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

Luminosity Functions of Elliptical Galaxies at z < 1.2

The luminosity functions of E/S0 galaxies are constructed in 3 different redshift bins (0.2 < z < 0.55, 0.55 < z < 0.8, 0.8 < z < 1.2), using the data from the Hubble Space Telescope Medium Deep Survey (HST MDS) and other HST surveys. These independent luminosity functions show the brightening in the luminosity of E/S0s by about 0.5~1.0 magnitude at z~1, and no sign of significant number evolution. This is the first direct measurement of the luminosity evolution of E/S0 galaxies, and our results support the hypothesis of a high redshift of formation (z > 1) for elliptical galaxies, together with weak evolution of the major merger rate at z < 1.Comment: To be published in ApJ Letters, 4 pages, AAS Latex, 4 figures, and 2 table