EIG - II. Intriguing characteristics of the most extremely isolated galaxies

TL;DR

This study investigates extremely isolated galaxies, revealing that their star formation is similar to field galaxies but showing environmental effects on their HI content and morphology, with more isolated galaxies tending to be early-types.

Contribution

First to show that highly isolated galaxies can have a higher fraction of early-types and lower HI content, challenging previous assumptions about isolation effects.

Findings

Isolated galaxies are mostly blue, star-forming, and follow main sequence relations.

More isolated galaxies tend to be early-types with lower HI content.

Star formation and morphology are governed by similar mechanisms regardless of type.

Abstract

We have selected a sample of 41 extremely isolated galaxies (EIGs) from the local universe using both optical and HI ALFALFA redshifts (Spector & Brosch 2016). Narrow band H$_\alpha$ and wide band imaging along with public data were used to derive star formation rates (SFRs), star formation histories (SFHs), and morphological classifications for the EIGs. We have found that the extreme isolation of the EIGs does not affect considerably their star-formation compared to field galaxies. EIGs are typically `blue cloud' galaxies that fit the `main sequence of star forming galaxies' and may show asymmetric star formation and strong compact star-forming regions. We discovered surprising environmental dependencies of the HI content, M$_{HI}$, and of the morphological type of EIGs; The most isolated galaxies (of subsample EIG-1) have lower M$_{HI}$ on average (with $2.5\,\sigma$ confidence) and a higher tendency to be early-types (with 0.94 confidence) compared to the less isolated galaxies of subsample EIG-2. To the best of our knowledge this is the first study that finds an effect in which an isolated sample shows a higher fraction of early-types compared to a less isolated sample. Both early-type and late-type EIGs follow the same colour-to-M$_{*}$, SFR-to-M$_{*}$ (`main sequence') and M$_{HI}$-to-$M_{*}$ relations. This indicates that the mechanisms and factors governing star formation, colour and the M$_{HI}$-to-M$_{*}$ relation are similar in early-type and late-type EIGs, and that the morphological type of EIGs is not governed by their M$_{HI}$ content, colour or SFR.

Tables

Table 1. Table 1:

EIG BR-XX

Table 2. Table 3:

a	No optical counterpart was found for EIG 1s-05 (an ALFALFA object).
b	EIG 1s-14 is projected close to a foreground bright star. Uncertainties were estimated to be 0.1 $mag$.
c	Converted from Landolt ($U$$B$$V$$R$$I$) magnitudes.
d	EIG 2s-06 has a significant foreground star in front of it. Uncertainties were estimated to be 0.2 $mag$.

Table 3. Table 5:

L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.038\cdot \nu L_{\nu }\left(12\mu \text{m}\right)$ L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.035\cdot \nu L_{\nu }\left(22\mu \text{m}\right)$

(4)

Table 4. Table 7:

L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.038\cdot \nu L_{\nu }\left(12\mu \text{m}\right)$ L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.035\cdot \nu L_{\nu }\left(22\mu \text{m}\right)$

(4)

Table 5. Table 9:

L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.038\cdot \nu L_{\nu }\left(12\mu \text{m}\right)$ L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.035\cdot \nu L_{\nu }\left(22\mu \text{m}\right)$

(4)

Table 6. Table 11:

L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.038\cdot \nu L_{\nu }\left(12\mu \text{m}\right)$ L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.035\cdot \nu L_{\nu }\left(22\mu \text{m}\right)$

(4)

Table 7. Table 13:

L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.038\cdot \nu L_{\nu }\left(12\mu \text{m}\right)$ L${}_{H_{\alpha },corr}$ $=$ L${}_{H_{\alpha },obs}{}^{}+0.035\cdot \nu L_{\nu }\left(22\mu \text{m}\right)$

(4)

Table 8. Table 15:

a	‘’ and ‘$>$’ indicate 0.95 confidence level upper and lower limits (respectively).

Table 9. Table 17:

$i\cong {\cos }^{-1}\sqrt{\frac{{\left(b_{24}/a_{24}\right)}^2-q_0^2}{1-q_0^2}}$

(6)

Table 10. Table 19:

$i\cong {\cos }^{-1}\sqrt{\frac{{\left(b_{24}/a_{24}\right)}^2-q_0^2}{1-q_0^2}}$

(6)

Table 11. Table 21:

$i\cong {\cos }^{-1}\sqrt{\frac{{\left(b_{24}/a_{24}\right)}^2-q_0^2}{1-q_0^2}}$

(6)

Table 12. Table 23:

a	Due to stars/galaxies projected by chance on EIG 1a-03 and interfering with the measurement $b_{24}/a_{24}$ was measured on the third largest ellipse isophote (${\mu }_{\text{R}}=24.93\pm 0.08{\text{mag\hspace{0.17em}arcsec}}^{-2}$).

Equations

$$\text{mbox}{cps}_{N,line}$$

$$\text{mbox}EW\cong \frac{\text{mbox}{cps}_{N,line}}{\text{mbox}{cps}_N-\text{mbox}{cps}_{N,line}}\cdot \frac{{\int }_0^{\infty }\text{mbox}{T}_N(\lambda )d\lambda }{\text{mbox}{T}_N({\lambda }_{line})}$$

$$\text{mbox}{F}_{line}\cong \frac{\text{mbox}{cps}_{N,line}}{\text{mbox}{T}_{atm,N}({\lambda }_{line})\text{mbox}{T}_N({\lambda }_{line})\text{mbox}{R}_{\lambda }({\lambda }_{line})}$$

$$\text{mbox}log\frac{SFR}{M_{\odot }\text{mbox}{yr}^{-1}}=L_{{\mathrm{H}}_{\alpha },corr}-41.27$$

$$v_{rot}=\frac{W_{50}}{2\cdot \sin i}$$

$$M_{dyn,24}=\frac{v_{rot}^2\cdot a_{24}}{G}$$

$$\text{mbox}log\left(\frac{\text{mbox}SFR}{M_{\odot }\text{mbox}{yr}^{-1}}\right)=\alpha \cdot \text{mbox}log\left(\frac{M_{\ast }}{M_{\odot }}\right)+\beta$$

$$\alpha$$

$$\text{mbox}log\left(M_{HI}/M_{\odot }\right)\cong 0.34\cdot \text{mbox}log\left(M_{\ast }/M_{\odot }\right)+6.20$$

$$log\left(\text{mbox}SFR/M_{\odot }\text{mbox}{yr}^{-1}\right)$$

Full text

††pagerange: EIG - II. Intriguing characteristics of the most extremely isolated galaxies–25††pubyear: 2017

1 Introduction

The research described here is part of an extensive study of star formation properties and evolution of galaxies in different environments and of various morphological types, conducted in the past few decades (e.g., Brosch, 1983; Almoznino, 1995; Almoznino & Brosch, 1998; Brosch et al., 1998; Heller, 2001; Brosch et al., 2006; Brosch et al., 2008; Zitrin & Brosch, 2008). Specifically, we studied galaxies in the most extremely underdense regions of the local Universe. These galaxies are particularly interesting since they evolved with little or no environmental interference in the last few Gyr, and are therefore useful for validating and calibrating galaxy evolution models. Furthermore, when compared to galaxies in denser regions, they illuminate the overall effects of the environment on the evolution of galaxies.

It is well-known that extremely dense environments can greatly influence the star formation (SF) in galaxies. Tidal interactions and mergers of galaxies can trigger extreme starbursts with SFR up to $10^{3}\,M_{\odot}\,\mbox{yr}^{-1}$, while isolated galaxies hardly ever exhibit SFR $>$20 $M_{\odot}\,yr^{-1}$ (Kennicutt, 1998). Although the effect on SFR may be extreme during mergers in clusters as well as in pairs and loose groups, the effect on SFR averaged over the whole history of a galaxy may be small (Bergvall et al., 2003; Rieke et al., 2009). In cluster environments, apart from the higher rate of interactions, ram pressure by the intracluster medium strips the galaxies of their gas and, therefore, reduces SF. It has also been suggested that in some cases the ram pressure might increase SF (Gavazzi & Jaffe, 1985).

Galaxies in isolated environments are generally considered to be gas-rich, fainter, bluer, of later type, and exhibit higher specific star formation rates (SSFRs; SFRs per unit stellar mass) than galaxies in average density environments (Dressler, 1980; Grogin & Geller, 1999; Pustilnik et al., 2002; Rojas et al., 2004, 2005; Deng et al., 2012; Kreckel et al., 2012; Melnyk et al., 2014; Moorman et al., 2016). Some claim that this is not just an effect of the higher abundance of late-type galaxies, and that the late-type galaxies themselves are fainter in under-dense regions than in average density regions (Varela et al., 2004; Croton et al., 2005; Sorrentino et al., 2006).

Numerous other studies also indicate that the properties of galaxies are influenced by their neighbourhood. Brosch & Shaviv (1982) found that the inner regions of isolated galaxies are bluer, compared to ‘field’ galaxies. This was later suggested to be a consequence of intensive formation of massive stars in the nuclei (Brosch & Isaacman, 1982). Varela et al. (2004) found that bars are less frequent in isolated galaxies than in perturbed galaxies. Fernández Lorenzo et al. (2014) found that bluer pseudo-bulges tend to reside in neighbourhoods with a higher probability of tidal perturbation. They suggest that the environment could be playing a role in rejuvenating pseudo-bulges. Wang & White (2012) found that satellite galaxies around isolated bright primary galaxies are systematically redder than field galaxies of the same stellar mass, except around primaries with $log\left(M_{*}/M_{\odot}\right)<10.8$, where the satellites’ colours were similar or even bluer.

This work attempts, among other things, to help resolve the question of ‘Nature vs. Nurture’; does the evolution of galaxies depend only on their content or do their large-scale environments have a significant evolutionary influence. Some argue that galaxy formation is driven predominantly by the mass of the host DM halo, and is nearly independent of the larger-scale halo environment (e.g., Croton & Farrar 2008; Tinker & Conroy 2009). This is supported by their simulation models that produce void galaxies conforming to some observed statistical properties (e.g., colour distribution, luminosity function and nearest neighbour statistics). However, since there are many galaxy properties that most simulations cannot predict (e.g., HI content), and since the halo mass of galaxies cannot be directly measured, this hypothesis is hard to prove or disprove.

We have chosen a sample of extremely isolated galaxies (EIGs) from the local universe based on a simple isolation criterion. The neighbourhood properties of this sample were analysed using both observational data and cosmological simulations. The cosmological simulations were further used to estimate the properties and histories of the dark matter (DM) haloes in which the sample EIGs reside. The sample and its analysis are described in detail in the first paper of this series, Spector & Brosch (2016) (SB16), and are summarized here in Section 2.

Extensive optical observations of the sample EIGs in broad-band and rest-frame Hα were performed using the one meter telescope of the Florence and George Wise Observatory111IAU code 097 - http://wise-obs.tau.ac.il/ (WO). Section 3 describes these observations and their processing. The results of these observations, along with public observational data, were used to measure the current SFRs and to estimate SFHs. These observational results are described in section 4. Analysis of these results is presented in section 5, and the findings are discussed in section 6.

Throughout this work, unless indicated otherwise, $\Lambda$ cold dark matter ($\Lambda$CDM) cosmology with the seven-year Wilkinson Microwave Anisotropy Probe data (WMAP7, Bennett et al. 2011) parameters are used, including the dimensionless Hubble parameter $h=0.704$. We adopt here the solar $g$-band absolute magnitude of $\mbox{M}_{g,\odot}=+5.12$ (according to the Sloan Digital Sky Survey, SDSS, DR7 web site222www.sdss.org/dr7/algorithms/sdssUBVRITransform.html

#vega_sun_colors).

2 The Sample

We have chosen the sample of EIGs using a simple isolation criterion: a galaxy is considered an EIG and is included in the sample if it has no known neighbours closer than a certain neighbour distance limit in 3D redshift space (200 km s*-1* or 300 km s*-1* as explained below) and if its redshift is in the range $2000<\mbox{cz}<7000\,{\rm km\,s}^{-1}$. No magnitude, HI mass or size limit was used in the selection of candidate neighbours. The use of such limits would have somewhat reduced the level of isolation of the sample (especially for the closer EIGs) and therefore was not preferred. Not using such limits, however, complicates somewhat the analysis of the sample’s isolation level (described in section 3 of SB16 and summarized below). It also causes the sensitivity limits (listed below) and the isolation level to depend on redshift. Higher redshift EIGs are less isolated on average than lower redshift EIGs. For this reason, the redshift of EIGs was limited to 7000 km s*-1*.

One of the unique advantages of the EIG sample we study here is that, apart from the optical redshift data commonly used to estimate environment density, it also utilized HI redshifts from the Arecibo Legacy Fast ALFA survey (ALFALFA) survey. The ALFALFA survey is a second-generation untargeted extragalactic HI survey initiated in 2005 (Giovanelli et al., 2005, 2007; Saintonge, 2007). This survey utilizes the superior sensitivity and angular resolution of the Arecibo 305 m radio telescope to conduct the deepest ever census of the local HI Universe. ALFALFA was particularly useful in verifying the isolation of the target galaxies, since by being an HI survey it easily measures redshifts of low surface brightness galaxies (LSBs) and other low-luminosity late-type neighbours that are often difficult to detect optically but abound with HI.

The ALFALFA dataset we used was the “$\alpha$.40 HI source catalogue” ($\alpha$.40; Haynes et al., 2011). This catalogue covers 40 percent of the final ALFALFA survey area ($\sim$2800 $\mbox{deg}^{2}$) and contains 15855 sources. The sensitivity limit of the ALFALFA dataset is given by eq. (6) and (7) of Haynes et al. (2011) as function of the velocity width of the HI line profile, $W_{50}$. For a typical value $W_{50}=100\,{\rm km\,s}^{-1}$ the sensitivity limit of the ALFALFA dataset is $\sim$0.6 Jy km s*-1*. For the redshift range of the EIG sample this translates to HI mass, $\mbox{log}\left(M_{HI}/M_{\odot}\right)$, sensitivity limit of $\sim$8.0 (for $\mbox{cz}=2000\,{\rm km\,s}^{-1}$) to $\sim$9.1 (for $\mbox{cz}=7000\,{\rm km\,s}^{-1}$).

The search criterion was applied to two sky regions, one in the spring sky (Spring) and the other in the autumn sky (Autumn) as described in Table 2. These particular regions were selected since they are covered by the $\alpha$.40 ALFALFA catalogue (Haynes et al., 2011). Both regions include mainly high Galactic latitudes. The Spring region is almost fully covered by spectroscopic data in SDSS DR10 (Ahn et al., 2014).