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Planck 2013 results. XXVI. Background geometry and topology of the Universe

The new cosmic microwave background (CMB) temperature maps from Planck provide the highest-quality full-sky view of the surface of last scattering available to date. This allows us to detect possible departures from the standard model of a globally homogeneous and isotropic cosmology on the largest scales. We search for correlations induced by a possible non-trivial topology with a fundamental domain intersecting, or nearly intersecting, the last scattering surface (at comoving distance χ rec ), both via a direct search for matched circular patterns at the intersections and by an optimal likelihood search for specific topologies. For the latter we consider flat spaces with cubic toroidal (T3), equal-sided chimney (T2) and slab (T1) topologies, three multi-connected spaces of constant positive curvature (dodecahedral, truncated cube and octahedral) and two compact negative-curvature spaces. These searches yield no detection of the compact topology with the scale below the diameter of the last scattering surface. For most compact topologies studied the likelihood maximized over the orientation of the space relative to the observed map shows some preference for multi-connected models just larger than the diameter of the last scattering surface. Since this effect is also present in simulated realizations of isotropic maps, we interpret it as the inevitable alignment of mild anisotropic correlations with chance features in a single sky realization; such a feature can also be present, in milder form, when the likelihood is marginalized over orientations. Thus marginalized, the limits on the radius R i of the largest sphere inscribed in topological domain (at log-likelihood-ratio Δln L > -5 relative to a simply-connected flat Planck best-fit model) are: in a flat Universe, R i > 0.92χ rec for the T3 cubic torus; R i > 0.71χ rec for the T2 chimney; R i > 0.50χ rec for the T1 slab; and in a positively curved Universe, R i > 1.03χ rec for the dodecahedral space; R i > 1.0χ rec for the truncated cube; and R i > 0.89χ rec for the octahedral space. The limit for a wider class of topologies, i.e., those predicting matching pairs of back-to-back circles, among them tori and the three spherical cases listed above, coming from the matched-circles search, is R i > 0.94χ rec at 99% confidence level. Similar limits apply to a wide, although not exhaustive, range of topologies. We also perform a Bayesian search for an anisotropic global Bianchi VII h geometry. In the non-physical setting where the Bianchi cosmology is decoupled from the standard cosmology, Planck data favour the inclusion of a Bianchi component with a Bayes factor of at least 1.5 units of log-evidence. Indeed, the Bianchi pattern is quite efficient at accounting for some of the large-scale anomalies found in Planck data. However, the cosmological parameters that generate this pattern are in strong disagreement with those found from CMB anisotropy data alone. In the physically motivated setting where the Bianchi parameters are coupled and fitted simultaneously with the standard cosmological parameters, we find no evidence for a Bianchi VII h cosmology and constrain the vorticity of such models to (ω/H) 0 < 8.1 × 10 -10 (95% confidence level).

79 ASTRONOMY AND ASTROPHYSICS↗

A Population Study of Gaseous Exoplanets

We present here the analysis of 30 gaseous extrasolar planets, with temperatures between 600 and 2400 K and radii between 0.35 and 1.9 R {sub Jup}. The quality of the HST/WFC3 spatially scanned data combined with our specialized analysis tools allow us to study the largest and most self-consistent sample of exoplanetary transmission spectra to date and examine the collective behavior of warm and hot gaseous planets rather than isolated case studies. We define a new metric, the Atmospheric Detectability Index (ADI) to evaluate the statistical significance of an atmospheric detection and find statistically significant atmospheres in around 16 planets out of the 30 analyzed. For most of the Jupiters in our sample, we find the detectability of their atmospheres to be dependent on the planetary radius but not on the planetary mass. This indicates that planetary gravity plays a secondary role in the state of gaseous planetary atmospheres. We detect the presence of water vapour in all of the statistically detectable atmospheres, and we cannot rule out its presence in the atmospheres of the others. In addition, TiO and/or VO signatures are detected with 4σ confidence in WASP-76 b, and they are most likely present in WASP-121 b. We find no correlation between expected signal-to-noise and atmospheric detectability for most targets. This has important implications for future large-scale surveys.

79 ASTRONOMY AND ASTROPHYSICS↗

Seismicity-constrained fault detection and characterization with a multitask machine learning model

Geological fault detection and characterization are crucial for understanding subsurface dynamics across scales. While methods for fault delineation based on either seismicity location analysis or seismic image reflector discontinuity are well-established, a systematic approach that integrates both data types remains absent. We develop a novel machine learning model that unifies seismic reflector images and seismicity location information to automatically identify geological faults and characterize their geometrical properties. The model encodes a seismic image and a seismicity location image separately, and fuses the encoded features with a spatial-channel attention fusion module to improve the learning of important features in both inputs. We design an automated strategy to generate high-quality synthetic training data and labels. To improve the realism of the seismicity location image, we include random seismicity noise and missing seismicity location associated with some of the faults. We validate the model’s efficacy and accuracy using synthetic data examples and two field data examples. Moreover, we show that fine-tuning the trained model with a small, domain-specific dataset enhances its fidelity for field data applications. The results demonstrate that integrating seismicity location and seismic images into a unified framework allows the end-to-end neural network to achieve higher fidelity and accuracy in delineating subsurface faults and their geometrical properties compared with image-only fault detection methods. Our approach offers an adaptive data-driven tool for geological fault characterization and seismic hazard mitigation, bridging the gap between seismicity location and image-based fault detection methods.

58 GEOSCIENCES↗

Three-Gorge Reservoir: A 'Controlled Experiment' for Calibration/Validation of Time-Variable Gravity Signals Detected from Space

With the advances of measurements, modern space geodesy has become a new type of remote sensing for the Earth dynamics, especially for mass transports in the geophysical fluids on large spatial scales. A case in point is the space gravity mission GRACE (Gravity Recovery And Climate Experiment) which has been in orbit collecting gravity data since early 2002. The data promise to be able to detect changes of water mass equivalent to sub-cm thickness on spatial scale of several hundred km every month or so. China s Three-Gorge Reservoir has already started the process of water impoundment in phases. By 2009,40 km3 of water will be stored behind one of the world s highest dams and spanning a section of middle Yangtze River about 600 km in length. For the GRACE observations, the Three-Gorge Reservoir would represent a geophysical controlled experiment , one that offers a unique opportunity to do detailed geophysical studies. -- Assuming a complete documentation of the water level and history of the water impoundment process and aided with a continual monitoring of the lithospheric loading response (such as in area gravity and deformation), one has at hand basically a classical forwardinverse modeling problem of surface loading, where the input and certain output are known. The invisible portion of the impounded water, i.e. underground storage, poses either added values as an observable or a complication as an unknown to be modeled. Wang (2000) has studied the possible loading effects on a local scale; we here aim for larger spatial scales upwards from several hundred km, with emphasis on the time-variable gravity signals that can be detected by GRACE and follow-on missions. Results using the Green s function approach on the PREM elastic Earth model indicate the geoid height variations reaching several millimeters on wavelengths of about a thousand kilometers. The corresponding vertical deformations have amplitude of a few centimeters. In terms of long-wavelength spherical harmonics, the induced geoid height variations are very close to the accuracy of GRACE- recoverable gravity field, while the low-degree (2 to 5) harmonics should be detectable. With a large regional time-variable gravity signal, the Three-Gorge experiment can serve as a useful calibration/verification for GRACE (including the elastic loading effects), and future gravity missions (especially for visco-elastic yielding as well as underground water variations).

Chao, Benjamin F.↗

Building Lunar Maps for Terrain Relative Navigation and Hazard Detection Applications

Terrain Relative Navigation (TRN) systems that localize a spacecraft with respect to a map of the surface by comparing descent imagery to that reference map can only be as accurate as the reference map itself. Accurate map products that are based on orbital reconnaissance data must be validated for navigation applications to ensure that all relevant error sources are minimized. Currently available map products have been generated for scientific applications, so the need for accurate TRN maps remains a gap to be filled for upcoming lunar lander missions, in particular missions to the South Pole region. Additionally, representative high-resolution maps that contain lander-scale features are needed for successful development and testing of Hazard Detection (HD) systems. This paper describes one of NASA’s current efforts to develop benchmark data sets that can be used for developing and testing TRN and HD algorithms as well as suggested processes and metrics for generating and validating lunar maps that can be used for navigation and hazard detection.

Beyer, Ross A.↗

Can SOHO SWAN detect CMEs?

We have investigated the possibility that the Solar Wind Anisotropies (SWAN) remote sensing instrument on SOHO may be able to detect coronal mass ejections (CMEs) in neutral Hydrogen Lyman-? emission. We have identified CMEs near the Sun in observations by the SOHO LASCO white-light coronagraphs and in extreme ultraviolet emissions using SOHO E n . There are very few methods of tracking CMEs after they leave the coronagraph's field-of-view, so this is an important topic to study. The primary science goal of the SWAN investigation is the measurement of large-scale structures in the solar wind, and these are obtained by detecting intensity fluctuations in Lyman-?. SWAN consists of a pair of Sensors on opposite panels of SOHO. The instantaneous field-of-view of each sensor unit is a So x So square, divided into lo pixels. A gimbaled periscope system allows each sensor to map the intensity distribution of Lyman-?, and the entire sky can be scanned in less than one day. This is the typical mode of operation for this instrument.

St.Cyr, O. C.↗

Building Lunar Maps for Terrain Relative Navigation and Hazard Detection Applications

Terrain Relative Navigation (TRN) systems localize a spacecraft with respect to a map of the surface by comparing descent imagery to that reference map. The spacecraft position estimates can only be as accurate as the reference map itself. Accurate map products that are based on orbital reconnaissance data must be validated for navigation applications to ensure that all relevant error sources are minimized. Currently available map products have been generated for scientific applications, so the need for accurate TRN maps remains a gap to be filled for upcoming lunar lander missions, in particular missions to the South Pole region. Additionally, representative high-resolution maps that contain lander-scale features are needed for successful development and testing of Hazard Detection (HD) systems. This paper describes one of NASA’s current efforts to develop benchmark data sets that can be used for developing and testing TRN and HD algorithms as well as suggested processes and metrics for generating and validating lunar maps that can be used for navigation and hazard detection.

Lunar Maps↗

An inertia-free filter line-search algorithm for large-scale nonlinear programming

We present a filter line-search algorithm that does not require inertia information of the linear system. This feature enables the use of a wide range of linear algebra strategies and libraries, which is essential to tackle large-scale problems on modern computing architectures. The proposed approach performs curvature tests along the search step to detect negative curvature and to trigger convexification. We prove that the approach is globally convergent and we implement the approach within a parallel interior-point framework to solve large-scale and highly nonlinear problems. Our numerical tests demonstrate that the inertia-free approach is as efficient as inertia detection via symmetric indefinite factorizations. We also demonstrate that the inertia-free approach can lead to reductions in solution time because it reduces the amount of convexification needed.

Chiang, Nai-Yuan↗

Einstein Inflationary Probe (EIP)

I will discuss plans to develop a concept for the Einstein Inflation Probe: a mission to detect gravity waves from inflation via the unique signature they impart to the cosmic microwave background (CMB) polarization. A sensitive CMB polarization satellite may be the only way to probe physics at the grand-unified theory (GUT) scale, exceeding by 12 orders of magnitude the energies studied at the Large Hadron Collider. A detection of gravity waves would represent a remarkable confirmation of the inflationary paradigm and set the energy scale at which inflation occurred when the universe was a fraction of a second old. Even a strong upper limit to the gravity wave amplitude would be significant, ruling out many common models of inflation, and pointing to inflation occurring at much lower energy, if at all. Measuring gravity waves via the CMB polarization will be challenging. We will undertake a comprehensive study to identify the critical scientific requirements for the mission and their derived instrumental performance requirements. At the core of the study will be an assessment of what is scientifically and experimentally optimal within the scope and purpose of the Einstein Inflation Probe.

Hinshaw, Gary↗

Extragalactic CO emission lines in the CMB experiments: A forgotten signal and a foreground

High resolution cosmic microwave background (CMB) experiments have allowed us to precisely measure the CMB temperature power spectrum down to very small scales (multipole ℓ ~ 3000). Such measurements at multiple frequencies enable separating the power spectrum of the primary CMB anisotropies with the power spectrum of other signals like CMB lensing, thermal and kinematic Sunyaev-Zel’dovich effects (tSZ and kSZ), and cosmic infrared background (CIB). Here, in this paper, we explore another signal of interest at these frequencies that should be present in the CMB maps: extragalactic CO molecular rotational line emissions, which are the most widely used tracers of molecular gas in the line intensity mapping experiments. Using the SIDES simulations adopted for top hat bandpasses at 150 and 220 GHz, we show that the cross-correlation of the CIB with CO lines has a contribution similar to the CIB-tSZ correlation and the kSZ power, thereby contributing a non-negligible amount to the total power at these scales. This signal, therefore, may significantly impact the recently reported ≥3σ detection of the kSZ power spectrum from the South Pole Telescope collaboration, as the contribution of the CO lines is not considered in such analyses. Our results also provide a new way of measuring the CO power spectrum in cross-correlation with the CIB. Finally, these results show that the CO emissions present in the CMB maps will have to be accounted for in all the CMB auto-power spectrum and cross-correlation studies involving a large-scale structure tracer.

79 ASTRONOMY AND ASTROPHYSICS↗

SU-F-I-10: Spatially Local Statistics for Adaptive Image Filtering

Purpose: To facilitate adaptive image filtering operations, addressing spatial variations in both noise and signal. Such issues are prevalent in cone-beam projections, where physical effects such as X-ray scattering result in spatially variant noise, violating common assumptions of homogeneous noise and challenging conventional filtering approaches to signal extraction and noise suppression. Methods: We present a computational mechanism for probing into and quantifying the spatial variance of noise throughout an image. The mechanism builds a pyramid of local statistics at multiple spatial scales; local statistical information at each scale includes (weighted) mean, median, standard deviation, median absolute deviation, as well as histogram or dynamic range after local mean/median shifting. Based on inter-scale differences of local statistics, the spatial scope of distinguishable noise variation is detected in a semi- or un-supervised manner. Additionally, we propose and demonstrate the incorporation of such information in globally parametrized (i.e., non-adaptive) filters, effectively transforming the latter into spatially adaptive filters. The multi-scale mechanism is materialized by efficient algorithms and implemented in parallel CPU/GPU architectures. Results: We demonstrate the impact of local statistics for adaptive image processing and analysis using cone-beam projections of a Catphan phantom, fitted within an annulus to increase X-ray scattering. The effective spatial scope of local statistics calculations is shown to vary throughout the image domain, necessitating multi-scale noise and signal structure analysis. Filtering results with and without spatial filter adaptation are compared visually, illustrating improvements in imaging signal extraction and noise suppression, and in preserving information in low-contrast regions. Conclusion: Local image statistics can be incorporated in filtering operations to equip them with spatial adaptivity to spatial signal/noise variations. An efficient multi-scale computational mechanism is developed to curtail processing latency. Spatially adaptive filtering may impact subsequent processing tasks such as reconstruction and numerical gradient computations for deformable registration. NIH Grant No. R01-184173.

60 APPLIED LIFE SCIENCES↗

Statistics of anisotropies in inflation with spectator vector fields

We study the statistics of the primordial power spectrum in models where massless gauge vectors are coupled to the inflaton, paying special attention to observational implications of having fundamental or effective horizons embedded in a bath of infrared fluctuations. As quantum infrared modes cross the horizon, they classicalize and build a background vector field. We find that the vector experiences a statistical precession phenomenon. Implications for primordial correlators and the interpretation thereof are considered. Firstly, we show how in general two, not only one, additional observables, a quadrupole amplitude and an intrinsic shape parameter, are necessary to fully describe the correction to the curvature power spectrum, and develop a unique parametrization for them. Secondly, we show that the observed anisotropic amplitude and the associated preferred direction depend on the volume of the patch being probed. We calculate non-zero priors for the expected deviations between detections based on microwave background data (which probes the entire Hubble patch) and large scale structure (which only probes a fraction of it)

79 ASTRONOMY AND ASTROPHYSICS↗

Prospects for dark matter detection with inelastic transitions of xenon

Dark matter can scatter and excite a nucleus to a low-lying excitation in a direct detection experiment. This signature is distinct from the canonical elastic scattering signal because the inelastic signal also contains the energy deposited from the subsequent prompt de-excitation of the nucleus. A measurement of the elastic and inelastic signal will allow a single experiment to distinguish between a spin-independent and spin-dependent interaction. For the first time, we characterise the inelastic signal for two-phase xenon detectors in which dark matter inelastically scatters off the {sup 129}Xe or {sup 131}Xe isotope. We do this by implementing a realistic simulation of a typical tonne-scale two-phase xenon detector and by carefully estimating the relevant background signals. With our detector simulation, we explore whether the inelastic signal from the axial-vector interaction is detectable with upcoming tonne-scale detectors. We find that two-phase detectors allow for some discrimination between signal and background so that it is possible to detect dark matter that inelastically scatters off either the {sup 129}Xe or {sup 131}Xe isotope for dark matter particles that are heavier than approximately 100 GeV. If, after two years of data, the XENON1T search for elastic scattering nuclei finds no evidence for dark matter, the possibility of ever detecting an inelastic signal from the axial-vector interaction will be almost entirely excluded.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Diffuse pionic gamma-ray emission from large-scale structures in the Fermi era

For more than a decade now, the complete origin of the diffuse gamma-ray emission background (EGRB) has been unknown. Major components like unresolved star-forming galaxies (making ≲ 50% of the EGRB) and blazars (≲ 23%), have failed to explain the entire background observed by Fermi. Another, though subdominant, contribution is expected to come from the process of large-scale structure formation. The growth of structures is accompanied by accretion and merger shocks, which would, with at least some magnetic field present, give rise to a population of structure-formation cosmic rays (SFCRs). Though expected, this cosmic-ray population is still hypothetical and only very weak limits have been placed to their contribution to the EGRB. The most promising insight into SFCRs was expected to come from Fermi-LAT observations of clusters of galaxies, however, only upper limits and no detection have been placed. Here, we build a model of gamma-ray emission from large-scale accretion shocks implementing a source evolution calibrated with the Fermi-LAT cluster observation limits. Though our limits to the SFCR gamma-ray emission are weak (above the observed EGRB) in some cases, in others, some of our models can provide a good fit to the observed EGRB. More importantly, we show that these large-scale shocks could still give an important contribution to the EGRB, especially at high energies. Future detections of cluster gamma-ray emission would help place tighter constraints on our models and give us a better insight into large-scale shocks forming around them.

79 ASTRONOMY AND ASTROPHYSICS↗

Cosmological cross-correlations and nearest neighbour distributions

ABSTRACT Cross-correlations between data sets are used in many different contexts in cosmological analyses. Recently, k-nearest neighbour cumulative distribution functions (kNN-CDF) were shown to be sensitive probes of cosmological (auto) clustering. In this paper, we extend the framework of NN measurements to describe joint distributions of, and correlations between, two data sets. We describe the measurement of joint kNN-CDFs, and show that these measurements are sensitive to all possible connected N-point functions that can be defined in terms of the two data sets. We describe how the cross-correlations can be isolated by combining measurements of the joint kNN-CDFs and those measured from individual data sets. We demonstrate the application of these measurements in the context of Gaussian density fields, as well as for fully non-linear cosmological data sets. Using a Fisher analysis, we show that measurements of the halo-matter cross-correlations, as measured through NN measurements are more sensitive to the underlying cosmological parameters, compared to traditional two-point cross-correlation measurements over the same range of scales. Finally, we demonstrate how the NN cross-correlations can robustly detect cross-correlations between sparse samples – the same regime where the two-point cross-correlation measurements are dominated by noise.

79 ASTRONOMY AND ASTROPHYSICS↗

Bio-inspired gas sensing: boosting performance with sensor optimization guided by “machine learning”

The performance of existing gas sensors often degrades in field conditions because of the loss of measurement accuracy in the presence of interferences. Thus, new sensing approaches are required with improved sensor selectivity. We are developing a new generation of gas sensors, known as multivariable sensors, that have several independent responses for multi-gas detection with a single sensor. In this study, we analyze the capabilities of natural and fabricated photonic three-dimensional (3-D) nanostructures as sensors for the detection of different gaseous species, such as vapors and non-condensable gases. We employed bare Morpho butterfly wing scales to control their gas selectivity with different illumination angles. Next, we chemically functionalized Morpho butterfly wing scales with a fluorinated silane to boost the response of these nanostructures to the vapors of interest and to suppress the response to ambient humidity. Further, we followed our previously developed design rules for sensing nanostructures and fabricated bioinspired inorganic 3-D nanostructures to achieve functionality beyond natural Morpho scales. These fabricated nanostructures have embedded catalytically active gold nanoparticles to operate at high temperatures of ≈300 °C for the detection of gases for solid oxide fuel cell (SOFC) applications. Our performance advances in the detection of multiple gaseous species with specific nanostructure designs were achieved by coupling the spectral responses of these nanostructures with machine learning (a.k.a. multivariate analysis, chemometrics) tools. Our newly acquired knowledge from studies of these natural and fabricated inorganic nanostructures coupled with machine learning data analytics allowed us to advance our design rules for sensing nanostructures toward the required gas selectivity for numerous gas monitoring scenarios at room and high temperatures for industrial, environmental, and other applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Computational Imaging for Intelligence in Highly Scattering Aerosols (Final Report)

Natural and man-made degraded visual environments pose major threats to national security. The random scattering and absorption of light by tiny particles suspended in the air reduces situational awareness and causes unacceptable down-time for critical systems and operations. To improve the situation, we have developed several approaches to interpret the information contained within scattered light to enhance sensing and imaging in scattering media. These approaches were tested at the Sandia National Laboratory Fog Chamber facility and with tabletop fog chambers. Computationally efficient light transport models were developed and leveraged for computational sensing. The models are based on a weak angular dependence approximation to the Boltzmann or radiative transfer equation that appears to be applicable in both the moderate and highly scattering regimes. After the new model was experimentally validated, statistical approaches for detection, localization, and imaging of objects hidden in fog were developed and demonstrated. A binary hypothesis test and the Neyman-Pearson lemma provided the highest theoretically possible probability of detection for a specified false alarm rate and signal-to-noise ratio. Maximum likelihood estimation allowed estimation of the fog optical properties as well as the position, size, and reflection coefficient of an object in fog. A computational dehazing approach was implemented to reduce the effects of scatter on images, making object features more readily discernible. We have developed, characterized, and deployed a new Tabletop Fog Chamber capable of repeatably generating multiple unique fog-analogues for optical testing in degraded visual environments. We characterized this chamber using both optical and microphysical techniques. In doing so we have explored the ability of droplet nucleation theory to describe the aerosols generated within the chamber, as well as Mie scattering theory to describe the attenuation of light by said aerosols, and correlated the aerosol microphysics to optical properties such as transmission and meteorological optical range (MOR). This chamber has proved highly valuable and has supported multiple efforts inclusive to and exclusive of this LDRD project to test optics in degraded visual environments. Circularly polarized light has been found to maintain its polarization state better than linearly polarized light when propagating through fog. This was demonstrated experimentally in both the visible and short-wave infrared (SWIR) by imaging targets made of different commercially available retroreflective films. It was found that active circularly polarized imaging can increase contrast and range compared to linearly polarized imaging. We have completed an initial investigation of the capability for machine learning methods to reduce the effects of light scattering when imaging through fog. Previously acquired experimental long-wave images were used to train an autoencoder denoising architecture. Overfitting was found to be a problem because of lack of variability in the object type in this data set. The lessons learned were used to collect a well labeled dataset with much more variability using the Tabletop Fog Chamber that will be available for future studies. We have developed several new sensing methods using speckle intensity correlations. First, the ability to image moving objects in fog was shown, establishing that our unique speckle imaging method can be implemented in dynamic scattering media. Second, the speckle decorrelation over time was found to be sensitive to fog composition, implying extensions to fog characterization. Third, the ability to distinguish macroscopically identical objects on a far-subwavelength scale was demonstrated, suggesting numerous applications ranging from nanoscale defect detection to security. Fourth, we have shown the capability to simultaneously image and localize hidden objects, allowing the speckle imaging method to be effective without prior object positional information. Finally, an interferometric effect was presented that illustrates a new approach for analyzing speckle intensity correlations that may lead to more effective ways to localize and image moving objects. All of these results represent significant developments that challenge the limits of the application of speckle imaging and open important application spaces. A theory was developed and simulations were performed to assess the potential transverse resolution benefit of relative motion in structured illumination for radar systems. Results for a simplified radar system model indicate that significant resolution benefits are possible using data from scanning a structured beam over the target, with the use of appropriate signal processing.

58 GEOSCIENCES↗

Leveraging Qubit Loss Detection in Fault-Tolerant Quantum Algorithms

Qubit loss errors constitute a dominant source of noise in many quantum hardware systems, particularly in neutral-atom quantum computers. We develop a theoretical framework to effectively detect and correct loss errors in logical algorithms and leverage such loss information in decoding. Considering general quantum error correction codes and logical circuits, we introduce a delayed-erasure decoder for experimentally motivated error models which leverages information from delayed loss detection to accurately correct loss errors, even when the precise moment of the error is unknown. Using this decoder, we identify strategies for detecting and correcting loss errors based on the logical circuit structure. For deep circuits prior to logical measurement, we explore methods to integrate loss detection into syndrome extraction with minimal overhead, identifying optimal strategies depending on the qubit loss fraction in the noise and hardware capabilities. In contrast, we find that many key algorithmic subroutines involve frequent gate teleportation, shortening the circuit depth before logical measurement and naturally replacing qubits with no additional experimental overhead. We simulate this setting using a toy model algorithm for small-angle synthesis and find a significant performance improvement as the loss fraction increases. These results provide a path forward for advancing large-scale fault-tolerant quantum computation in systems with loss error detection.

atoms↗