Bayesian Optimization with Dimension Scheduling: Application to Biological Systems

Bayesian Optimization (BO) is a data-efficient method for global black-box optimization of an expensive-to-evaluate fitness function. BO typically assumes that computation cost of BO is cheap, but experiments are time consuming or costly. In practice, this allows us to optimize ten or fewer critical parameters in up to 1,000 experiments. But experiments may be less expensive than BO methods assume: In some simulation models, we may be able to conduct multiple thousands of experiments in a few hours, and the computational burden of BO is no longer negligible compared to experimentation time. To address this challenge we introduce a new Dimension Scheduling Algorithm (DSA), which reduces the computational burden of BO for many experiments. The key idea is that DSA optimizes the fitness function only along a small set of dimensions at each iteration. This DSA strategy (1) reduces the necessary computation time, (2) finds good solutions faster than the traditional BO method, and (3) can be parallelized straightforwardly. We evaluate the DSA in the context of optimizing parameters of dynamic models of microalgae metabolism and show faster convergence than traditional BO

XTCE and XML Database Evolution and Lessons from JWST, LandSat, and Constellation

The database organizations within three different NASA projects have advanced current practices by creating database synergy between the various spacecraft life cycle stakeholders and educating users in the benefits of the Consultative Committee for Space Data Systems (CCSDS) XML Telemetry and Command Exchange (XTCE) format. The combination of XML for managing program data and CCSDS XTCE for exchange is a robust approach that will meet all user requirements using Standards and Non proprietary tools. COTS tools for XTCEKML are very wide and varied. To combine together various low cost and free tools can be more expensive in the long run than choosing a more expensive COTS tool that meets all the needs. This was especially important when deploying in 32 remote sites with no need for licenses. A common mission XTCEKML format between dissimilar systems is possible and is not difficult. Command XMLKTCE is more complex than telemetry and the use of XTCEKML metadata to describe pages and scripts is needed due to the proprietary nature of most current ground systems. Other mission and science products such as spacecraft loads, science image catalogs, and mission operation procedures can all be described with XML as well to increase there flexibility as systems evolve and change. Figure 10 is an example of a spacecraft table load. The word is out and the XTCE community is growing, The f ~ sXt TCE user group was held in October and in addition to ESAESOC, SC02000, and CNES identified several systems based on XTCE. The second XTCE user group is scheduled for March 10, 2008 with LDMC and others joining. As the experience with XTCE grows and the user community receives the promised benefits of using XTCE and XML the interest is growing fast

NoSOCS in SDSS. I. Sample Definition and Comparison of Mass Estimates

We use Sloan Digital Sky Survey (SDSS) data to investigate galaxy cluster properties of systems first detected within DPOSS. With the high quality photometry of SDSS we derived new photometric redshifts and estimated richness and optical luminosity. For a subset of low redshift ($z \le 0.1$) clusters, we have used SDSS spectroscopic data to identify groups in redshift space in the region of each cluster, complemented with massive systems from the literature to assure the continuous mass sampling. A method to remove interlopers is applied, and a virial analysis is performed resulting in estimates of velocity dispersion, mass, and a physical radius for each low-$z$ system. We discuss the choice of maximum radius and luminosity range in the dynamical analysis, showing that a spectroscopic survey must be complete to at least M$^*+1$ if one wishes to obtain accurate and unbiased estimates of velocity dispersion and mass. We have measured X-ray luminosity for all clusters using archival data from RASS. For a smaller subset (twenty-one clusters) we selected temperature measures from the literature and estimated mass from the M-T$_X$ relation, finding that they show good agreement with the virial estimate. However, these two mass estimates tend to disagree with the caustic results. We measured the presence of substructure in all clusters of the sample and found that clusters with substructure have virial masses higher than those derived from T$_X$. This trend is not seen when comparing the caustic and X-ray masses. That happens because the caustic mass is estimated directly from the mass profile, so it is less affected by substructure.Comment: 21 pages, 17 figures, 5 tables, Accepted to MNRA

Standardization of XML Database Exchanges and the James Webb Space Telescope Experience

Personnel from the National Aeronautics and Space Administration (NASA) James Webb Space Telescope (JWST) Project have been working with various standard communities such the Object Management Group (OMG) and the Consultative Committee for Space Data Systems (CCSDS) to assist in the definition of a common extensible Markup Language (XML) for database exchange format. The CCSDS and OMG standards are intended for the exchange of core command and telemetry information, not for all database information needed to exercise a NASA space mission. The mission-specific database, containing all the information needed for a space mission, is translated from/to the standard using a translator. The standard is meant to provide a system that encompasses 90% of the information needed for command and telemetry processing. This paper will discuss standardization of the XML database exchange format, tools used, and the JWST experience, as well as future work with XML standard groups both commercial and government

James Webb Space Telescope - Applying Lessons Learned to I&T

The James Webb Space Telescope (JWST) is part of a new generation of spacecraft acquiring large data volumes from remote regions in space. To support a mission such as the JWST, it is imperative that lessons learned from the development of previous missions such as the Hubble Space Telescope and the Earth Observing System mission set be applied throughout the development and operational lifecycles. One example of a key lesson that should be applied is that core components, such as the command and telemetry system and the project database, should be developed early, used throughout development and testing, and evolved into the operational system. The purpose of applying lessons learned is to reap benefits in programmatic or technical parameters such as risk reduction, end product quality, cost efficiency, and schedule optimization. In the cited example, the early development and use of the operational command and telemetry system as well as the establishment of the intended operational database will allow these components to be used by the developers of various spacecraft components such that development, testing, and operations will all use the same core components. This will reduce risk through the elimination of transitions between development and operational components and improve end product quality by extending the verification of those components through continual use. This paper will discuss key lessons learned that have been or are being applied to the JWST Ground Segment integration and test program

James Webb Space Telescope - L2 Communications for Science Data Processing

JWST is the first NASA mission at the second Lagrange point (L2) to identify the need for data rates higher than 10 megabits per second (Mbps). JWST will produce approximately 235 Gigabits of science data every day that will be downlinked to the Deep Space Network (DSN). To get the data rates desired required moving away from X-band frequencies to Ka-band frequencies. To accomplish this transition, the DSN is upgrading its infrastructure. This new range of frequencies are becoming the new standard for high data rate science missions at L2. With the new frequency range, the issues of alternatives antenna deployment, off nominal scenarios, NASA implementation of the Ka-band 26 GHz, and navigation requirements will be discussed in this paper. JWST is also using Consultative Committee for Space Data Systems (CCSDS) standard process for reliable file transfer using CCSDS File Delivery Protocol (CFDP). For JWST the use of the CFDP protocol provides level zero processing at the DSN site. This paper will address NASA implementations of Ground Stations in support of Ka-band 26 GHz and lesson learned from implementing a file base (CFDP) protocol operational system

NoSOCS in SDSS. II. Mass Calibration of Low Redshift Galaxy Clusters with Optical and X-ray Properties

We use SDSS data to investigate the scaling relations of 127 NoSOCS and 56 CIRS galaxy clusters at low redshift ($z \le 0.10$). We show that richness and both optical and X-ray luminosities are reliable mass proxies. The scatter in mass at fixed observable is $\sim$ 40%, depending on the aperture, sample and observable considered. For example, for the massive CIRS systems $\sigma_{lnM500|N500}$ = 0.33 $\pm$ 0.05 and $\sigma_{lnM500|Lx}$ = 0.48 $\pm$ 0.06. For the full sample $\sigma_{lnM500|N500}$ = 0.43 $\pm$ 0.03 and $\sigma_{lnM500|Lx}$ = 0.56 $\pm$ 0.06. We estimate substructure using two and three dimensional optical data, verifying that substructure has no significant effect on the cluster scaling relations (intercepts and slopes), independent of which substructure test we use. For a subset of twenty-one clusters, we estimate masses from the M-T$_X$ relation using temperature measures from BAX. The scaling relations derived from the optical and X-ray masses are indeed very similar, indicating that our method consistently estimates the cluster mass and yields equivalent results regardless of the wavelength from which we measure mass. For massive systems, we represent the mass-richness relation by a function with the form ${\rm ln (M_{200}) = A + B \times ln(N_{200}/60)}$, with M$_{200}$ being expressed in units of 10$^{14}$ M$_{\odot}$. Using the virial mass, for CIRS clusters, we find A = (1.39 $\pm$ 0.07) and B = (1.00 $\pm$ 0.11). The relations based on the virial mass have a scatter of $\sigma_{lnM200|N200}$ = 0.37 $\pm$ 0.05, while $\sigma_{lnM200|N200}$ = 0.77 $\pm$ 0.22 for the caustic mass and $\sigma_{lnM200|N200}$ = 0.34 $\pm$ 0.08 for the temperature based mass (abridged).Comment: 27 pages, 22 figures, 12 tables, Accepted to MNRA