The Shane Wirtanen counts: Observability of the galaxy correlation function

For an explicit test of the ability to recover the galaxy two-point correlation function from the Lick catalog of Shane and Wirtanen, we have applied the reduction and analysis methods of Seidner et al. and Groth and Peebles to model galaxy distributions that have known plate and field "errors" and that are high-fidelity simulations of the Lick sample. The model galaxy space distribution is constructed with the Soneira-Peebles prescription, which generates model distributions which have two-, three-, and four-point correlation functions in good agreement with the observed correlation functions. The space distribution is projected onto the sky with and without plate "errors." The Seidner et al. analysis recovers the plate factors in the former case with an error of 6.3%, as originally estimated. The two-point correlation function estimated from the "corrected" model catalog reproduces the built-in correlation function including the break from the power law. This is also true if the angular scale of the break is increased or decreased by a factor of 1.76 from the observed value. We also compare a map of the corrected counts with a map of the counts projected without plate errors and find that the corrected map is a good visual representation of the galaxy distribution. Finally, we construct a simulation which includes systematic variations in plate sensitivity with observer and time-so called "plate shape gradients." Once again, the correlation function of the model catalog reproduces the built in correlation function

Semantic Association Rule Learning from Time Series Data and Knowledge Graphs

Digital Twins (DT) are a promising concept in cyber-physical systems research due to their advanced features including monitoring and automated reasoning. Semantic technologies such as Knowledge Graphs (KG) are recently being utilized in DTs especially for information modelling. Building on this move, this paper proposes a pipeline for semantic association rule learning in DTs using KGs and time series data. In addition to this initial pipeline, we also propose new semantic association rule criterion. The approach is evaluated on an industrial water network scenario. Initial evaluation shows that the proposed approach is able to learn a high number of association rules with semantic information whichare more generalizable. The paper aims to set a foundation for further work on using semantic association rule learning especially in the context of industrial applications

Production and use of metals and oxygen for lunar propulsion

Production, power, and propulsion technologies for using oxygen and metals derived from lunar resources are discussed. The production process is described, and several of the more developed processes are discussed. Power requirements for chemical, thermal, and electrical production methods are compared. The discussion includes potential impact of ongoing power technology programs on lunar production requirements. The performance potential of several possible metal fuels including aluminum, silicon, iron, and titanium are compared. Space propulsion technology in the area of metal/oxygen rocket engines is discussed

Detection of Cosmic Shear with the HST Survey Strip

Weak lensing by large-scale structure provides a unique method to directly measure matter fluctuations in the universe, and has recently been detected from the ground. Here, we report the first detection of this `cosmic shear' based on space-based images. The detection was derived from the Hubble Space Telescope (HST) Survey Strip (or Groth Strip), a 4' by 42' set of 28 contiguous WFPC2 pointings with I<27. The small size of the HST Point-Spread Function (PSF) affords both a lower statistical noise, and a much weaker sensitivity to systematic effects, a crucial limiting factor of cosmic shear measurements. Our method and treatment of systematic effects were discussed in an earlier paper (Rhodes, Refregier & Groth 2000). We measure an rms shear of 1.8% on the WFPC2 chip scale (1.27'), in agreement with the predictions of cluster-normalized CDM models. Using a Maximum Likelihood (ML) analysis, we show that our detection is significant at the 99.5% confidence level (CL), and measure the normalization of the matter power spectrum to be sigma8*Omega_m^(0.48) = 0.51 (+0.14,-0.17), in a LambdaCDM universe. These 68% CL errors include (Gaussian) cosmic variance, systematic effects and the uncertainty in the redshift distribution of the background galaxies. Our result is consistent with earlier lensing measurements from the ground, and with the normalization derived from cluster abundance. We discuss how our measurement can be improved with the analysis of a large number of independent WFPC2 fields.Comment: 4 pages, 2 figure