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K-band luminosity–density relation at fixed parameters or for different galaxy families

  • Xin-Fa Deng EMAIL logo , Xiao-Qing Wen , Yong Xin , Xiao-Ping Qi and Ying-Ping Ding
Published/Copyright: July 9, 2020
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Open Physics
From the journal Open Physics Volume 18 Issue 1

Abstract

Using the apparent magnitude-limited Main galaxy sample of the Sloan Digital Sky Survey Data Release 10, we examine the K-band luminosity–density relation at fixed parameters or for different galaxy families. It is found that the limiting or fixing galaxy properties, such as galaxy morphology, stellar mass, and color, exert substantial influence on the environmental dependence of the K-band luminosity of galaxies, which suggests that the K-band luminosity–density relation is likely attributable to the relation between these galaxy properties and density.

Keywords: fundamental parameters of galaxies, statistic of galaxy, distance and redshifts

1 Introduction

In the past few decades, the environmental dependence of galaxy luminosity was investigated by many authors (e.g., [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]). Some studies demonstrated that the luminosity–density relation of galaxies likely follows different trends in different bands [18]. In this situation, one often wishes to examine the environmental dependence of different band luminosities and to understand the luminosity–density relation all round. The K band is a standard near-infrared photometric filter. Deng et al. [21] examined the environmental dependence of the K-band luminosity in different galaxy samples of Sloan Digital Sky Survey Data Release 10 (SDSS DR10) [22]. It was found that in the luminous volume-limited Main galaxy sample with the luminosity −22.5 ≤ M r ≤ −20.5, the environmental dependence of the K-band luminosity still can be observed: luminous galaxies tend to reside in dense environments, whereas faint galaxies tend to reside in low-density regions, which is consistent with widely accepted conclusion obtained in previous studies (e.g., [10,11,14,15,17]). But in the faint volume-limited Main galaxy sample with the luminosity −20.5 ≤ M r ≤ −18.5, this dependence is very weak. Deng et al. [21] also applied the apparent magnitude-limited Main galaxy sample, divided the Main galaxy sample of the SDSS DR10 into subsamples with a redshift binning size of Δz = 0.01, investigated the environmental dependence of the K-band luminosity of subsamples in each redshift bin, and concluded that the K-band luminosity of Main galaxies shows substantial correlation with the local environment in many redshift bins.

Due to the close correlations among galaxy properties (e.g., [10,23,24,25,26,27,28,29,30,31]), it is difficult to study the environmental dependence of galaxy properties. The strong environmental dependence of a galaxy property is likely due to the environmental dependence of other galaxy properties and tight correlations between this galaxy property and other ones. Hence, following the discovery of strong correlations between certain properties of galaxies and environments, it is common to further examine the environmental dependence of these properties at fixed parameters or for different galaxy families (e.g., [11,16,27,32,33,34,35,36,37,38]). In this work, we also further examine the environmental dependence of the K-band luminosity at fixed parameters or for different galaxy families.

The outline of this article is as follows. Section 2 describes the data used. In Section 3, we discuss the environmental dependence of the K-band luminosity at fixed parameters or for different galaxy families. Our main results and conclusions are summarized in Section 4.

For calculating distances, we used a cosmological model with a matter density of Ω 0 = 0.3, a cosmological constant of Ω Λ = 0.7, and a Hubble constant of H 0 = 70 km s−1 Mpc−1.

2 Data

The Main galaxy sample [39] of the SDSS includes galaxies brighter than r petro = 17.77 (r-band apparent Petrosian magnitude), in which most galaxies are located within the redshift range of 0.02 ≤ z ≤ 0.2. Deng [40] downloaded the data of the Main galaxy sample from the Catalog Archive Server of SDSS Data Release 10 [22] using the SDSS SQL Search (http://www.sdss3.org/dr10/), extracted 633172 Main galaxies with redshifts of 0.02 ≤ z ≤ 0.2 (the Main galaxy sample corresponds to LEGACY_TARGET1 & (64|128|256) > 0), and constructed an apparent magnitude-limited Main galaxy sample. In this work, we also use this galaxy sample. The data set of the K-band luminosity measurements was downloaded from the StellarMassStarformingPort table.

3 Environmental dependence of the K-band luminosity at fixed parameters or for different galaxy families

Following previous studies [18,38,40], we apply the projected local density Σ 5 = N / π d 5 2 (galaxies Mpc−2), where d 5 is the distance to the fifth nearest neighbor within ±1,000 km s−1 in redshift (e.g., [27,41,42]), and divide the entire apparent magnitude-limited Main galaxy sample into subsamples with a redshift binning size of Δz = 0.01. As was done by Deng [18], in each subsample, we arrange galaxies in the density order from the smallest to largest and then select approximately 5% of the galaxies to construct each of two samples from both extremes of density. The low-density sample contains the first 5% of the galaxies in the density order, whereas the high-density sample consists of the final 5% of the galaxies in the density order.

3.1 Environmental dependence of the K-band luminosity at fixed morphology

Deng [38] studied the environmental dependence of U-band luminosity at fixed morphology and found that the abnormal environmental dependence of U-band luminosity for late-type galaxies is fairly strong in the redshift range of 0.03 ≤ z ≤ 0.09, whereas this environmental dependence for early-type galaxies is very weak in nearly all redshift bins. Deng [40] also demonstrated that at a given galaxy morphology, the environmental dependence of the stellar velocity dispersion of the galaxies is substantially reduced, but can still be observed in several redshift bins for the late types. These results show that the limiting or fixing galaxy morphology exerts substantial influence on the environmental dependence of some galaxy properties, which suggests that much of some galaxy property–density relations is most likely attributable to the relation between morphology and density. In this study, we examine the environmental dependence of the K-band luminosity for the early-type and late-type galaxies.

We use the early-type and late-type samples constructed by Deng [40]. As the concentration index is closely correlated with morphological type [43,44,45,46,47], it often served as a morphology-classification parameter [45,46,47,48]. Similar to Deng [38], Deng [40] applied the r-band concentration index c i = R 90/R 50 to discriminate early-type (c i ≥ 2.86) galaxies from late-type (c i < 2.86) galaxies [45,46]. R 50 and R 90 are the radii enclosing 50% and 90% of the Petrosian flux, respectively. From the apparent magnitude-limited Main galaxy sample of the SDSS DR10, Deng [40] constructed an early-type sample (containing 201630 galaxies) and a late-type sample (containing 431542 galaxies).

Figures 1 and 2 demonstrate the K-band luminosity distributions at both extremes of density in various redshift bins for the early-type and late-type samples. The Kolmogorov–Smirnov (KS) test demonstrates the degree of similarity or difference between two independent distributions in a figure, by calculating a probability value. It can serve as a quantitative comparison. The lower the probability value is, the less likely the two distributions are similar. Conversely, the higher the probability value is, the more similar the two distributions are. When the probability value is 1, the two distributions are completely same. Table 1 lists the statistical results of the KS test. As indicated by Figures 1, 2 and Table 1, for the early-type and late-type samples, the environmental dependence of the K-band luminosity is fairly weak nearly in all redshift bins. We again note that the limiting or fixing galaxy morphology exerts substantial influence on the environmental dependence of the K-band luminosity of galaxies.

Figure 1 K-band luminosity distributions of early-type galaxies at both extremes of density in various redshift bins: the red solid lines represent the high-density samples, and the blue dashed lines represent the low-density samples. The error bars on the blue lines are 1σ Poisson errors. The error bars on the red lines are omitted for clarity.
Figure 1

K-band luminosity distributions of early-type galaxies at both extremes of density in various redshift bins: the red solid lines represent the high-density samples, and the blue dashed lines represent the low-density samples. The error bars on the blue lines are 1σ Poisson errors. The error bars on the red lines are omitted for clarity.

Figure 2 Same as Figure 1, but for the K-band luminosity distributions of late-type galaxies at both extremes of density in various redshift bins.
Figure 2

Same as Figure 1, but for the K-band luminosity distributions of late-type galaxies at both extremes of density in various redshift bins.

Table 1

KS test probabilities that the two independent distributions in each redshift bin of Figures 1 and 2 are drawn from the same parent distribution

Redshift bin Early type Late type
Galaxy number P Galaxy number P
0.02–0.03 4571 0.23 19350 0.0013
0.03–0.04 6177 0.12 23517 0.00031
0.04–0.05 7075 0.49 25167 0.00040
0.05–0.06 8372 0.0028 28009 0.0017
0.06–0.07 11561 0.21 35390 0.21
0.07–0.08 15181 0.022 42233 2.42 × 10−6
0.08–0.09 15263 0.094 39731 0.00082
0.09–0.10 13007 0.035 31309 0.0053
0.10–0.11 13668 0.049 30365 0.00022
0.11–0.12 15875 0.0010 31015 0.0010
0.12–0.13 14602 0.00066 26622 1.61 × 10−6
0.13–0.14 15531 0.0019 25198 7.28 × 10−6
0.14–0.15 13057 0.00051 19603 0.017
0.15–0.16 11872 0.039 16316 0.041
0.16–0.17 10857 0.054 13011 0.035
0.17–0.18 9486 0.014 10409 0.018
0.18–0.19 8412 0.78 8170 0.079
0.19–0.20 7063 0.49 6127 0.52

3.2 Environmental dependence of the K-band luminosity for high stellar mass (HSM) and low stellar mass (LSM) galaxies

From the apparent magnitude-limited Main galaxy sample of the SDSS DR10, Deng [40] constructed an HSM sample (containing 250653 galaxies) and an LSM sample (containing 382519 galaxies), above and below the threshold (3 × 1010 M⊙) developed by Kauffmann et al. [49]. In this work, we also use these two samples.

Previous studies demonstrated that the environmental dependence of galaxy properties at fixed stellar mass is somewhat complicated. Kauffmann et al. [32] and Bamford et al. [34] reported that at fixed stellar mass, some galaxy properties, such as color, star formation, and nuclear activity, still have a strong environmental dependence, whereas the morphology, size, and concentration weakly depend on the environment. Deng et al. [36] showed that for HSM and LSM galaxies, the color, morphology, and star-formation activity strongly depend on the environment, but the size has only a weak environmental dependence. When investigating such a subject, some works shed light on the apparent magnitude-limited Main galaxy sample. Deng [38] found that the abnormal environmental dependence of U-band luminosity for LSM galaxies is fairly strong in the redshift range of 0.03 ≤ z ≤ 0.09, whereas this dependence for HSM galaxies is very weak in nearly all redshift bins. Deng [40] showed that the environmental dependence of the stellar velocity dispersion for HSM galaxies and LSM galaxies is much weaker than that for the entire apparent magnitude-limited Main galaxy sample, but can still be observed in certain redshift bins.

Figures 3 and 4 show the K-band luminosity distributions at both extremes of density in various redshift bins for the HSM and LSM samples. Table 2 also lists K–S probabilities. As shown by Figures 3 and 4 and Table 2, the environmental dependence of the K-band luminosity for HSM galaxies and LSM galaxies is very weak nearly in all redshift bins, which demonstrate that the limiting or fixing stellar mass exerts substantial influence on the environmental dependence of the K-band luminosity of galaxies.

Figure 3 Same as Figure 1, but for the K-band luminosity distributions of HSM galaxies at both extremes of density in various redshift bins.
Figure 3

Same as Figure 1, but for the K-band luminosity distributions of HSM galaxies at both extremes of density in various redshift bins.

Figure 4 Same as Figure 1, but for the K-band luminosity distributions of LSM galaxies at both extremes of density in various redshift bins.
Figure 4

Same as Figure 1, but for the K-band luminosity distributions of LSM galaxies at both extremes of density in various redshift bins.

Table 2

KS test probabilities that the two independent distributions in each redshift bin of Figures 3 and 4 are drawn from the same parent distribution

Redshift bin High mass Low mass
Galaxy number P Galaxy number P
0.02–0.03 2065 0.015 21856 4.50 × 10−8
0.03–0.04 3196 0.20 26498 5.69 × 10−8
0.04–0.05 4364 0.065 27878 8.02 × 10−6
0.05–0.06 5747 0.0020 30634 0.0028
0.06–0.07 9231 0.0032 37720 0.26
0.07–0.08 13995 0.093 43419 0.025
0.08–0.09 16155 0.052 38839 0.18
0.09–0.10 15118 0.070 29198 0.050
0.10–0.11 17307 0.00012 26726 0.0010
0.11–0.12 21273 0.0011 25617 0.065
0.12–0.13 20473 0.00096 20751 0.042
0.13–0.14 22552 0.032 18177 0.021
0.14–0.15 19920 0.0057 12740 0.61
0.15–0.16 19144 0.092 9044 0.59
0.16–0.17 17834 0.034 6034 0.51
0.17–0.18 16069 0.00011 3826 0.95
0.18–0.19 14354 0.051 2228 0.62
0.19–0.20 11856 0.016 1334 0.94

3.3 Environmental dependence of the K-band luminosity for blue and red galaxies

We use the red and blue samples constructed by Deng [40]. According to the threshold (the observed u–r color = 2.22) defined by Strateva et al. [24], Deng [40] classified galaxies in the apparent magnitude-limited main galaxy sample of the SDSS DR 10 as “red” and “blue”, respectively. The red sample contains 354337 galaxies. The blue sample includes 278835 galaxies. In this section, we examine the environmental dependence of the K-band luminosity of blue and red galaxies.

Galaxy color is a parameter that is most predictive of the environments [11]. Some studies demonstrated that limiting the color exerts substantial influence on the environmental dependence of some galaxy properties [35,37,38,50,51]. In the apparent magnitude-limited Main galaxy sample, Deng [38] demonstrated that the abnormal environmental dependence of the U-band luminosity for blue and red galaxies is fairly weak in nearly all redshift bins. However, Deng [40] noted that the environmental dependence of the stellar velocity dispersion for red galaxies is very strong in certain redshift bins, but that this dependence for blue galaxies is fairly weak in all redshift bins. Deng et al. [37] also found that the environmental dependence of the star formation rate and specific star formation rate for blue galaxies is very weak, whereas that for red galaxies is fairly strong. In Figures 5 and 6, we plot the K-band luminosity distributions at both extremes of density in various redshift bins for the red and blue samples. K–S probabilities are listed in Table 3. As shown by Figures 5, 6 and Table 3, the environmental dependence of the K-band luminosity for blue and red galaxies is fairly weak in nearly all redshift bins, as well as the U-band luminosity.

Figure 5 Same as Figure 1, but for the K-band luminosity distributions of red galaxies at both extremes of density in various redshift bins.
Figure 5

Same as Figure 1, but for the K-band luminosity distributions of red galaxies at both extremes of density in various redshift bins.

Figure 6 Same as Figure 1, but for the K-band luminosity distributions of blue galaxies at both extremes of density in various redshift bins.
Figure 6

Same as Figure 1, but for the K-band luminosity distributions of blue galaxies at both extremes of density in various redshift bins.

Table 3

KS test probabilities that the two independent distributions in each redshift bin of Figures 5 and 6 are drawn from the same parent distribution

Redshift bin Red Blue
Galaxy number P Galaxy number P
0.02–0.03 6310 0.011 17611 0.024
0.03–0.04 9376 2.63 × 10−5 20318 0.0011
0.04–0.05 12076 0.14 20166 0.00023
0.05–0.06 14956 0.0078 21425 0.011
0.06–0.07 21635 0.0010 25316 0.023
0.07–0.08 29385 5.17 × 10−5 28029 4.28 × 10−7
0.08–0.09 30042 0.0087 24952 0.00032
0.09–0.10 24814 0.0064 19502 0.00093
0.10–0.11 26051 1.06 × 10−9 17982 0.0010
0.11–0.12 29017 2.68 × 10−5 17873 0.0029
0.12–0.13 26187 0.00089 15037 0.0047
0.13–0.14 27000 0.0047 13729 0.0018
0.14–0.15 22510 6.90 × 10−5 10150 0.20
0.15–0.16 19824 0.0075 8364 0.12
0.16–0.17 17345 0.0066 6523 0.44
0.17–0.18 14792 0.00048 5103 0.24
0.18–0.19 12766 0.027 3816 0.67
0.19–0.20 10251 0.030 2939 0.69

4 Summary

Using the apparent magnitude-limited Main galaxy sample of SDSS Data Release 10 [22], we investigate the environmental dependence of the K-band luminosity at fixed parameters or for different galaxy families. Following Deng [18], we divide the entire apparent magnitude-limited Main galaxy sample into subsamples with a redshift binning size of Δz = 0.01 and perform statistical analyses in each redshift bin. Overall, our results demonstrate that the limiting or fixing galaxy properties, such as galaxy morphology, stellar mass, and color, exerts substantial influence on the environmental dependence of the K-band luminosity of galaxies, which suggests that the K-band luminosity–density relation is likely attributable to the relation between these galaxy properties and density.

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Acknowledgments

The authors thank the anonymous referee for many useful comments and suggestions. This study was supported by the National Natural Science Foundation of China (NSFC, Grant nos 11533004 and 11563005). Funding for SDSS-III has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, and the U.S. Department of Energy. The SDSS-III web site is http://www.sdss3.org/. SDSS-III is managed by the Astrophysical Research Consortium for the Participating Institutions of the SDSS-III Collaboration including the University of Arizona, the Brazilian Participation Group, Brookhaven National Laboratory, University of Cambridge, University of Florida, the French Participation Group, the German Participation Group, the Instituto de Astrofisica de Canarias, the Michigan State/Notre Dame/JINA Participation Group, Johns Hopkins University, Lawrence Berkeley National Laboratory, Max Planck Institute for Astrophysics, New Mexico State University, New York University, Ohio State University, Pennsylvania State University, University of Portsmouth, Princeton University, the Spanish Participation Group, University of Tokyo, University of Utah, Vanderbilt University, University of Virginia, University of Washington, and Yale University.

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Received: 2018-05-29
Revised: 2020-02-14
Accepted: 2020-02-14
Published Online: 2020-07-09

© 2020 Xin-Fa Deng et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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https://doi.org/10.1515/phys-2020-0016
Keywords for this article
fundamental parameters of galaxies, statistic of galaxy, distance and redshifts
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BY 4.0

Articles in the same Issue

  1. Regular Articles
  2. Model of electric charge distribution in the trap of a close-contact TENG system
  3. Dynamics of Online Collective Attention as Hawkes Self-exciting Process
  4. Enhanced Entanglement in Hybrid Cavity Mediated by a Two-way Coupled Quantum Dot
  5. The nonlinear integro-differential Ito dynamical equation via three modified mathematical methods and its analytical solutions
  6. Diagnostic model of low visibility events based on C4.5 algorithm
  7. Electronic temperature characteristics of laser-induced Fe plasma in fruits
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  9. Characterization of the effects of a plasma injector driven by AC dielectric barrier discharge on ethylene-air diffusion flame structure
  10. Impact of double-diffusive convection and motile gyrotactic microorganisms on magnetohydrodynamics bioconvection tangent hyperbolic nanofluid
  11. Dependence of the crossover zone on the regularization method in the two-flavor Nambu–Jona-Lasinio model
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  13. Heuristic decision of planned shop visit products based on similar reasoning method: From the perspective of organizational quality-specific immune
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  15. Dynamic triaxial constitutive model for rock subjected to initial stress
  16. Automatic target recognition method for multitemporal remote sensing image
  17. Gaussons: optical solitons with log-law nonlinearity by Laplace–Adomian decomposition method
  18. Adaptive magnetic suspension anti-rolling device based on frequency modulation
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  20. The heuristic model of energy propagation in free space, based on the detection of a current induced in a conductor inside a continuously covered conducting enclosure by an external radio frequency source
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  44. Simultaneous measurement of ionizing radiation and heart rate using a smartphone camera
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  48. Testing algorithm for heat transfer performance of nanofluid-filled heat pipe based on neural network
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  55. Mining reasonable distance of horizontal concave slope based on variable scale chaotic algorithms
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  57. Hopkinson rod test results and constitutive description of TRIP780 steel resistance spot welding material
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  59. Analytical solution of one-dimensional Pennes’ bioheat equation
  60. MHD squeezed Darcy–Forchheimer nanofluid flow between two h–distance apart horizontal plates
  61. Analysis of irregularity measures of zigzag, rhombic, and honeycomb benzenoid systems
  62. A clustering algorithm based on nonuniform partition for WSNs
  63. An extension of Gronwall inequality in the theory of bodies with voids
  64. Rheological properties of oil–water Pickering emulsion stabilized by Fe3O4 solid nanoparticles
  65. Review Article
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  67. Review of research, development and application of photovoltaic/thermal water systems
  68. Special Issue on Fundamental Physics of Thermal Transports and Energy Conversions
  69. Numerical analysis of sulfur dioxide absorption in water droplets
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  72. Prediction of capillary suction in porous media based on micro-CT technology and B–C model
  73. Energy equilibrium analysis in the effervescent atomization
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  79. Kinetics and thermodynamics of SO2 adsorption on metal-loaded multiwalled carbon nanotubes
  80. Effect of the inner-surface baffles on the tangential acoustic mode in the cylindrical combustor
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  82. Conserved vectors with conformable derivative for certain systems of partial differential equations with physical applications
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  84. Exact optical solitons of the perturbed nonlinear Schrödinger–Hirota equation with Kerr law nonlinearity in nonlinear fiber optics
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  88. Stable solutions to the nonlinear RLC transmission line equation and the Sinh–Poisson equation arising in mathematical physics
  89. Calculation of focal values for first-order non-autonomous equation with algebraic and trigonometric coefficients
  90. Influence of interfacial electrokinetic on MHD radiative nanofluid flow in a permeable microchannel with Brownian motion and thermophoresis effects
  91. Standard routine techniques of modeling of tick-borne encephalitis
  92. Fractional residual power series method for the analytical and approximate studies of fractional physical phenomena
  93. Exact solutions of space–time fractional KdV–MKdV equation and Konopelchenko–Dubrovsky equation
  94. Approximate analytical fractional view of convection–diffusion equations
  95. Heat and mass transport investigation in radiative and chemically reacting fluid over a differentially heated surface and internal heating
  96. On solitary wave solutions of a peptide group system with higher order saturable nonlinearity
  97. Extension of optimal homotopy asymptotic method with use of Daftardar–Jeffery polynomials to Hirota–Satsuma coupled system of Korteweg–de Vries equations
  98. Unsteady nano-bioconvective channel flow with effect of nth order chemical reaction
  99. On the flow of MHD generalized maxwell fluid via porous rectangular duct
  100. Study on the applications of two analytical methods for the construction of traveling wave solutions of the modified equal width equation
  101. Numerical solution of two-term time-fractional PDE models arising in mathematical physics using local meshless method
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  103. Fundamental solutions for the long–short-wave interaction system
  104. Role of fractal-fractional operators in modeling of rubella epidemic with optimized orders
  105. Exact solutions of the Laplace fractional boundary value problems via natural decomposition method
  106. Special Issue on 19th International Symposium on Electromagnetic Fields in Mechatronics, Electrical and Electronic Engineering
  107. Joint use of eddy current imaging and fuzzy similarities to assess the integrity of steel plates
  108. Uncertainty quantification in the design of wireless power transfer systems
  109. Influence of unequal stator tooth width on the performance of outer-rotor permanent magnet machines
  110. New elements within finite element modeling of magnetostriction phenomenon in BLDC motor
  111. Evaluation of localized heat transfer coefficient for induction heating apparatus by thermal fluid analysis based on the HSMAC method
  112. Experimental set up for magnetomechanical measurements with a closed flux path sample
  113. Influence of the earth connections of the PWM drive on the voltage constraints endured by the motor insulation
  114. High temperature machine: Characterization of materials for the electrical insulation
  115. Architecture choices for high-temperature synchronous machines
  116. Analytical study of air-gap surface force – application to electrical machines
  117. High-power density induction machines with increased windings temperature
  118. Influence of modern magnetic and insulation materials on dimensions and losses of large induction machines
  119. New emotional model environment for navigation in a virtual reality
  120. Performance comparison of axial-flux switched reluctance machines with non-oriented and grain-oriented electrical steel rotors
  121. Erratum
  122. Erratum to “Conserved vectors with conformable derivative for certain systems of partial differential equations with physical applications”
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