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Results for “optimization methods”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Vertex-finding and reconstruction of contained two-track neutrino events in the MicroBooNE detector
In this work, we describe algorithms developed to isolate and accurately reconstruct two-track events that are contained within the MicroBooNE detector. This method is optimized to reconstruct two tracks of lengths longer than 5cm. This code has applications to searches for neutrino oscillations and measurements of cross sections using quasi-elastic-like charged current events. The algorithms we discuss will be applicable to all detectors running in Fermilab's Short Baseline Neutrino program (SBN), and to any future liquid argon time projection chamber (LArTPC) experiment with beam energies ~ 1 GeV. The algorithms are publicly available on a GITHUB repository. This reconstruction offers a complementary and independent alternative to the Pandora reconstruction package currently in use in LArTPC experiments, and provides similar reconstruction performance for two-track events.
HARPS-N OBSERVES THE SUN AS A STAR
Radial velocity (RV) perturbations induced by stellar surface inhomogeneities including spots, plages and granules currently limit the detection of Earth-twins using Doppler spectroscopy. Such stellar noise is poorly understood for stars other than the Sun because their surface is unresolved. In particular, the effects of stellar surface inhomogeneities on observed stellar radial velocities are extremely difficult to characterize, and thus developing optimal correction techniques to extract true stellar radial velocities is extremely challenging. In this paper, we present preliminary results of a solar telescope built to feed full-disk sunlight into the HARPS-N spectrograph, which is in turn calibrated with an astro-comb. This setup enables long-term observation of the Sun as a star with state-of-the-art sensitivity to RV changes. Over seven days of observing in 2014, we show an average 50 cm s{sup −1} RV rms over a few hours of observation. After correcting observed radial velocities for spot and plage perturbations using full-disk photometry of the Sun, we lower by a factor of two the weekly RV rms to 60 cm s{sup −1}. The solar telescope is now entering routine operation, and will observe the Sun every clear day for several hours. We will use these radial velocities combined with data from solar satellites to improve our understanding of stellar noise and develop optimal correction methods. If successful, these new methods should enable the detection of Venus over the next two to three years, thus demonstrating the possibility of detecting Earth-twins around other solar-like stars using the RV technique.
The extended Baryon Oscillation Spectroscopic Survey: testing a new approach to measure the evolution of the structure growth
We report that the extended Baryon Oscillation Spectroscopic Survey (eBOSS) is one of the first of a new generation of galaxy redshift surveys that will cover a large range in redshift with sufficient resolution to measure the baryon acoustic oscillations signal. For surveys covering a large redshift range we can no longer ignore cosmological evolution, meaning that either the redshift shells analysed have to be significantly narrower than the survey, or we have to allow for the averaging over evolving quantities. Both of these have the potential to remove signal: analysing small volumes increases the size of the Fourier window function, reducing the large-scale information, while averaging over evolving quantities can, if not performed carefully, remove differential information. It will be important to measure cosmological evolution from these surveys to explore and discriminate between models. We apply a method to optimally extract this differential information to mock catalogues designed to mimic the eBOSS quasar sample. By applying a set of weights to extract redshift-space distortion measurements as a function of redshift, we demonstrate an analysis that does not invoke the problems discussed above. Finally, we show that our estimator gives unbiased constraints.
Gauge-fixing quantum density operators at scale
We provide a theory, algorithms, and simulations of nonequilibrium quantum systems using a one-dimensional (1D) completely positive (CP), matrix-product (MP) density-operator (𝜌) representation. By generalizing the matrix product state's orthogonality center, to additionally store positive classical mixture correlations, the MP𝜌 factorization naturally emerges. In this setting, we analytically and numerically examine the virtual gauge freedoms associated with the representation of quantum density operators. Based on this perspective, we simplify algorithms in certain limits to speed up the integration of the canonical-form master-equation dynamics. This enables us to quickly evolve under the dynamics of two-body quantum channels without resorting to optimization-based methods. In addition to this technical advance, we also scale up numerical examples and discuss implications for accurately modeling hardware architectures and predicting their performance in the near term. This includes an example of the quantum to classical transition of informationally leaky, i.e., decohering, qubits. In this setting, because of loss from environmental interactions, nonlocal complex coherence correlations are converted into global incoherent classical statistical mixture correlations. Lastly, the representation of both global and local correlations is discussed. We expect this work to have applications in additional nonequilibrium settings, beyond qubit engineering.
Simulating scalar field theories on quantum computers with limited resources
Here we present a quantum algorithm for implementing φ 4 lattice scalar field theory on qubit computers. The field is represented in the discretized field amplitude basis. The number of qubits and elementary gates required by the implementation of the evolution operator is proportional to the lattice size. The algorithm allows efficient φ 4 state preparation for a large range of input parameters in both the normal and broken-symmetry phases. The states are prepared using a combination of variational and adiabatic evolution methods. First, the ground state of a local Hamiltonian, which includes the φ 4 self-interaction, is prepared using short variational circuits. Next, this state is evolved by switching on the coupling between the lattice sites adiabatically. The parameters defining the local Hamiltonian are adjustable and constitute the input of our algorithm. We present a method to optimize these parameters in order to reduce the adiabatic time required for state preparation. For preparing broken-symmetry states, the adiabatic evolution problems caused by crossing the phase transition critical line and by the degeneracy of the broken-symmetry ground state can be addressed using an auxiliary external field which gradually turns off during the adiabatic process. We show that the time dependence of the external field during the adiabatic evolution is important for addressing the broken-symmetry ground state degeneracy. The adiabatic time dependence on the inverse error tolerance can be reduced from quadratic to linear by using a field strength that decreases exponentially in time relative to one that decreases linearly.
Reconstructing the neutron star equation of state from observational data via automatic differentiation
Neutron star observables like masses, radii, and tidal deformability are direct probes to the dense matter equation of state (EoS). Here, a novel deep learning method that optimizes an EoS in the automatic differentiation framework of solving inverse problems is presented. The trained neural network EoS yields narrow bands for the relationship between the pressure and speed of sound as a function of the mass density. The results are consistent with those obtained from conventional approaches and the observational bound on the tidal deformability inferred from the gravitational wave event, GW170817.
Information divergences to parametrize astrophysical uncertainties in dark matter direct detection
Astrophysical uncertainties in dark matter direct detection experiments are typically addressed by parametrizing the velocity distribution in terms of a few uncertain parameters that vary around some central values. Here we propose a method to optimize over all velocity distributions lying within a given distance measure from a central distribution. We discretize the dark matter velocity distribution as a superposition of streams and use a variety of information divergences to parametrize its uncertainties. With this, we bracket the limits on the dark matter–nucleon and dark matter–electron scattering cross sections, when the true dark matter velocity distribution deviates from the commonly assumed Maxwell-Boltzmann form. The methodology pursued is general and could be applied to other physics scenarios where a given physical observable depends on a function that is uncertain.
Implementing two-qubit gates at the quantum speed limit
The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine-learning-inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus the method is applicable to a variety of platforms, including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates.
Polarization transmission in the Hadron Storage Ring of the Electron-Ion Collider
The successful operation of the future Electron-Ion Collider is contingent on maintaining high hadron beam polarization up to 275 GeV. The Hadron Storage Ring lattice, however, features a symmetry-breaking interaction region that excites strong, nonsystematic spin resonances, posing a significant threat to polarization preservation. This paper systematically investigates two complementary strategies to ensure high polarization transmission. The first method involves optimizing the vertical betatron phase advance between Siberian snakes to orchestrate a cancellation of depolarizing kicks across the ring. We demonstrate through simulations that both dynamic and fixed-optics solutions based on this principle can successfully preserve polarization through the strongest resonances. The second, more powerful approach involves optimizing the snake rotation axes to suppress resonance driving terms at their source. We revisit established symmetric configurations, such as the Lee-Courant schemes, and introduce a novel, highly symmetric “Doubly Lee-Courant” (DLC) scheme, which enforces a local 𝜋 spin phase advance across every consecutive pair of snakes. Our analysis reveals a clear performance hierarchy, with the DLC configuration providing an exceptionally robust and energy-insensitive baseline for polarization preservation. We conclude that a hybrid strategy, using a DLC snake scheme as a symmetric foundation and betatron phase tuning for fine corrections, offers the most effective path forward for the EIC and future high-energy polarized-beam facilities.
Distributionally Robust Decision Making Leveraging Conditional Distributions
Distributionally robust optimization (DRO) is a powerful tool for decision making under uncertainty. It is particularly appealing because of its ability to leverage existing data. However, many practical problems call for decision-making with some auxiliary information, and DRO in the context of conditional distributions is not straightforward. We propose a conditional kernel distributionally robust optimization (CKDRO) method that enables robust decision making under conditional distributions through kernel DRO and the conditional mean operator in the reproducing kernel Hilbert space (RKHS). In particular, we consider problems where there is a correlation between the unknown variable y and an auxiliary observable variable x. Given past data of the two variables and a queried auxiliary variable, CKDRO represents the conditional distribution P(y|x) as the conditional mean operator in the RKHS space and quantifies the ambiguity set in the RKHS as well, which depends on the size of the dataset as well as the query point. To justify the use of RKHS, we demonstrate that the ambiguity set defined in RKHS can be viewed as a ball under a metric that is similar to the Wasserstein metric. The DRO is then dualized and solved via a finite dimensional convex program. The proposed CKDRO approach is applied to a generation scheduling problem and shows that the result of CKDRO is superior to common benchmarks in terms of quality and robustness.
Optimal Sizing and Operation of a Hybrid Clean Energy Center
In this paper, we consider the deployment of battery energy storage systems (BESSs) paired with photovoltaics (PV) to form a hybrid clean energy center (HCEC), where the BESS is equipped for two main purposes: i) reducing the volatility of PV output and ii) shifting energy to meet an export limit. We develop a linear dynamical model to characterize the output of an HCEC and express the volatility and export power as functions of the battery's dispatch operations. We also propose an optimal sizing method via second-order cone programming (SOCP) to maximize the net benefits of the HCEC. Numerical studies are performed to illustrate and validate the proposed method.
Methods and Results for Quantum Optimal Pulse Control on Superconducting Qubit Systems
The effective use of current Noisy Intermediate-Scale Quantum (NISQ) devices is often limited by the noise which is caused by interaction with the environment and affects the fidelity of quantum gates. In transmon qubit systems, the quantum gate fidelity can be improved by applying control pulses that can minimize the effects of the environmental noise. In this work, we employ physics-guided quantum optimal control strategies to design optimal pulses driving quantum gates on superconducting qubit systems. We test our results by conducting experiments on the IBM quantum hardware using their OpenPulse API. We compare the performance of our pulse-optimized quantum gates against the default quantum gates and show that the optimized pulses improve the fidelity of the quantum gates, in particular the single-qubit gates. Here we discuss the challenges we encountered in our work and point to possible future improvements.
Physics-Based Inversion of a Millimeter-Wave Diagnostic for Liquid Metal Additive Manufacturing
Not Available
Structure-aware Initialization via Numerical Continuation and Informed Priors
Scientific machine learning (SciML) often operates in ill-conditioned, weakly identifiable regimes due to limited data or indirect observations. In such settings, optimization and inference are highly sensitive to the starting point, making initialization--often under-reported--a consequential degree of freedom. Random initialization is not a neutral default as it induces an implicit prior over candidate solutions and can systematically bias the result, producing large run-to-run variability. Here, we formalize this view by treating initialization as a hidden confounder in SciML and develop a unifying theory for structure-aware initialization via numerical continuation, constructing warm starts from related problem instances. Across representative tasks, including physics-informed neural networks, maximum likelihood estimation, and variational inference, warm starts have been shown to consistently reduce optimization effort and improve reliability.
Quantifying Energy Justice Goals in the Power Sector: Developing and Using Metrics
New policy goals are explicitly guiding the future grid toward greater energy equity. At the same time, policy goals also guide the grid toward decarbonization and resilience, while maintaining cost, security, and reliability cornerstone requirements. In conclusion, these pressures create a dilemma: moving urgently to address climate change and respond to energy disruptions, but also slowly to engage communities on the climate frontlines in earnest.
Multi-Energy Microgrid Planning Considering Heat Flow Dynamics
In this paper, we present a multi-energy microgrid optimal planning method, considering the intra-hour dynamics of the heating system as constraints of the energy dispatch, and consequently of the sizing of the Combined Heat and Power units. The fixed-mass flow rate method and finite difference technique are applied to linearize the dynamic constraints. Moreover, to determine the values of the mass flow rates, a critical scenario representing the slowest heat dynamic process is put forward. Finally, a realistic microgrid case study is presented to illustrate the effect of considering the heat flow dynamics in the overall solution.
Remote temperature distribution sensing using permanent magnets
Remote temperature sensing is essential for applications in enclosed vessels where feedthroughs or optical access points are not possible. A unique sensing method for measuring the temperature of multiple closely-spaced points is proposed using permanent magnets and several three-axis magnetic field sensors. The magnetic field theory for multiple magnets is discussed and a solution technique is presented. Experimental calibration procedures, solution inversion considerations and methods for optimizing the magnet orientations are described in order to obtain low-noise temperature estimates. The experimental setup and the properties of permanent magnets are shown. Finally, experiments were conducted to determine the temperature of nine magnets in different configurations over a temperature range of 5 to 60 degrees Celsius and for a sensor-to-magnet distance of up to 35 mm. Furthermore, to show the possible applications of this sensing system for measuring temperatures through metal walls, additional experiments were conducted inside an opaque 304 stainless steel cylinder.