This paper was converted on www.awesomepapers.org from LaTeX by an anonymous user.
Want to know more? Visit the Converter page.

Do galactic bars depend on environment?: An information theoretic analysis of Galaxy Zoo 2

Suman Sarkar1, Biswajit Pandey1, Snehasish Bhattacharjee2
1 Department of Physics, Visva-Bharati University, Santiniketan, Birbhum, 731235, India
2Department of Astronomy, Osmania University, Hyderabad, 500007, India
[email protected]@[email protected]
Abstract

We use an information theoretic framework to analyze data from the Galaxy Zoo 2 project and study if there are any statistically significant correlations between the presence of bars in spiral galaxies and their environment. We measure the mutual information between the barredness of galaxies and their environments in a volume limited sample (Mr21M_{r}\leq-21) and compare it with the same in datasets where (i) the bar/unbar classifications are randomized and (ii) the spatial distribution of galaxies are shuffled on different length scales. We assess the statistical significance of the differences in the mutual information using a t-test and find that both randomization of morphological classifications and shuffling of spatial distribution do not alter the mutual information in a statistically significant way. The non-zero mutual information between barredness and environment arises due to the finite and discrete nature of the dataset which can be entirely explained by mock Poisson distributions. We also separately compare the cumulative distribution functions of the barred and unbarred galaxies as a function of their local density. Using a Kolmogorov-Smirnov test, we find that the null hypothesis can not be rejected even at 75%75\% confidence level. Our analysis indicates that environments do not play a significant role in the formation of a bar, which is largely determined by the internal processes of the host galaxy.

keywords:
methods: statistical - data analysis - galaxies: formation - evolution - cosmology: large scale structure of the Universe.
pubyear: 2020pagerange: Do galactic bars depend on environment?: An information theoretic analysis of Galaxy Zoo 2References

1 Introduction

Observations suggest that a significant fraction of spiral galaxies in the present universe are barred (Eskridge et al., 2000; Marinova & Jogee, 2007; Barazza, Jogee, & Marinova, 2008). Even our Milky Way is known to host a bar-like structure (Binney et al., 1991; Wegg, Gerhard, & Portail, 2015). Bars are extended linear structures that results from disc instabilities (Toomre, 1964). The stellar bars transfer angular momentum to the outer disc (Lynden-Bell, 1979; Athanassoula, 2002; Berentzen et al., 2007) and also help to redistribute angular momentum between the disk and the surrounding dark matter halo (Weinberg, 1985; Debattista & Sellwood, 2000; Athanassoula, 2003; Berentzen, Shlosman, & Jogee, 2006). They are efficient in driving gas into the central regions of galaxies which can trigger starbursts and AGN activity and also help to grow a central bulge component (Schwarz, 1981; Kormendy, 1982; Shlosman, Frank, & Begelman, 1989; Hunt & Malkan, 1999; Knapen et al., 1995; Knapen, Shlosman, & Peletier, 2000; Kormendy & Kennicutt, 2004; Jogee, Scoville, & Kenney, 2005; Laurikainen, Salo, & Buta, 2004; Sheth et al., 2005; Laurikainen et al., 2007). The bars can thus play a driving role in the evolution of disk galaxies.

It is still not clear if the formation and evolution of bars are purely governed by internal secular processes. Galaxies form and evolve in diverse environments in the cosmic web. They form at the centre of the dark matter halos (White & Rees, 1978) which are embedded in different environments of the cosmic web. The mass, shape and angular momentum of the dark matter halos are known to be influenced by their large-scale environments (Hahn et al., 2007, 2007b). So the environment may impart an indirect influence on the formation and evolution of galactic bars. The different assembly history of the dark matter halos causes the early-forming low mass halos to cluster more strongly as compared to the late-forming halos of similar mass (Croton, Gao & White, 2007; Gao & White, 2007). Such clustering bias of dark matter halos may indirectly affect the formation of galactic bars. The direct external influence of environments may also play a role in the formation and evolution of bars in spiral galaxies. Studies with N-body simulations suggest that tidal interactions and the passage of a companion galaxy can trigger the formation of bars in disk galaxies (Byrd & Valtonen, 1990; Gerin, Combes, & Athanassoula, 1990; Berentzen et al., 2004; Martinez-Valpuesta et al., 2017; Łokas, 2018; Ghosh et al., 2020). These trends have been also supported by observations (Elmegreen, Elmegreen, & Bellin, 1990; Giuricin et al., 1993; Méndez-Abreu et al., 2012).

It would be interesting to know whether the formation and evolution of galactic bars are influenced by their environment. A significant number of studies have been carried out to test the correlation between the environment and the presence of bars in spiral galaxies. Thompson (1981) studies the radial distribution of barred galaxies in the Coma cluster and find that a significantly larger fraction of barred galaxies is found at the cluster core. Giuricin et al. (1993) use NGC catalogue to study the effect of local galaxy density on the presence of bars and find that the early type and low luminosity spiral galaxies in high density environments tend to be barred. Eskridge et al. (2000) find a slightly higher fraction of barred galaxies in the Fornax and Virgo clusters compared to the fields. Barway, Wadadekar, & Kembhavi (2011) study bar fraction in lenticular galaxies using SDSS DR7 and find a higher fraction of barred galaxies in clusters than in the fields. Skibba et al. (2012) study the environmental dependence of bars in spiral galaxies using the Galaxy Zoo 2 project and find that the redder galaxies with higher stellar mass are more likely to have bars. They reported a significant bar-environment correlation which shows that the barred galaxies more frequently occur in denser environments than their unbarred counterparts.

Contrary to these findings, several studies reported no dependence of bars on the environment. van den Bergh (2002) investigate the dependence of bar frequency in fields, groups and cluster environments and find no evidence for any role of environment on the formation of bars. Li et al. (2009) studied the projected redshift-space two-point cross-correlation functions of barred and unbarred galaxies in the SDSS and find that at a fixed stellar mass, the clustering of barred and unbarred galaxies are indistinguishable over the scales 20 kpc - 30 Mpc. Barazza et al. (2009) find that the fraction and properties of bars in clusters and fields are quite similar. Aguerri, Méndez-Abreu, & Corsini (2009) study bar fraction as a function of local galaxy density using SDSS DR5 and find that there is no difference in the local galaxy density of barred and unbarred galaxies. Cameron et al. (2010) investigate the evolution of bar fraction in the COSMOS field and reported that the evolution of the barred galaxy populations does not depend on the large-scale environmental density. Méndez-Abreu, Sánchez-Janssen, & Aguerri (2010) use HST ACS data to study the bar fraction in the Coma cluster and find that the bar fraction does not vary significantly in the centre and outskirts of the cluster. Martínez & Muriel (2011) study the relationship between the fraction of barred spirals and a number of environmental parameters and find that the fraction of barred spirals is insensitive to their environment. Lee et al. (2012) use SDSS DR7 to study the dependence of bars on environment and find that the fraction of barred galaxies are independent of their large-scale environment when the other galaxy properties are fixed. Marinova et al. (2012) study bars in massive disk galaxies using data from the HST ACS Treasury survey of the Coma cluster and find that the bar fraction does not show a statistically significant variation across environments.

Clearly, the correlation between the occurrence of bars in spirals and their environment still remains a debated issue and there are no clear consensus on the bar-environment correlation.

Most of the studies in this field are plagued by smaller size of data samples which made it difficult to derive statistically meaningful conclusions. The SDSS (York, et al., 2000) is the largest and most successful redshift survey to date which has provided the most detailed three dimensional map of the nearby universe and a wealth of information about the individual galaxies. The Galaxy Zoo (Lintott et al., 2008, 2011) is a citizen science project based on the SDSS and HST data which invites volunteers to help in the morphological classification of a large number of galaxies by visual inspection of their images. We plan to use data from the Galaxy Zoo 2 project (Willett et al., 2013) for the present work.

Pandey & Sarkar (2017) proposed an information theoretic framework to study the correlation between the morphology of a galaxy and its large-scale environment. They considered spirals and ellipticals as two distinct morphological classes and find a synergic interaction between morphology and environment up to a length scale of 30h1Mpc30{\,h^{-1}\,{\rm Mpc}}. Recently Sarkar & Pandey (2020) show that the observed excess mutual information between morphology and environment are statistically significant at 99.9%99.9\% confidence level. Another study by Bhattacharjee, Pandey, & Sarkar (2020) show that a conditioning on stellar mass does not explain the statistically significant mutual information between morphology and environment on larger length scales. Galactic bar is an important morphological feature based on which a spiral galaxy is further classified as barred or unbarred. It would be natural to ask if there exists a bar-environment correlation similar to the correlation observed between morphology and environment.

We would like to measure the mutual information between barredness of a galaxy and its environment on different length scales. One can randomize the information about barredness and also shuffle the spatial distribution after dividing it into smaller sub-cubes. Comparing the mutual information in these distributions with that from the original distribution would allow us to test the statistical significance of any observed correlation between barredness and environment (Sarkar & Pandey, 2020). In this work, we use this information theoretic framework to test the large-scale environmental dependence of galactic bars if any.

The local density of galaxies governs the various external influences such as tidal interactions which may act as an external trigger for bar formation. Comparing the number density of galaxies at the locations of the barred and unbarred spirals can elucidate this issue. Keeping this in mind, we also separately test any effects of local density on the presence of galactic bars in spiral galaxies.

2 DATA

2.1 SDSS and Galaxy Zoo 2 data

We use data from the Sloan Digital Sky survey (SDSS) for the present analysis. We use Structured Query Language (SQL) to extract the required data from the SDSS SkyServer111https://skyserver.sdss.org/casjobs/. The SDSS (York, et al., 2000) covers 9,3769,376 square degrees of sky for spectroscopy where 2,863,6352,863,635 galaxies were chosen as targets. The morphological information of galaxies in the SDSS main-sample (Abazajian et al., 2009) is provided by Galaxy Zoo 2 (GZ2) (Willett et al., 2013). GZ2 is the second phase of the original Galaxy zoo project(GZ1) (Lintott et al., 2008) 222http://zoo1.galaxyzoo.org which is a citizen scientist programme for morphological classification of galaxies through visual inspection of images. GZ1 provides morphological classifications of 900000\sim 900000 galaxies drawn from the SDSS. GZ2 targets a subset of 300000\sim 300000 galaxies from GZ1 for a more detailed morphological classifications. The GZ2 decision tree consists of a total of 11 tasks. It differentiates the galaxies having a disk from the smooth (E/S0) ones and also record the various prominent features of the galaxies like presence of bars, number of spiral arms, arc or lens shapes. We combine the zoo2Mainspeczzoo2Mainspecz table with specobjAllspecobjAll and photozphotoz to retrieve the required information. We use a critical value of debiased vote fraction to select the spirals (t04_spiral_a08_spiral_debiased>0.6t04\_spiral\_a08\_spiral\_debiased>0.6). The barred and unbarred spirals are selected using a similar cut-off in the value of the debiased vote fraction (t03_bar_a06_bar_debiased>0.6t03\_bar\_a06\_bar\_debiased>0.6 and t03_bar_a07_no_bar_debiased>0.6t03\_bar\_a07\_no\_bar\_debiased>0.6). The cut-off values for the debiased vote fractions were chosen so as to have a reasonable number of galaxies in the volume limited sample to be prepared. We identify a contiguous region in the northern galactic hemisphere and select all the classified barred and unbarred spirals between right ascension 135135^{\circ} and 255255^{\circ} and declination 00^{\circ} and 6060^{\circ}. We prepare a volume limited sample by restricting the r-band Petrosian absolute magnitude to Mr21M_{r}\leq-21. The galactic extinction corrected r-band Petrosian apparent magnitude limit of the sample is mr<17m_{r}<17. We get a volume limited sample which extends upto redshift z0.087z\leq 0.087 and contains a total 1126011260 galaxies (22142214 barred and 90469046 unbarred). We then extract all the galaxies within a cubic region of sides 132h1Mpc132{\,h^{-1}\,{\rm Mpc}} from the volume limited sample. This is the largest cube that can fit within the volume limited sample. The resulting cube contains a total 34203420 galaxies of which 690690 are barred and 27302730 are unbarred. We show the definition of the volume limited sample, the spatial distributions of the galaxies in the sample and the variations of comoving number density in it in Figure 1.

We use the Λ\LambdaCDM cosmological model with Ωm0=0.315\Omega_{m0}=0.315, ΩΛ0=0.685\Omega_{\Lambda 0}=0.685 and h=0.674h=0.674 (Planck Collaboration, et al., 2018) for conversion of redshifts to comoving distances.

2.2 Mock Poisson samples

We generate 1010 mock Poisson samples each with 34203420 points distributed within a cubic region of side 132h1Mpc132{\,h^{-1}\,{\rm Mpc}}. We randomly label 690690 points as barred and 27302730 points as unbarred in each of these distributions. These mock data sets have the same number of galaxies as in the actual SDSS data cube. The ratio of barred to unbarred spirals in the mock samples are also kept same as the actual data.

Refer to caption
Refer to caption
Refer to caption
Refer to caption
Figure 1: The top left panel shows the definition of the volume limited sample used in the present analysis. The projected distribution of the galaxies in the volume limited sample is shown in the top right panel. The galaxies within the cubic region are shown with a different colour in the same panel. The bottom left panel shows the spatial distribution of the barred and unbarred spirals in the cubic region defined within the volume limited sample. The bottom right panel shows the comoving number density of galaxies as a function of distance along three different axes within the cubic region. The comoving number densities are obtained in uniform slices of thickness 10.15h1Mpc10.15{\,h^{-1}\,{\rm Mpc}}.

3 Method of analysis

We now have a magnitude limited sample of the spiral galaxies defined within a cubic region of size Lh1MpcL{\,h^{-1}\,{\rm Mpc}}. We divide this cube in Nd=ng3N_{d}=n_{g}^{3} voxels with size d=(Lng)h1Mpcd=(\frac{L}{n_{g}}){\,h^{-1}\,{\rm Mpc}}. One can have different sets of voxels by changing the number of grids ngn_{g}. Different choices of ngn_{g} allow us to study the environment on different length scales.

Let us now define a discrete random variable XX that represents the environment at a certain length scale. The probability of finding a randomly selected galaxy in the ithi^{th} voxel will be p(Xi)=NiNp(X_{i})=\frac{N_{i}}{N}, where NiN_{i} is the number of galaxies in the ithi^{th} voxel and N=i=1NdNiN=\sum_{i=1}^{N_{d}}{N_{i}} is the total number of galaxies in the entire cube. The random variable XX have NdN_{d} outcomes ({Xi:i=1,2,.Nd}\{X_{i}:i=1,2,....N_{d}\}).

The information entropy (Shannon, 1948) associated with the random variable XX on length scale of dh1Mpcd{\,h^{-1}\,{\rm Mpc}} is given by,

H(X)\displaystyle H(X) =\displaystyle= i=1Ndp(Xi)logp(Xi)\displaystyle-\sum_{i=1}^{N_{d}}p(X_{i})\log p(X_{i}) (1)
=\displaystyle= logNi=1NdNilogNiN.\displaystyle\log N-\sum_{i=1}^{N_{d}}\frac{N_{i}\log N_{i}}{N}.

Let us now define another variable YY that represents the presence or absence of bar in spiral galaxies. Here, YY only takes two values Y1Y_{1} for barred & Y2Y_{2} for unbarred. If there are N1N_{1} barred and N2N_{2} unbarred galaxies in the cube then the Shannon entropy associated with the variable YY will be

H(Y)\displaystyle H(Y) =\displaystyle= (N1NlogN1N+N2NlogN2N)\displaystyle-\left(\frac{N_{1}}{N}\log\frac{N_{1}}{N}+\frac{N_{2}}{N}\log\frac{N_{2}}{N}\right) (2)
=\displaystyle= logNN1logN1+N2logN2N\displaystyle\log N-\frac{N_{1}\log N_{1}+N_{2}\log N_{2}}{N}

Here, N=N1+N2N=N_{1}+N_{2} is the total number of galaxies in the sample. Any variation in the voxel size will not change the number of barred and unbarred galaxies and the value of H(Y)H(Y) is independent of the grid size.

We also calculate the joint entropy for the variables XX and YY. Which is given by,

H(X,Y)\displaystyle H(X,Y) =\displaystyle= i=1Ndj=22p(Xi,Yj)logp(Xi,Yj)\displaystyle-\sum_{i=1}^{N_{d}}\sum_{j=2}^{2}p(X_{i},Y_{j})\log p(X_{i},Y_{j}) (3)
=\displaystyle= logN1Ni=1Ndj=12NijlogNij.\displaystyle\log N-\frac{1}{N}\sum_{i=1}^{N_{d}}\sum_{j=1}^{2}N_{ij}\log N_{ij}.

Here NijN_{ij} is the number of galaxies that resides in the ithi^{th} voxel and belongs to the jthj^{th} morphology. So we have,

i=1Ndj=12Nij=N.\displaystyle\sum_{i=1}^{N_{d}}{\sum_{j=1}^{2}{N_{ij}}}=N.

If the two variables XX and YY are uncorrelated then H(X)+H(Y)=H(X,Y)H(X)+H(Y)=H(X,Y). Otherwise the joint entropy would be smaller than the some of the individual entropies, i.e. H(X,Y)<H(X)+H(Y)H(X,Y)<H(X)+H(Y).

We calculate the mutual information between the two variables XX and YY as,

I(X;Y)\displaystyle I(X;Y) =\displaystyle= H(X)+H(Y)H(X,Y)\displaystyle H(X)+H(Y)-H(X,Y) (4)

The mutual information measures the amount of information shared between two random variables. In other words, it is the reduction in uncertainty in the outcome of one random variable due to the pre-existing knowledge of the other. Higher the mutual information, greater is the association between the two variables. The mutual information measures the association between two random variables irrespective of the nature of the random variables and their relationship.

3.1 Randomizing the classification of barred and unbarred galaxies

We temporarily obliterate the actual bar/unbar classifications of the spiral galaxies in the cube and randomly tag each of them as barred or unbarred. We do this in such a way that the total number of barred and unbarred galaxies in the resulting distribution remains the same as before. Such randomization of classifications would not change H(X)H(X) or H(Y)H(Y) but the joint entropy H(X,Y)H(X,Y) of the resulting distribution is expected to change when the variables are correlated. The randomization procedure destroys any existing correlations between XX and YY turning them into independent random variables. So any mutual information of physical origin should ideally diminish to zero after the randomization. We generate 10 randomized datasets from the actual data for our analysis.

3.2 Shuffling the spatial distribution

The SDSS data cube of sides L=132h1MpcL=132{\,h^{-1}\,{\rm Mpc}} is divided into Nc=ns3N_{c}={n_{s}}^{3} sub cubes of size ls=Lnsl_{s}=\frac{L}{n_{s}}, where nsn_{s} is the number of segments on each side. We shuffle (Bhavsar & Ling, 1988) the sub-cubes to obtain a new distribution which contains same number of galaxies distributed within the same volume. This will destroy any existing correlations between the environment and the barredness beyond the size of the sub-cubes used for shuffling. A detail description of the shuffling procedure can be found in Sarkar & Pandey (2020). The sub cubes are randomly interchanged with random rotations in multiples of 9090^{\circ} along any of the three axes. We repeated this process for 100×Nc100\times N_{c} times so as to allow each of the sub-cubes to swap its position multiple times, with other sub-cubes. The shuffling exercise is performed for three different sizes of shuffling unit, ns=3n_{s}=3, ns=7n_{s}=7 and ns=11n_{s}=11 which corresponds to ls=44h1Mpcl_{s}=44{\,h^{-1}\,{\rm Mpc}}, ls19h1Mpcl_{s}\sim 19{\,h^{-1}\,{\rm Mpc}} and ls=12h1Mpcl_{s}=12{\,h^{-1}\,{\rm Mpc}} respectively. In order to avoid any spurious correlations, we chose the size of the shuffling units to be different from the grid sizes used for the estimation of mutual information. We also ensure that the size of the shuffling units are not integral multiple of grid sizes and vice versa. We generate 10 shuffled realizations from the original SDSS data for the present analysis.

3.3 Testing statistical significance of mutual information with t-test

An equal variance tt-test is used to estimate the statistical significance of the mutual information between environment and barredness of the spiral galaxies in the actual SDSS data. At each length scale, we compare the mutual information obtained for randomized or shuffled data with that from the original SDSS data. The tt-score at each length scale is given by,

t=|μ1μ2|σs1n1+1n2.\displaystyle t=\frac{|{\mu_{1}}-{\mu_{2}}|}{\sigma_{s}\sqrt{\frac{1}{n_{1}}+\frac{1}{n_{2}}}}. (5)

Here μ1{\mu_{1}} and μ2{\mu_{2}} are the mean values and σ1\sigma_{1} and σ2\sigma_{2} are the standard deviations at a given length scale for the two data sets under consideration. σs=(n11)σ12+(n21)σ22n1+n22\sigma_{s}=\sqrt{\frac{(n_{1}-1)\sigma_{1}^{2}+(n_{2}-1)\sigma_{2}^{2}}{n_{1}+n_{2}-2}}, where n1n_{1} and n2n_{2} are the number of samples used to estimate mean and standard deviation of the two data sets. (n1+n22)(n_{1}+n_{2}-2) is the degree of freedom associated with the test.

Refer to caption
Refer to caption
Refer to caption
Refer to caption
Figure 2: The top left panel shows the mutual information between barredness and environment as a function of length scales in the original SDSS data cube, mock Poisson data cubes and SDSS data cubes with randomized bar/unbar classification. The 1σ1-\sigma errorbars for the SDSS (randomized) and Poisson random datasets are obtained using 10 different realizations for each. We estimate the 1σ1-\sigma errorbars for the original SDSS data using 1010 jack-knife samples drawn from the original dataset. The top right panel shows the t score between original and randomized SDSS data at different length scales. The bottom left and bottom right panels of this figure show the same but for the shuffled SDSS data along with original SDSS data and mock Poisson datasets. The spatial distribution of galaxies within the SDSS data cube is shuffled with three different shuffling lengths and the corresponding results are shown together in the bottom left and bottom right panel of this figure. In each case, we ensure that the size of the sub-cubes used for shuffling the data, is not equal or integral multiple of the grid sizes used for calculating the mutual information.
Refer to caption
Figure 3: This shows the cumulative distribution function of barred and unbarred spirals as a function of local galaxy density.
Table 1: This table shows the tt scores between actual SDSS data and SDSS data with randomized bar/unbar classifications, at different length scales. The pp values associated with the tt scores are also listed in the same table.
Grid size ( h1Mpc{\,h^{-1}\,{\rm Mpc}} ) tt score pp value
11.00 1.551 6.91×1026.91\times 10^{-2}
12.00 1.292 1.06×1011.06\times 10^{-1}
13.20 2.366 1.47×1021.47\times 10^{-2}
14.67 2.182 2.13×1022.13\times 10^{-2}
16.50 1.819 4.28×1024.28\times 10^{-2}
18.86 3.912 5.11×1045.11\times 10^{-4}
22.00 1.086 1.46×1011.46\times 10^{-1}
26.40 2.255 1.84×1021.84\times 10^{-2}
33.00 1.547 6.96×1026.96\times 10^{-2}
44.00 0.788 2.20×1012.20\times 10^{-1}
66.00 2.700 7.32×1037.32\times 10^{-3}
Table 2: This table shows the tt scores between original SDSS data and shuffled versions of the SDSS data, at different length scales. Three different shuffling length scales were used in the analysis. We ensure that for each nsn_{s}, the size of the sub-cubes employed for shuffling the data is not equal or integral multiple of the grid size used for the estimation of mutual information. The pp values associated with the tt scores are also tabulated here.
Grid size ns=3n_{s}=3 ns=7n_{s}=7 ns=11n_{s}=11
( h1Mpc{\,h^{-1}\,{\rm Mpc}} ) tt score pp value tt score pp value tt score pp value
11.00 - - 11.785 3.37×10103.37\times 10^{-10} 17.841 3.42×10133.42\times 10^{-13}
12.00 0.314 3.79×1013.79\times 10^{-1} 4.775 7.56×1057.56\times 10^{-5} - -
13.20 2.341 1.55×1021.55\times 10^{-2} 5.783 8.82×1068.82\times 10^{-6} 9.027 2.10×1082.10\times 10^{-8}
14.67 - - 3.281 2.08×1032.08\times 10^{-3} 2.590 9.24×1039.24\times 10^{-3}
16.50 0.721 2.40×1012.40\times 10^{-1} 1.452 8.19×1028.19\times 10^{-2} 0.903 1.89×1011.89\times 10^{-1}
18.86 0.029 4.89×1014.89\times 10^{-1} - - 1.239 1.16×1011.16\times 10^{-1}
22.00 - - 2.792 6.02×1036.02\times 10^{-3} 2.295 1.70×1021.70\times 10^{-2}
26.40 0.056 4.78×1014.78\times 10^{-1} 0.524 3.03×1013.03\times 10^{-1} 0.154 4.40×1014.40\times 10^{-1}
33.00 0.104 4.59×1014.59\times 10^{-1} 0.095 4.63×1014.63\times 10^{-1} 0.254 4.01×1014.01\times 10^{-1}
44.00 - - 0.424 3.38×1013.38\times 10^{-1} 0.787 2.21×1012.21\times 10^{-1}
66.00 2.250 1.86×1021.86\times 10^{-2} 1.336 9.91×1029.91\times 10^{-2} 2.539 1.03×1021.03\times 10^{-2}
Table 3: This table lists the critical values DKS(α)D_{KS}(\alpha) for different significance level α\alpha in the Kolmogorov-Smirnov test
significance level (α\alpha) 0.005 0.01 0.05 0.1 0.25
Confidence level 99.5% 99% 95% 90% 75%
DKS(α)D_{KS}(\alpha) 0.085584 0.080481 0.067154 0.060517 0.050420

3.4 Local density of barred and unbarred spirals

We find the distance to the kthk^{th} nearest neighbour for each galaxies in the cube and estimate the local number density of galaxies around it. The kthk^{th} nearest neighbour density (Casertano & Hut, 1985) around a galaxy is defined as

ηk=k1V(rk)\displaystyle\eta_{k}=\frac{k-1}{V(r_{k})} (6)

Here rkr_{k} is the distance to the kthk^{th} nearest neighbour and V(rk)=43πrk3V(r_{k})=\frac{4}{3}\pi r_{k}^{3} is the volume of the sphere having a radius rkr_{k}. In this work, we have used k=4k=4. The value of rkr_{k} could be overestimated for the galaxies near the boundary of the cube. Consequently, the local density of the galaxies near the boundary could be underestimated. We calculate the local density for only those galaxies which have rk<rbr_{k}<r_{b}, where rbr_{b} is the closest distance of the galaxy from the boundary wall. After using this criteria we are left with N1=514N_{1}^{{}^{\prime}}=514 barred galaxies and N2=2002N_{2}^{{}^{\prime}}=2002 unbarred galaxies. We estimate the densities at the locations of the barred and unbarred galaxies using Equation 6. The local density is estimated in units of h3Mpc3h^{3}\,\rm{Mpc}^{-3}.

3.5 Testing the difference in the local density of barred and unbarred spirals with Kolmogorov-Smirnov test

We use the two-sample Kolmogorov-Smirnov test to compare the cumulative distributions of density for the barred and unbarred spirals. The Kolmogorov-Smirnov test is a non-parametric test which makes no assumption about the distributions. The null hypothesis assumes that both barred and unbarred galaxies are sampled from populations with identical distributions. We calculate the maximum difference between the two cumulative distributions. The supremum difference between the two cumulative distribution functions DKSD_{KS} is defined as

DKS\displaystyle D_{KS} =\displaystyle= supηk{|f1,m(ηk)f2,m(ηk)|}\displaystyle\sup_{\eta_{k}}\,\,\{\,\,|f_{1,m}(\eta_{k})-f_{2,m}(\eta_{k})|\,\,\} (7)

f1,m(ηk)f_{1,m}(\eta_{k}) and f2,m(ηk)f_{2,m}(\eta_{k}) are the cumulative distribution functions for barred and unbarred spirals at the mthm^{th} bin where m{1,2,3.,N}m\in\{1,2,3....,N^{{}^{\prime}}\}. sup\sup is the operator that finds the supremum of all the (N1+N2N_{1}^{{}^{\prime}}+N_{2}^{{}^{\prime}}) differences. Here m=1Nf1,m(ηk)=m=1Nf2,m(ηk)=1\sum_{m=1}^{N^{{}^{\prime}}}{f_{1,m}(\eta_{k})}=\sum_{m=1}^{N^{{}^{\prime}}}{f_{2,m}(\eta_{k})}=1.

The critical value of DKSD_{KS} for a given significance level (α\alpha) is given by,

DKS(α)\displaystyle D_{KS}(\alpha) =\displaystyle= ln(α2)N1+N22N1N2\displaystyle\sqrt{-\ln\left(\frac{\alpha}{2}\right)\,\,\,\frac{N_{1}^{{}^{\prime}}+N_{2}^{{}^{\prime}}}{2N_{1}^{{}^{\prime}}N_{2}^{{}^{\prime}}}} (8)

where N1N_{1}^{{}^{\prime}} and N2N_{2}^{{}^{\prime}} are the number of barred and unbarred spirals in the sample. If DKS>DKS(α)D_{KS}>D_{KS}(\alpha) then the null hypothesis can be rejected at level α\alpha. We test the null hypothesis at different significance level to find if the distributions of barred and unbarred spirals are same or different.

4 Results

4.1 Effects of large-scale environments on galactic bars

We compare the mutual information in the randomized and shuffled data sets to that with the original SDSS data in Figure 2. The mutual information between barredness and environment in the SDSS data is shown in the top left panel of Figure 2. We find a non-zero mutual information between the barredness and the environment in the SDSS data, which decreases with increasing length scales. We would like to test the statistical significance of these non-zero mutual information. When we compare the mutual information in the original data with the randomized data sets, we find that the randomization of barred/unbarred classifications have no impact on the mutual information between barredness and environment. We also compare our results to mock Poisson distributions to understand the relevance of the observed non-zero mutual information. We find that the observed mutual information in the SDSS and Poisson data sets are nearly identical beyond 15h1Mpc15{\,h^{-1}\,{\rm Mpc}}. The mock Poisson samples show a relatively higher mutual information than the original and randomized data at length scales below 15h1Mpc15{\,h^{-1}\,{\rm Mpc}}. This may purely arise due to the dominance of Poisson noise on smaller length scales. This suggests that the observed non-zero mutual information originate from the finite and discrete character of the distributions. We use an equal variance Student’s t-test to identify any statistically significant differences between the original and randomized data sets. The t-scores at different length scales together with the critical value of t-score at 99.9%99.9\% confidence level are shown in the top right panel of Figure 2. We tabulate the t-score and the associated p-value at each length scale in Table 1. The results show that the null hypothesis can not be rejected at this confidence interval. This analysis suggests that the large-scale environment of galaxies have no influence on galactic bars.

We then compare the mutual information in the original and shuffled data sets in the bottom left panel of Figure 2. The plot shows that shufffling the spatial distribution of galaxies with three different shuffling lengths have little to no influence on the mutual information. The process of shuffling the data is expected to destroy the mutual information at all length scales beyond the shuffling length. However, we do not observe any such decrease of mutual information when the data is shuffled on different length scales. We notice an increase in the mutual information in shuffled data sets at the smallest length scales. This is contrary to what one would expect in a shuffled distribution. When we compare our results with that from mock Poisson distribution, it shows that an even higher mutual information is observed in the Poisson distributions at the smallest length scale. Shuffling the data randomizes the spatial distribution of galaxies and enhances the Poisson character of the distribution. It may be noted that the mean-intergalactic separation of our sample is 9h1Mpc\sim 9{\,h^{-1}\,{\rm Mpc}} and the measurements of mutual information near these length scales would be dominated by Poisson noise. Evidently, we do not assign any physical significance to the increase in mutual information in the shuffled data at smallest length scales. The t-scores at different length scales in the shuffled data sets are shown in the bottom right panel of Figure 2. The critical t-score at 99.9%99.9\% confidence level is also shown together in the same panel. The t-score and the associated p-value at each length scale for each shuffling length are tabulated in Table 2. The results clearly show that the null hypothesis can not be rejected at 99.9%99.9\% confidence level. Shuffling the spatial distribution on different length scales do not alter the mutual information between the barredness and the environment in a statistically significant way. This again suggests that the large-scale environment do not play a significant role on the formation of a bar in spiral galaxy.

4.2 Effects of small-scale environments on galactic bars

We also test if the local density of barred and unbarred galaxies are different in a statistically significant way. We estimate the local densities at the locations of barred and unbarred spiral galaxies. We compare their cumulative distribution functions as a function of local density in Figure 3. We perform a Kolmogorov-Smirnov test to assess the statistical significance of the differences between the two distributions. We find the maximum difference between the two cumulative distribution functions to be DKS=0.044240D_{KS}=0.044240. The critical values of DKS(α)D_{KS}(\alpha) for different significance level α\alpha are tabulated in Table 3. The null hypothesis can be rejected if DKS>DKS(α)D_{KS}>D_{KS}(\alpha). We find that the null hypothesis can not be rejected even at 75%75\% confidence level. This implies that the local density field of the barred and unbarred galaxies do not differ in a statistically significant manner and they reside in similar environments on smaller length scales. So the small-scale environment can not be held accountable for presence or absence of galactic bars in spiral galaxies.

5 Conclusions

Understanding the role of bars in spiral galaxies are central to understanding their formation. Bars are known to be the most efficient means to redistribute materials inside a galaxy. It is yet not clearly known why some spiral galaxies host a bar while others do not. The detail process of bar formation may be governed by several factors. Both the internal secular processes and external triggers may induce the bar formation in a galaxy. Besides, the large-scale environment and the assembly history of dark matter halos may also have an influence on the formation of galactic bars. All these possibilities must be tested against observations to identify the most influential factors governing the formation of galactic bars.

We have calculated the mutual information between the barredness of a galaxy and its environment on different length scales using the SDSS and Galaxy Zoo 2 data. We randomize the bar/unbar classification of galaxies and measure the mutual information between the barredness and environment. There are no statistically significant change in the mutual information between barredness and environment after the classifications are randomized. We also shuffle the spatial distribution of SDSS galaxies after dividing it in smaller sub-cubes and randomly interchanging their spatial locations along with random rotations. We also do not observe any statistically significant difference in the mutual information between the barredness and the environment after the data is shuffled on different length scales. The analysis do not provide any strong evidence against the null hypothesis which suggests that the large-scale environment of barred and unbarred galaxies are similar and there are no correlations between the barredness of a galaxy and its large-scale environment.

We also separately test any possible influence of local density on the presence of galactic bars. We measure the local density at the locations of barred and unbarred galaxies and then compare their cumulative distribution functions using a Kolmogorov-Smirnov test. The test favours the null hypothesis which indicates that the local density of barred and unbarred galaxies are quite similar. A study of the bar fraction in nearby galaxy clusters suggests that the bar formation in low-mass galaxies are expected to be more susceptible to their environment than the bright or massive galaxies (Méndez-Abreu et al., 2012). The volume limited sample analyzed in this work consists of brighter galaxies for which the bar formation may be unaffected by their environment.

In the present work, we explore any possible role of the small-scale and large-scale environments of galaxies on the formation of galactic bars. Our analysis clearly indicates that the presence or absence of bars in spiral galaxies do not depend on either their small-scale or large-scale environments. This suggests that the formation of galactic bar is largely decided by the internal processes of the host galaxy.

6 Data availability

The data underlying this article is available in https://skyserver.sdss.org/casjobs/ . The datasets were derived from sources in the public domain: https://www.sdss.org/ and http://zoo1.galaxyzoo.org .

7 ACKNOWLEDGEMENT

We sincerely thank an anonymous reviewer for insightful comments and suggestions. The authors would like to thank the SDSS and Galaxy Zoo team for making the data public. The authors also acknowledge the efforts of the Galaxy Zoo 2 volunteers for the detailed visual morphological classifications of the SDSS galaxies, without which this work would not be possible.

BP would like to acknowledge financial support from the SERB, DST, Government of India through the project CRG/2019/001110. BP would also like to acknowledge IUCAA, Pune for providing support through associateship programme.

Funding for the SDSS and SDSS-II has been provided by the Alfred P. Sloan Foundation, the Participating Institutions, the National Science Foundation, the U.S. Department of Energy, the National Aeronautics and Space Administration, the Japanese Monbukagakusho, the Max Planck Society, and the Higher Education Funding Council for England. The SDSS website is http://www.sdss.org/.

The SDSS is managed by the Astrophysical Research Consortium for the Participating Institutions. The Participating Institutions are the American Museum of Natural History, Astrophysical Institute Potsdam, University of Basel, University of Cambridge, Case Western Reserve University, University of Chicago, Drexel University, Fermilab, the Institute for Advanced Study, the Japan Participation Group, Johns Hopkins University, the Joint Institute for Nuclear Astrophysics, the Kavli Institute for Particle Astrophysics and Cosmology, the Korean Scientist Group, the Chinese Academy of Sciences (LAMOST), Los Alamos National Laboratory, the Max-Planck-Institute for Astronomy (MPIA), the Max-Planck-Institute for Astrophysics (MPA), New Mexico State University, Ohio State University, University of Pittsburgh, University of Portsmouth, Princeton University, the United States Naval Observatory, and the University of Washington.

References

  • Abazajian et al. (2009) Abazajian K. N., Adelman-McCarthy J. K., Agüeros M. A., Allam S. S., Allende Prieto C., An D., Anderson K. S. J., et al., 2009, ApJS, 182, 543
  • Aguerri, Méndez-Abreu, & Corsini (2009) Aguerri J. A. L., Méndez-Abreu J., Corsini E. M., 2009, A&A, 495, 491
  • Athanassoula (2002) Athanassoula E., 2002, ApJL, 569, L83
  • Athanassoula (2003) Athanassoula E., 2003, MNRAS, 341, 1179
  • Barazza, Jogee, & Marinova (2008) Barazza F. D., Jogee S., Marinova I., 2008, ApJ, 675, 1194
  • Barazza et al. (2009) Barazza F. D., Jablonka P., Desai V., Jogee S., Aragón-Salamanca A., De Lucia G., Saglia R. P., et al., 2009, A&A, 497, 713
  • Barway, Wadadekar, & Kembhavi (2011) Barway S., Wadadekar Y., Kembhavi A. K., 2011, MNRAS, 410, L18
  • Berentzen et al. (2004) Berentzen I., Athanassoula E., Heller C. H., Fricke K. J., 2004, MNRAS, 347, 220
  • Berentzen, Shlosman, & Jogee (2006) Berentzen I., Shlosman I., Jogee S., 2006, ApJ, 637, 582
  • Berentzen et al. (2007) Berentzen I., Shlosman I., Martinez-Valpuesta I., Heller C. H., 2007, ApJ, 666, 189
  • Bhattacharjee, Pandey, & Sarkar (2020) Bhattacharjee S., Pandey B., Sarkar S., 2020, JCAP, 039 (2020)
  • Bhavsar & Ling (1988) Bhavsar, S. P. & Ling, E. N. 1988, ApJ Letters, 331, L63
  • Binney et al. (1991) Binney J., Gerhard O. E., Stark A. A., Bally J., Uchida K. I., 1991, MNRAS, 252, 210
  • Byrd & Valtonen (1990) Byrd G., Valtonen M., 1990, ApJ, 350, 89
  • Cameron et al. (2010) Cameron E., Carollo C. M., Oesch P., Aller M. C., Bschorr T., Cerulo P., Aussel H., et al., 2010, MNRAS, 409, 346
  • Casertano & Hut (1985) Casertano S., Hut P., 1985, ApJ, 298, 80
  • Croton, Gao & White (2007) Croton D. J., Gao L., White S. D. M., 2007, MNRAS, 374, 1303
  • Debattista & Sellwood (2000) Debattista V. P., Sellwood J. A., 2000, ApJ, 543, 704
  • Elmegreen, Elmegreen, & Bellin (1990) Elmegreen D. M., Elmegreen B. G., Bellin A. D., 1990, ApJ, 364, 415
  • Eskridge et al. (2000) Eskridge P. B., Frogel J. A., Pogge R. W., Quillen A. C., Davies R. L., DePoy D. L., Houdashelt M. L., et al., 2000, AJ, 119, 536
  • Gao & White (2007) Gao L., White S. D. M., 2007, MNRAS, 377, L5
  • Gerin, Combes, & Athanassoula (1990) Gerin M., Combes F., Athanassoula E., 1990, A&A, 230, 37
  • Ghosh et al. (2020) Ghosh S., Saha K., Di Matteo P., Combes F., 2020, arXiv, arXiv:2008.04942
  • Giuricin et al. (1993) Giuricin G., Mardirossian F., Mezzetti M., Monaco P., 1993, ApJ, 407, 22
  • Hahn et al. (2007) Hahn, O., Porciani, C., Carollo, C. M., & Dekel, A. 2007, MNRAS, 375, 489
  • Hahn et al. (2007b) Hahn, O., Carollo, C. M., Porciani, C., & Dekel, A. 2007, MNRAS, 381, 41
  • Hunt & Malkan (1999) Hunt L. K., Malkan M. A., 1999, ApJ, 516, 660
  • Jogee, Scoville, & Kenney (2005) Jogee S., Scoville N., Kenney J. D. P., 2005, ApJ, 630, 837
  • Knapen et al. (1995) Knapen J. H., Beckman J. E., Heller C. H., Shlosman I., de Jong R. S., 1995, ApJ, 454, 623
  • Knapen, Shlosman, & Peletier (2000) Knapen J. H., Shlosman I., Peletier R. F., 2000, ApJ, 529, 93
  • Kormendy (1982) Kormendy J., 1982, ApJ, 257, 75
  • Kormendy & Kennicutt (2004) Kormendy J., Kennicutt R. C., 2004, ARA&A, 42, 603
  • Laurikainen, Salo, & Buta (2004) Laurikainen E., Salo H., Buta R., 2004, ApJ, 607, 103
  • Laurikainen et al. (2007) Laurikainen E., Salo H., Buta R., Knapen J. H., 2007, MNRAS, 381, 401
  • Lee et al. (2012) Lee G.-H., Park C., Lee M. G., Choi Y.-Y., 2012, ApJ, 745, 125
  • Li et al. (2009) Li C., Gadotti D. A., Mao S., Kauffmann G., 2009, MNRAS, 397, 726
  • Lintott et al. (2008) Lintott, C. J., Schawinski, K., Slosar, A., et al. 2008, MNRAS, 389, 1179
  • Lintott et al. (2011) Lintott, C., Schawinski, K., Bamford, S., et al. 2011, MNRAS, 410, 166
  • Łokas (2018) Łokas E. L., 2018, ApJ, 857, 6
  • Lynden-Bell (1979) Lynden-Bell D., 1979, MNRAS, 187, 101
  • Marinova & Jogee (2007) Marinova I., Jogee S., 2007, ApJ, 659, 1176
  • Marinova et al. (2012) Marinova I., Jogee S., Weinzirl T., Erwin P., Trentham N., Ferguson H. C., Hammer D., et al., 2012, ApJ, 746, 136
  • Martínez & Muriel (2011) Martínez H. J., Muriel H., 2011, MNRAS, 418, L148
  • Martinez-Valpuesta et al. (2017) Martinez-Valpuesta I., Aguerri J. A. L., González-García A. C., Dalla Vecchia C., Stringer M., 2017, MNRAS, 464, 1502
  • Méndez-Abreu, Sánchez-Janssen, & Aguerri (2010) Méndez-Abreu J., Sánchez-Janssen R., Aguerri J. A. L., 2010, ApJL, 711, L61
  • Méndez-Abreu et al. (2012) Méndez-Abreu J., Sánchez-Janssen R., Aguerri J. A. L., Corsini E. M., Zarattini S., 2012, ApJL, 761, L6
  • Pandey & Sarkar (2017) Pandey B., Sarkar S., 2017, MNRAS, 467, L6
  • Planck Collaboration, et al. (2018) Planck Collaboration, et al., 2018, arXiv, arXiv:1807.06209
  • Sarkar & Pandey (2020) Sarkar S., Pandey B., 2020, MNRAS, 497, 4077
  • Schwarz (1981) Schwarz M. P., 1981, ApJ, 247, 77
  • Shannon (1948) Shannon, C. E.  1948, Bell System Technical Journal, 27, 379-423, 623-656
  • Sheth et al. (2005) Sheth K., Vogel S. N., Regan M. W., Thornley M. D., Teuben P. J., 2005, ApJ, 632, 217
  • Shlosman, Frank, & Begelman (1989) Shlosman I., Frank J., Begelman M. C., 1989, Nature, 338, 45
  • Skibba et al. (2012) Skibba R. A., Masters K. L., Nichol R. C., Zehavi I., Hoyle B., Edmondson E. M., Bamford S. P., et al., 2012, MNRAS, 423, 1485
  • Thompson (1981) Thompson L. A., 1981, ApJL, 244, L43
  • Toomre (1964) Toomre A., 1964, ApJ, 139, 1217
  • van den Bergh (2002) van den Bergh S., 2002, AJ, 124, 782
  • Wegg, Gerhard, & Portail (2015) Wegg C., Gerhard O., Portail M., 2015, MNRAS, 450, 4050
  • Weinberg (1985) Weinberg M. D., 1985, MNRAS, 213, 451
  • White & Rees (1978) White, S. D. M., & Rees, M. J. 1978, MNRAS, 183, 341
  • Willett et al. (2013) Willett K. W., Lintott C. J., Bamford S. P., Masters K. L., Simmons B. D., Casteels K. R. V., Edmondson E. M., et al., 2013, MNRAS, 435, 2835
  • York, et al. (2000) York D. G., et al., 2000, AJ, 120, 1579