ cm−2) and broadband saturable absorption (2.0–3.9 µm) is investigated. The effective tuning of the nonlinear optical response of CdO is demonstrated by adjusting the carrier concentration via a tungsten (W)‐doping scheme. The saturable absorption is improved and carrier relaxation process is accelerated after increasing the W‐dopant level. Based on these findings, the robust CdO‐SAs with customizable parameters are realized and Q‐switched lasers with decreasing pulse width from 372 to 254.3 ns are obtained at 2 µm. The design flexibility provided by CdO opens up a large parameter space that enables the continuous improvement of compact and high‐performance MIR ultrafast lasers.

Oxidation Driven Thin‐Film Solid‐State Metal Dealloying Forming Bicontinuous Nanostructures

AbstractSolid‐state metal dealloying (SSMD) is a promising method for fabricating nanoscale metallic composites and nanoporous metals across a range of materials. Thin‐film SSMD is particularly attractive due to its ability to create fine features via solid‐state interfacial reactions within a thin‐film geometry, which can be integrated into devices for various applications. This work examines a new dealloying couple, namely the Nb–Al alloy with the dealloying agent Sc, as previously predicted in the machine‐learning (ML) models. Prior ML predictions aimed to guide the design of nanoarchitectured materials through dealloying, relying on intuition‐driven discovery within a large parameter space. However, this work reveals that at the nanoscale, the involvement of oxygen in thin film processing may instead drive the dealloying process, resulting in the formation of bicontinuous nanostructures similar to those formed by metal‐agent dealloying. The phase evolution, as well as chemical and morphological changes, are closely analyzed using a combination of X‐ray absorption spectroscopy, diffraction, and scanning transmission electron microscopy to understand the mechanisms behind nanostructure formation. The findings suggest a potential pathway for utilizing oxygen to drive the formation of bicontinuous metal–metal oxide nanocomposites, paving the way for further development of functional nanoporous materials in diverse fields.

Two-Dimensional Chiral Metasurfaces Obtained by Geometrically Simple Meta-atom Rotations.

Two-dimensional chiral metasurfaces seem to contradict Lord Kelvin's geometric definition of chirality since they can be made to coincide by performing rotational operations. Nevertheless, most planar chiral metasurface designs often use complex meta-atom shapes to create flat versions of three-dimensional helices, although the visual appearance does not improve their chiroptical response but complicates their optimization and fabrication due to the resulting large parameter space. Here we present one of the geometrically simplest two-dimensional chiral metasurface platforms consisting of achiral dielectric rods arranged in a square lattice. Chirality is created by rotating the individual meta-atoms, making their arrangement chiral and leading to chiroptical responses that are stronger or comparable to more complex designs. We show that resonances depending on the arrangement are robust against geometric variations and behave similarly in experiments and simulations. Finally, we explain the origin of chirality and behavior of our platform by simple considerations of the geometric asymmetry and gap size.

Nanoscale printed tunable specimen geometry enables high-throughput miniaturized fracture testing

Two-photon lithography (TPL) enables the design of novel micromechanical specimens, down to sub-micron resolution, thus extending the possibilities for device and material characterisation and pushing the boundaries of a broad range of miniaturized technologies such as optics, analytics, and medicine. Employing a push-to-pull geometry, incorporating double edge notched tension specimens loaded in mode I, the specimen manufacturing and testing can be automated to a large extent. This allows for the use of large parameter space characterisation methods as the essential work of fracture, with an experimentally simpler to realize compression testing setup. Within this work, a methodology is outlined for automated specimen direct laser writing with a TPL-device and subsequent testing via a nanoindenter. In total, 2100 specimens were manufactured, of which 1997 could be used for evaluation. Estimations for the essential work of fracture of the used photoresist is presented, with regards to influencing parameters such as testing displacement rate and laser writing power. A discussion of its statistical robustness and validity considerations is included. This will act as a basis framework for further statistical fracture evaluation schemes for other resin materials, as well as for probing thin film systems.

A Discussion on Geodynamic Modeling Methodology: Inferences from Numerical Models in the Anatolian Plate

Numerical models have found widespread use in geosciences, mainly due to high-resolution datasets and the development of supercomputing facilities with powerful data processing and storage capabilities during the past two decades. Instantaneous and time-dependent geodynamic modeling studies were carried out in many regions of the Alpine-Himalayan orogenic belt, including the Anatolian Plate, to investigate mantle dynamics such as lower lithosphere deformation, upper mantle flow, and their surface implications. 
 This study focuses on the instantaneous numerical modeling technique by considering multidimensional thermomechanical models in the Central and East Anatolian plateaus. To this end, conventional geodynamic modeling processes are explained with a conceptual flow chart that shows a feed-forward backpropagation modeling architecture which is nonlinearly fed by a large parameter space. While addressing a complex natural phenomenon controlled by variables on a wide range of space-time scales, the limitations as well as advantages of numerical models are analyzed. 
 In addition to conventional techniques, systematic data improvement is discussed as a new strategy in data/parameter-dependent numerical model design through an iterative process based on the Grounded Theory method for the construction of an explanatory theory from the model. This involves not refinement but (re)construction of the data (i.e., measurement/analysis/scaling) as an effective way to reveal theory/information grounded in data. 
 It is speculated that this procedure, which includes problem-oriented data reconstruction accompanying the numerical modeling process, may provide an integrated perspective for instantaneous numerical modelling.

Task graph-based performance analysis of parallel-in-time methods

In this paper, we present a performance model based on task graphs for various iterative parallel-in-time (PinT) methods. PinT methods have been developed to speed up the simulation time of time-dependent problems using modern parallel supercomputers. The performance model is based on a data-driven notation of the methods, from which a task graph is generated. Based on this task graph and a distribution of time points across processes typical for PinT methods, a theoretical lower runtime bound for the method can be obtained, as well as a prediction of the runtime for a given number of processes. In particular, the model is able to cover the large parameter space of PinT methods and make predictions for arbitrary parameter settings. Here, we describe a general procedure for generating task graphs based on three iterative PinT methods, namely, Parareal, multigrid-reduction-in-time (MGRIT), and the parallel full approximation scheme in space and time (PFASST). Furthermore, we discuss how these task graphs can be used to analyze the performance of the methods. In addition, we compare the predictions of the model with parallel simulation times using five different PinT libraries.

Revisiting UV/optical continuum time lags in AGN

ABSTRACT In this paper, we present an updated version of our model (KYNXiltr) which considers thermal reverberation of a standard Novikov–Thorne accretion disc illuminated by an X-ray point-like source. Previously, the model considered only two cases of black hole spins, and assumed a colour correction factor fcol = 2.4. Now, we extend the model to any spin value and fcol. In addition, we consider two scenarios of powering the X-ray corona, either via accretion, or external to the accretion disc. We use KYNXiltr to fit the observed time lags obtained from intense monitoring of four local Seyfert galaxies (NGC 5548, NGC 4593, Mrk 817, and Fairall 9). We consider various combinations of black hole spin, colour correction, corona height, and fraction of accretion power transferred to the corona. The model fits well the overall time-lag spectrum in these sources (for a large parameter space). For NGC 4593 only, we detect a significant excess of delays in the U band. The contribution of the diffuse BLR emission in the time-lag spectrum of this source is significant. It is possible to reduce the large best-fitting parameter space by combining the results with additional information, such as the observed Eddington ratio and average X-ray luminosity. We also provide an update to the analytic expression provided by Kammoun et al., for an X-ray source that is not powered by the accretion process, which can be used for any value of fcol, and for two values of the black hole spin (0 and 0.998).

An inflationary disk phase to explain extended protoplanetary dust disks

Context. Understanding planetesimal formation is an essential first step towards understanding planet formation. The distribution of these first solid bodies drives the locations where planetary embryos, which eventually form fully-fledged planets, grow. Aims. We seek to understand the parameter space of possible protoplanetary disk formation and evolution models of our Solar System. A good protoplanetary disk scenario for the Solar System must meet at least the following three criteria: (1) It must produce an extended gas and dust disk (e.g. 45 au for the dust); (2) within the disk, the local dust-to-gas ratio in at least two distinct locations must sufficiently increase to explain the early formation of the parent bodies of non-carbonaceous and carbonaceous iron meteorites; and (3) dust particles, which have condensed at high temperatures (i.e. calcium–aluminium-rich inclusions), must be transported to the outer disk. Though current protoplanetary disk models are able to satisfy one or two of these criteria, none have been successful in recreating all three. We aim to find scenarios that satisfy all three. Methods. In this study we used a 1D disk model that tracks the evolution of the gas and dust disks. Planetesimals are formed within the disk at locations where the streaming instability can be triggered. We explored a large parameter space to study the effect of the disk viscosity, the timescale of infall of material into the disk, the distance within which material is deposited into the disk, and the fragmentation threshold of dust particles. Results. We find that scenarios with a large initial disk viscosity (α > 0.05), a relatively short infall timescale (Tinfall < 100–200kyr), and a small centrifugal radius (RC ~ 0.4 au; i.e. the distance within which material falls into the disk) result in disks that satisfy all three criteria needed to represent the protoplanetary disk of the Solar System. The large initial viscosity and short infall timescale result in a rapid initial expansion of the disk, which we dub the ‘inflationary phase’ of the disk. Furthermore, a temperature-dependent fragmentation threshold, which accounts for cold icy particles breaking more easily, results in larger and more massive disks. This, in turn, results in more ‘icy’ than ‘rocky’ planetesimals. Such scenarios are also better in line with our Solar System, which has small terrestrial planets and massive giant planet cores. Finally, we find that scenarios with large RC cannot transport calcium–aluminium-rich inclusions to the outer disk and do not produce planetesimals at two locations within the disk.

Revisiting the properties of GW190814 and its formation history

ABSTRACT GW190814 was reported during LIGO’s and Virgo’s third observing run with the most asymmetric component masses (an ∼23 M⊙ black hole and an ∼2.6 M⊙ compact object). Under the assumption that this event is a binary black hole (BBH) merger formed through the isolated binary evolution channel, we reanalyse the publicly released data of GW190814 with the modified astrophysical priors on the effective spin χeff, and further explore its formation history using detailed binary modelling. We show that GW190814 is likely to have been formed through the classical common envelope channel. Our findings show that the properties inferred using the modified astrophysical priors are consistent with those inferred by the uniform priors. With the newly inferred properties of GW190814, we perform detailed binary evolution of the immediate progenitor of the BBH (namely a close binary system composed of a BH and a helium star) in a large parameter space, taking into account mass-loss, internal differential rotation, supernova kicks, and tidal interactions between the helium star and the BH companion. Our findings show that GW190814-like events could be formed in limited initial conditions just after the common envelope phase: an ∼23 M⊙ BH and a helium star of MZamsHe ∼ 8.5 M⊙ at solar metallicity (∼ 7.5 M⊙ at 10 per cent solar metallicity) with an initial orbital period at around 1.0 d. Additionally, the inferred low spin of the secondary indicates that the required metallicity for reproducing GW190814-like events should not be too low (e.g. Z ≳ 0.1 Z⊙).

CoAxNN: Optimizing on-device deep learning with conditional approximate neural networks

While deep neural networks have achieved superior performance in a variety of intelligent applications, the increasing computational complexity makes them difficult to be deployed on resource-constrained devices. To improve the performance of on-device inference, prior studies have explored various approximate strategies, such as neural network pruning, to optimize models based on different principles. However, when combining these approximate strategies, a large parameter space needs to be explored. Meanwhile, different configuration parameters may interfere with each other, damaging the performance optimization effect. In this paper, we propose a novel model optimization framework, CoAxNN, which effectively combines different approximate strategies, to facilitate on-device deep learning via model approximation. Based on the principles of different approximate optimizations, our approach constructs the design space and automatically finds reasonable configurations through genetic algorithm-based design space exploration. By combining the strengths of different approximation methods, CoAxNN enables efficient conditional inference for models at runtime. We evaluate our approach by leveraging state-of-the-art neural networks on a representative intelligent edge platform, Jetson AGX Orin. The experimental results demonstrate the effectiveness of CoAxNN, which achieves up to 1.53× speedup while reducing energy by up to 34.61%, with trivial accuracy loss on CIFAR-10/100 and CINIC-10 datasets.

Constraining Cosmology with Machine Learning and Galaxy Clustering: The CAMELS-SAM Suite

As the next generation of large galaxy surveys come online, it is becoming increasingly important to develop and understand the machine-learning tools that analyze big astronomical data. Neural networks are powerful and capable of probing deep patterns in data, but they must be trained carefully on large and representative data sets. We present a new “hump” of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project: CAMELS-SAM, encompassing one thousand dark-matter-only simulations of (100 h −1 cMpc)3 with different cosmological parameters (Ω m and σ 8) and run through the Santa Cruz semi-analytic model for galaxy formation over a broad range of astrophysical parameters. As a proof of concept for the power of this vast suite of simulated galaxies in a large volume and broad parameter space, we probe the power of simple clustering summary statistics to marginalize over astrophysics and constrain cosmology using neural networks. We use the two-point correlation, count-in-cells, and void probability functions, and we probe nonlinear and linear scales across 0.68 < R <27 h −1 cMpc. We find our neural networks can both marginalize over the uncertainties in astrophysics to constrain cosmology to 3%–8% error across various types of galaxy selections, while simultaneously learning about the SC-SAM astrophysical parameters. This work encompasses vital first steps toward creating algorithms able to marginalize over the uncertainties in our galaxy formation models and measure the underlying cosmology of our Universe. CAMELS-SAM has been publicly released alongside the rest of CAMELS, and it offers great potential to many applications of machine learning in astrophysics: https://camels-sam.readthedocs.io.

FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning.

Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.

Precise determination of quadrupole and hexadecapole deformation parameters of the sd-shell nucleus, 28Si

Quasi-elastic (QEL) scattering measurements have been performed using a 28Si projectile off a 90Zr target at energies around the Coulomb barrier. A Bayesian analysis within the framework of coupled channels (CC) calculations is performed in a large parameter space of quadrupole and hexadecapole deformations (β2 and β4) of 28Si. Our results unambiguously show that 28Si is an oblate shaped nucleus with β2=-0.38±0.01 which is in excellent agreement with results from electromagnetic probes. The sign and magnitude of quadrupole deformation along with a precise value of hexadecapole deformation (β4=+0.03±0.01) of 28Si have been determined for the first time using QEL scattering. A remarkable agreement is obtained between the experimental and calculated β4 values of 28Si based on Skyrme-Hartree-Fock method. The present results demonstrate the strong sensitivity of the quasi-elastic scattering to the sign and magnitude to the ground state deformation parameters, thus affirming its suitability to be used for rare exotic nuclei using low intensity RIBs.

An Iterative Wiener Filter Based on a Fourth-Order Tensor Decomposition

This work focuses on linear system identification problems in the framework of the Wiener filter. Specifically, it addresses the challenging identification of systems characterized by impulse responses of long length, which poses significant difficulties due to the existence of large parameter space. The proposed solution targets a dimensionality reduction of the problem by involving the decomposition of a fourth-order tensor, using low-rank approximations in conjunction with the nearest Kronecker product. In addition, the rank of the tensor is controlled and limited to a known value without involving any approximation technique. The final estimate is obtained based on a combination of four (shorter) optimal filters, which are alternatively iterated. As a result, the designed iterative Wiener filter outperforms the traditional counterpart, being more robust to the accuracy of the statistics’ estimates and/or noisy conditions. In addition, simulations performed in the context of acoustic echo cancellation indicate that the proposed iterative Wiener filter that exploits this fourth-order tensor decomposition achieves better performance as compared to some previously developed solutions based on lower decomposition levels. This study could further lead to the development of computationally efficient tensor-based adaptive filtering algorithms.

A thin-film modeling approach for analysis of carbon capture sorbent-based devices

The threat of climate change has driven scientists and engineers to develop new methods reducing carbon emitted as well as capturing CO2 from the atmosphere and other point sources. Carbon capture technology is still in its infancy and requires significant system efficiency improvements before commercial or industrial adoption is not cost prohibitive. Contactor designs must optimize capture efficiency while minimizing pumping losses through this stage, and typical contactors employ a packing material that is wetted with a fluid spray to create a film. The capture efficiency is determined by a complex relationship of operating parameters, contactor geometry, and the chemical kinetics of the CO2 and chosen capture fluid. Due in part to the large parameter space, as well as the physical difficulty of accurately determining capture efficiency, numerical modeling has become an important tool in research and development of contactors for carbon capture. Historically, modeling approaches used for CO2 capture simulations have been too computationally intensive for design work. This work examines the effectiveness of using a thin-film approximation approach to model the reacting fluid, thus avoiding the expensive grid resolution requirements of the volume-of-fluid (VOF) method. Results showed that changes in the reaction model and dissolution constants have a significant impact in the capture efficiency predictions. CO2 capture was largely dictated by the supply of MEA in the film, with capture efficiency reductions as MEA was depleted. Changes in gas temperatures and velocities also had some impact in the CO2 mass transfer. Overall the results validation shows that in a non-flooded contactor, the thin-film approximation is capable of predicting experimental results with sufficient accuracy. Therefore, this model can be used to quickly evaluate contactor designs.

Scratch-based Reflection Art via Differentiable Rendering

The 3D visual optical arts create fascinating special effects by carefully designing interactions between objects and light sources. One of the essential types is 3D reflection art, which aims to create reflectors that can display different images when viewed from different directions. Existing works produce impressive visual effects. Unfortunately, previous works discretize the reflector surface with regular grids/facets, leading to a large parameter space and a high optimization time cost. In this paper, we introduce a new type of 3D reflection art - scratch-based reflection art , which allows for a more compact parameter space, easier fabrication, and computationally efficient optimization. To design a 3D reflection art with scratches, we formulate it as a multi-view optimization problem and introduce differentiable rendering to enable efficient gradient-based optimizers. For that, we propose an analytical scratch rendering approach, together with a high-performance rendering pipeline, allowing efficient differentiable rendering. As a consequence, we could display multiple images on a single metallic board with only several minutes for optimization. We demonstrate our work by showing virtual objects and manufacturing our designed reflectors with a carving machine.

Scaling for selectivity in finite nanopores for 1:1 electrolytes: The dependence of predictability of device behavior on system parameters

The scaling properties of a cation selective nanopore mean that the device function (selectivity) depends on the input parameters (pore radius, pore length, surface charge density, electrolyte concentration, voltage) via a single scaling parameter that is a simple analytical function of the input parameters. In our previous study (Sarkadi et al., (2022) [5]), we showed that a parameter inspired by the Dukhin number (therefore, we also call it a Dukhin number) is an appropriate scaling parameter for infinitely long nanopores. Here, we extend that study to finite pores in a membrane and provide a detailed analyzes over a large parameter space for 1:1 electrolytes obtained from the Nernst-Planck transport equation coupled either to the Local Equilibrium Monte Carlo method or to the Poisson-Botzmann theory. Scaling is exact in a limiting case, where an analytical solution, such as Linearized Poisson-Boltzmann, is available (infinite pore). Here we show that scaling can work even if the solution is numerical or provided by computer simulations (finite pore). We discuss how these cases approach the limiting case as functions of the system parameters.

Same-sided successive-shock HED instability experiments

Inertial confinement fusion (ICF) and high-energy density (HED) physics experiments experience complicated forcing for instability growth and mix due to the ubiquitous presence of multiple shocks interacting with perturbations on multiple material interfaces. One common driver of instability growth is successive shocks from the same direction. However, there is a severe lack of analytic work and modeling validation for same-sided successive shocks since they are extremely difficult to achieve with conventional (non-HED) drivers. Successive shocks access a large instability parameter space; idealized fluid theory [K. O. Mikaelian, Phys. Rev. A 31, 410 (1985)] predicts 15 different interface evolution scenarios for a sinusoidal perturbation. Growth becomes more complex for multi-mode, compressible HED systems. The Mshock campaign is the first experiment in any fluid regime to probe a wide portion of successive shock parameter space. This is enabled by our development of a hybrid direct/indirect drive platform capable of creating independently controllable successive shocks on the National Ignition Facility. These experiments have delivered the first data capable of rigorously challenging our models and their ability to accurately capture Richtmyer–Meshkov growth under successive shocks. Single-mode and two-mode experiments have successfully demonstrated the ability to access and control the various growth scenarios of the shocked interface, including re-inversion, freeze out, and continued growth. Simulations and theoretical modeling are shown to accurately capture the experimental observations in the linear growth phase, giving us confidence in our ICF/HED design codes.

Theoretical Considerations for Optimizing the Use of Optogenetics with Complex Behavior.

Optogenetic approaches have allowed researchers to address complex questions about behavior that were previously unanswerable. However, as optogenetic procedures involve a large parameter space across multiple dimensions, it is crucial to consider such parameters in conjunction with the behaviors under study. Here, we discuss strategies to optimize optogenetic approaches with complex behavior by identifying critical experimental design considerations, including frequency specificity, temporal precision, activity-controlled optogenetics, stimulation pattern, and cell-type specificity. We highlight potential limitations or theoretical considerations to be made when manipulating each of these factors of optogenetic experiments. This overview emphasizes the importance of optimizing optogenetic study design to enhance the conclusions that can be drawn about the neuroscience of behavior. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.