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All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

Development of response models for the Earth Radiation Budget Experiment (ERBE) sensors. Part 4: Preliminary nonscanner models and count conversion algorithms

Two count conversion algorithms and the associated dynamic sensor model for the M/WFOV nonscanner radiometers are defined. The sensor model provides and updates the constants necessary for the conversion algorithms, though the frequency with which these updates were needed was uncertain. This analysis therefore develops mathematical models for the conversion of irradiance at the sensor field of view (FOV) limiter into data counts, derives from this model two algorithms for the conversion of data counts to irradiance at the sensor FOV aperture and develops measurement models which account for a specific target source together with a sensor. The resulting algorithms are of the gain/offset and Kalman filter types. The gain/offset algorithm was chosen since it provided sufficient accuracy using simpler computations.

Halyo, Nesim↗
NASA NTRS2023

Alternate Approach to Multi Layer Insulation Modeling to Reduce Node Count

For models with a limitation on the overall node count, the typical approach to Multi-Layer Insulation (MLI) modeling may generate nodes that are necessary for the analysis, but do not represent components of particular interest. This leaves fewer nodes that can be utilized to model components of greater importance than the MLI. A common approach to modeling MLI is to include a separate MLI node representing the outer layer of the insulation and a radiative coupling based on the area multiplied by an effective emissivity. Therefore, wherever insulation is included, one node is needed for the underlying surface and another node for the insulation. Since many spacecraft and instruments include MLI covering a sizable portion of their designs, this may result in a considerable number of nodes being used for MLI. An alternate method to MLI modeling was developed that eliminates the MLI node, while still preserving the effect of the insulation for the underlying surface, thereby increasing the available nodes that could be used elsewhere in the model. This approach relies on preserving the baseline reflectivity, while reducing the absorptivity (based on the blanket effective emittance) and including a transparency. An inactive second surface is placed just behind the base surface that fully absorbs any energy that is transmitted without including its effect in the model. In essence, this approach applies only the energy that makes it through the blanket to the underlying surface. This method was tested out on the Roman Space Telescope model in local areas in preparation for its use in the generation of a launch model, which is constrained in the allowable node count. This paper documents the performance of the method and presents a comparison between the One-Node MLI method and the traditional two node MLI approach.

Thermal↗
NASA NTRS1994

Cluster evolution and microwave source counts

We present the modeled counts for the expected Sunyaev-Zel'dovich microwave sources associated with clusters of galaxies, predicted for experiments with arcminute-scale spatial resolution, assuming self-similar cluster evolution, for different spectra of the primordial density fluctuations and values of the cosmological density parameter Omega. Our simulations show that the source counts should be a powerful test of the evolution of very high redshift clusters. Experiments with 1 - 2 min spatial resolution, with moderate sensitivity but covering a large area of the sky, would be most effective for studying the SZ source population. Recent arcminute-scale radio experiments, the Owens Valley Radio Observatory (OVRO) RING experiment and VLA deep imaging, achieved sensitivity and sky coverage close to that needed for the detection of negative sources associated with very distant clusters. From the absence of cluster detections in these experiments, we rule out, with 90% confidence, models with Omega less than 0.3 and n = +1 as predicting too many bright sources; or there is no hot gas in clusters more distant than z(sub max) = 5 in such models. If the single negative source detected in the RING experiment is a distant cluster, the Omega = 1, n = -2 model also may be ruled out as it predicts too few sources. The new generation of telescopes, including the new SUZIE and Ryle instruments, will soon be able to detect distant clusters. The cluster population in the past has been modeled by scaling the observed present-day sample of X-ray clusters back to high redshifts, an approach which makes the best use of the observed cluster gas parameters, and makes the simulations robust to the assumed evolution at very early epochs. Although the pure self-similar model may be incompatible with the variety of observed evolutionary effects, we show that reasonable modifications to the intracluster gas history in that model, proposed to reconcile the self-similar evolution of cluster mass and the observed evolution of their X-ray luminosity, do not considerably change our microwave predictions made using the pure self-similar model. That is, the results of our simulations are applicable to the wide class of evolutionary models in which the cluster gas mass times gas temperature evolves as the dark mass times cluster virial temperature.

Markevitch, M.↗
NASA NTRSDate not supplied

QuantifyML: How good is my machine learning model?

This paper presents an approach, QuantifyML, which employs model counting to assess the learnability and robustness of machine learning models. Typically the efficacy of machine learning models is determined by computing their accuracy statistically on test data sets. However, this may be misleading, if the test data is not representative of the problem that is being studied. Further, two different models may have the same accuracy on a given data set, measured statistically, but may be very different in their behavior on unseen data. Also, models with high accuracy could have poor adversarial robustness. In QuantifyML, our goal is to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into a C program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise counts w.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, expressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the robustness of trained models around given inputs. Our evaluation demonstrates these applications of QuantifyML on decision trees and neural networks trained to learn relational properties of graphs, for which we know the ground truth, and to perform image classification, for which we do not have the ground truth, but we can quantify local robustness.

Deep Neural Networks↗
NASA NTRSDate not supplied

QuantifyML: How Good is My Machine Learning Model?

We present QuantifyML, which applies model counting to assess the learn ability, safety, and robustness of machine learning models. Typically, the efficacy of machine learning models is determined by computing their accuracy statistically on test datasets. However, this may be misleading, if the test data is not representative of the problem that is being studied. With QuantifyML we aim to precisely quantify the extent to which machine learning models have learned and generalized from the given data. In QuantifyML, a trained model is translated into aC program, which is fed to the CBMC model checking tool to produce a formula in Conjunctive Normal Form (CNF), which in turn is analyzed with state-of-the-art model counters to efficiently obtain precise countsw.r.t different outputs. QuantifyML enables i) evaluating the learnability of models by comparing the counts for the outputs to ground truth, ex-pressed as logical predicates (if available), ii) comparing the performance of different models that may be built with different machine learning algorithms (e.g., decision-trees vs. neural networks), and iii) quantifying the safety and robustness of trained models.

Deep Neural Networks↗
NASA NTRS2011

The Atacama Cosmology Telescope: Extragalactic Sources at 148 GHz in the 2008 Survey

We report on extragalactic sources detected in a 455 square-degree map of the southern sky made with data at a frequency of 148 GHz from the Atacama Cosmology Telescope 2008 observing season. We provide a catalog of 157 sources with flux densities spanning two orders of magnitude: from 15 mJy to 1500 mJy. Comparison to other catalogs shows that 98% of the ACT detections correspond to sources detected at lower radio frequencies. Three of the sources appear to be associated with the brightest cluster galaxies of low redshift X-ray selected galaxy clusters. Estimates of the radio to mm-wave spectral indices and differential counts of the sources further bolster the hypothesis that they are nearly all radio sources, and that their emission is not dominated by re-emission from warm dust. In a bright (> 50 mJy) 148 GHz-selected sample with complete cross-identifications from the Australia Telescope 20 GHz survey, we observe an average steepening of the spectra between .5, 20, and 148 GHz with median spectral indices of alp[ha (sub 5-20) = -0.07 +/- 0.06, alpha (sub 20-148) -0.39 +/- 0.04, and alpha (sub 5-148) = -0.20 +/- 0.03. When the measured spectral indices are taken into account, the 148 GHz differential source counts are consistent with previous measurements at 30 GHz in the context of a source count model dominated by radio sources. Extrapolating with an appropriately rescaled model for the radio source counts, the Poisson contribution to the spatial power spectrum from synchrotron-dominated sources with flux density less than 20 mJy is C(sup Sync) = (2.8 +/- 0.3) x 1O (exp-6) micro K(exp 2).

Marriage, T. A.↗
NASA NTRS1974

Utility of ERTS for monitoring the breeding habit of migratory waterfowl

Waterfowl breeding-ground surveys conducted each year by the Bureau of Sport Fisheries and Wildlife extend over a vast region of the United States and Canada. Data from these surveys are used to estimate waterfowl production by means of a mathematical model. Counts of May and July ponds are some the variables used in this model. Annual production estimates are used to predict fall flights of ducks. This information is then used for establishing waterfowl hunting regulations. Work to date indicates that satellite remote sensing techniques hold considerable promise for the accurate and rapid assessment of waterfowl breeding habitat, especially changes in pond numbers and distribution. Development of an operational system utilizing satellite sensors as a primary source of data appears to be a realistic goal for the future.

Work, E. W., Jr.↗
NASA NTRS1980

Software reliability: A comparison of results obtained from established software reliability models

Two models of the software error detection process are compared, the Jelinski-Moranda model and a Bayes inference model. Simulation techniques are used to generate software related system failure data which is analyzed by both models. Point estimates and confidence limits are compared. It is demonstrated that uncertainty may be considerable for reasonable samples sizes and should be considered in any application of these techniques. The Jelinski-Moranda model is sensitive to the failure of data to follow internal assumptions of the model, often not providing any point estimates, a factor which may limit its usefulness in many real world situations. The Bayes model is shown to respond to the introduction of additional errors in the software correction process, a condition where error counting models such as the Jelinski-Moranda generally fail to converge.

Horn, M. H.↗
NASA NTRS1986

A SIRTF interdiciplinary scientist proposal

Segal's chronometric cosmology provides an adequate fit to the radio source counts only for an unrealistic choice of spectral index. Since the typical observed spectral index of 0.75 gives a completely unacceptable X squared = 136 with 24 (or fewer) degrees of freedom, it is concluded that the actual Universe does not fit the chronometric model. Counts of ultraviolet excess quasistellar objects also show a steep N(S) curve that the chronometric cosmology cannot explain. Claims to the contrary by Segal, Loncaric, and Segal (1980) and Segal and Nicoll (1986) depend on a seemingly innocuous assumption that in fact destroys the power of the N(S) test. Even though the chronometric model gives a better fit that other non-evolving models it must be ruled out along with all non-evolving cosmologies.

Wright, E. L.↗
NASA NTRS2007

GISMO, a 2 mm Bolometer Camera Optimized for the Study of High Redshift Galaxies

The 2mm spectral range provides a unique terrestrial window enabling ground based observations of the earliest active dusty galaxies in the universe and thereby allowing a better constraint on the star formation rate in these objects. We present a progress report for our bolometer camera GISMO (the Goddard-IRAM Superconducting 2-Millimeter Observer), which will obtain large and sensitive sky maps at this wavelength. The instrument will be used at the IRAM 30 m telescope and we expect to install it at the telescope in 2007. The camera uses an 8 x 16 planar array of multiplexed TES bolometers, which incorporates our recently designed Backshort Under Grid (BUG) architecture. GISMO will be very efficient at detecting sources serendipitously in large sky surveys. With the background limited performance of the detectors, the camera provides significantly greater imaging sensitivity and mapping speed at this wavelength than has previously been possible. The major scientific driver for the instrument is to provide the IRAM 30 m telescope with the capability to rapidly observe galactic and extragalactic dust emission, in particular from high-zeta ULI RGs and quasar s, even in the summer season. The instrument will fill in the SEDs of high redshift galaxies at the Rayleigh-Jeans part of the dust emission spectrum, even at the highest redshifts. Our source count models predict that GISMO will serendipitously detect one galaxy every four hours on the blank sky, and that one quarter of these galaxies will be at a redshift of zeta 6.5.

Staguhn, J.↗
NASA NTRS2017

Models of Sector Aircraft Counts in the Presence of Local, Regional and Airport Constraints

Recently, the ATM community has made important progress in collaborative trajectory management through the introduction of a new FAA traffic management initiative called a Collaborative Trajectory Options Program (CTOP). FAA can use CTOPs to manage air traffic under multiple constraints (manifested as flow constrained areas or FCAs) in the system, and it allows flight operators to indicate their preferences for routing and delay options. CTOPs also permits better management of the overall trajectory of flights by considering both routing and departure delay options simultaneously. However, adoption of CTOPs in airspace has been hampered by many factors that include challenges in how to identify constrained areas and how to set rates for the FCAs. Decision support tools providing assistance would be particularly helpful in effective use of CTOPs. Such DSTs tools would need models of demand and capacity in the presence of multiple constraints. This study examines different approaches to using historical data to create and validate models of maximum flows in sectors and other airspace regions in the presence of multiple constraints. A challenge in creating an empirical model of flows under multiple constraints is a lack of sufficient historical data that captures diverse situations involving combinations of multiple constraints especially those with severe weather. The approach taken here to deal with this is two-fold. First, we create a generalized sector model encompassing multiple sectors rather than individual sectors in order to increase the amount of data used for creating the model by an order of magnitude. Secondly, we decompose the problem so that the amount of data needed is reduced. This involves creating a baseline demand model plus a separate weather constrained flow reduction model and then composing these into a single integrated model. A nominal demand model is a flow model (gdem) in the presence of clear local weather. This defines the flow as a function of weather constraints in neighboring regions, airport constraints and weather in locations that can cause re-routes to the location of interest. A weather constrained flow reduction model (fwx-red) is a model of reduction in baseline counts as a function of local weather. Because the number of independent variables associated with each of the two decomposed models is smaller than that with a single model, need for amount of data is reduced. Finally, a composite model that combines these two can be represented as fwx-red (gdem(e), l) where e represents non-local constraints and l represents local weather. The approaches studied to developing these models are divided into three categories: (1) Point estimation models (2) Empirical models (3) Theoretical models. Errors in predictions of these different types of models have been estimated. In situations when there is abundant data, point estimation models tend to be very accurate. In contrast, empirical models do better than theoretical models when there is some data available. The biggest benefit of theoretical models is their general applicability in wider range situations once the degree of accuracy of these has been established.

Kulkarni, Deepak↗
NASA NTRS1994

Galaxy formation in Lambda greater than 0 Friedmann models: Consequences for the number counts versus redshift test

We study the effect of the cosmological constant Lambda on galaxy formation using a simple spherical top-hat overdensity model. We consider models with Omega(sub 0) = 0.2, lambda(sub 0) = 0, and Omega(sub 0) = 0.2, lambda(sub 0) = 0.8 (where Omega(sub 0) is the density parameter, and lambda(sub 0) identically equal Lambda/3 H(sub 0 exp 2) where H(sub 0) is the Hubble constant). We adjust the initial power spectrum amplitude so that both models reproduce the same large-scale structures. The galaxy formation era in the lambda(sub 0) = 0 model occurs early (z approximately 6) and is very short, whereas in the lambda(sub 0) = 0.8 model the galaxy formation era starts later (z approximately 4), and last much longer, possibly all the way to the present. Consequently, galaxies at low redshift (z less than 1) are significantly more evolved in the lambda(sub 0) = 0 model than in the lambda(sub 0) = 0.8 model. This result implies that previous attempts to determine Lambda using the number counts versus redshift test are probably unreliable.

Martel, Hugo↗