Large Parameter Space Research Articles

Binary Comb Models for FRB 121102

Abstract The first repeating fast radio burst source, FRB 121102, is observed to emit bursts with a periodically modulated rate. We show that FRB 121102 can be interpreted as an interacting neutron star binary system with an orbital period of ∼159 days. We develop a binary comb model by introducing an eccentricity into the orbit. Besides the original funnel mode of the binary comb model, which was applied to FRB 180916.J0158+65 by Ioka & Zhang, we also identify two new modes of the binary comb model, i.e., the τ-crossing mode and the inverse funnel mode, and apply them to interpret FRB 121102. These new developments expand the applicable parameter space, allowing the companion star to be a massive star, a massive black hole, or a supermassive black hole, with the latter two having larger parameter spaces. These models are also consistent with other observations, such as the persistent bright radio counterpart associated with the source. We also argue that the observed frequency dependence of the active window does not disfavor the binary comb model, in contrast to recent claims, and propose two possible scenarios to interpret the data.

Refined fundamental parameters of Canopus from combined near-IR interferometry and spectral energy distribution

Context. Canopus, the brightest and closest yellow supergiant to our Solar System, offers a unique laboratory for understanding the physics of evolved massive stars. Aims. We aim at quantitatively exploring a large space of fundamental parameters of Canopus based on the combined analysis of its spectral energy distribution (SED) and optical-IR long baseline interferometry. Methods. We use the most recent high resolution near-IR data from the VLTI focal beam combiners PIONIER (H and K bands) and AMBER (K band), together with precise spectrophotometric measures that cover the SED of Canopus, from the UV to the IR, taken from ground and space observatories. Results. The accurate and precise PIONIER data allowed us to simultaneously measure the angular diameter and the limb darkening (LD) profile using different analytical laws. We found that the power-law LD, being also in agreement with predictions from stellar atmosphere models, reproduces the interferometric data well. For this model we measured an angular diameter of 7.184 ± 0.0017 ± 0.029 mas and an LD coefficient of 0.1438 ± 0.0015, which are respectively ≳5 and ~15−25 more precise than in our previous A&A paper on Canopus from 2008. From a dedicated analysis of the interferometric data, we also provide new constraints on the putative presence of weak surface inhomogeneities. Additionally, we analyzed the SED in a innovative way by simultaneously fitting the reddening-related parameters and the stellar effective temperature and gravity. We find that a model based on two effective temperatures is much better at reproducing the whole SED, from which we derived several parameters, including a new bolometric flux estimate: fbol = (59.22 ± 2.45) × 10−6 erg cm−2 s−1. We were also able to estimate the stellar mass from these measurements, and it is shown to be in agreement with additional predictions from evolutionary models, from which we inferred the age of Canopus as well. Conclusions. The Canopus angular diameter and LD measured in this work with PIONIER are the most precise to date, with a direct impact on several related fundamental parameters. Moreover, thanks to our joint analysis, we were able to determine a set of fundamental parameters that simultaneously reproduces both high-precision interferometric data and a good quality SED and, at the same time, agrees with stellar evolution models. This refined set of fundamental parameters constitutes a careful balance between the different methodologies used, providing invaluable observationally based constraints to models of stellar structure and evolution, which still present difficulties in simulating stars such as Canopus in detail.

Model-Agnostic and Efficient Exploration of Numerical Congestion Control State Space of Real-World TCP Implementations

The significant impact of TCP congestion control on the Internet highlights the importance of testing congestion control algorithm implementations (CCAIs) in various network environments. Many CCAI testing problems can be solved by exploring the numerical state space of CCAIs, which is defined by a group of numerical (and nonnumerical) state variables of the CCAIs. However, the current practices for automated numerical state space exploration are either limited by the approximate abstract CCAI models or inefficient due to the large space of network environment parameters and the complicated relation between the CCAI states and network environment parameters. In this paper, we propose an automated numerical state space exploration method, called ACT, which leverages the model-agnostic feature of random testing and greatly improves its efficiency by guiding random testing under the feedback iteratively obtained in a test. Our experiments on five representative Linux TCP CCAIs show that ACT can more efficiently explore a large numerical state space than manual testing, undirected random testing, and symbolic execution based testing, while without requiring an abstract CCAI model. ACT detects multiple design and implementation bugs of these Linux TCP CCAIs, including some new bugs not reported before.

Adaptive machine learning for time-varying systems: low dimensional latent space tuning

Machine learning (ML) tools such as encoder-decoder convolutional neural networks (CNN) can represent incredibly complex nonlinear functions which map between combinations of images and scalars. For example, CNNs can be used to map combinations of accelerator parameters and images which are 2D projections of the 6D phase space distributions of charged particle beams as they are transported between various particle accelerator locations. Despite their strengths, applying ML to time-varying systems, or systems with shifting distributions, is an open problem, especially for large systems for which collecting new data for re-training is impractical or interrupts operations. Particle accelerators are one example of large time-varying systems for which collecting detailed training data requires lengthy dedicated beam measurements which may no longer be available during regular operations. We present a novel method of adaptive ML for time-varying systems. Our approach is to map very high (N ≈ 100k) dimensional inputs (a combination of scalar parameters and images) into the low dimensional (N ≈ 2) latent space at the output of the encoder section of an encoder-decoder CNN. We then actively tune the low dimensional latent space-based representation of complex system dynamics by the addition of an adaptively tuned feedback vector directly before the decoder sections builds back up to our image-based high-dimensional phase space density representations. This method allows us to learn correlations within and to quickly tune the characteristics of incredibly large parameter space systems and to track their evolution in real time based on feedback without massive new data sets for re-training. We demonstrate that our method can accurately predict and track the phase space of charged particle beams at various locations in a particle accelerator by adaptively adjusting in real-time while the unknown input beam distribution of the accelerator is changing in shape, charge, and offset and while the RF system of the accelerator itself is also changing in an unpredictable way. For FACET-II we demonstrate that such an approach has the potential to use transverse deflecting cavity and energy spread spectrum beam measurements to accurately predict 2D projections of the 6D phase space of the electron beam at the plasma wakefield acceleration interaction point where such diagnostics are unavailable.

Self-calibrating the look-elsewhere effect: fast evaluation of the statistical significance using peak heights

ABSTRACT In experiments where one searches a large parameter space for an anomaly, one often finds many spurious noise-induced peaks in the likelihood. This is known as the look-elsewhere effect, and must be corrected for when performing statistical analysis. This paper introduces a method to calibrate the false alarm probability (FAP), or p-value, for a given dataset by considering the heights of the highest peaks in the likelihood. Specifically, we derive an equation relating the global p-value to the rank and height of local maxima. In the simplest form of self-calibration, the look-elsewhere-corrected $\chi^2$ of a physical peak is approximated by the $\chi^2$ of the peak minus the $\chi^2$ of the highest noise-induced peak, with accuracy improved by considering lower peaks. In contrast to alternative methods, this approach has negligible computational cost as peaks in the likelihood are a byproduct of every peak-search analysis. We apply to examples from astronomy, including planet detection, periodograms, and cosmology.

Control of gene expression in engineered mammalian cells with a programmable shear-stress inducer.

In humans, cellular mechanoperception serves as the basis of touch sensation and proprioception, contributes to the proper programming of cell fate during embryonic development, and plays a pivotal role in the development of mechanosensitive tissues. Molecular mechanoreceptors can respond to their environment by mediating transient adjustments of ion homeostasis, which subsequently trigger calcium‐dependent alteration of gene expression via specific signaling pathways such as the nuclear factor of the activated T‐cells pathway. Although, mechanoreceptors are potential drug targets for various diseases, current techniques to study mechanically gated processes are often based on custom‐tailored microfluidic systems, which require special setups or have limited throughput. Here, we present a platform to characterize shear‐stress‐triggered, calcium‐mediated gene expression, which employs a programmable, 96‐well‐format, shear‐stress induction device to examine the effects of imposing various mechanical loads on mammalian adherent cell lines. The presented method is suitable for high‐throughput experiments and provides a large tunable parameter space to optimize conditions for different cell types. Our findings indicate that the device is an effective tool to explore conditions in terms of frequency, intensity, intervals as well as extracellular matrix composition alongside the evaluation of different combinations of mechanosensitive proteins for mechanically activated gene expression. We believe our results can serve as a platform for further investigations into shear stress‐controlled gene expression in basic research and drug screening.

Generalized model of MnSi-like spiral magnets

A general, symmetry-allowed model of nearest-neighbour interactions for MnSi-like magnets is presented. A left-handed helical magnet phase occurs within a large parameter space of the model, which is explored via numerical simulation. The relations between microscopic features of the spiral structure and various model parameters, including an external magnetic field, are determined and show good agreement with predictions from free energy considerations. A skyrmion structure is stabilized near the boundary.

Body-caudal fin fish-inspired self-propulsion study on burst-and-coast and continuous swimming of a hydrofoil model

The present study examines the energy efficiency of self-propelled hydrofoils for various modes and kinematics of swimming adopted by various body-caudal fin fish. In particular, this work considers the intermittent burst-and-coast (B&C) and continuous swimming modes, and examines the effect of the undulating and/or pitching swimming kinematics, adopted by the undulating body of anguilliform fish and pitching caudal fin of carangiform and thunniform fish. Notably, B&C swimming is adopted in nature mostly by the latter class but rarely by the former. This fact forms the basis of our study on the hydrodynamics and propulsion performance for both classes of fish-inspired swimming using a NACA0012 hydrofoil model. This analysis explores a large parameter space covering undulation wavelength, 0.8≤λ*<∞, Reynolds number, 50≤Ref≤1500, and duty cycle (DC), 0.1≤DC≤1, with the DC representing the fraction of time in B&C swimming. The fluid–structure dynamics-based vortex-shedding-process is investigated, where B&C swimming results in either an asymmetric reverse von Karman (RVK) or forward von Karman vortex street, rather than a symmetric RVK vortex street observed during continuous swimming. It is demonstrated that the B&C swimming results in an energy saving, although there is a concomitant increase in the travel time. Moreover, our results show that B&C swimming is effective for carangiform and thunniform tail-like kinematics but not for anguilliform body-like kinematics of the hydrofoil. Thus, the predictions are consistent with the observed swimming behavior adopted by a fish in nature and provide input into the efficient design of unmanned underwater vehicles.

The role of fluid–structure interaction in pulsating bubble dynamics near a movable structure

In the present study, the nonlinear bubble–plate interaction is investigated via boundary integral (BI) simulations with a particular focus on the effect of fluid–structure interaction (FSI). A series of experiments of electric discharge cavitation bubbles are also conducted to gain more physical insights. Good agreement between BI simulations and high-speed recordings is obtained. The effects of two geometrical parameters including the normalized stand-off distance γ and the plate-bubble size ratio θ (both scaled by the maximum bubble radius) are investigated systematically. We find four types of distinct bubble collapse patterns, namely, contacting jet that impacts the plate directly, non-contacting jet that impacts a layer of liquid, collision of two axial jets and no evident jet. A detailed phase diagram for the bubble collapse pattern is given in a large parameter space. Through the comparison of bubble dynamics near a movable and a fixed plate, the FSI effect is found to induce a significant increase in the jet impact velocity and the corresponding impact pressure. We also give a criterion condition for neglecting the FSI effect. Finally, we find two scaling laws for the plate motion with respect to γ, which can be explained using a semi-analytical model.

Looking forward to millicharged dark sectors at the LHC

We identify potentially the world's most sensitive location to search for millicharged particles in the 10 MeV to 100 GeV mass range: the forward region at the LHC. We propose constructing a scintillator-based experiment, FORward MicrOcharge SeArch (FORMOSA) in this location, and estimate the corresponding sensitivity projection. We show that FORMOSA can discover millicharged particles in a large and unexplored parameter space, and study strongly interacting dark matter that cannot be detected by ground-based direct-detection experiments. The newly proposed LHC Forward Physics Facility (FPF) provides an ideal structure to host the full FORMOSA experiment.

Skyrmion Qubits: A New Class of Quantum Logic Elements Based on Nanoscale Magnetization.

We introduce a new class of primitive building blocks for realizing quantum logic elements based on nanoscale magnetization textures called skyrmions. In a skyrmion qubit, information is stored in the quantum degree of helicity, and the logical states can be adjusted by electric and magnetic fields, offering a rich operation regime with high anharmonicity. By exploring a large parameter space, we propose two skyrmion qubit variants depending on their quantized state. We discuss appropriate microwave pulses required to generate single-qubit gates for quantum computing, and skyrmion multiqubit schemes for a scalable architecture with tailored couplings. Scalability, controllability by microwave fields, operation time scales, and readout by nonvolatile techniques converge to make the skyrmion qubit highly attractive as a logical element of a quantum processor.

The evolution of Materials Acceleration Platforms: toward the laboratory of the future with AMANDA

The development of complex functional materials poses a multi-objective optimization problem in a large multi-dimensional parameter space. Solving it requires reproducible, user-independent laboratory work and intelligent preselection of experiments. However, experimental materials science is a field where manual routines are still predominant, although other domains like pharmacy or chemistry have long used robotics and automation. As the number of publications on Materials Acceleration Platforms (MAPs) increases steadily, we review selected systems and fit them into the stages of a general material development process to examine the evolution of MAPs. Subsequently, we present our approach to laboratory automation in materials science. We introduce AMANDA (Autonomous Materials and Device Application Platform - www.amanda-platform.com), a generic platform for distributed materials research comprising a self-developed software backbone and several MAPs. One of them, LineOne (L1), is specifically designed to produce and characterize solution-processed thin-film devices like organic solar cells (OSC). It is designed to perform precise closed-loop screenings of up to 272 device variations per day yet allows further upscaling. Each individual solar cell is fully characterized, and all process steps are comprehensively documented. We want to demonstrate the capabilities of AMANDA L1 with OSCs based on PM6:Y6 with 13.7% efficiency when processed in air. Further, we discuss challenges and opportunities of highly automated research platforms and elaborate on the future integration of additional techniques, methods and algorithms in order to advance to fully autonomous self-optimizing systems—a paradigm shift in functional materials development leading to the laboratory of the future.

The impact of digital contact tracing on the SARS-CoV-2 pandemic\u2014a comprehensive modelling study

Contact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD).Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models.For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions ({R_{0}} at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected.When DCT is deployed in a population with an ongoing outbreak where mathcal{O} (0.1%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.

Stellar winds and metal enrichment from fast-rotating Population III stars

ABSTRACT Stellar winds from fast-rotating Population III (Pop III) stars have long been suspected to make important contributions to early metal enrichment, as features in the nucleosynthesis of such ‘spinstars’ are consistent with the chemical abundance patterns of some metal-poor stars in the local Universe. Particularly, stellar winds rich in light elements can provide another pathway towards explaining the carbon enhancement in carbon-enhanced metal-poor (CEMP) stars. In this work, we focus on the feedback of Pop III stellar winds combined with supernovae (SNe), and derive the resulting chemical signatures in the enriched medium. We explore a large parameter space of Pop III star formation, feedback, yields from winds, and SNe with a semi-analytical model. The predicted pattern of carbon and iron abundances of second-generation stars agrees well with observations of CEMP-no stars ($[\rm Ba/Fe]\lt 0$) at $[\rm Fe/H]\lesssim -3$ and A(C) ≲ 7, under the optimistic assumption of significant mass-loss by winds from massive ($\gtrsim 25\ \rm M_{\odot }$) stars that collapse into BHs without SNe. In this scenario, carbon-rich but iron-free second-generation stars can form in systems dominated by enrichment from winds, gaining trace amounts of iron by accretion from the interstellar medium, to become the most iron-poor and carbon-enhanced stars seen in observations ($[\rm Fe/H]\lesssim -4$, $[\rm C/Fe]\gtrsim 2$). We conclude that the observed CEMP-no stars can be explained by both our winds + ISM accretion channel as well as the well-studied faint SN scenario. Wind feedback from Pop III spinstars deserves more detailed modelling in early cosmic structure formation.

Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis

Multi-dimensional spectral-imaging is a mainstay of the scanning probe and electron microscopies, micro-Raman, and various forms of chemical imaging. In many cases, individual spectra can be fit to a specific functional form, with the model parameter maps, providing direct insight into material properties. Since spectra are often acquired across a spatial grid of points, spatially adjacent spectra are likely to be similar to one another; yet, this fact is almost never used when considering parameter estimation for functional fits. On datasets tried here, we show that by utilizing proximal information, whether it be in the spatial or spectral domains, it is possible to improve the reliability and increase the speed of such functional fits by ∼2–3×, as compared to random priors. We explore and compare three distinct new methods: (a) spatially averaging neighborhood spectra, and propagating priors based on functional fits to the averaged case, (b) hierarchical clustering-based methods where spectra are grouped hierarchically based on response, with the priors propagated progressively down the hierarchy, and (c) regular clustering without hierarchical methods with priors propagated from fits to cluster means. Our results highlight that utilizing spatial and spectral neighborhood information is often critical for accurate parameter estimation in noisy environments, which we show for ferroelectric hysteresis loops acquired on a prototypical PbTiO3 thin film with piezoresponse spectroscopy. This method is general and applicable to any spatially measured spectra where functional forms are available. Examples include exploring the superconducting gap with tunneling spectroscopy, using the Dynes formula, or current–voltage curve fits in conductive atomic force microscopy mapping. Here we explore the problem for ferroelectric hysteresis, which, given its large parameter space, constitutes a more difficult task than, for example, fitting current–voltage curves with a Schottky emission formula (Chiu 2014 Adv. Mater. Sci. Eng. 2014 578168).

Parametric modeling of a solid state Ericsson cycle heat engine

The Johnson Thermoelectric Converter (JTEC) operates as an approximation of an Ericsson cycle thermodynamic heat engine with no moving parts. During operation, hydrogen flows from a high-temperature high-pressure region to a low-pressure region by way of a membrane-electrode assembly. In passing through the assembly, the hydrogen is stripped of its electrons which flow through the circuit and reform with protons to the recover hydrogen on the low-pressure side. Some of the electrical power extracted is used to electrochemically “pump” the hydrogen back to the low-temperature high-pressure side and sustain the pressure differential. The objective of the work presented here was to mathematically characterize a JTEC system and to develop the coupled relationships between design goals like efficiency, net power production, and power density; and design parameters like high versus low operating temperatures and pressures, device geometry, and thermophysical properties of the device materials and working fluid. Power production is related to the operating pressure ratio, the ratio of high to low device operating temperatures, the high operating temperature, and the effective heat transfer area of the hot end. The efficiency is related to several non-dimensional and dimensional number groups (especially the recuperator effectiveness). The power density or volume of the device is related to a different recuperator parameter, the high temperature of the heat addition source, the cold temperature of the thermal rejection source, and the internal device geometry. Even with reasonable simplifications and assumptions, the design space contains a large number of variable parameters. The model equations were exercised over the large parametric trade space.

Bayesian Dynamic Feature Partitioning in High-Dimensional Regression With Big Data

Bayesian computation of high-dimensional linear regression models using Markov chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated if the data size is large or data arrive sequentially over time (streaming or online settings). This article proposes a novel dynamic feature partitioned regression (DFP) for efficient online inference for high-dimensional linear regressions with large or streaming data. DFP constructs a pseudo posterior density of the parameters at every time point, and quickly updates the pseudo posterior when a new block of data (data shard) arrives. DFP updates the pseudo posterior at every time point suitably and partitions the set of parameters to exploit parallelization for efficient posterior computation. The proposed approach is applied to high-dimensional linear regression models with Gaussian scale mixture priors and spike-and-slab priors on large parameter spaces, along with large data, and is found to yield state-of-the-art inferential performance. The algorithm enjoys theoretical support with pseudoposterior densities over time being arbitrarily close to the full posterior as the data size grows, as shown in the supplementary material. Supplementary material also contains details of the DFP algorithm applied to different priors. Package to implement DFP is available in https://github.com/Rene-Gutierrez/DynParRegReg. The dataset is available in https://github.com/Rene-Gutierrez/DynParRegReg\\_Implementation.

Rapid Microwave-Only Characterization and Readout of Quantum Dots Using Multiplexed Gigahertz-Frequency Resonators

For mesoscopic quantum devices, efficient characterization and high-fidelity readout using only microwave resonators is of great practical interest, especially to investigate the large parameter space frequently occurring in semiconducting devices. This work shows how dc conductance measurements can be substituted with rf measurements through the implementation of gigahertz-frequency resonators. It also demonstrates multiplexed dispersive gate sensing, which detects electron hybridization with high sensitivity, compatible with applications in quantum information processing. These results present intriguing opportunities for high-speed measurement of quantum transport.

Fast Multipole Methods for N-body Simulations of Collisional Star Systems

Abstract Direct N-body simulations of star clusters are accurate but expensive, largely due to the numerous  ( N 2 ) pairwise force calculations. To solve the post-million-body problem, it will be necessary to use approximate force solvers, such as tree codes. In this work, we adapt a tree-based, optimized Fast Multipole Method (FMM) to the collisional N-body problem. The use of a rotation-accelerated translation operator and an error-controlled cell opening criterion leads to a code that can be tuned to arbitrary accuracy. We demonstrate that our code, Taichi, can be as accurate as direct summation when N > 104. This opens up the possibility of performing large-N, star-by-star simulations of massive stellar clusters and would permit large parameter space studies that would require years with the current generation of direct summation codes. Using a series of tests and idealized models, we show that Taichi can accurately model collisional effects, such as dynamical friction and the core-collapse time of idealized clusters, producing results in strong agreement with benchmarks from other collisional codes such as NBODY6++GPU or PeTar. Parallelized using OpenMP and AVX, Taichi is demonstrated to be more efficient than other CPU-based direct N-body codes for simulating large systems. With future improvements to the handling of close encounters and binary evolution, we clearly demonstrate the potential of an optimized FMM for the modeling of collisional stellar systems, opening the door to accurate simulations of massive globular clusters, super star clusters, and even galactic nuclei.

Deep reinforcement learning for efficient measurement of quantum devices

Deep reinforcement learning is an emerging machine-learning approach that can teach a computer to learn from their actions and rewards similar to the way humans learn from experience. It offers many advantages in automating decision processes to navigate large parameter spaces. This paper proposes an approach to the efficient measurement of quantum devices based on deep reinforcement learning. We focus on double quantum dot devices, demonstrating the fully automatic identification of specific transport features called bias triangles. Measurements targeting these features are difficult to automate, since bias triangles are found in otherwise featureless regions of the parameter space. Our algorithm identifies bias triangles in a mean time of <30 min, and sometimes as little as 1 min. This approach, based on dueling deep Q-networks, can be adapted to a broad range of devices and target transport features. This is a crucial demonstration of the utility of deep reinforcement learning for decision making in the measurement and operation of quantum devices.