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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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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Characterizing climate pathways using feature importance on echo state networks

The 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data-driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black-box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. Furthermore, we are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

black-box models↗
DOE OSTI2018

Utah FORGE: Deep Well 58-32 (MU-ESW1) Core Data

These datasets, images, and graphics were derived from core drilling and core that was extracted from Utah Forge deep well 58-32 (originally called MU-ESW1), near Roosevelt Hot Springs.

15 GEOTHERMAL ENERGY↗
NASA NTRS2011

Stardust-NExT, Deep Impact, and the Accelerating Spin of 9P/Tempel One

The evolution of the spin rate of comet 9P/Tempel 1 through two perihelion passages (CYs 2000 and 2005) is determined from 1922 Earth-based observations taken over a period of 13y as part of a World-Wide observing campaign and 2888 observations taken over a period of 50d from the Deep Impact spacecraft. We determine the following sidereal spin rates (periods): 209.023 +/- 0.025 degrees /day (41.335 +/- 0.005 h) prior to the 2000 perihelion passage, 210.448 +/- 0.016 degrees/day (41.055 +/- 0.003 h) for the interval between the 2000 and 2005 perihelion passages, 211.856 +/- 0.030 degrees/day (40.783 +/- 0.006 h) from Deep Impact photometry just prior to the 2005 perihelion passage, and 211.625 +/- 0.012 degrees /day (40.827 +/- 0.002 h) in the interval 2006-2010 following the 2005 perihelion passage. The period decreased by 16.8 +/- 0.3 min during the 2000 passage and by 13.7 +/- 0.2 min during the 2005 passage suggesting a secular decrease in the net torque. The change in spin rate is asymmetric with respect to perihelion with the maximum net torque being applied on approach to perihelion. The Deep Impact data alone show that the spin rate was increasing at a rate of 0.024 +/- 0.003 degree/d/d at JD2453530.60510 (i.e., 25.134 d before impact) and provides independent confirmation of the change seen in the Earth-based observations. The rotational phase of the nucleus at times before and after each perihelion and at the Deep Impact encounter is estimated based on the Thomas et al. pole and longitude system. The possibility of a 180 degree error in the rotational phase is assessed and found to be significant. Analytical and physical modeling of the behavior of the spin rate through of each perihelion is presented and used as a basis to predict the rotational state of the nucleus at the time of the nominal (i.e., prior to February 2010) Stardust-NExT encounter on 2011 February 14 20:42. We find that a net torque in the range of 0.3 - 2.5 x 10(exp 7) kg.square m2/square s acts on the nucleus during perihelion passage. The spin rate initially slows down on approach to perihelion and then passes through a minimum. It then accelerates rapidly as it passes through perihelion eventually reaching a maximum post-perihelion. It then decreases to a stable value as the nucleus moves away from the sun. We find that the pole direction is unlikely to precess by more than approximately 1 degree/perihelion passage. The trend of the period with time and the fact that the modeled peak torque that occurs before perihelion is in agreement with published accounts of trends in water production rate and suggests that widespread H2O out-gassing from the surface is largely responsible for the observed spin-up.

Belton, Michael J. S.↗
NASA NTRS2011

Stardust-NExT, Deep Impact, and the Accelerating Spin of 9P/Tempel 1

The evolution of the spin rate of Comet 9P/Tempel 1 through two perihelion passages (in 2000 and 2005) is determined from 1922 Earth-based observations taken over a period of 13 year as part of a World-Wide observing campaign and from 2888 observations taken over a period of 50 days from the Deep Impact spacecraft. We determine the following sidereal spin rates (periods): 209.023 +/- 0.025deg/dy (41.335 0.005 h) prior to the 2000 perihelion passage, 210.448 +/- 0.016deg/dy (41.055 +/- 0.003 h) for the interval between the 2000 and 2005 perihelion passages, 211.856 +/- 0.030deg/dy (40.783 +/- 0.006 h) from Deep Impact photometry just prior to the 2005 perihelion passage, and 211.625 +/- 0.012deg/dy (40.827 +/- 0.002 h) in the interval 2006-2010 following the 2005 perihelion passage. The period decreased by 16.8 +/- 0.3 min during the 2000 passage and by 13.7 +/- 0.2 min during the 2005 passage suggesting a secular decrease in the net torque. The change in spin rate is asymmetric with respect to perihelion with the maximum net torque being applied on approach to perihelion. The Deep Impact data alone show that the spin rate was increasing at a rate of 0.024 +/- 0.003deg/dy/dy at JD2453530.60510 (i.e., 25.134 dy before impact), which provides independent confirmation of the change seen in the Earth-based observations. The rotational phase of the nucleus at times before and after each perihelion and at the Deep Impact encounter is estimated based on the Thomas et al. (Thomas et al. [2007]. Icarus 187, 4-15) pole and longitude system. The possibility of a 180deg error in the rotational phase is assessed and found to be significant. Analytical and physical modeling of the behavior of the spin rate through of each perihelion is presented and used as a basis to predict the rotational state of the nucleus at the time of the nominal (i.e., prior to February 2010) Stardust-NExT encounter on 2011 February 14 at 20:42. We find that a net torque in the range of 0.3-2.5 x 10(exp 7) kg/sq m/sq s acts on the nucleus during perihelion passage. The spin rate initially slows down on approach to perihelion and then passes through a minimum. It then accelerates rapidly as it passes through perihelion eventually reaching a maximum post-perihelion. It then decreases to a stable value as the nucleus moves away from the Sun. We find that the pole direction is unlikely to precess by more than approx. 1deg per perihelion passage. The trend of the period with time and the fact that the modeled peak torque occurs before perihelion are in agreement with published accounts of trends in water production rate and suggests that widespread H2O out-gassing from the surface is largely responsible for the observed spin-up.

Belton, Michael J. S.↗
DOE OSTI2022

Real-time deep-learning inversion of seismic full waveform data for CO 2 saturation and uncertainty in geological carbon storage monitoring

Deep-learning inversion has recently drawn attention in geological carbon storage research due to its potential of imaging and monitoring carbon storage in real time, significantly improving efficiency and safety of carbon storage operations. We present a deep-learning full waveform inversion method that after the neural network has been trained can image CO 2 saturation and its uncertainty in real time. Our deep-learning inversion method is based on the U-Net architecture with the neural network trained on pairs of synthetic seismic data and CO 2 saturation models. Accordingly, our training establishes a mapping relationship between seismic data and CO 2 saturation models and once fully trained directly estimates CO 2 saturation as a function of subsurface location. We further quantify uncertainties of CO 2 saturation estimates using the Monte Carlo dropout method and a bootstrap aggregating method. For this proof-of-concept study, the CO 2 training models and data are derived from the Kimberlina 1.2 model, a hypothetical 3D geological carbon storage model that is constructed based on various geological and hydrological data from the Southern San Joaquin Basin, California. We perform deep-learning inversion experiments using noise-free and noisy training and test data sets and compare the results. Our modelling experiments show that (1) the deep-learning inversion can estimate 2D distributions of CO 2 fairly well even in the presence of Gaussian random noise and (2) both CO 2 saturation imaging and uncertainty quantification can be done in real time. Our results suggest that the deep-learning inversion method can serve as a robust real-time monitoring tool for geological carbon storage and/or other time-varying reservoir/aquifer properties that result from injection, extraction, and/or other subsurface transport phenomena.

58 GEOSCIENCES↗
DOE OSTI2022

Introduction to Special Section: Machine Learning for Image-based Geologic Interpretation

Image-based geological interpretation has been a labor-intensive and time-consuming process because it requires well-trained geoscientists to identify geological structures, features, and textures from various types of images. These images include scanning electron microscopic images, optical microscopic images, optical photos, resistivity images, seismic volumes, remote-sensing images, etc. With fast-evolving machine learning (ML) technology and computing power in recent decades, computers can achieve nearhuman-level to super-human-level performance with scalable high efficiency in the computer vision field. These technological revolutions facilitated image-based geological interpretation in petroleum exploration and production. For example, a fault picking method applied to 3-D seismic volume data using deep learning can achieve superior performance in comparison to conventional auto-picking methods. In addition, under the new normal of low oil prices, the petroleum industry seeks cost-effective strategies such as automating traditionally labor-intensive processes. Nevertheless, the potential of applying ML to geological image interpretation is still facing a few key challenges including data scarcity, data distribution, poor data and/or label quality, data leakage, learning algorithms, model architecture, training methodologies, testing and evaluation metrics, hyper-parameters optimization, model drift, production deployment, and the like.

58 GEOSCIENCES↗
DOE OSTI2018

Detecting the permafrost carbon feedback: talik formation and increased cold-season respiration as precursors to sink-to-source transitions

Abstract. Thaw and release of permafrost carbon (C) due to climate change is likely to offset increased vegetation C uptake in northern high-latitude (NHL) terrestrial ecosystems. Models project that this permafrost C feedback may act as a slow leak, in which case detection and attribution of the feedback may be difficult. The formation of talik, a subsurface layer of perennially thawed soil, can accelerate permafrost degradation and soil respiration, ultimately shifting the C balance of permafrost-affected ecosystems from long-term C sinks to long-term C sources. It is imperative to understand and characterize mechanistic links between talik, permafrost thaw, and respiration of deep soil C to detect and quantify the permafrost C feedback. Here, we use the Community Land Model (CLM) version 4.5, a permafrost and biogeochemistry model, in comparison to long-term deep borehole data along North American and Siberian transects, to investigate thaw-driven C sources in NHL (> 55∘ N) from 2000 to 2300. Widespread talik at depth is projected across most of the NHL permafrost region (14 million km2) by 2300, 6.2 million km2 of which is projected to become a long-term C source, emitting 10 Pg C by 2100, 50 Pg C by 2200, and 120 Pg C by 2300, with few signs of slowing. Roughly half of the projected C source region is in predominantly warm sub-Arctic permafrost following talik onset. This region emits only 20 Pg C by 2300, but the CLM4.5 estimate may be biased low by not accounting for deep C in yedoma. Accelerated decomposition of deep soil C following talik onset shifts the ecosystem C balance away from surface dominant processes (photosynthesis and litter respiration), but sink-to-source transition dates are delayed by 20–200 years by high ecosystem productivity, such that talik peaks early (∼ 2050s, although borehole data suggest sooner) and C source transition peaks late (∼ 2150–2200). The remaining C source region in cold northern Arctic permafrost, which shifts to a net source early (late 21st century), emits 5 times more C (95 Pg C) by 2300, and prior to talik formation due to the high decomposition rates of shallow, young C in organic-rich soils coupled with low productivity. Our results provide important clues signaling imminent talik onset and C source transition, including (1) late cold-season (January–February) soil warming at depth (∼ 2 m), (2) increasing cold-season emissions (November–April), and (3) enhanced respiration of deep, old C in warm permafrost and young, shallow C in organic-rich cold permafrost soils. Our results suggest a mosaic of processes that govern carbon source-to-sink transitions at high latitudes and emphasize the urgency of monitoring soil thermal profiles, organic C age and content, cold-season CO2 emissions, and atmospheric 14CO2 as key indicators of the permafrost C feedback.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Deep nonparametric estimation of intrinsic data structures by chart autoencoders: Generalization error and robustness

Autoencoders have demonstrated remarkable success in learning low-dimensional latent features of high-dimensional data across various applications. Assuming that data are sampled near a low-dimensional manifold, we employ chart autoencoders, which encode data into low-dimensional latent features on a collection of charts, preserving the topology and geometry of the data manifold. Our paper establishes statistical guarantees on the generalization error of chart autoencoders, and we demonstrate their denoising capabilities by considering n noisy training samples, along with their noise-free counterparts, on a d-dimensional manifold. By training autoencoders, we show that chart autoencoders can effectively denoise the input data with normal noise. We prove that, under proper network architectures, chart autoencoders achieve a squared generalization error in the order of n–$\frac{2}{d+2}$log 4 n, which depends on the intrinsic dimension of the manifold and only weakly depends on the ambient dimension and noise level. We further extend our theory on data with noise containing both normal and tangential components, where chart autoencoders still exhibit a denoising effect for the normal component. As a special case, our theory also applies to classical autoencoders, as long as the data manifold has a global parametrization. Furthermore, our results provide a solid theoretical foundation for the effectiveness of autoencoders, which is further validated through several numerical experiments.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

BICEP/Keck. XVI. Characterizing Dust Polarization through Correlations with Neutral Hydrogen

We characterize Galactic dust filaments by correlating BICEP/Keck and Planck data with polarization templates based on neutral hydrogen (H i) observations. Dust polarization is important for both our understanding of astrophysical processes in the interstellar medium (ISM) and the search for primordial gravitational waves in the cosmic microwave background (CMB). In the diffuse ISM, H i is strongly correlated with the dust and partly organized into filaments that are aligned with the local magnetic field. We analyze the deep BICEP/Keck data at 95, 150, and 220 GHz, over the low-column-density region of sky where BICEP/Keck has set the best limits on primordial gravitational waves. We separate the H i emission into distinct velocity components and detect dust polarization correlated with the local Galactic H i but not with the H i associated with Magellanic Stream i. We present a robust, multifrequency detection of polarized dust emission correlated with the filamentary H i morphology template down to 95 GHz. For assessing its utility for foreground cleaning, we report that the Hi morphology template correlates in B modes at a ~10%–65% level over the multipole range 20 < ℓ < 200 with the BICEP/Keck maps, which contain contributions from dust, CMB, and noise components. We measure the spectral index of the filamentary dust component spectral energy distribution to be β = 1.54 ± 0.13. We find no evidence for decorrelation in this region between the filaments and the rest of the dust field or from the inclusion of dust associated with the intermediate velocity H i. Finally, we explore the morphological parameter space in the H i-based filamentary model.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Low-Level Mesoscale and Cloud-Scale Interactions Promoting Deep Convection Initiation

Data from scanning radars, radiosondes, and vertical profilers deployed during three field campaigns are analyzed to study interactions between cloud-scale updrafts associated with initiating deep moist convection and the surrounding environment. Three cases are analyzed in which the radar networks permitted dual-Doppler wind retrievals in clear air preceding and during the onset of surface precipitation. These observations capture the evolution of (i) the mesoscale and boundary layer flow, and (ii) low-level updrafts associated with deep moist convection initiation (CI) events yielding sustained or short-lived precipitating storms. The elimination of convective inhibition did not distinguish between sustained and unsustained CI events, though the vertical distribution of convective available potential energy may have played a role. The clearest signal differentiating the initiation of sustained versus unsustained precipitating deep convection was the depth of the low-level horizontal wind convergence associated with the mesoscale flow feature triggering CI, a sharp surface wind shift boundary, or orographic upslope flow. The depth of the boundary layer relative to the height of the LFC failed to be a consistent indicator of CI potential. Widths of the earliest detectable low-level updrafts associated with sustained precipitating deep convection were ~3–5 km, larger than updrafts associated with surrounding boundary layer turbulence (~1–3 km wide). It is hypothesized that updrafts of this larger size are important for initiating cells to survive the destructive effects of buoyancy dilution via entrainment.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗
DOE OSTI2017

Deep Learning on Operational Facility Data Related to Large-Scale Distributed Area Scientific Workflows

Abstract—Distributed computing platforms provide a robust mechanism to perform large-scale computations by splitting the task and data among multiple locations, possibly located thousands of miles apart geographically. This distribution of resources can lead to benefits such as redundancy of data, engagement with scientific teams that involve a diverse group of domain experts distributed spatially around the globe. It allows scientists to share their expertise and data with everyone and achieve insights by deploying collective intelligence of entire team. However, this distributed computing comes with its associated problems such as rampant duplication of file transfers increasing congestion, long job completion times, unexpected site crashing, suboptimal data transfer rates, unpredictable reliability in a time range, overshooting congestion, suboptimal usage of storage elements. In addition, each sub-system becomes a potential failure node that can trigger system wide disruptions.

Machine Learning, Provenance, Time Series, Semanti↗
DOE OSTI2022

A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

Here, we present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.

42 ENGINEERING↗
NASA NTRS1994

The HST Quasar Absorption-Line Key Project. 8: CCD imaging of Hubble Space Telescope quasar fields

We present an analysis of CCD images of more than 100 quasars; many of the objects have been or will be observed in the Hubble Space Telescope (HST) Key Project Quasar Absorption-Line Survey. The data set consists of B, g, and i photometry of 117 quasars and deeper g (limiting magnitude of approximately equal 22) images of 101 quasar fields. Positions accurate to approximately equal 1 sec, measured with the Space Telescope Science Institute's Astrometric Support Program, are listed for all of the quasars in this study. Positions, magnitudes, and classifications of stars and galaxies located within approximately equal 100 sec of the quasars are given for all of the deep g data. The positions of the stars and galaxies relative to the quasar are accurate to approximately equal 0.5 sec. The results presented here can be used to prepare spectroscopic programs designed to obtain redshifts of galaxies in the fields of these quasars.

Kirhakos, Sofia↗
DOE OSTI2020

Where Is the Provenance? Ethical Replicability and Reproducibility in GIScience and Its Critical Applications

As replicability and reproducibility (R&R) crises develop within emerging convergent inquiry, ethical use of provenance information is central to the establishment and preservation of trust in critical applications of GIScience and geospatial technologies. Today large volumes of geospatial data are generated at high velocity from satellite sensors and unmanned aircraft systems, citizen sensors, geolocation-based data services, global navigation satellite systems, and so on. The extensive use of these data for applications such as disaster and humanitarian response raises the issue of R&R from competing perspectives of location privacy and geospatial data quality. Although geospatial data can be integrated and linked with contextual information to identify individuals’ movements, steps taken to ensure privacy can complicate the multiuser development of high-quality geospatial workflows. Provenance information as digital records of historical (retrospective) and potential future (prospective) geospatial processes is often overlooked, misunderstood, or inadequately addressed. We explore the relationship between provenance information, location privacy, and geospatial data quality in the context of R&R with a focus on disaster analytics. Here, we argue that in the era of big data and deep learning, GIScientists and associated institutions bear greater responsibility both for geospatial workflow quality and for location privacy. Given vastly heterogenous computational landscapes, we provide practical recommendations for ethically driven provenance and R&R research and development within the GIScience community and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2019

Evidence for the Cross-correlation between Cosmic Microwave Background Polarization Lensing from Polarbear and Cosmic Shear from Subaru Hyper Suprime-Cam

In this work, we introduce the first measurement of cross-correlation between the lensing potential, reconstructed from cosmic microwave background (CMB) polarization data, and the cosmic shear field from galaxy shapes. This measurement is made using data from the P OLARBEAR CMB experiment and the Subaru Hyper Suprime-Cam (HSC) survey. By analyzing an 11 deg 2 overlapping region, we reject the null hypothesis at 3.5σ and constrain the amplitude of the cross-spectrum to $\widehat{A}$ lens = 1.70 ± 0.48, where $\widehat{A}$ lens is the amplitude normalized with respect to the Planck 2018 prediction, based on the flat Λ cold dark matter cosmology. The first measurement of this cross-spectrum without relying on CMB temperature measurements is possible owing to the deep P OLARBEAR map with a noise level of ~6 μK arcmin, as well as the deep HSC data with a high galaxy number density of n g = 23 arcmin -2 . We present a detailed study of the systematics budget to show that residual systematics in our findings are negligibly small, which demonstrates the future potential of this cross-correlation technique.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2013

SEDS: The Spitzer Extended Deep Survey. Survey Design, Photometry, and Deep IRAC Source Counts

The Spitzer Extended Deep Survey (SEDS) is a very deep infrared survey within five well-known extragalactic science fields: the UKIDSS Ultra-Deep Survey, the Extended Chandra Deep Field South, COSMOS, the Hubble Deep Field North, and the Extended Groth Strip. SEDS covers a total area of 1.46 deg(exp 2) to a depth of 26 AB mag (3sigma) in both of the warm Infrared Array Camera (IRAC) bands at 3.6 and 4.5 micron. Because of its uniform depth of coverage in so many widely-separated fields, SEDS is subject to roughly 25% smaller errors due to cosmic variance than a single-field survey of the same size. SEDS was designed to detect and characterize galaxies from intermediate to high redshifts (z = 2-7) with a built-in means of assessing the impact of cosmic variance on the individual fields. Because the full SEDS depth was accumulated in at least three separate visits to each field, typically with six-month intervals between visits, SEDS also furnishes an opportunity to assess the infrared variability of faint objects. This paper describes the SEDS survey design, processing, and publicly-available data products. Deep IRAC counts for the more than 300,000 galaxies detected by SEDS are consistent with models based on known galaxy populations. Discrete IRAC sources contribute 5.6 +/- 1.0 and 4.4 +/- 0.8 nW / square m/sr at 3.6 and 4.5 micron to the diffuse cosmic infrared background (CIB). IRAC sources cannot contribute more than half of the total CIB flux estimated from DIRBE data. Barring an unexpected error in the DIRBE flux estimates, half the CIB flux must therefore come from a diffuse component.

Spitzer↗