A state-space method of parameter identification
State-space method of parameter tracking for adaptive control, using Riccati equation and Kalman filtering
Currie, M. G.↗
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An analysis of parameter compression and Full-Modeling techniques with Velocileptors for DESI 2024 and beyond
In anticipation of forthcoming data releases of current and future spectroscopic surveys, we present the validation tests and analysis of systematic effects within velocileptors modeling pipeline when fitting mock data from the AbacusSummit N-body simulations. We compare the constraints obtained from parameter compression methods to the direct fitting (Full-Modeling) approaches of modeling the galaxy power spectra, and show that the ShapeFit extension to the traditional template method is consistent with the Full-Modeling method within the standard ΛCDM parameter space. We show the dependence on scale cuts when fitting the different redshift bins using the ShapeFit and Full-Modeling methods. We test the ability to jointly fit data from multiple redshift bins as well as joint analysis of the pre-reconstruction power spectrum with the post-reconstruction BAO correlation function signal. We further demonstrate the behavior of the model when opening up the parameter space beyond ΛCDM and also when combining likelihoods with external datasets, namely the Planck CMB priors. Finally, we describe different parametrization options for the galaxy bias, counterterm, and stochastic parameters, and employ the halo model in order to physically motivate suitable priors that are necessary to ensure the stability of the perturbation theory.
79 ASTRONOMY AND ASTROPHYSICS↗
Filling the gap: hunting for vector bosons at the MUonE experiment with displaced decay signature
The upcoming MUonE experiment aims to precisely measure the running of the fine structure constant via elastic muon-electron scattering, to shed light on the current tension in the muon’s anomalous magnetic moment. In addition to its primary function as a precision experiment, MUonE also offers a unique testing ground to probe long-lived vector bosons. Such vector bosons can be produced via μe → μeV or μN → μNV scattering and decay into an electron/positron pair a few centimeters away from the interaction point. With its high-resolution tracking system and unique geometric design, MUonE is well-suited to reconstruct displaced vertices close to the target, allowing it to probe parameter space previously unattainable at colliders and longer-baseline beam dump experiments. We present a comprehensive study of the discovery potential of BSM vector boson mediators at the MUonE experiment. We show that MUonE can fill the long-standing gap in the parameter space of vector boson mediators with masses up to around 100 MeV.
Models for Dark Matter↗
Probing the Cosmic Axion Background via Axion-Photon Conversion in Filaments
The cosmic axion background (CaB) is a hypothetical population of relativistic axions produced in the early universe. If the CaB is produced from dark matter decays, the axions in this population can convert to photons in the magnetic fields of cosmological filaments, resulting in an isotropic gamma ray background flux. We present new indirect detection constraints on the axion mass and axion-photon coupling, for GeV-TeV dark matter with a decay lifetime below $10^{30}$ sec, by comparing this flux against experimental data. We exclude significant parameter space for axion masses below $10^{-5}$~eV for a broad range of dark matter masses and lifetimes, assuming conservative filament magnetic field strengths ($\sim 1$ nG). For large filament fields ($\sim 100$ nG), our strategy also constrains a portion of the QCD-axion parameter space for TeV-scale dark matter masses.
Rocha, Duncan [Chicago U., EFI; Chicago U., KICP]↗
Gravitational production of completely dark photons with nonminimal couplings to gravity
Dark photons are a theorized massive spin-1 particle which can be produced via various mechanisms, including cosmological gravitational particle production (GPP) in the early universe. In this work, we extend previous results for GPP of dark photons to include nonminimal couplings to gravity. We find that nonminimal couplings can induce a ghost instability or lead to runaway particle production at high momentum and discuss the constraints on the parameter space such that the theory is free of instabilities. Within the instability-free regime we numerically calculate the particle production and find that the inclusion of nonminimal couplings can lead to an enhancement of the particle number. As a result, GPP of nonminimally coupled dark photons can open the parameter space for production of a cosmological relevant relic density (constituting all or part of the dark matter) as compared to the minimally-coupled theory. These results are independent of the choice of inflation model, which we demonstrate by repeating the analysis for a class of rapid-turn multi-field inflation models.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Physiological parameters in space settlement design
One of the major goals of space settlement design is the provision of an environment which will allow full health and effective performance for all members of the population. Attention is given to questions concerning an alternation of 1 G-0 G environment, the physiology of weightlessness, the transit between earth and settlement, research on physiological parameters, and the need for a sensitivity analysis.
Billingham, J.↗
Experimental validation of a collision-radiation dataset for molecular hydrogen in plasmas
Quantitative spectroscopy of molecular hydrogen has generated substantial demand, leading to the accumulation of diverse elementary process data encompassing radiative transitions, electron-impact transitions, predissociations, and quenching. However, their rates currently available are still sparse, and there are inconsistencies among those proposed by different authors. In this study, we demonstrate an experimental validation of such a molecular dataset by composing a collisional-radiative model (CRM) for molecular hydrogen and comparing experimentally obtained vibronic populations across multiple levels. From the population kinetics of molecular hydrogen, the importance of each elementary process in various parameter space is studied. In low-density plasmas (electron density ne≲1017 m−3) the excitation rates from the ground states and radiative decay rates, both of which have been reported previously, determine the excited state population. The inconsistency in the excitation rates affects the population distribution the most significantly in this parameter space. However, in higher density plasmas (ne≳1018 m−3), the excitation rates from excited states become important, which have never been reported in the literature, and may need to be approximated in some way. In order to validate these molecular datasets and approximated rates, we carried out experimental observations for two different hydrogen plasmas; a low-density radio frequency heated plasma (ne≈1016 m−3) and the Large Helical Device (LHD) divertor plasma (ne≳1018 m−3). The visible emission lines from EF1Σg+, HH¯1Σg+, D1Πu±, GK1Σg+, I1Πg±, J1Δg±, h3Σg+, e3Σu+, d3Πu±,g3Σg+, i3Πg±, and j3Δg± states were observed simultaneously and their population distributions were obtained from their intensities. We compared the observed population distributions with the CRM prediction, in particular the CRM with the rates compiled by Janev et al., Miles et al., and those calculated with the molecular convergent close-coupling (MCCC) method. The MCCC prediction gives the best agreement with the experiment, particularly for the emission from the low-density plasma. However, the population distribution in the LHD divertor shows a worse agreement with the CRM than those from low-density plasma, indicating the necessity of the precise excitation rates from excited states. We also found that the rates for the electron attachment is inconsistent with experimental results. This requires further investigation.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
Ecological Niches for Space Missions in the Far-Infrared
The far-infrared and submillimeter region (30 micron-750 micron) has perhaps the greatest potential of all wavelengths for advancement in astronomy. When viewed in terms of the cosmic backgrounds, the far-IR is extremely important: half of the total luminosity in the Universe is emitted at rest wavelengths approximately 80-100 microns. At the highest known galaxy redshifts, this energy is redshifted to approximately 600 microns. Existing and planned missions have a broad range of capabilities defined in terms of their spectral coverage, spectral resolution, angular resolution, survey speed, and sensitivity. In this parameter space, the opportunity for future far-IR and submillimeter missions with great discovery potential is evident. Such missions will answer fundamental questions about the history of energy release in the Universe, the formation and evolution of galaxies, and formation of stellar and protoplanetary systems. We discuss the parameter space that can be filled by a few well-chosen space missions, specifically a submillimeter all-sky survey and a far-IR to submillimeter observatory.
Benford, D. J.↗
Tool Support for Parametric Analysis of Large Software Simulation Systems
The analysis of large and complex parameterized software systems, e.g., systems simulation in aerospace, is very complicated and time-consuming due to the large parameter space, and the complex, highly coupled nonlinear nature of the different system components. Thus, such systems are generally validated only in regions local to anticipated operating points rather than through characterization of the entire feasible operational envelope of the system. We have addressed the factors deterring such an analysis with a tool to support envelope assessment: we utilize a combination of advanced Monte Carlo generation with n-factor combinatorial parameter variations to limit the number of cases, but still explore important interactions in the parameter space in a systematic fashion. Additional test-cases, automatically generated from models (e.g., UML, Simulink, Stateflow) improve the coverage. The distributed test runs of the software system produce vast amounts of data, making manual analysis impossible. Our tool automatically analyzes the generated data through a combination of unsupervised Bayesian clustering techniques (AutoBayes) and supervised learning of critical parameter ranges using the treatment learner TAR3. The tool has been developed around the Trick simulation environment, which is widely used within NASA. We will present this tool with a GN&C (Guidance, Navigation and Control) simulation of a small satellite system.
Schumann, Johann↗
AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems
Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.
97 MATHEMATICS AND COMPUTING↗
Maximizing Science Capability for Far-Infrared Space Missions
The far-infrared and submillimeter region (20 microns-800 microns) has perhaps the greatest potential of all wavelengths for advancement in astronomy. When viewed in terms of the cosmic backgrounds, the far-IR is extremely important: half of the total luminosity in the Universe is emitted at rest wavelengths approximately 80-100 microns. At the highest known galaxy redshifts (z approximately equal to 6) this energy is redshifted to approximately 600 microns. Existing and planned missions have a broad range of capabilities, defined in terms of their spectral coverage, spectral resolution, angular resolution, mapping speed, and sensitivity. In this 5-dimensional parameter space, the far-IR is substantially be-hind most other wavelength bands. The opportunity for future missions with great discovery potential is evident. Such missions will be well-suited to answering fundamental questions about the history of energy release in the Universe, the formation and evolution of galaxies, and formation of stellar and protoplanetary systems. We discuss the parameter space that can be filled by a few well-chosen space missions, specifically a submillimeter all-sky survey and a far-IR to submillimeter observatory. Ultimately, a long baseline submillimeter interferometer is necessary to provide sensitivity and angular resolution.
Benford, Dominic↗
Ecological Niches for Space Missions in the Far-Infrared
The far-infrared and submillimeter region (30 micrometers-750 micrometers) has perhaps the greatest potential of all wavelengths for advancement in astronomy. When viewed in terms of the cosmic backgrounds, the fares is extremely important: half of the total luminosity in the Universe is emitted at rest wavelengths approximately 80 - 100 micrometers. At the highest known galaxy redshifts (z approximately equal to 6) this energy is redshifted to approximately 600 micrometers. Existing and planned survey missions have a broad range of capabilities, defined in terms of their spectral coverage, spectral resolution, survey area, and sensitivity. In this 4-dimensional parameter space, the flares is found to be substantially behind most other wavelength bands. The opportunity for future missions with great discovery potential is evident. Such missions will be wellsuited to answering fundamental questions about the history of energy release in the Universe, the formation and evolution of galaxies, and formation of stellar and protoplanetary systems. We discuss the parameter space that can be filled by a few well-chosen space missions, specifically a submillimeter all-sky survey and a far-IR to submillimeter observatory.
Benford, Dominic J.↗
Thrust Augmentation Measurements for a Pulse Detonation Engine Driven Ejector
Thrust augmentation results of an ongoing study of pulse detonation engine driven ejectors are presented and discussed. The experiments were conducted using a pulse detonation engine (PDE) setup with various ejector configurations. The PDE used in these experiments utilizes ethylene (C2H4) as the fuel, and an equi-molar mixture of oxygen and nitrogen as the oxidizer at an equivalence ratio of one. High fidelity thrust measurements were made using an integrated spring damper system. The baseline thrust of the PDE engine was first measured and agrees with experimental and modeling results found in the literature. Thrust augmentation measurements were then made for constant diameter ejectors. The parameter space for the study included ejector length, PDE tube exit to ejector tube inlet overlap distance, and straight versus rounded ejector inlets. The relationship between the thrust augmentation results and various physical phenomena is described. To further understand the flow dynamics, shadow graph images of the exiting shock wave front from the PDE were also made. For the studied parameter space, the results showed a maximum augmentation of 40%. Further increase in augmentation is possible if the geometry of the ejector is tailored, a topic currently studied by numerous groups in the field.
Pal, S.↗
Upper bound on Parity breaking scale for doublet WIMP dark matter
A bstract We consider weakly interacting massive particle (WIMP) dark matter in a Parity solution to the strong CP problem. The WIMP phenomenology can be drastically affected by the presence of Parity partners of the WIMP and electroweak gauge bosons. We focus on a Parity extension of SU(2) L -doublet fermion dark matter, identify the viable parameter space, and derive the predictions of the theory. We find that the Parity symmetry breaking scale is bounded from above, with the bound given by 25–60 TeV, depending on whether or not dark matter and its Parity partner coannihilate with each other. The High-Luminosity Large Hadron Collider, future colliders, and direct and indirect detection experiments can probe the parameter space further, with correlated signal rates.
Baldwin, Matthew J. [University of Chicago; Univer↗
Upper Bound on Parity Breaking Scale for Doublet WIMP Dark Matter
We consider weakly interacting massive particle (WIMP) dark matter in a Parity solution to the strong CP problem. The WIMP phenomenology can be drastically affected by the presence of Parity partners of the WIMP and electroweak gauge bosons. We focus on a Parity extension of $SU(2)_L$-doublet fermion dark matter, identify the viable parameter space, and derive the predictions of the theory. We find that the Parity symmetry breaking scale is bounded from above, with the bound given by $25-60$ TeV, depending on whether or not dark matter and its Parity partner coannihilate with each other. The High-Luminosity Large Hadron Collider, future colliders, and direct and indirect detection experiments can probe the parameter space further, with correlated signal rates.
Baldwin, Matthew J. [Chicago U.; Chicago U., KICP]↗
Search for ultralight dark matter in the SuperMAG high-fidelity dataset
Ultralight dark matter, such as kinetically mixed dark-photon dark matter (DPDM) or axion-like-particle dark matter (axion DM), can source an oscillating magnetic-field signal at Earth’s surface. Previous work searched for this signal in a publicly available dataset of global magnetometer measurements maintained by the SuperMAG collaboration. This “low-fidelity” dataset reported measurements with a 1-min time resolution, allowing the search to set leading direct constraints on DPDM and axion DM with Compton frequencies f DM ≤ 1 / ( 1 min ) (corresponding to masses m DM ≤ 7 × 10 − 17 eV ). More recently, a dedicated experiment undertaken by the SNIPE Hunt collaboration has also searched for this same signal at higher frequencies f DM ≥ 0.5 Hz (or m DM ≥ 2 × 10 − 15 eV ). In this work, we search for this signal of ultralight DM in the SuperMAG “high-fidelity” dataset, which features a 1-sec time resolution, allowing us to probe the gap in parameter space between the low-fidelity dataset and the SNIPE Hunt experiment. The high-fidelity dataset exhibits lower geomagnetic noise than the low-fidelity dataset and features more data than the SNIPE Hunt experiment, making it a powerful probe of ultralight DM. Our search finds no robust DPDM or axion DM candidates. We set constraints on DPDM and axion DM parameter space for 10 − 3 Hz ≤ f DM ≤ 0.98 Hz (or 4 × 10 − 18 eV ≤ m DM ≤ 4 × 10 − 15 eV ). Our results are the leading direct constraints on both DPDM and axion DM in this mass range, and our DPDM constraint surpasses the leading astrophysical constraint in a narrow range around m A ′ ≈ 2 × 10 − 15 eV . Published by the American Physical Society 2024
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Deep operator network surrogate for phase-field modeling of metal grain growth during solidification
A deep operator network (DeepONet) has been constructed that generates accurate representations of phase-field model simulations for evolving two dimensional metal grain morphology growing from melt. These representations serve as lower resolution, computationally efficient stand-ins for quick parameter space exploration of solutions to the the Allen-Cahn equations that dictate the phase-field model simulations. The experimental target for the phase-field model is a uranium casting system cooling a 434 g uranium charge from a maximum temperature of 1400° C at an average rate of 30° C / min , traversing the crystallographic phases of the pure metal. Experimental parameters inform the phase-field model, whose higher resolution computational model solutions are used to train the DeepONet in a given parameter space with the aim of developing a faster, more efficient method for predicting the solidifying metal's microstructure at different potential experimental values. The final DeepONet generates high accuracy, lower resolution predictions with cumulative relative approximation error over all timesteps of less than 0.5%, while ensuring solutions remain within physically feasible ranges. Further, these relative error values are comparable with other state-of-the-art DeepONet models for microstructure evolution, while significantly reducing the amount of training data required. Training a convolutional neural network simultaneously with the DeepONet, enforcing realistic values at the complex metal grain boundaries, and mathematically encoding boundary conditions into the structure of the DeepONet improved prediction accuracy and computational efficiency over a standard DeepONet model.
36 MATERIALS SCIENCE↗
Design and analysis of numerical experiments
Methods for the design and analysis of numerical experiments that are especially useful and efficient in multidimensional parameter spaces are presented. The analysis method, which is similar to kriging in the spatial analysis literature, fits a statistical model to the output of the numerical model. The method is applied to a fully nonlinear, global, equivalent-barotropic dynamical model. The statistical model also provides estimates for the uncertainty of predicted numerical model output, which can provide guidance on where in the parameter space to conduct further experiments, if necessary. The method can provide significant improvements in the efficiency with which numerical sensitivity experiments are conducted.
Bowman, Kenneth P.↗