Control co-design of commercial building chiller plant using Bayesian optimization
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Many scientific problems can be formulated as sparse regression, i.e., regression onto a set of parameters when there is a desire or expectation that some of the parameters are exactly zero or do not substantially contribute. This includes many problems in signal and image processing, system identification, optimization, and parameter estimation methods such as Gaussian process regression. Sparsity facilitates exploring high-dimensional spaces while finding parsimonious and interpretable solutions. In the present work, we illustrate some of the important ways in which sparse regression appears in plasma physics and point out recent contributions and remaining challenges to solving these problems in this field. Further, a brief review is provided for the optimization problem and the state-of-the-art solvers, especially for constrained and high-dimensional sparse regression.
In the presence of an AC drive, multiterminal Josephson junctions exhibit the inverse AC Josephson effect, where the oscillations of the superconducting phase of each junction can lock onto one another or onto the external drive. The competition between these different phase-locked states results in a complex array of quantized voltage plateaus whose stability strongly depend on the circuit parameters of the shunted junctions. This phase diagram cannot be explored with low-temperature transport experiments alone, given the breadth of the parameter space, so we present an easily tunable analog circuit whose dynamical properties emulate those of a three-terminal junction. We focus on the observation of the multiterminal inverse AC Josephson effect, and we discuss how to identify Shapiro steps associated with each of the three junctions as well as their quartet states. We only observe integer phase-locked states in strongly overdamped networks, but fractional Shapiro steps appear as well when the quality factor of the junctions increases. Finally, we discuss the role of transverse coupling in the synchronization of the junctions.
Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.
Among the most challenging aspects of simulating thermal decomposition of fibre reinforced polymers is the determination of appropriate reaction parameters. Thermogravimetric analysis is typically used to generate decomposition data, to which the reaction parameters are then fit. When designing mixed fibre materials (e.g. combinations of glass and carbon fibres), the number of TGA experiments needed to explore the entire design space may be intractable. Here, we demonstrate the creation of two candidate proxy mixed fibre mechanisms and compare them to a mechanism created by fitting parameters from TGA on the mixed fibre composite. These mechanisms are then demonstrated in a 2D axisymmetric numerical decomposition, heat transfer, and porous flow model. We find a maximum 11% uncertainty in mass and 4% in temperature difference when using a proxy mechanism.
The effects of oscillations in gas composition, known as lean/rich dithering, on the performance of a commercial Pd-based three-way catalyst (TWC) for stoichiometric natural gas (NG) engines were evaluated using synthetic exhaust flow reactor experiments. Under simulated NG exhaust conditions, NO conversion was intimately correlated to CH 4 conversion at slightly fuel-rich operating conditions. CH 4 conversion significantly varied with lambda (or O 2 concentration) and depended on the direction of the lambda change. The dynamic CH 4 conversion is likely related to the change of catalyst oxidation state and structures of Pd active sites. The CH 4 -NO cross-over point was found at a rich-biased lambda rather than stoichiometry. Compared to static operation, catalyst performance was much higher under a realistic dithering condition. The impacts of dithering parameters including amplitude and frequency on CH 4 and NO conversions were explored. O 2 dosage is proposed as an effective descriptor of the TWC dithering performance. In the O 2 -dosage space, the performance of distinct dithering parameters collapse, helping to elucidate the fundamental influence of dithering parameters on TWC performance. With the help of O2 dosage, an optimal operating window leading to high CH 4 and NO conversions was identified. A higher dithering amplitude is required to keep the catalyst in a reduced state for higher CH 4 and NO conversions, while an appropriate O 2 dosage that does not exceed the breakthrough OSC is necessary to avoid excess O 2 that results in NO slip. The dithering amplitude and O 2 -dosage-metric maps provide a new method for studying dynamic TWC performance and identifying optimum operation strategies.
We investigate how recent updates to neutrino oscillation parameters and the sum of neutrino masses influence the sensitivity of neutrinoless double-beta ( 0 ν β β ) decay experiments. Incorporating the latest cosmological constraints on the sum of neutrino masses and laboratory measurements on oscillations, we determine the sum of neutrino masses for both the normal hierarchy (NH) and the inverted hierarchy (IH). Our analysis reveals a narrow range for the sum of neutrino masses, approximately 0.06 eV / c 2 for NH and 0.102 eV / c 2 for IH. Utilizing these constraints, we calculate the effective Majorana masses for both NH and IH scenarios, establishing the corresponding allowed regions. Importantly, we find that the minimum neutrino mass is nonzero, as constrained by the current oscillation parameters. Additionally, we estimate the half-life of 0 ν β β decay using these effective Majorana masses for both NH and IH. Our results suggest that upcoming ton-scale experiments will comprehensively explore the IH scenario, while 100-ton-scale experiments will effectively probe the parameter space for the NH scenario, provided the background index can achieve 1 event/kton-year in the region of interest. Published by the American Physical Society 2024
Phase-grating moiré interferometers (PGMIs) have emerged as promising candidates for the next generation of neutron interferometry, enabling the use of a polychromatic beam and manifesting interference patterns that can be directly imaged by existing neutron cameras. However, the modeling of the various PGMI configurations is limited to cumbersome numerical calculations and backward propagation models which often do not enable one to explore the setup parameters. Here we generalize the Fresnel scaling theorem to introduce a k -space model for PGMI setups illuminated by a cone beam, thus enabling an intuitive forward propagation model for a wide range of parameters and experimental setups. The interference manifested by a PGMI is shown to be a special case of the Talbot effect, and the optimal fringe visibility is shown to occur at the moiré location of the Talbot distances. We derive analytical expressions for the contrast and the propagating intensity profiles in various conditions and provide the first analysis of the PGMI dark-field imaging signal when considering sample characterization. The model's predictions are compared to experimental measurements and good agreement is found between them. Last, we propose and experimentally verify a method to recover contrast at typically inaccessible PGMI autocorrelation lengths. The presented work provides a toolbox for analyzing and understanding existing PGMI setups and their future applications, for example extensions to two-dimensional PGMIs and characterization of samples with nontrivial structures. Published by the American Physical Society 2024
We present a search for the pair production of Higgsinos in final states with large missing transverse momentum and either two reconstructed muons or a reconstructed lepton (muon or electron) and an isolated track. The analyzed data correspond to proton-proton collisions with an integrated luminosity of 137 fb −1 , collected by the CMS experiment at $\sqrt{𝑠}$ =13 TeV in 2016, 2017, and 2018. The signal scenario assumes four nearly mass degenerate Higgsino mass eigenstates: two neutralino states $\tilde{𝜒}^0_2$ and $\tilde{𝜒}^0_1$ with a small mass difference in the range 1–10 GeV and two chargino states $\tilde{𝜒}^±_1$ with an intermediate mass. The analysis focuses on the decay of the heavier neutralino into the lighter one and a virtual 𝑍 boson, which decays into two same-flavor leptons. The leptons have small transverse momentum and/or a small opening angle between the identified muons. An isolated track is used to recover events in which only one of the two leptons is identified. Multivariate discriminants are used to enhance the sensitivity by efficiently rejecting backgrounds from SM processes or misreconstructed tracks and/or leptons. The search explores a unique phase space and probes a previously unexplored region of the signal model parameter space. Mass differences between the two neutralinos are probed down to 1.5 GeV, assuming a Higgsino mass of 100 GeV. The maximum excluded Higgsino mass is 115 GeV.
Effective Field Theory (EFT) extensions of the Standard Model are tools to compute observables (e.g. cross sections with partonic center-of-mass energy √s^) as a systematically improvable expansion suppressed by a new physics scale M. If one is interested in EFT predictions in the parameter space where M < √s^, concerns of self-consistency emerge, which can manifest as a violation of perturbative partial-wave unitarity. However, when we search for the effects of an EFT at a hadron collider with center-of-mass energy √s using an inclusive strategy, we typically do not have access to the event-by-event value of √s^. This motivates the need for a formalism that incorporates parton distribution functions into the perturbative partial-wave unitarity analysis. Developing such a framework and initiating an exploration of its implications is the goal of this work. Our approach opens up a potentially valid region of the EFT parameter space where M << √s. We provide evidence that there exist valid EFTs in this parameter space. The perturbative unitarity bounds are sensitive to the details of a given search, an effect we investigate by varying kinematic cuts.
In geophysical inversions, lower and upper model parameter bounds are a means of solution stabilization. Further, constraints that intend to let only geologically plausible inverse solutions pass are amenable to lower and upper bounds. Reliable prior information is paramount to construct such bound constraints. It is common practice to narrow and widen bound intervals for regions of, respectively, more and less certain prior information. Contrary to this practice, we experiment with widened bound intervals in zones that are poorly resolved by a given survey configuration but where prior information would suggest structural anomalies of interest. The purpose of enlarged parameter bounds that correlate spatially with predefined targets is to let the inversion explore a larger solution space, thus increasing the potential to resolve otherwise hidden anomalies. Application of the method is based on a carbon-sequestration baseline (pre-injection) crosswell electromagnetic (EM) field survey at the Containment and Monitoring Institute Field Research Station (Alberta, Canada), where impeded measurements led to generally reduced sensitivities for the interwell region. Synthetic-data proofs of concept use augmented bounds designed to boost the resolution of artificial plume targets, indicating an enhanced illumination compared to constant bounds. Comparative field data inversions with spatially variable bounds constructed from prior resistivity and velocity information highlight non-horizontal baseline structures.
Light-curve variability is well-suited to characterizing objects in surveys with high cadence and a long baseline. This is especially relevant in view of the large datasets to be produced by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). We aim to determine variability parameters for objects in the Palomar Transient Factory (PTF) and explore differences between quasars (QSOs), stars, and galaxies. We relate variability and colour information in preparation for future surveys. We fit joint likelihoods to structure functions (SFs) of 71 million PTF light curves with a Markov chain Monte Carlo method. For each object, we assume a power-law SF and extract two parameters: the amplitude on timescales of one year, A, and a power-law index, γ. With these parameters and colours in the optical (Pan-STARRS1) and mid-infrared (WISE), we identify regions of parameter space dominated by different types of spectroscopically confirmed objects from SDSS. Candidate QSOs, stars, and galaxies are selected to show their parameter distributions. QSOs show high-amplitude variations in the R band, and the highest γ values. Galaxies have a broader range of amplitudes and their variability shows relatively little dependency on timescale. With variability and colours, we achieve a photometric selection purity of 99.3% for QSOs. Even though hard cuts in monochromatic variability alone are not as effective as seven-band magnitude cuts, variability is useful in characterizing object subclasses. Through variability, we also find QSOs that were erroneously classified as stars in the SDSS. We discuss perspectives and computational solutions in view of the upcoming LSST.
We point out that neutrinos can have enhanced couplings to photons, if light (pseudo)scalar mediators are present, resulting in a potentially measurable neutrino polarizability. We show that the expected suppression from small neutrino masses can be compensated by the light mediator mass, generating dimension 7 Rayleigh operators at low scales. We explore the rich phenomenology of such models, computing in detail the constraints on the viable parameter space, spanned by the couplings of the mediator to neutrinos and photons. Finally, we build several explicit models that lead to an enhanced neutrino polarizability by modifying the inverse see-saw majoron, i.e., the pseudo-Nambu-Goldstone boson of the U(1) L global lepton number responsible for generating small neutrino masses.
Distributed Acoustic Sensing (DAS) is a rapidly developing technology that can record acoustic wavefields at high sampling rates and with dense spatial spacing difficult to achieve with seismometers. However, the geophysical community has not fully explored DAS survey parameters to characterize the ideal array design. A better quantitative understanding of DAS array behavior prior to SPE Phase III acquisition can help improve the quality of the data recorded by guiding the DAS array design. Here we use array response functions as well as beamforming and backprojection results from forward modelling calculations to assess the performance of varying DAS array geometries to record regional and local sources. A seven-sided polygon DAS array demonstrated improved capabilities for recording regional sources over segmented linear arrays, with potential improvements in recording and locating local sources. These results help reveal DAS array performance as a function of geometry.
We consider a model with a complex scalar field that couples to (e, μ) or (μ, τ) within the “longevity” window: [|m l 1 – m l 2 |, m l 1 + m l 2 ] in which l 1 and l 2 are the two different charged leptons. Within such a mass window, even a relatively large coupling (e.g. of the size commensurate with the current accuracy/discrepancy in the muon g – 2 experiment) leads to long lifetimes and macroscopic propagation distance between production and decay points. We propose to exploit several existing neutrino experiments and one future experiment to probe the parameter space of this model. For the μ – e sector, we exploit the muonium decay branching ratio and the production and decay sequence at the LSND experiment, excluding the parametric region suggested by g μ – 2 anomaly. For the τ – μ sector, we analyze three main production mechanisms of scalars at beam dump experiments: the Drell-Yan process, the heavy meson decay, and the muon scattering. We explore the constraints from the past CHARM and NuTeV experiments, and evaluate sensitivity for the proposed beam dump experiment, SHiP. The latter can thoroughly probe the parameter space relevant for the g μ – 2 anomaly.
We study the cosmological evolution of a light QCD axion and identify the parameter space to obtain the correct relic dark matter abundance. The axion potential is flattened at the origin, corresponding to the only minimum, while it is unsuppressed at π. These potential features arise by assuming a mirror sector with the strong CP phase $\overline{θ}$ shifted by π compared to the SM sector, which allows the mirror axion potential to be tuned against the usual QCD axion potential. Before the QCD phase transition, assuming the mirror sector is decoupled and much colder than the SM thermal bath, the mirror sector potential dominates, causing the axion to initially roll to a temporary minimum at π. However, after the QCD phase transition, the potential minimum changes, and the axion relaxes from the newly created “hilltop” near π to the CP-conserving minimum at the origin. As the axion adiabatically tracks this shift in the potential minimum through the QCD phase transition, with non-adiabatic evolution near π and 0, it alters the usual prediction of the dark matter abundance. Consequently, this “hilltop” misalignment mechanism opens new regions of axion parameter space, with the correct relic abundance while still solving the strong CP problem, that could be explored in future experiments.
Particle physics theories, such as those which explain neutrino flavor mixing, arise from a vast landscape of model-building possibilities. A model’s construction typically relies on the intuition of theorists. It also requires considerable effort to identify appropriate symmetry groups, assign field representations, and extract predictions for comparison with experimental data. We develop Autonomous Model Builder (AMBer), a framework in which a reinforcement learning agent interacts with a streamlined physics software pipeline to search these spaces efficiently. AMBer selects symmetry groups, particle content, and group representation assignments to construct models while minimizing the number of free parameters introduced. We validate our approach in well-studied regions of theory space and extend the exploration to a previously unexamined symmetry group. While demonstrated in the context of neutrino flavor theories, this approach of reinforcement learning with physics software feedback may be extended to other theoretical model-building problems in the future.
The use of experiments searching for light axion dark matter as high-frequency gravitational wave detectors has garnered increasing attention in recent years. We explore the capabilities of the Broadband Reflector Experiment for Axion Detection (BREAD) in probing the GW parameter space and study the directional dependence of its coverage. This detector can investigate frequencies ranging from 0.05 to 200 THz. We find that by employing single photon detectors, BREAD is sensitive to GWs with characteristic strains as low as 10 −21 at 0.1 THz and 10 −25 at 200 THz with a year exposure time, making it competitive with other proposals operating at similar frequencies.