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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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NASA NTRS2016

Calibration and Validation of Landsat Tree Cover in the Taiga-Tundra Ecotone

Monitoring current forest characteristics in the taiga-tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover greater than 80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

Montesano, Paul Mannix↗
NASA NTRS2016

Impacts of the Angular Dependence of the Solar Diffuser BRDF Degradation Factor on the SNPP VIIRS Reflective Solar Band On-Orbit Radiometric Calibration

Using an onboard sunlit solar diffuser (SD) as the primary radiance source, the visible infrared imaging radiometer suite (VIIRS) on the Suomi National Polar-orbiting Partnership satellite regularly performs radiometric calibration of its reflective solar bands (RSBs). The SD bidirectional reflectance distribution function (BRDF) value decreases over time. A numerical degradation factor is used to quantify the degradation and is determined by an onboard SD stability monitor (SDSM), which observes the sun and the sunlit SD at almost the same time. We had shown previously that the BRDF degradation factor was angle-dependent. Consequently, due to that the SDSM and the RSB view the SD at very different angles relative to both the solar and the SD surface normal vectors, directly applying the BRDF degradation factor determined by the SDSM to the VIIRS RSB calibration can result in large systematic errors. We develop a phenomenological model to calculate the BRDF degradation factor for the RSB SD view from the degradation factor for the SDSM SD view. Using the yearly undulations observed in the VIIRS detector gains for the M1-M4 bands calculated with the SD BRDF degradation factor for the SDSM SD view and the difference between the VIIRS detector gains calculated from the SD and the lunar observations, we obtain the model parameter values and thus establish the relation between the BRDF degradation factors for the RSB and the SDSM SD view directions.

Lei, Ning↗
NASA NTRS2016

The Kepler Science Data Processing Pipeline Source Code Road Map

We give an overview of the operational concepts and architecture of the Kepler Science Processing Pipeline. Designed, developed, operated, and maintained by the Kepler Science Operations Center (SOC) at NASA Ames Research Center, the Science Processing Pipeline is a central element of the Kepler Ground Data System. The SOC consists of an office at Ames Research Center, software development and operations departments, and a data center which hosts the computers required to perform data analysis. The SOC's charter is to analyze stellar photometric data from the Kepler spacecraft and report results to the Kepler Science Office for further analysis. We describe how this is accomplished via the Kepler Science Processing Pipeline, including, the software algorithms. We present the high-performance, parallel computing software modules of the pipeline that perform transit photometry, pixel-level calibration, systematic error correction, attitude determination, stellar target management, and instrument characterization.

Kepler pipeline software↗
NASA NTRS2017

Two-Season Atacama Cosmology Telescope Polarimeter Lensing Power Spectrum

We report a measurement of the power spectrum of cosmic microwave background (CMB) lensing from two seasons of Atacama Cosmology Telescope polarimeter (ACTPol) CMB data. The CMB lensing power spectrum is extracted from both temperature and polarization data using quadratic estimators. We obtain results that are consistent with the expectation from the best-fit Planck CDM model over a range of multipoles L 80-2100, with an amplitude of lensing A(sub lens) = 1.06 +/- 0.15 stat +/- 0.06 sys relative to Planck. Our measurement of the CMB lensing power spectrum gives sigma 8 omega m(sup 0.25) = 0.643 +/- 0.054; including baryon acoustic oscillation scale data, we constrain the amplitude of density fluctuations to be sigma 8 = 0.831 +/- 0.053. We also update constraints on the neutrino mass sum. We verify our lensing measurement with a number of null tests and systematic checks, finding no evidence of significant systematic errors. This measurement relies on a small fraction of the ACTPol data already taken; more precise lensing results can therefore be expected from the full ACTPol data set.

Shewin, Blake D.↗
NASA NTRS2016

The Primordial Inflation Explorer (PIXIE)

The Primordial Inflation Explorer is an Explorer-class mission to open new windows on the early universe through measurements of the polarization and absolute frequency spectrum of the cosmic microwave background. PIXIE will measure the gravitational-wave signature of primordial inflation through its distinctive imprint in linear polarization, and characterize the thermal history of the universe through precision measurements of distortions in the blackbody spectrum. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning over 7 octaves in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). Multi-moded non-imaging optics feed a polarizing Fourier Transform Spectrometer to produce a set of interference fringes, proportional to the difference spectrum between orthogonal linear polarizations from the two input beams. Multiple levels of symmetry and signal modulation combine to reduce systematic errors to negligible levels. PIXIE will map the full sky in Stokes I, Q, and U parameters with angular resolution 2.6 degrees and sensitivity 70 nK per 1degree square pixel. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r < 10(exp. -3) at 5 standard deviations. The PIXIE mission complements anticipated ground-based polarization measurements such as CMBS4, providing a cosmic-variance-limited determination of the large-scale E-mode signal to measure the optical depth, constrain models of reionization, and provide a firm detection of the neutrino mass (the last unknown parameter in the Standard Model of particle physics). In addition, PIXIE will measure the absolute frequency spectrum to characterize deviations from a blackbody with sensitivity 3 orders of magnitude beyond the seminal COBE/FIRAS limits. The sky cannot be black at this level; the expected results will constrain physical processes ranging from inflation to the nature of the first stars and the physical conditions within the interstellar medium of the Galaxy. We describe the PIXIE instrument and mission architecture required to measure the CMB to the limits imposed by astrophysical foregrounds.

Kogut, Alan↗
NASA NTRS2011

Design of the PIXIE Cryogenic System

The Primordial Inflation Explorer (PIXIE) is a proposed m1ss1on to study the polarization of the remnant cosmic microwave background with the goal of finding and understanding primordial gravity waves. The instrument has been designed to capture this information across the entire sky by rejecting foreground signals and suppressing systematic error by multiple differencing methods. The instrument operates at a temperature very close to the Cosmic Microwave Background of 2.7 K, while the detectors operate at 0.1 K. The PIXIE cryogenic system provides this in low Earth orbit by making use of 3 subsystems. Lightweight, simply deployed shields provide protection against the Earth and Sun while passively cooling wiring and instrument supports at 150 K. A mechanical cryocooler precools wires and supports at 68, 17, and 4.5 K while its compressors operate at room temperature. And finally two adiabatic demagnetization refrigerators cool the instrument from 4.5 to 2.7 K and cool the detectors to 0.1 K. Staged cooling in this manner allows a thermodynamically efficient use of relatively mature technologies that can be fully demonstrated before flight..

DiPirro, Michael↗
NASA NTRS2018

Heterogeneous Distribution of Chromium on Mercury

Measurements made with geochemical instruments on the MESSENGER spacecraft revealed that Mercury's crust is surprisingly rich in volatile elements, including S, Na, K, Cl, and C, and that it is enriched in Mg and depleted in Al, Ca, and Fe, relative to other terrestrial planets. Geochemical maps also indicated the presence of a number of distinct geochemical terranes. The MESSENGER X-ray Spectrometer (XRS) detected X-ray fluorescence, induced by incident solar X-rays, from the top approx. 10s of micrometers of Mercury's surface. Like Fe, Cr was only detectable by XRS during large solar flares. However, accurate Cr measurements are more susceptible to systematic errors than other elements measured by the XRS. Therefore, to date, Cr data have been published for only 11 XRS measurements, but we have recently derived a map of Cr/Si across Mercury's surface. This map is based on data acquired through the complete MESSENGER mission and reveals clear spatial heterogeneity in Cr.

Nittler, L. R.↗
NASA NTRS2017

Retrieval of Water Vapor Column Abundance and Aerosol Properties from ChemCam Passive Sky Spectroscopy

We derive water vapor column abundances and aerosol properties from Mars Science Laboratory (MSL) ChemCam passive mode observations of scattered sky light. This paper covers the methodology and initial results for water vapor and also provides preliminary results for aerosols. The data set presented here includes the results of 113 observations spanning from Mars Year 31 L(s) = 291 deg (March 30, 2013) to Mars Year 33 L(s) = 127 deg (March 24, 2016). Each ChemCam passive sky observation acquires spectra at two different elevation angles. We fit these spectra with a discrete-ordinates multiple scattering radiative transfer model, using the correlated-k approximation for gas absorption bands. The retrieval proceeds by first fitting the continuum of the ratio of the two elevation angles to solve for aerosol properties, and then fitting the continuum-removed ratio to solve for gas abundances. The final step of the retrieval makes use of the observed CO 2 absorptions and the known CO 2 abundance to correct the retrieved water vapor abundance for the effects of the vertical distribution of scattering aerosols and to derive an aerosol scale height parameter. Our water vapor results give water vapor column abundance with a precision of +/-0.6 precipitable microns and systematic errors no larger than +/-0.3 precipitable microns, assuming uniform vertical mixing. The ChemCam-retrieved water abundances show, with only a few exceptions, the same seasonal behavior and the same timing of seasonal minima and maxima as the TES, CRISM, and REMS-H data sets that we compare them to. However ChemCam-retrieved water abundances are generally lower than zonal and regional scale from-orbit water vapor data, while at the same time being significantly larger than pre-dawn REMS-H abundances. Pending further analysis of REMS-H volume mixing ratio uncertainties, the differences between ChemCam and REMS-H pre-dawn mixing ratios appear to be much too large to be explained by large scale circulations and thus they tend to support the hypothesis of substantial diurnal interactions of water vapor with the surface. Our preliminary aerosol results, meanwhile, show the expected seasonal pattern in dust particle size but also indicate a surprising interannual increase in water-ice cloud opacities.

Mcconnochie, Timothy H.↗
NASA NTRS2017

Similarity Assessment of Land Surface Model Outputs in the North American Land Data Assimilation System

Multimodel ensembles are often used to produce ensemble mean estimates that tend to have increased simulation skill over any individual model output. If multimodel outputs are too similar, an individual LSM would add little additional information to the multimodel ensemble, whereas if the models are too dissimilar, it may be indicative of systematic errors in their formulations or configurations. The article presents a formal similarity assessment of the North American Land Data Assimilation System (NLDAS) multimodel ensemble outputs to assess their utility to the ensemble, using a confirmatory factor analysis. Outputs from four NLDAS Phase 2 models currently running in operations at NOAA/NCEP and four new/ upgraded models that are under consideration for the next phase of NLDAS are employed in this study. The results show that the runoff estimates from the LSMs were most dissimilar whereas the models showed greater similarity for root zone soil moisture, snow water equivalent, and terrestrial water storage. Generally, the NLDAS operational models showed weaker association with the common factor of the ensemble and the newer versions of the LSMs showed stronger association with the common factor, with the model similarity increasing at longer time scales. Trade-offs between the similarity metrics and accuracy measures indicated that the NLDAS operational models demonstrate a larger span in the similarity-accuracy space compared to the new LSMs. The results of the article indicate that simultaneous consideration of model similarity and accuracy at the relevant time scales is necessary in the development of multimodel ensemble.

land surface models↗
NASA NTRS2018

Multi-Parameter Nonlinear Gain Correction of X-Ray Transition Edge Sensors for the X-Ray Integral Field Unit

With its array of 3840 Transition Edge Sensors (TESs), the Athena X-ray Integral Field Unit (X-IFU) will provide spatially resolved high-resolution spectroscopy (2.5 eV up to 7 keV) from 0.2 to 12 keV, with an absolute energy scale accuracy of 0.4 eV. Slight changes in the TES operating environment can cause significant variations in its energy response function, which may result in systematic errors in the absolute energy scale. We plan to monitor such changes at pixel level via onboard X-ray calibration sources and correct the energy scale accordingly using a linear or quadratic interpolation of gain curves obtained during ground calibration. However, this may not be sufficient to meet the 0.4 eV accuracy required for the XIFU. In this contribution, we introduce a newtwo-parameter gain correction technique, based on both the pulse-height estimate of a fiducial line and the baseline value of the pixels. Using gain functions that simulate ground calibration data, we show that this technique can accurately correct deviations in detector gain due to changes in TES operating conditions such as heat sink temperature, bias voltage, thermal radiation loading and linear amplifier gain. We also address potential optimisations of the onboard calibration source and compare the performance of this new technique with those previously used.

Cucchetti, E.↗
NASA NTRS2018

Characterization of On-Orbit GPS Transmit Antenna Patterns for Space Users

The GPS Antenna Characterization Experiment (GPS ACE) has made extensive observations of GPS L1 signals received at geosynchronous (GEO) altitude, with the objective of developing comprehensive models of the signal levels and signal performance in the GPS transmit antenna side lobes. The experiment was originally motivated by the fact that data on the characteristics and performance of the GPS signals available in GEO and other high Earth orbits was limited. The lack of knowledge of the power and accuracy of the side lobe signals on-orbit added risk to missions seeking to employ the side lobes to meet navigation requirements or improve performance. The GPS ACE Project lled that knowledge gap through a collaboration between The Aerospace Corporation and NASA Goddard Space Fight Center to collect and analyze observations from GPS side lobe transmissions to a satellite at GEO using a highly-sensitive GPS receiver installed at the ground station. The GPS ACE architecture has been in place collecting observations of the GPS constellation with extreme sensitivity for several years. This sensitivity combined with around-the-clock, all-in-view processing enabled full azimuthal coverage of the GPS transmit gain patterns over time to angles beyond 90 degrees off-boresight. Results discussed in this paper include the reconstructed transmit gain patterns, with comparisons to available pre-fight gain measurements from the GPS vehicle contractors. For GPS blocks with extensive ground measurements, the GPS ACE results show remarkable agreement with ground based measurements. For blocks without extensive ground measurements, the GPS ACE results provide the only existing assessments of the full transmit gain patterns. The paper also includes results of pseudorange deviation analysis to assess systematic errors associated with GPS side lobe signals.

space↗
NASA NTRS2018

Characterization of On-Orbit GPS Transmit Antenna Patterns for Space Users

The GPS Antenna Characterization Experiment (GPS ACE) has made extensive observations of GPS L1 signals received at geosynchronous (GEO) altitude, with the objective of developing comprehensive models of the signal levels and signal performance in the GPS transmit antenna side lobes. The experiment was originally motivated by the fact that data on the characteristics and performance of the GPS signals available in GEO and other high Earth orbits was limited. The lack of knowledge of the power and accuracy of the side lobe signals on-orbit added risk to missions seeking to employ the side lobes to meet navigation requirements or improve performance. The GPS ACE Project lled that knowledge gap through a collaboration between The Aerospace Corporation and NASA Goddard Space Fight Center to collect and analyze observations from GPS side lobe transmissions to a satellite at GEO using a highly-sensitive GPS receiver installed at the ground station. The GPS ACE architecture has been in place collecting observations of the GPS constellation with extreme sensitivity for several years. This sensitivity combined with around-the-clock, all-in-view processing enabled full azimuthal coverage of the GPS transmit gain patterns over time to angles beyond 90 degrees o -boresight. Results discussed in this paper include the reconstructed transmit gain patterns, with comparisons to available pre-fight gain measurements from the GPS vehicle contractors. For GPS blocks with extensive ground measurements, the GPS ACE results show remarkable agreement with ground based measurements. For blocks without extensive ground measurements, the GPS ACE results provide the only existing assessments of the full transmit gain patterns. The paper also includes results of pseudorange deviation analysis to assess systematic errors associated with GPS side lobe signals.

Donaldson, Jennifer E.↗
NASA NTRS2018

The Challenges of Measuring Methane from Space with a Lidar

The global and regional quantification of methane fluxes and identification of its sources and sinks has been highlighted as one of the goals of the NASA 2017 Earth Science Decadal Survey. Detecting methane from space and airborne platforms with an active (laser) remote sensing instrument presents several unique technology and measurement challenges. The instrument must have a single frequency, narrow-linewidth light source, and photon-sensitive detector at the right spectral region to make continuous measurements from orbit, day and night, all seasons and at all latitudes. It must have a high signal to noise ratio and must be relatively immune to biases from aerosol/cloud scattering, spectroscopic and meteorological data uncertainties, and instrument systematic errors. At Goddard Space Flight Center (GSFC), in collaboration with industry, we have developed an airborne instrument to measure methane. Our instrument is a nadir-viewing lidar that uses Integrated Path Differential Absorption (IPDA), to measure methane near 1.65 µm. We sample the absorption line using multiple wavelengths from a narrow linewidth laser source and a sensitive photodetector. This measurement approach provides maximum information content about the CH4 column, and minimizes biases in the XCH4 retrieval. In this paper, we will review our progress to date and discuss the technology challenges, options and tradeoffs to measure methane from space and airborne platforms.

airborne instruments↗
NASA NTRS2018

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

In this paper we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

Earth observations↗
NASA NTRS2019

Soil Moisture Data Assimilation

Accurate knowledge of soil moisture at the continental scale is important for improving predictions of weather, agricultural productivity and natural hazards, but observations of soil moisture at such scales are limited to indirect measurements, either obtained through satellite remote sensing or from meteorological networks. Land surface models simulate soil moisture processes, using observation-based meteorological forcing data, and auxiliary information about soil, terrain and vegetation characteristics. Enhanced estimates of soil moisture and other land surface variables, along with their uncertainty, can be obtained by assimilating observations of soil moisture into land surface models. These assimilation results are of direct relevance for the initialization of hydro-meteorological ensemble forecasting systems. The success of the assimilation depends on the choice of the assimilation technique, the nature of the model and the assimilated observations, and, most importantly, the characterization of model and observation error. Systematic differences between satellite-based microwave observations or satellite-retrieved soil moisture and their simulated counterparts require special attention. Other challenges include inferring root-zone soil moisture information from observations that pertain to a shallow surface soil layer, propagating information to unobserved areas and downscaling of coarse information to finer-scale soil moisture estimates. This chapter summarizes state-of-the-art solutions to these issues with conceptual data assimilation examples, using techniques ranging from simplified optimal interpolation to spatial ensemble Kalman filtering. In addition, operational soil moisture assimilation systems are discussed that support numerical weather prediction at ECMWF and provide value-added soil moisture products for the NASA Soil Moisture Active Passive mission.

radar backscatter↗
NASA NTRS2019

Evaluation Analysis of NASA SMAP L3 and L4 and SPoRT-LIS Soil Moisture Data in the United States

Soil moisture has a critical role in the development, frequency and persistence of climatic and hydrologic extremes such as drought, heat wave and flooding events. In situ soil moisture data are uneven and sparse in time and space. This highlights the need to utilize other soil moisture sources to fill this spatiotemporal gap. The goal of this study is to validate one satellite-based and two model-based soil moisture datasets with in situ data across the United States. Soil moisture information from the Soil Moisture Active Passive (SMAP) enhanced level 3 (L3) (SMAP L3) and modeled level 4 (L4) (SMAP L4) data at 9-km resolution and Short-term Prediction Research and Transition-Land Information System (SPoRT-LIS) modeled at 3-km resolution were selected for evaluation. SPoRT-LIS is a near real-time, high resolution operational land analysis data. Ground-based data were obtained from the North American Soil Moisture Database (NASMD) for 362 stations. Seven statistical indicators including anomaly, Spearman, and Pearson correlation coefficients, the systematic error (Bias), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and normalized standard deviation (SDV) were used to evaluate the satellite- and model-derived soil moisture data. In addition, the triple collocation (TC) error model was used to measure the error among SMAP L4, SPoRT-LIS and ground-based data. This study assesses which satellite or modeled dataset is most appropriate for specific times and locations to use as a surrogate for in situ observations. Temporal and spatial analysis demonstrated that, overall, SMAP L4 performed better than SMAP L3 and SPoRT-LIS. Strong agreement was observed between SMAP L4 and in situ observations (ρ = 0.53, Bias = −0.006) in all seasons and most regions with various land covers, especially in winter and in the central regions of the United States. For croplands, SMAP L4 presented the best agreement with in situ data, analyzing all period (ρ = 0.60) and non-winter period (ρ = 0.61) separately.

Tavakol, Ameneh↗
NASA NTRS2019

The ILRS: Approaching 20 Years and Planning for the Future

The International Laser Ranging Service (ILRS) was established by the International Association of Geodesy (IAG) in 1998 to support programs in geodesy, geophysics, fundamental constants and lunar research, and to provide the International Earth Rotation Service with data products that are essential to the maintenance and improvement in the International Terrestrial Reference Frame (ITRF), the basis for metric measurements of changes in the Earth and Earth–Moon system. Other scientific products derived from laser ranging include precise geocentric positions and motions of ground stations, satellite orbits, components of Earth’s gravity field and their temporal variations, Earth Orientation Parameters, precise lunar ephemerides and information about the internal structure of the Moon. Laser ranging systems are already measuring the one-way distance to remote optical receivers in space and are performing very accurate time transfer between remote sites in the Earth and in Space. The ILRS works closely with the IAG’s Global Geodetic Observing System. The ILRS develops (1) the standards and specifications necessary for product consistency, and (2) the priorities and tracking strategies required to maximize network efficiency. The service collects, merges, analyzes, archives and distributes satellite and lunar laser ranging data to satisfy a variety of scientific, engineering, and operational needs and encourages the application of new technologies to enhance the quality, quantity, and cost effectiveness of its data products. The ILRS works with (1) new satellite missions in the design and building of retroreflector targets to maximize data quality and quantity, and (2) science programs to optimize scientific data yield. Since its inception, the ILRS has grown to include forty laser ranging stations distributed around the world. The ILRS stations track more than ninety satellites from low Earth orbit (LEO) to the geosynchronous orbit altitude as well as retroreflector arrays on the surface of the Moon. Applications have been expanded to include time transfer, asynchronous ranging for targets at extended ranges, free space quantum telecommunications, and the tracking of space debris. Laser ranging technology is moving to lower energy, higher repetition rates (kHz), single-photon-sensitive detectors, shorter pulse widths, shorter normal point intervals for faster data acquisition, and increased pass interleaving, automated to autonomous operation with remote access, and embedded software for real-time updates and decision making. An example of pass interleaving is presented for the Yarragadee station (see Fig. 4); tracking of LEO satellites is often accommodated during break in LEO and GNSS passes. New satellites arrays provide more compact targets and work continues on the development of lighter less expensive arrays for satellites and the moon. The service now provides operational ITRF products including daily/ weekly station positions and daily resolution Earth orientation products; the flow of weekly combination of satellite orbit files for LAGEOS/Etalon-1 and -2 has recently been established. New products are under testing through a pilot project on systematic error monitoring currently underway. The article will give an overview of activities underway within the service, paths forward presently envisioned, and current issues and challenges.

Laser retroreflectors↗
NASA NTRS2019

LiteBIRD: An All-Sky Cosmic Microwave Background Probe of Inflation

The LiteBIRD mission will map polarized fluctuations in the cosmic microwave background (CMB) to search for the signature of gravitational waves from inflation, potentially opening a window on the Universe a fraction of a second after the Big Bang. CMB measurements from space give access to the largest angular scales and the full frequency range to constrain Galactic foregrounds, and LiteBIRD has been designed to take best advantage of the unique window of space. LiteBIRD will have a powerful ability to separate Galactic foreground emission from the CMB due to its 15 frequency bands spaced between 40 and 402 GHz and sensitive 100-mK bolometers. LiteBIRD will provide stringent control of systematic errors due to the benign thermal environment at the second Lagrange point, L2, 20-K rapidly rotating half-wave plates on each telescope, and the ability to crosscheck its results by measuring both the reionization and recombination peaks in the B-mode power spectrum. LiteBIRD would be the next step in the series of CMB space missions, COBE, WMAP, and Planck, each of which has given landmark scientific discoveries.

Lee, Adrian T.↗