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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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Constraints on dark photon dark matter from Lyman- α forest simulations and an ultrahigh signal-to-noise quasar spectrum
The ultralight dark photon is a well-motivated, hypothetical dark matter candidate. In a dilute plasma, they can resonantly convert into photons, and heat up the intergalactic medium between galaxies. In this work, we explore the dark photon dark matter parameter space by comparing synthetic Lyman- α forest data from cosmological hydrodynamical simulations to observational data from VLT/UVES of the quasar HE0940-1050 ( z em = 3.09 ). We use a novel flux normalization technique that targets underdense gas, reshaping the flux probability distribution. Not only do we place robust constraints on the kinetic mixing parameter of dark photon dark matter, but notably our findings suggest that this model can still reconcile simulated and observed Doppler parameter distributions of z ∼ 0 Lyman- α lines, as seen by HST/COS. This work opens new pathways for the use of the Lyman- α forest to explore new physics, and can be extended to other scenarios such as primordial black hole evaporation, dark matter decay, and annihilation.
SQUID Readout of a High-$Q$ Superconducting $LC$ Resonator
We demonstrate the readout of a superconducting $LC$ resonator, with $Q \approx 2 \times 10^{6}$ at approximately 250 kHz, using a dc SQUID followed by a SQUID series-array amplifier readout chain. We find that $Q$ depends on the SQUID flux-bias point, increasing near the shallow-slope point of the SQUID modulation curve and decreasing near the steep-slope point, consistent with the previously observed SQUID damping effects. From this variation, we infer the SQUID effective input impedance. We further infer the effective temperature of the resonator circuit from the resonance peak in the noise spectrum and show that it also depends on the SQUID flux-bias point, suggesting a possible contribution from SQUID back-action noise. This system provides a working prototype for future experiments based on lumped-element resonators with SQUID readout, in particular low-mass axion searches within the DMRadio program.
Implementation of Johnson Noise Thermometry into Radiation Hardened Electronics
The random motion of charge carriers in an electrical conductor is a function of its temperature. This phenomenon generates an electrical signal, referred to as Johnson noise, that is present in all analog signals. Conversely, the temperature dependence of the Johnson noise spectrum also enables a direct measurement of absolute temperature, which can provide a secondary measurement of temperature when quantified using sensors such as resistive temperature devices (RTDs) in a process called Johnson noise thermometry (JNT). For nuclear environments, this is of particular interest in correcting for RTDs signal drift caused by accumulation, transmutation, and displacement damage caused by neutron and gamma irradiation.
Disentangling the impact of quasiparticles and two-level systems on the statistics of superconducting-qubit lifetime
Temporal fluctuations in the superconducting qubit lifetime, T 1 , present additional challenges in the pursuit of fault-tolerant quantum computing. Although the exact mechanisms remain unclear, T 1 fluctuations are generally attributed to strong coupling between the qubit and a few near-resonant two-level systems (TLSs), which can exchange energy with an ensemble of thermally fluctuating two-level fluctuators (TLFs) at low frequencies. Here, we report T 1 measurements of qubits with varying geometrical footprints and surface dielectrics as a function of temperature. By analyzing the noise spectrum of the qubit depolarization rate, Γ 1 = 1 / T 1 , we disentangle the contributions of TLSs, nonequilibrium quasiparticles (QPs), and equilibrium (thermally excited) QPs to the variance in Γ 1 . We find that the Γ 1 variance in qubits with smaller footprints is more susceptible to QP and TLS fluctuations than that in larger-footprint qubits. Furthermore, the QP-induced variances in all qubits align with the theoretical framework of QP diffusion and fluctuation. These findings offer valuable insights for future qubit design and engineering optimization.
Dephasing in Fluxonium Qubits from Coherent Quantum Phase Slips
Phase slips occur across all Josephson junctions (JJs) at a rate that increases with the impedance of the junction. In superconducting qubits composed of JJ-array superinductors—such as fluxonium—phase slips in the array can lead to decoherence. In particular, phase-slip processes at the individual array junctions can coherently interfere, each with an Aharonov-Casher phase that depends on the offset charges of the array islands. These coherent quantum phase slips (CQPS) perturbatively modify the qubit frequency, and therefore charge noise on the array islands will lead to dephasing. By varying the impedance of the array junctions, we design a set of fluxonium qubits in which the expected phase-slip rate within the JJ array changes by several orders of magnitude. We characterize the coherence times of these qubits and demonstrate that the scaling of CQPS-induced dephasing rates agrees with our theoretical model. Furthermore, we perform noise spectroscopy of two qubits in regimes dominated by either CQPS or flux noise. We find that the noise power spectrum associated with CQPS dephasing appears to be featureless at low frequencies and not 1 / f . Numerical simulations indicate that this behavior is consistent with charge noise generated by charge-parity fluctuations within the array. Our findings broadly inform JJ-array-design trade-offs, relevant for the numerous superconducting-qubit designs employing JJ-array superinductors. Published by the American Physical Society 2024
Fine structure of current noise spectra in nanoelectromechanical resonators
Here, we study the frequency-dependent noise of a suspended carbon nanotube quantum dot nanoelectromechanical resonator induced by electron-vibration coupling. Using a rigorous Keldysh diagrammatic technique, we establish a formal framework connecting the vibrational properties to electrical measurements. We find that the noise power spectrum exhibits a narrow resonant peak at the frequency of the vibrational modes. However, this fine structure tends to disappear due to a coherent cancellation effect when the tunneling barriers are tuned to a symmetric point. Notably, measuring the electrical current noise spectra provides a sensitive alternative method for detecting the damping and dephasing of quantum vibrational modes.
Observable CMB B -modes from cosmological phase transitions
A B -mode polarization signal in the cosmic microwave background (CMB) is widely regarded as smoking gun evidence for gravitational waves produced during inflation. Here, we demonstrate that tensor perturbations sourced during noninflationary epochs can yield non-negligible B -mode signals, which can in principle complicate the interpretation of future observational data. As a case study, we consider tensor perturbations sourced in the bubble collision stage of a first-order cosmological phase transition occurring in a secluded dark sector. Although phase transitions arise from causal subhorizon physics, they nevertheless exhibit a white noise power spectrum on superhorizon scales. Power is suppressed on the large scales relevant for CMB B -mode polarization, but it is not necessarily negligible. We show that for appropriately chosen phase transition parameters, the maximal B -mode amplitude can compete with inflationary predictions that can be tested with current and future experiments. These scenarios can be differentiated by performing measurements on multiple angular scales, since the phase transition signal predicts peak power on smaller scales.
New Probe of Cosmic Birefringence Using Galaxy Polarization and Shapes
We propose a novel statistical method to measure cosmic birefringence and demonstrate its power in probing parity violation due to axions. Exploiting an empirical correlation between the integrated radio polarization direction of a spiral galaxy and its apparent shape, we devise an unbiased minimum-variance estimator for the rotation angle, which should achieve an uncertainty of 5°–15° per galaxy. In conclusion, large galaxy samples from the forthcoming SKA continuum surveys, together with optical shape catalogs, promise a comparable or even lower noise power spectrum for the rotation angle than in the CMB Stage-IV (CMB-S4) experiment, with different systematics.
Unveiling Hidden Lyman Alpha Emitters in the DESI DR1 Data
We present an automatic method based on machine-learning convolutional neural network (CNN) architecture to detect Lyman alpha emitters (LAE) hidden in the Data Release 1 spectroscopic dataset of the Dark Energy Spectroscopic Instrument (DESI). Those LAEs mostly have incorrect redshift estimations because the current DESI pipeline is not designed to detect and measure the redshifts of galaxies at $z>2$. To uncover those sources, we first visually inspect thousands of DESI spectra and construct a sample, consisting of both LAEs and non-LAEs, for training and testing the CNN-based model to (1) detect LAEs in DESI spectra and (2) determine their Ly$α$ redshifts. The final model yields $95.2\%$ purity and $95.9\%$ completeness for detecting LAEs. We apply this model to approximately $2\times10^{6}$ spectra of sources targeted as emission-line galaxies and detect 19,685 LAEs from $z\sim2$ to $3.5$ within 12 minutes with a single GPU, illustrating the high efficiency of this model for identifying LAEs. The detected LAEs are mostly at the bright end of the luminosity function with Ly$α$ luminosity $L_{\rm Lyα} \gtrsim 10^{43}$ erg/s. The high signal-to-noise composite spectrum of the detected LAEs further shows various spectral features, including P-Cygni profiles of metal lines and MgII emission lines, possible indicators of Lyman continuum escape fraction, revealing the rich astrophysical information in this LAE sample. Finally, this sample can be used to train and validate the pipelines for redshift determination of LAEs for the preparation of the DESI-II survey.
Using time reversal with long duration broadband noise signals to achieve high amplitude and a desired spectrum at a target location
Time Reversal (TR) is a signal processing technique that can be used to focus acoustic waves to a specific location in space, with most applications aiming to create an impulsive focus. Here, this study instead aims to focus long-duration noise signals using TR. This paper seeks to generate higher amplitude noise at a desired location over an existing method of broadcasting equalized noise. Additionally, this paper explores various characteristics associated with focusing long duration noise using TR. The dependence of the focal amplitude on the duration of the focused signal is explored as well as the implications of using multiple sources when focusing noise. The focal amplitude decreases with longer duration and then levels off when the duration exceeds a few seconds. Coherent addition of focused noise is observed if all loudspeakers have coherent noise signals convolved with their reversed impulse responses. Lastly, focusing noise with a desired spectrum is explored.
The Average Spectrum Norm and Near-Optimal Tensor Completion
We propose the average spectrum norm to study the minimum number of measurements required to approximate a multidimensional array (i.e., sample complexity) via low-rank tensor recovery. Our focus is on the tensor completion problem, where the aim is to estimate a multiway array using a subset of tensor entries corrupted by noise. Our average spectrum norm-based analysis provides near-optimal sample complexities, exhibiting dependence on the ambient dimensions and rank that do not suffer from exponential scaling as the order increases.
Ultra-narrow-linewidth hybrid-integrated self-injection locked laser at 780 nm
Narrow-linewidth lasers are essential across a wide range of applications, including classical and quantum sensing, trapped ion systems, position/navigation/timing systems, optical clocks, and microwave frequency synthesizers. In the visible and near-visible spectrum, low-noise lasers are particularly important for laser trapping and cooling techniques, which are vital for trapped ion quantum computing, sensing, and atomic clocks. In this context, our work showcases a hybrid-integrated narrow-linewidth laser that operates at 780 nm, achieving a self-heterodyne linewidth of 105 Hz. To validate the experimental results, we performed a numerical analysis that combines insights from a many-body theory applied to the gain region with a travelling-wave model to capture the laser dynamics. Our investigation further delves into how the linewidth of the self-injection locked lasers is influenced by the parameters of micro-ring resonators, aiming to assess the potential for achieving Hz-level integrated laser linewidths at 780 nm. This work not only demonstrates the technical feasibility of Hz-level narrow-linewidth lasers but also lays the groundwork for future explorations in the field.
Quantum Zeno Control of Superconducting Qubit Coherence
The Quantum Zeno Effect (QZE) dictates how the dynamics of a quantum system can be modified through continuous or discrete measurements [1]. It has gained increasing attention in quantum computing community in recent years, both due to its inevitable implications for qubit readout as well as for its promise for enabling new methods of quantum state control such as reservoir engineering and Zeno-dragging. Here, we investigate Zeno effects implemented via weak measurements for controlling superconducting-qubit coherence during gate and readout operations in a multi-qubit setup. Building on prior observations [2] that measurement backaction can both suppress and enhance qubit relaxation, we predict and measure QZE-altered qubit coherence times by manipulating the spectral overlap of qubit spectrum with background noise sources. We also discuss modifications and opportunities for controllable QZE due to anharmonic effects in multi-level superconducting atoms. [1] S. Greenfield, A. Kamal, J. Dressel, E. Levenson-Falk, arXiv:2506.12679 (2025) [2] Thorbeck, Z. Xiao, L. Govia, A. Kamal, Phys. Rev. Lett. 132, 090602 (2024)
Cosmology From CMB Lensing and Delensed EE Power Spectra Using 2019-2020 SPT-3G Polarization Data
From CMB polarization data alone we reconstruct the CMB lensing power spectrum, comparable in overall constraining power to previous temperature-based reconstructions, and an unlensed E -mode power spectrum, with clear detections of the third through tenth acoustic peaks. The observations, taken in 2019 and 2020 with the South Pole Telescope (SPT) and the SPT-3G camera, cover 1500 deg 2 at 95, 150, and 220 GHz with arcminute resolution and roughly 4.9 µ K-arcmin coadded noise in polarization. The power spectrum estimates, together with systematic parameter estimates and a joint covariance matrix, follow from a Bayesian analysis using the Marginal Unbiased Score Expansion (MUSE) method. The E -mode spectrum at ℓ > 2000 and lensing spectrum at L > 350 are the most precise to date. Assuming the ΛCDM model, and using only these SPT data and priors on τ and absolute calibration from Planck, we find H 0 = 66.81 ± 0.81 km/s/Mpc, comparable in precision to the Planck determination and in 5.4 σ tension with the most precise H 0 inference derived via the distance ladder. We also find S 8 ≡ σ 8 (Ω m /0.3) 0.5 = 0.850 ± 0.017, providing further independent evidence of a slight tension with low-redshift structure probes. The ΛCDM model provides a good simultaneous fit to the combined Planck, ACT, and SPT data, and thus passes a powerful test. Combining these CMB datasets with BAO observations, we explore extensions to the ΛCDM model. We find that the effective number of neutrino species, spatial curvature, and primordial helium fraction are consistent with standard model values, and that the 95% confidence upper limit on the neutrino mass sum is 0.075 eV, close to the minimum sum expected from observations of solar and atmospheric neutrino oscillations. The SPT data are consistent with the somewhat weak (< 3 σ ) preference for excess lensing power seen in Planck and ACT data relative to predictions of the ΛCDM model given the combined Planck, ACT, and BAO data sets. Finally, we also detect at greater than 3 σ the influence of non-linear evolution in the CMB lensing power spectrum and discuss it in the context of the S 8 tension. Forthcoming SPT-3G analyses will feature deeper and wider observations in temperature and polarization, providing even tighter constraints and more powerful tests of the ΛCDM model.
The Atacama Cosmology Telescope: R eionization kSZ trispectrum methodology and limits
Patchy reionization generates kinematic Sunyaev–Zel’dovich (kSZ) anisotropies in the cosmic microwave background (CMB). Large-scale velocity perturbations along the line of sight modulate the small-scale kSZ power spectrum, leading to a trispectrum (or four-point function) in the CMB that depends on the physics of reionization. We investigate the challenges in detecting this trispectrum and use tools developed for CMB lensing, such as realization-dependent bias subtraction and cross-correlation based estimators, to counter uncertainties in the instrumental noise and assumed CMB power spectrum. We also find that both lensing and extragalactic foregrounds can impart larger trispectrum contributions than the reionization kSZ signal. We present a range of mitigation methods for both of these sources of contamination, validated on microwave-sky simulations. We use ACT DR6 and Planck data to calculate an upper limit on the reionization kSZ trispectrum from a measurement dominated by foregrounds. The upper limit is about 50 times the signal predicted from recent simulations.
Statistical White-Line Analysis in High-Throughput TXM-XANES for Chemical State Quantification
The transmission X-ray microscopy (TXM) based X-ray absorption near-edge structure (XANES) technique provides three-dimensional mapping of element-specific chemical states at nanometer-scale spatial resolution and micrometer-scale fields of view. However, compared to conventional volume-averaged XANES (VA-XANES) measurements, the inherently small voxel size in TXM-XANES leads to a lower signal-to-noise ratio, making full-spectrum analysis computationally demanding and less robust. Here, we present the structural and compositional conditions for a statistical white-line analysis framework under which chemical state information can be directly extracted from the white-line peak position in voxel spectra without the need for voxel-wise background subtraction or normalization, under well-defined structural and compositional conditions. The method is validated on layered oxide cathode materials, where low-order polynomial fitting accurately reproduces white-line features, and the extracted energy distributions correlate strongly with VA-XANES results. This statistical approach enables high-throughput, dose-efficient, and noise-robust chemical state quantification in TXM-XANES, offering broad applicability to functional materials requiring nanoscale oxidation-state mapping.
Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning
Fuel cladding chemical interaction (FCCI) is one of the main performance limiting factors for metallic nuclear fuels. The interaction destabilizes the martensitic microstructure and deteriorates mechanical properties of HT-9 cladding. The detection of low atomic number elements (Z<10) and overlapping of elemental peaks can be problematic in interpreting energy dispersive X-ray spectroscopy (EDS) data. Electron energy loss spectroscopy (EELS) provides precise elemental edge energy values and can detect elements with a low atomic number. This work utilizes EELS to study the distribution of lanthanides and light elements at the interaction region. The sample was prepared from the FCCI region of a U-10Zr (wt.%) solid fuel with HT-9 cladding, irradiated to a burnup of 13.2 at.%. Processing the EELS data included three major steps: 1) enhance the signal to noise ratio by denoising the spectrum with principal component analysis (PCA) method, removing background and performing deconvolution; 2) identify chemical elements with core energy loss edges; 3) confirm different phases using a popular machine learning method, K-means. This work presents qualitative assessment of lanthanides and light elements like carbon (C) and oxygen (O) enhanced by the application of machine learning algorithms. By comparing with EDS elemental maps, EELS provides higher resolution chemical maps, reveals the distribution of carbon at the interaction region supporting the formation of zirconium carbide, a rind-like microstructure feature that was proposed to mitigate the chemical interaction. Furthermore, the plasmon peak map was also found to indicate an energy shift associated with the formation of phases/compounds. K-means clustering method was used on the processed electron energy loss (EEL) spectrum to automatically reveal different phases. The resulting clustered maps from K-means clustering align well with elemental maps confirming certain phases, especially Fe-Ce and Zr-C, in the FCCI region.