Preservation and reflection in specification

Lopes, Antónia; Fiadeiro, José Luiz

doi:10.1007/bfb0000484

Cited by 11 publications

(9 citation statements)

References 16 publications

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“…Besides that we see that the fraction of blue galaxies is also larger for the middle and bottom panels. If we consider (g − r)0 = 0.75 as a typical color for red galaxies (Lopes 2007;Fukugita, Shimasaku, & Ichikawa 1995) and call blue galaxies with (g − r)0 0.65 (typical color minus 0.10) we see there is only a small number of blue objects that are also bulges in the three most dense bins (upper right panels). In all the other panels the number of blue objects increases, even for bulges in the two lowest density bins (upper left panels).…”

Section: Relations Of Physical Properties Of Galaxies and Their Envirmentioning

confidence: 99%

NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters

Lopes¹,

Ribeiro²,

Rembold³

2013

Monthly Notices of the Royal Astronomical Society

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We are able to extend the investigation of the color-morphology-density-radius relations, for bright and faint galaxies, to R 3 × R 200 and to very low density regions, probing the transition region between cluster and field galaxies, and finding a smooth variation between these two populations. We investigate the environmental variation of galaxy properties (and their relations), such as color, spectral type and concentration. Our sample comprises 6,415 galaxies that were previously selected as cluster members from 152 systems with z 0.100. This sample is further divided in complete subsamples of 5,106 galaxies with M r M * + 1 (from clusters at z 0.100) and 1,309 galaxies with M * + 1 < M r M * + 3 (from objects at z 0.045). We characterize the environment as a function of the local galaxy density and global cluster related parameters, such as radial distance, substructure, X-ray luminosity and velocity dispersion. For a sample of field galaxies we also trace their environmental dependence using a local galaxy density estimate. Our main findings are: (i) The fraction of discs is generally higher than the ones for blue and star-forming galaxies, indicating a faster transformation of color and star-formation compared to structural parameters. (ii) Regarding the distance to the cluster center we find a small variation in the galaxy populations outside the virial radius. Once within that radius the fractions of each population change fast, decreasing even faster within R ∼ 0.3 × R 200 . (iii) We also find a small increase in the fraction of blue faint galaxies within R ∼ 0.4 × R 200 , before decreasing again to the most central bin. (iv) Our results do not indicate a significant dependence on cluster mass, except for the disc fraction in the core of clusters.(v) The relations between galaxy properties also point to no dependence on cluster mass, except for the scatter of the color stellar mass relation. Our results corroborate a scenario on which pre-processing in groups leads to a strong evolution in galaxy properties, before they are accreted by large clusters.

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Section: Relations Of Physical Properties Of Galaxies and Their Envirmentioning

confidence: 99%

NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters

Lopes¹,

Ribeiro²,

Rembold³

2013

Monthly Notices of the Royal Astronomical Society

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“…Details regarding SDSS data extraction are provided in Paper I. There, we used SDSS photometric data to estimate more accurate photometric redshifts (z photo ;Lopes 2007), richnesses and optical luminosities for the full NoSOCS supplemental catalogue. We found 7414 systems well sampled in SDSS DR5.…”

Section: Data a N D M E T H O D Smentioning

confidence: 99%

NoSOCS in SDSS �� II. Mass calibration of low redshift galaxy clusters with optical and X-ray properties

Lopes

Carvalho

Kohl-Moreira

et al. 2009

Monthly Notices of the Royal Astronomical Society

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We use SDSS data to investigate the scaling relations of 127 No SOCS and 56 CIRS galaxy clusters at low redshift (z≤ 0.10). We show that richness and both optical and X‐ray luminosities are reliable mass proxies. The scatter in mass at a fixed observable is ∼40 per cent, depending on the aperture, sample and observable considered. For example, for the massive CIRS systems σlnM500|N500= 0.33 ± 0.05 and σlnM500|Lx= 0.48 ± 0.06. For the full sample σlnM500|N500= 0.43 ± 0.03 and σllnM500|Lx= 0.56 ± 0.06. The scaling relations based only on the richer systems (CIRS) are slightly flatter than those based on the full sample, but the discrepancies are within 1σ. We estimate substructure using 2D and 3D 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 21 clusters, we estimate masses from the M–TX relation using temperature measures from Base de Données Amas de Galaxies X. 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 ln(M200) =A+B× ln(N200/60), with M200 being expressed in units of 1014 M⊙. Using the virial mass, for CIRS clusters, we find A= (1.39 ± 0.07) and B= (1.00 ± 0.11). For the same sample, but using the masses obtained by the caustic method, we get A= (0.64 ± 0.14) and B= (1.35 ± 0.34). If we consider the mass as estimated from TX (for the subset of 21 clusters with TX available) we derive A= (0.90 ± 0.10) and B= (0.92 ± 0.10). The relations based on the virial mass have a scatter of σlnM200|N200= 0.37 ± 0.05, while σlnM200|N200= 0.77 ± 0.22 for the caustic mass and σlnM200|N200= 0.34 ± 0.08 for the temperature‐based mass.

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“…The work reported in this paper made it clear that separate notions of morphism are necessary for modelling component interconnection during system configuration, and refinement for moving between different levels of abstraction. Similar distinctions were already recognised in [14] for specifications of system behaviour in temporal logic. Work is underway towards the integration of specifications and CommUnity designs in the proposed categorical framework.…”

Section: Discussionmentioning

confidence: 78%

Using Explicit State to Describe Architectures

Lopes

Fiadeiro

1999

Fundamental Approaches to Software Engineering

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Abstract. In order to achieve higher levels of abstraction in architectural design, we investigate extensions to parallel program design based on the use of explicit state variables to accommodate the action-based discipline of interaction that is typical of architecture description languages. Our study focus on primitives that support non-determinism, choice and fairness i n guarded-command based languages, and on refinement principles that are compositional with respect to interconnection.

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Preservation and reflection in specification

Cited by 11 publications

References 16 publications

NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters

NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters

NoSOCS in SDSS �� II. Mass calibration of low redshift galaxy clusters with optical and X-ray properties

Using Explicit State to Describe Architectures

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Preservation and reflection in specification

Cited by 11 publications

References 16 publications

NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters

NoSOCS in SDSS – IV. The role of environment beyond the extent of galaxy clusters

NoSOCS in SDSS ��� II. Mass calibration of low redshift galaxy clusters with optical and X-ray properties

Using Explicit State to Describe Architectures

Contact Info

Product

Resources

About

NoSOCS in SDSS �� II. Mass calibration of low redshift galaxy clusters with optical and X-ray properties