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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 NTRS2011

A Climate-Data Record (CDR) of the "Clear Sky" Surface Temperature of the Greenland Ice Sheet

To quantify the ice-surface temperature (IST) we are developing a climate-data record (CDR) of monthly IST of the Greenland ice sheet, from 1982 to the present using Advanced Very High Resolution Radiometer (AVHRR) and Moderate-Resolution Imaging Spectroradiometer (MODIS) data at 5-km resolution. "Clear-sky" surface temperature increases have been measured from the early 1980s to the early 2000s in the Arctic using AVHRR data, showing increases ranging from 0.57-0.02 (Wang and Key, 2005) to 0.72 0.10 deg C per decade (Comiso, 2006). Arctic warming has implications for ice-sheet mass balance because much of the periphery of the ice sheet is near 0 deg C in the melt season and is thus vulnerable to more extensive melting (Hanna et al., 2008). The algorithm used for this work has a long history of measuring IST in the Arctic with AVHRR (Key and Haefliger, 1992). The data are currently available from 1981 to 2004 in the AVHRR Polar Pathfinder (APP) dataset (Fowler et al., 2000). J. Key1NOAA modified the AVHRR algorithm for use with MODIS (Hall et al., 2004). The MODIS algorithm is now being processed over Greenland. Issues being addressed in the production of the CDR are: time-series bias caused by cloud cover, and cross-calibration between AVHRR and MODIS instruments. Because of uncertainties, time series of satellite ISTs do not necessarily correspond with actual surface temperatures. The CDR will be validated by comparing results with in-situ (see Koenig and Hall, in press) and automatic-weather station data (e.g., Shuman et al., 2001).

Hall, Dorothy K.↗
NASA NTRS2013

The NASA Meter Class Autonomous Telescope: Ascension Island

The Meter Class Autonomous Telescope (MCAT) is the newest optical sensor dedicated to NASA's mission to characterize the space debris environment. It is the successor to a series of optical telescopes developed and operated by the JSC Orbital Debris Program Office (ODPO) to monitor and assess the debris environment in (1) Low Earth Orbit (LEO), (2) Medium Earth Orbit (MEO), and (3) Geosynchronous Orbit (GEO), with emphasis on LEO and GEO altitudes. A joint NASA - Air Force Research Labs project, MCAT is a 1.3m optical telescope dedicated to debris research. Its optical path and sensor yield a large survey fence at the cutting edge of current detector performance. It has four primary operational observing modes, two of which were not computationally feasible a decade ago. Operations are supported by a sophisticated software suite that monitors clouds and weather conditions, and controls everything from data collection to dome rotation to processing tens of gigabytes of image data nightly. With fainter detection limits, precision detection, acquisition and tracking of targets, multi-color photometry, precision astrometry, automated re-acquisition capability, and the ability to process all data at the acquisition rate, MCAT is capable of producing and processing a volume and quality of data far in excess of any current (or prior) ODPO operations. This means higher fidelity population inputs and eliminating the multi-year backlog from acquisition-to-product typical of optical campaigns. All of this is possible given a suitable observing location. Ascension Island offers numerous advantages. As a British overseas territory with a US Air Force base presence, the necessary infrastructure and support already exists. It is located mid-way between Brazil and Africa at 7.93S latitude and 14.37 W longitude. With the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) asset in Moron, Spain shutting down, this presents access to the sky from a unique latitude/longitude for an optical telescope. Constant trade winds from the SSE, originating from Africa, give promise to a steady laminar airflow over an island, a trait sought after to create stable atmospheric and good astronomical 'seeing' conditions with very low annual rainfall values. This combination of attributes created the necessary compelling argument to redirect MCAT to its final destination: Ascension Island.

Lederer, S. M.↗
NASA NTRS2004

How Consistent are Recent Variations in the Tropical Energy and Water Cycle Resolved by Satellite Measurements?

One notable aspect of Earth's climate is that although the planet appears to be very close to radiative balance at top-of-atmosphere (TOA), the atmosphere itself and underlying surface are not. Profound exchanges of energy between the atmosphere and oceans, land and cryosphere occur over a range of time scales. Recent evidence from broadband satellite measurements suggests that even these TOA fluxes contain some detectable variations. Our ability to measure and reconstruct radiative fluxes at the surface and at the top of atmosphere is improving rapidly. One question is 'How consistent, physically, are these diverse remotely-sensed data sets'? The answer is of crucial importance to understanding climate processes, improving physical models, and improving remote sensing algorithms. In this work we will evaluate two recently released estimates of radiative fluxes, focusing primarily on surface estimates. The International Satellite Cloud Climatology Project 'FD' radiative flux profiles are available from mid-1983 to near present and have been constructed by driving the radiative transfer physics from the Goddard Institute for Space Studies (GISS) global model with ISCCP clouds and TOVS (TIROS Operational Vertical Sounder)thermodynamic profiles. Full and clear sky SW and LW fluxes are produced. A similar product from the NASA/GEWEX Surface Radiation Budget Project using different radiative flux codes and thermodynamics from the NASA/Goddard Earth Observing System (GEOS-1) assimilation model makes a similar calculation of surface fluxes. However this data set currently extends only through 1995. We also employ precipitation measurements from the Global Precipitation Climatology Project (GPCP) and the Tropical Rainfall Measuring Mission (TRMM). Finally, ocean evaporation estimates from the Special Sensor Microwave Imager (SSM/I) are considered as well as derived evaporation from the NCAR/NCEP Reanalysis. Additional information is included in the original extended abstract.

Robertson, F. R.↗
DOE OSTI2024

Simulating image coaddition with the Nancy Grace Roman Space Telescope – II. Analysis of the simulated images and implications for weak lensing

One challenge for applying current weak lensing analysis tools to the Nancy Grace Roman Space Telescope is that individual images will be undersampled. Our companion paper presented an initial application of Imcom – an algorithm that builds an optimal mapping from input to output pixels to reconstruct a fully sampled combined image – on the Roman image simulations. In this paper, we measure the output noise power spectra, identify the sources of the major features in the power spectra, and show that simple analytic models that ignore sampling effects underestimate the power spectra of the coadded noise images. We compute the moments of both idealized injected stars and fully simulated stars in the coadded images, and their one- and two-point statistics. We show that the idealized injected stars have root-mean-square ellipticity errors (1–6) × 10 -4 per component depending on the band; the correlation functions are ≥2 orders of magnitude below requirements, indicating that the image combination step itself is using a small fraction of the overall Roman second moment error budget, although the fourth moments are larger and warrant further investigation. The stars in the simulated sky images, which include blending and chromaticity effects, have correlation functions near the requirement level (and below the requirement level in a wide-band image constructed by stacking all four filters). We evaluate the noise-induced biases in the ellipticities of injected stars, and explain the resulting trends with an analytical model. We conclude by enumerating the next steps in developing an image coaddition pipeline for Roman.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2005

X-Ray Spectroscopy of Optically Bright Planets using the Chandra Observatory

Since its launch in July 1999, Chandra's Advanced CCD Imaging Spectrometer (ACIS) has observed several planets (Venus, Mars, Jupiter and Saturn) and 6 comets. At 0.5 arc-second spatial resolution, ACIS detects individual x-ray photons with good quantum efficiency (25% at 0.6 KeV) and energy resolution (20% FWHM at 0.6 KeV). However, the ACIS CCDs are also sensitive to optical and near-infrared light, which is absorbed by optical blocking filters (OBFs) that eliminate optical contamination from all but the brightest extended sources, e.g., planets. .Jupiter at opposition subseconds approx.45 arc-seconds (90 CCD pixels.) Since Chandra is incapable of tracking a moving target, the planet takes 10 - 20 kiloseconds to move across the most sensitive ACIS CCD, after which the observatory must be re-pointed. Meanwhile, the OBF covering that CCD adds an opt,ical signal equivalent to approx.110 eV to each pixel that lies within thc outline of the Jovian disk. This has three consequences: (1) the observatory must be pointed away from Jupiter while CCD bias maps are constructed; (2) most x-rays from within the optical image will be misidentified as charged-particle background and ignored; and (3) those x-rays that are reported will bc assigned anomalously high energies. The same also applies to thc other planets, but is less serious since they are either dimmer at optical wavelengths, or they show less apparent motion across the sky, permitting reduced CCD exposure times: the optical contamination from Saturn acids approx.15 eV per pixel, and from Mars and Venus approx.31 eV. After analyzing a series of short .Jupiter observations in December 2000, ACIS parameters were optimized for the February 2003 opposition. CCD bias maps were constructed while Chandra pointed away from Jupiter, and the subsequent observations employed on-board software to ignore any pixel that contained less charge than that expected from optical leakage. In addition, ACIS was commanded to report 5 x 5 arrays of pixel values surrounding each x-ray event, and the outlying values were employed during ground processing to correct for the optical contamination.

Ford, P. G.↗
NASA NTRS2019

All-Sky Medium-Energy Gamma-Ray Observatory (AMEGO)

The gamma-ray energy range from a few hundred keV (kiloelectronvolts) to a few hundred MeV (megaelectronvolts) has remained largely unexplored since the pioneering but limited observations by COMPTEL (The Imaging Compton Telescope) on the CGRO (Compton Gamma Ray Observatory) (1991-2000). Fundamental astrophysics questions can be addressed by a mission in the MeV range, from astrophysical jets and extreme physics of compact objects to a large population of unidentified objects. Such a mission will also provide critical inputs for multimessenger astrophysics by identifying and exploring the astrophysical objects that produce gravitational waves and neutrinos. To address these questions, we are developing AMEGO: All-sky Medium Energy Gamma-ray Observatory, as a NASA probe-class mission, to investigate the energy range from 200 keV to greater than10 GeV with good energy (ranging from less than 1 percent at the low end to approximately 10 percent at the high end) and angular resolution (from 2 to 6 degrees depending on energy) and with sensitivity a factor of 20-50 better than previous instruments. Measurements at these energies are challenging, mainly due to the fact that two photon interaction processes, Compton scattering and pair production, compete. These interaction processes require different approaches in both detection and data analysis, and consequently in the instrument concept. AMEGO will be capable of measuring both Compton-scattering events at lower energies and pair-production events at higher energies. AMEGO will also have sensitivity to linear polarization of detected radiation at a level of 20 percent minimum detectable polarization from a source 1 percent of the Crab intensity, observed for 106 seconds. AMEGO will be operating mainly in scanning (discovery) mode with a field-of-view of 2.5 sr (Special Relativity) (20 percent of the sky observation any time), with the capability to be pointed to particular regions of interest..

Moiseev, Alexander↗
NASA NTRS2019

Three Red Suns in the Sky: A Transiting, Terrestrial Planet in a Triple M-dwarf System at 6.9 pc

We present the discovery from Transiting Exoplanet Survey Satellite (TESS) data of LTT 1445Ab. At a distance of 6.9 pc, it is the second nearest transiting exoplanet system found to date, and the closest one known for which the primary is an M dwarf. The host stellar system consists of three mid-to-late M dwarfs in a hierarchical configuration, which are blended in one TESS pixel. We use MEarth data and results from the Science Processing Operations Center data validation report to determine that the planet transits the primary star in the system. The planet has a radius of {1.38}_{-0.12}^{+0.13} {R}_{\oplus }, an orbital period of {5.35882}_{-0.00031}^{+0.00030} days, and an equilibrium temperature of {433}_{-27}^{+28} K. With radial velocities from the High Accuracy Radial Velocity Planet Searcher, we place a 3σ upper mass limit of 8.4 {M}_{\oplus } on the planet. LTT 1445Ab provides one of the best opportunities to date for the spectroscopic study of the atmosphere of a terrestrial world. We also present a detailed characterization of the host stellar system. We use high-resolution spectroscopy and imaging to rule out the presence of any other close stellar or brown dwarf companions. Nineteen years of photometric monitoring of A and BC indicate a moderate amount of variability, in agreement with that observed in the TESS light-curve data. We derive a preliminary astrometric orbit for the BC pair that reveals an edge-on and eccentric configuration. The presence of a transiting planet in this system hints that the entire system may be co-planar, implying that the system may have formed from the early fragmentation of an individual protostellar core.

Binaries↗
DOE OSTI2023

Optimizing cloud motion estimation on the edge with phase correlation and optical flow

Abstract. Phase correlation (PC) is a well-known method for estimating cloud motion vectors (CMVs) from infrared and visible spectrum images. Commonly, phase shift is computed in the small blocks of the images using the fast Fourier transform. In this study, we investigate the performance and the stability of the blockwise PC method by changing the block size, the frame interval, and combinations of red, green, and blue (RGB) channels from the total sky imager (TSI) at the United States Atmospheric Radiation Measurement user facility's Southern Great Plains site. We find that shorter frame intervals, followed by larger block sizes, are responsible for stable estimates of the CMV, as suggested by the higher autocorrelations. The choice of RGB channels has a limited effect on the quality of CMVs, and the red and the grayscale images are marginally more reliable than the other combinations during rapidly evolving low-level clouds. The stability of CMVs was tested at different image resolutions with an implementation of the optimized algorithm on the Sage cyberinfrastructure test bed. We find that doubling the frame rate outperforms quadrupling the image resolution in achieving CMV stability. The correlations of CMVs with the wind data are significant in the range of 0.38–0.59 with a 95 % confidence interval, despite the uncertainties and limitations of both datasets. A comparison of the PC method with constructed data and the optical flow method suggests that the post-processing of the vector field has a significant effect on the quality of the CMV. The raindrop-contaminated images can be identified by the rotation of the TSI mirror in the motion field. The results of this study are critical to optimizing algorithms for edge-computing sensor systems.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2017

Simultaneous Observations of Traveling Convection Vortices: Ionosphere-Thermosphere Coupling

We present simultaneous observations of magnetosphere-ionosphere-thermosphere coupling over Svalbard during a traveling convection vortex (TCV) event. Various spaceborne and ground-based instruments made coordinated measurements, including magnetometers, particle detectors, an all-sky camera, European Incoherent Scatter (EISCAT) Svalbard Radar, Super Dual Auroral Radar Network (SuperDARN), and SCANning Doppler Imager (SCANDI). The instruments recorded TCVs associated with a sudden change in solar wind dynamic pressure. The data display typical features of TCVs including vortical ionospheric convection patterns seen by the ground magnetometers and SuperDARN radars and auroral precipitation near the cusp observed by the all-sky camera. Simultaneously, electron and ion temperature enhancements with corresponding density increase from soft precipitation are also observed by the EISCAT Svalbard Radar. The ground magnetometers also detected electromagnetic ion cyclotron waves at the approximate time of the TCV arrival. This implies that they were generated by a temperature anisotropy resulting from a compression on the dayside magnetosphere. SCANDI data show a divergence in thermospheric winds during the TCVs, presumably due to thermospheric heating associated with the current closure linked to a field-aligned current system generated by the TCVs. We conclude that solar wind pressure impulse-related transient phenomena can affect even the upper atmospheric dynamics via current systems established by a magnetosphere-ionosphere-thermosphere coupling process.

Kim, Hyomin↗
DOE OSTI2021

DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ~20%. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

galaxies: interactions↗
DOE OSTI2021

Enhance Low Level Temperature and Moisture Profiles Through Combining NUCAPS, ABI Observations, and RTMA Analysis

Thermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real-Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re-analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root-mean-squared-error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface-based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2000

Improving Global Analysis and Short-Range Forecast Using Rainfall and Moisture Observations Derived from TRMM and SSM/I Passive Microwave Instruments

The Global Precipitation Mission, a satellite project under consideration as a follow-on to the Tropical Rainfall Measuring Mission (TRMM) by the National Aeronautics and Space Agency (NASA) in the United States, the National Space Development Agency (NASDA) in Japan, and other international partners, comprises an improved TRMM-like satellite and a constellation of 8 satellites carrying passive microwave radiometers to provide global rainfall measurements at 3-hour intervals. The success of this concept relies on the merits of rainfall estimates derived from passive microwave radiometers. This article offers a proof-of-concept demonstration of the benefits of using, rainfall and total precipitable water (TPW) information derived from such instruments in global data assimilation with observations from the TRMM Microwave Imager (TMI) and 2 Special Sensor Microwave/Imager (SSM/I) instruments. Global analyses that optimally combine observations from diverse sources with physical models of atmospheric and land processes can provide a comprehensive description of the climate systems. Currently, such data analyses contain significant errors in primary hydrological fields such as precipitation and evaporation, especially in the tropics. We show that assimilating the 6-h averaged TMI and SSM/I surface rainrate and TPW retrievals improves not only the hydrological cycle but also key climate parameters such as clouds, radiation, and the upper tropospheric moisture in the analysis produced by the Goddard Earth Observing System (GEOS) Data Assimilation System, as verified against radiation measurements by the Clouds and the Earth's Radiant Energy System (CERES) instrument and brightness temperature observations by the TIROS Operational Vertical Sounder (TOVS) instruments. Typically, rainfall assimilation improves clouds and radiation in areas of active convection, as well as the latent heating and large-scale motions in the tropics, while TPW assimilation leads to reduced moisture biases and improved radiative fluxes in clear-sky regions. Ensemble forecasts initialized with analyses that incorporate TMI and SSM/I rainfall and TPW data also yield better short-range predictions of geopotential heights, winds, and precipitation in the tropics. This study offers a compelling illustration of the potential of using rainfall and TPW information derived from passive microwave instruments to significantly improve the quality of 4-dimensional global datasets for climate analysis and weather forecasting applications.

Hou, Arthur Y.↗
DOE OSTI2023

The 100-month Swift catalogue of supergiant fast X–ray transients

Supergiant fast X-ray transients (SFXTs) are high mass X-ray binaries (HMXBs) displaying X-ray outbursts that can reach peak luminosities up to 10 38 erg s -1 and spend most of their lives in more quiescent states with luminosities as low as 10 32 -10 33 erg s -1 . During the quiescent states, less luminous flares are also frequently observed with luminosities of 10 34 -10 35 erg s -1 . The main goal of the comprehensive and uniform analysis of the SFXT Swift triggers presented in this paper is to provide tools to predict whether a transient that has no known X-ray counterpart may be an SFXT candidate. These tools can be exploited for the development of future missions exploring the variable X-ray sky through large field-of-view instruments. We examined all available data on outbursts of SFXTs that triggered the Swift/Burst Alert Telescope (BAT) collected between 2005 August 30 and 2014 December 31, in particular those for which broad-band data, including the Swift/X-ray Telescope (XRT) data, are also available. This work complements and extends our previous catalogue of SFXT flares detected by BAT from 2005 February 12 to 2013 May 31, since we now include the additional BAT triggers recorded until the end of 2014 (i.e. beyond the formal first 100 months of the Swift mission). Due to a change in the mission’s observational strategy, virtually no SFXT triggers obtained a broad-band response after 2014. We processed all BAT and XRT data uniformly by using the Swift Burst Analyser to produce spectral evolution dependent flux light curves for each outburst in the sample. The BAT data allowed us to infer useful diagnostics to set SFXT triggers apart from the general γ-ray burst population, showing that SFXTs uniquely give rise to image triggers and are simultaneously very long, faint, and ‘soft’ hard-X-ray transients. We find that the BAT data alone can discriminate very well the SFXTs from other classes of fast transients, such as anomalous X-ray pulsars and soft gamma repeaters. On the contrary, the XRT data collected around the time of the BAT triggers are shown to be decisive for distinguishing SFXTs from, for instance, accreting millisecond X-ray pulsars and jetted tidal disruption events. The XRT observations of 35 (out of 52 in total) SFXT BAT triggers show that in the soft X-ray energy band, SFXTs display a decay in flux from the peak of the outburst of at least three orders of magnitude within a day and rarely undergo large re-brightening episodes, favouring in most cases a rapid decay down to the quiescent level within three to five days (at most).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Galaxy Spin Classification. I. Z-wise versus S-wise Spirals with the Chirality Equivariant Residual Network

The angular momentum of galaxies (galaxy spin) contains rich information about the initial condition of the universe, yet it is challenging to efficiently measure the spin direction for the tremendous amount of galaxies that are being mapped by ongoing and forthcoming cosmological surveys. We present a machine-learning-based classifier for the Z-wise versus S-wise spirals, which can help to break the degeneracy in the galaxy spin direction measurement. The proposed chirality equivariant residual network (CE-ResNet) is manifestly equivariant under a reflection of the input image, which guarantees that there is no inherent asymmetry between the Z-wise and S-wise probability estimators. We train the model with Sloan Digital Sky Survey images, with the training labels given by the Galaxy Zoo 1 project. A combination of data augmentation techniques is used during the training, making the model more robust to be applied to other surveys. We find an ~30% increase in both types of spirals when Dark Energy Spectroscopic Instrument (DESI) images are used for classification, due to the better imaging quality of DESI. We verify that the ~7σ difference between the numbers of Z-wise and S-wise spirals is due to human bias, since the discrepancy drops to <1.8σ with our CE-ResNet classification results. We discuss the potential systematics relevant to future cosmological applications.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2010

Optical Imaging and Radiometric Modeling and Simulation

OPTOOL software is a general-purpose optical systems analysis tool that was developed to offer a solution to problems associated with computational programs written for the James Webb Space Telescope optical system. It integrates existing routines into coherent processes, and provides a structure with reusable capabilities that allow additional processes to be quickly developed and integrated. It has an extensive graphical user interface, which makes the tool more intuitive and friendly. OPTOOL is implemented using MATLAB with a Fourier optics-based approach for point spread function (PSF) calculations. It features parametric and Monte Carlo simulation capabilities, and uses a direct integration calculation to permit high spatial sampling of the PSF. Exit pupil optical path difference (OPD) maps can be generated using combinations of Zernike polynomials or shaped power spectral densities. The graphical user interface allows rapid creation of arbitrary pupil geometries, and entry of all other modeling parameters to support basic imaging and radiometric analyses. OPTOOL provides the capability to generate wavefront-error (WFE) maps for arbitrary grid sizes. These maps are 2D arrays containing digital sampled versions of functions ranging from Zernike polynomials to combination of sinusoidal wave functions in 2D, to functions generated from a spatial frequency power spectral distribution (PSD). It also can generate optical transfer functions (OTFs), which are incorporated into the PSF calculation. The user can specify radiometrics for the target and sky background, and key performance parameters for the instrument s focal plane array (FPA). This radiometric and detector model setup is fairly extensive, and includes parameters such as zodiacal background, thermal emission noise, read noise, and dark current. The setup also includes target spectral energy distribution as a function of wavelength for polychromatic sources, detector pixel size, and the FPA s charge diffusion modulation transfer function (MTF).

Ha, Kong Q.↗
DOE OSTI2025

Neural Posterior Estimation for Cataloging Astronomical Images with Spatially Varying Backgrounds and Point Spread Functions

Neural posterior estimation (NPE), a type of amortized variational inference, is a computationally efficient means of constructing probabilistic catalogs of light sources from astronomical images. To date, NPE has not been used to perform inference in models with spatially varying covariates. However, ground-based astronomical images exhibit spatially varying sky backgrounds and point spread functions (PSFs), and accounting for this variation is essential for constructing accurate catalogs of imaged light sources. In this work, we introduce a novel NPE-based cataloging method that trains an inference network with semisynthetic astronomical images generated using PSFs and backgrounds sampled from the Sloan Digital Sky Survey. In experiments with semisynthetic images, we evaluate the method on key cataloging tasks: light source detection, star/galaxy separation, and flux measurement. A “generalist” inference network—trained with diverse PSFs and backgrounds—performs as well as a “specialist” network even when both are evaluated on the specialist’s particular PSF/background combination. This result suggests that a single NPE network can generalize across spatial variations, eliminating the need for retraining on each observational condition.

astronomy image processing↗
DOE OSTI2020

X-ray properties of the X-CLASS-redMaPPer galaxy cluster sample: the luminosity–temperature relation

ABSTRACT This article presents the results of a spectroscopic analysis of the X-CLASS-redMaPPer (XC1-RM) galaxy cluster sample. X-CLASS is a serendipitous search for clusters in X-ray wavebands based on the XMM–Newton archive, whereas redMaPPer is an optical cluster catalogue derived from the Sloan Digital Sky Survey (SDSS). The present sample comprises 92 X-ray extended sources identified in optical images within 1 arcmin separation. The area covered by the cluster sample is ∼ 27 deg2. The clusters span a wide redshift range (0.05 < z < 0.6) and 88 clusters benefit from spectrosopically confirmed redshifts using data from SDSS Data Release 14. We present an automated pipeline to derive the X-ray properties of the clusters in three distinct apertures: R500 (at fixed mass overdensity), Rfit (at fixed signal-to-noise ratio) and ${R}_{300\, {\rm kpc}}$ (fixed physical radius). The sample extends over wide temperature and luminosity ranges: from 1–10 keV and from 6 × 1042 to 11 × 1044 erg s−1, respectively. We investigate the luminosity–temperature (L–T) relation of the XC1-RM sample and find a slope equal to 3.03 ± 0.26. It is steeper than predicted by self-similar assumptions, in agreement with independent studies. A simplified approach is developed to estimate the amount and impact of selection biases that might be affecting our recoveredL–Tparameters. The result of this simulation process suggests that the measuredL–Trelation is biased to a steeper slope and higher normalization.

Molham, Mona↗
DOE OSTI2021

Fractal dimension of optical cirrus in Stripe82

ABSTRACT The geometric characteristics of dust clouds provide important information on the physical processes that structure such clouds. One of such characteristics is the 2D fractal dimension D of a cloud projected on to the sky plane. In previous studies, which were mostly based on infrared (IR) data, the fractal dimension of individual clouds was found to be in a range from 1.1 to 1.7 with a preferred value of 1.2–1.4. In this work, we use data from Stripe82 of the Sloan Digital Sky Survey to measure the fractal dimension of the cirrus clouds. This is done here for the first time for optical data with significantly better resolution as compared to IR data. To determine the fractal dimension, the perimeter-area method is employed. We also consider IR (IRAS and Herschel) counterparts of the corresponding optical fields to compare the results between the optical and IR. We find that the averaged fractal dimension across all clouds in the optical is $\langle D \rangle =1.69^{+0.05}_{-0.05}$ which is significantly larger than the fractal dimension of its IR counterparts $\langle D\rangle =1.38^{+0.07}_{-0.06}$. We examine several reasons for this discrepancy (choice of masking and minimal contour level, image and angular resolution, etc.) and find that for approximately half of our fields the different angular resolution (point spread function) of the optical and IR data can explain the difference between the corresponding fractal dimensions. For the other half of the fields, the fractal dimensions of the IR and visual data remain inconsistent, which can be associated with physical properties of the clouds, but further physical simulations are required to prove it.

79 ASTRONOMY AND ASTROPHYSICS↗