H i CLOUDS IN THE LOWER HALO: I. THE GALACTIC ALL-SKY SURVEY PILOT REGION
We have detected over H i clouds in the lower halo of the Galaxy within the pilot region of the Galactic All-Sky Survey (GASS), a region of the fourth quadrant that spans in longitude, in latitude and is centered on the Galactic equator. These clouds have a median peak brightness temperature of K, a median velocity width of km s, and angular sizes . The motion of these clouds is dominated by Galactic rotation with a random cloud-to-cloud velocity dispersion of km s. A sample of clouds likely to be near tangent points was analyzed in detail. These clouds have radii on the order of pc and a median H i mass of . The population has a vertical scale height of pc and is concentrated in Galactocentric radius, peaking at kpc. This confined structure suggests that the clouds are linked to spiral features, while morphological evidence that many clouds are aligned with loops and filaments is suggestive of a relationship with star formation. The clouds might result from supernovae and stellar winds in the form of fragmenting shells and gas that has been pushed into the halo rather than from a galactic fountain.
Subject headings:galaxies: structure — Galaxy: halo — ISM: clouds — ISM: structure — radio lines: ISM
Neutral atomic hydrogen (H i) is ubiquitous throughout the Galaxy with a wide variety of morphologies and kinematics, and exhibits many complex structures, including worms (Koo et al., 1992), sheets and filaments (Heiles, 1967; Dickey & Lockman, 1990), shells (Heiles, 1979; McClure-Griffiths et al., 2002), and clouds (Lockman, 2002). The Galactic H i disk extends to Galactocentric radii, kpc and its thickness varies from pc inside kpc to kpc in the outer Galaxy, with a roughly uniform thickness of pc between kpc and the solar circle (see Ferrière 2001 and references within). H i is also known to extend far beyond the thin H i disk as a layer into the Galactic halo (Lockman, 1984). However, recent high angular resolution observations using the Green Bank Telescope (GBT) have revealed that this layer is not smooth but instead is composed of small H i clouds with sizes on the order of a few tens of parsecs and masses of (Lockman, 2002); confusion may limit the detectability of such clouds at low heights, in which case they may not be confined to the halo (see, e.g., Stil et al. 2006). These clouds follow Galactic rotation and are discrete clumps of H i that are localized in space and velocity. Although they are sometimes related to larger structures, for example, embedded in filaments, each cloud appears to be a distinct object. The gross properties of the Milky Way’s thick H i layer may in fact be a consequence of the statistical properties of these H i clouds and it is possible that thick H i disks in other galaxies, once observed with sufficient resolution, would reveal a similar structure.
While the origin of the halo clouds is unknown, one possible explanation is a “galactic fountain” model, where hot gas produced by supernovae rises into the halo of the Galaxy, cools and condenses into H i clouds, which then fall back to the plane (Shapiro & Field, 1976; Bregman, 1980). Houck & Bregman (1990) predicted that, from a lower temperature fountain, gas in Galactic rotation could be formed at heights close to the plane. This scenario is supported by observations of intermediate velocity clouds (IVCs), such as those of cloud g1, whose location, kinematics and abundances match those expected (Wakker et al., 2008). The abundances in the IV Arch (Richter et al., 2001b) and LLIV Arch (Richter et al., 2001a) also suggest a fountain origin; as their abundances are near solar it is likely that they originated from material enriched from the disk. The location of these IVCs also support this, as they are roughly kpc from the disk.
Another possibility is that the halo clouds originate in environments where supernovae and stellar winds disrupt the surrounding medium. Such events can result in the formation of a bubble, and models suggest that with a large enough energy source it is possible for these bubbles to expand beyond the thickness of the Galactic disk (e.g., Tomisaka & Ikeuchi 1988; Heiles 1990). These bubbles are encompassed by an H i shell as a result of either radiative cooling, which then accumulates more H i as the bubble continues to expand, or solely the sweeping up of ambient material, depending on the wind speed (Koo & McKee, 1992). The shell remains as a single entity until in some cases Rayleigh-Taylor instabilities cause it to fragment (Mac Low et al., 1989). Once this fragmentation has occurred, gas that has been shock heated is expelled outwards, mixing material from the disk with that in the halo (Norman & Ikeuchi, 1989) and the remaining fragments of the H i shell may be the observed halo clouds (McClure-Griffiths et al., 2006). It is also possible for the hot gas that has been expelled to cool and recombine, as seen in models of de Avillez (2000), or perhaps energy from increased supernova activity in areas of active star formation has simply pushed disk gas into the halo, forming clumps of H i.
Large samples of halo clouds are required to constrain the properties and distribution of the population and provide insight into their origin. As the Lockman (2002) sample only has clouds in a small region of the Galaxy, the properties of the population that constitute the H i layer are currently not well-determined. In this paper we present a catalog of over H i clouds in the lower halo of the inner Galaxy, which we have detected in the Galactic All-Sky Survey pilot region, along with an analysis and discussion of their properties and distribution. We begin with an overview of the observations and data in §2 and present the observed properties of all clouds in §3. In §4 we determine the physical properties of a subset of these clouds that can be assumed to be located at tangent points, where the observer’s line-of-sight is tangent to circles of constant Galactocentric radius. An analysis of the cloud properties is presented in §5 and implications of these results are discussed in §6. We summarize the results and discuss future work in §7.
2. Observations and Data
The data presented in this paper are from the Galactic All-Sky Survey (GASS), a fully sampled Galactic H i survey that covers the entire sky south of declination . GASS data were taken with the 21 cm Multibeam receiver (Staveley-Smith et al., 1996) at the Parkes Radio Telescope and cover km s, where is the velocity with respect to the local standard of rest. The spectral resolution of the data is km s and the half power beam width of the Multibeam is .
GASS observations began in 2005 January and were completed by the end of 2006. Data reduction was performed using the Livedata package, which is part of the ATNF subset of the aips++ distribution. The bandpass correction was performed using an algorithm designed specifically for GASS data and the Doppler correction was applied. Fluxes were calibrated from observations of the standards S6, S8 and S9 (Williams, 1973). The reduced data were gridded into a 3D data cube with voxel dimensions of km s using the Gridzilla package, which is also part of the ATNF subset of the aips++ distribution, and was based on the gridding algorithm described in Barnes et al. (2001). The rms spectral noise, mK, was determined by measuring brightness temperature fluctuations in a square degree region of the survey that spanned 48 velocity channels free of any obvious emission. The final GASS data release will be corrected for stray radiation according to the procedure described in Kalberla et al. (2005). Further details on GASS can be found in McClure-Griffiths et al. (2006), while extensive details will be presented in a future paper.
In this paper we present results from the GASS pilot region, a region that was chosen for a preliminary study on halo clouds to develop techniques that will be applied to the entire survey in the future. The GASS pilot region is in the fourth quadrant in the inner Galaxy and spans , , and km s (see Figure 1). Our focus is on spatially discrete H i features with an angular size . These features are not characteristic of stray radiation, so although these data have not been corrected for stray radiation, it is unlikely that any of the clouds discussed here are a spurious result of this effect.
3. Hi Clouds in the Gass Pilot Region
We detect numerous discrete H i features in the lower halo of the Galaxy with angular sizes , similar to the population of halo clouds discovered by Lockman (2002). Samples of these clouds can be seen in Figures 2 and 3, where we display a longitude-latitude image at km s and a latitude-velocity image at , respectively. The curved lines at the top and bottom of Figure 2 represent the boundaries of the region studied in this paper. The contour in Figure 3 represents K. Where the emission is brighter than this it is difficult to distinguish clouds because of confusion, although we do detect some clouds above this threshold. These figures clearly demonstrate the presence of discrete H i clouds at a variety of longitudes, latitudes and velocities, which are seen both close to the disk and into the halo. Some clouds are extended and some compact, and many appear to be related to diffuse and filamentary structures.
3.1. Search Method and Criteria
We chose the following selection criteria to generate a homogeneously
selected catalog of halo clouds:
1. The cloud must be within the GASS pilot region, i.e., within , (see Figure 2 for exact boundaries), and km s.
2. The cloud must span four or more pixels and be clearly visible over three or more channels in the spectra. Most cloud detections were made with , where mK.
3. The cloud must be distinguishable from unrelated background emission. It was impossible to separate clouds from this emission at low latitudes in the least negative velocity channels, where the emission is particularly complex.
We believe that we have identified all obvious clouds that meet the criteria listed above. However, it should be noted that some clouds appeared to have double-peaked velocity profiles. In these circumstances, each peak was cataloged as an individual cloud because confusion may be important. If so, confusion may have resulted in the merging of profiles of multiple clouds at similar locations but with distinct velocities, resulting in a double-peaked profile. We will discuss such effects in detail in a subsequent publication.
Most of the clouds are not isolated and spherical, but are instead often part of nebulous, filamentary structures and/or sitting in a fluctuating diffuse background. As a result, all automated cloud finding algorithms that we tested had difficulty differentiating clouds from neighbouring clouds and from background emission. Clouds were therefore identified and their properties measured by visual inspection of the data cubes.
3.2. Properties of the Entire Cloud Sample
Using the criteria presented in §3.1 we measured H i clouds in the GASS pilot region. The observed properties of these clouds are presented in Table 1. We provide a description of each property and how it was determined below, along with sample spectra in Figure 4. An integrated intensity map, which has the summed intensities over the velocity range for a given cloud, was used to aid in the determination of some properties. These maps have had a background subtracted that was the mean flux of unrelated emission in three interactively chosen areas surrounding the cloud. It is apparent from Figure 5 that at the low end of the peak brightness temperature distribution ( K) there is significant incompleteness in regions of higher background levels. We are confident in our background subtraction because beyond incompleteness there is no trend between the peak brightness temperature and mean background level.
The columns in Table 1 are:
Columns 1-2: The Galactic longitude, , and latitude, , of the cloud at the position of the peak brightness temperature.
Column 3: The cloud velocity with respect to the local standard of rest, , in km s, measured as the velocity of the cloud’s peak brightness temperature after background subtraction. The background level was determined by fitting a line between each edge of the cloud velocity profile where it merges with surrounding emission.
Column 4: The cloud’s peak brightness temperature after background subtraction, , in K. Most clouds that we measured had K prior to background subtraction, as clouds with greater than this tended to be found only in areas of high confusion.
Column 5: is the full-width at half-maximum (FWHM) of the velocity profile, determined by inspection after background subtraction, in km s.
Column 6: The H i column density, , at the cloud center is cm in the optically thin limit, an assumption that is reasonable because the emission is faint. was determined after we subtracted a background from the integrated intensity map. The background level was highly variable from cloud to cloud being mainly dependent on the latitude and velocity of the cloud.
Column 7: and are the maximum and minimum extent of the cloud in arcminutes and were determined by inspection from the integrated intensity map of the cloud. Many of the clouds are unresolved in at least one dimension but we have not deconvolved their angular sizes due to the uncertainties associated with the fluctuating background levels of the integrated intensity maps. These values therefore represent upper limits of the angular extent.
Column 8: represents the H i mass of the cloud in units of kpc, where is the distance to the cloud in kpc. A background was subtracted from the integrated intensity map, thereby leaving only the flux of the cloud, which was then summed. The mass also relies on the assumption that the H i is optically thin.
Column 9: Details of prior detections in the literature, if any, are noted.
3.3. Uncertainties in Observed Properties
: The difference between and the velocity where the profile decreases from the peak by , assuming the profile can be approximated by a Gaussian, where is the error on (see below) and is the rms noise.
: This error is assumed to be , where the first term represents the error in prior to the background subtraction and the second term represents the error of the mean of the two points defining the background.
: Each of the two half maximum points on the profile is defined as the point where the profile, with error , crosses , with error . We translate these errors into velocity errors using the slope of a Gaussian profile. We also add the channel width in quadrature.
: The error on the H i column density, .
and : The uncertainties due to the interactive nature of the angular extent measurements and the fluctuating background levels of the integrated intensity maps dominate over the nominal statistical error. The uncertainties were therefore calibrated by comparing values obtained during different trials for a randomly selected subset of clouds and are estimated to be approximately of the determined angular extent.
: The errors introduced by the interactive process of determining the cloud area and subtracting the background dominate the mass uncertainties and are estimated to be of the determined mass, based on the comparison of different trials of randomly selected clouds mentioned above.
3.4. Observed Properties
The as a function of longitude for all clouds is shown in Figure 6, along with a solid line denoting the terminal velocity, , which, in the fourth quadrant, is the most negative velocity permitted by Galactic rotation. The clouds are abundant at velocities allowed by Galactic rotation and there is clearly a decline in the number of clouds beyond (more negative than) , which demonstrates that the motions of this cloud population are dominated by Galactic rotation. Clouds beyond are said to have “forbidden velocities” and the amount their differs from is the deviation velocity, , as defined by Wakker (1991). For , we used determined by McClure-Griffiths & Dickey (2007) from H i observations from the Southern Galactic Plane Survey (McClure-Griffiths et al., 2005). For all remaining longitudes we used determined by Luna et al. (2006) from the Columbia-Universidad de Chile CO surveys.
In Figure 7 we display the latitude distribution with deviation velocity. For clouds beyond the terminal velocity (with negative deviation velocities), those at lower latitudes have larger absolute deviation velocities. This is likely an artefact, as the number of clouds would naturally fall off with both more negative deviation velocities and larger latitudes if they were dominated by Galactic rotation. There are, however, some outliers at large positive latitudes with very negative deviation velocities.
Histograms of , and angular size are presented in Figures 8a–c, respectively. The majority of clouds have K, with the median K. The number of clouds decreases sharply below and suggests that the sample is sensitivity limited. The median FWHM of the velocity profiles is km s and very few clouds have linewidths larger than km s (Figure 8b). All but one of the linewidths are greater than km s; as the velocity resolution of the survey is km s, most lines are therefore well resolved. The median angular diameter of the clouds is , which is approximately twice the beam size. This value and the steep cutoff at small angular sizes seen in Figure 8c are most likely due to the spatial resolution limit of the data and suggest that many of the clouds are unresolved.
4. Selection of a set of “Tangent Point” Clouds
The largest magnitude velocity from Galactic rotation in the inner Galaxy occurs at the tangent point, defined as the location where the line-of-sight is perpendicular to a circle of constant Galactocentric radius (see Figure 1). Here and the LSR velocity from Galactic rotation is where is the radius of the solar circle, is the circular velocity and is the circular velocity at the solar circle. We adopt kpc and km s, as recommended by the IAU (Kerr & Lynden-Bell, 1986). Clouds in pure Galactic rotation cannot have a circular velocity beyond . However, the random motion of a cloud near the tangent point might increase the cloud’s beyond . Clouds in Galactic rotation with must therefore lie near the tangent point and thus at a known distance, . While an assumption, it is reasonable to adopt a distance of for clouds with given the rapid decline in the number of clouds beyond the terminal velocity, as shown in Figure 6. Tangent point clouds constitute a sample uniquely suited for investigating the population’s distribution and properties, such as physical size and mass.
From the population of clouds detected in the GASS pilot region, we define the tangent point sample as all clouds with km s, where km s is one channel width, and assume that they are at the tangent point; we assess the effect of this assumption in §5.3. We also assume that Galactic rotation is constant with distance from the plane, i.e., ; deviations from cylindrical rotation will be discussed in a subsequent work. As the tangent point clouds in the GASS pilot region are nearly all at the same distance and the latitude boundary of the region corresponds to a constant height of kpc (at tangent points), they provide a uniformly selected sample.
4.1. Derived Properties
The physical properties and positions of the tangent point clouds are presented in Table 2, while descriptions of the derived quantities are presented below.
Columns 1-3: As in Table 1.
Column 4: The deviation velocity, (Wakker, 1991), where is the most negative velocity expected from Galactic rotation in the fourth quadrant, in km s, and was determined by McClure-Griffiths & Dickey (2007) from H i observations for and by Luna et al. (2006) from CO observations for all remaining longitudes.
Column 5: The distance, , along the line-of-sight from the Sun to the cloud determined by assuming the cloud is at the tangent point: , in kpc.
Column 6: The Galactocentric radius, , of the tangent point at the cloud’s location, in kpc.
Column 7: The height, , of the cloud from the plane of the Galaxy, determined geometrically to be , in kpc.
Column 8: The radius, , of the cloud in pc, determined by , where , , and and are from Table 1.
Column 9: The physical mass of H i in the cloud, , determined as in Column 8 of §3.2 but with the tangent point distance assumed, in .
4.2. Uncertainties in Derived Properties
Here we present error estimates for the derived properties in Table 2.
: As in Table 1.
: This error is , where km s for clouds located at longitudes where was determined using H i (; McClure-Griffiths & Dickey 2007). For all other longitudes, where the terminal velocity was determined using CO observations (Luna et al., 2006), the error is assumed to be km s, as suggested from the scatter on Figure 7 of McClure-Griffiths & Dickey (2007).
: Distance errors, which are inherent to the assumption that the clouds are located at tangent points, were estimated using a simulated population of clouds (see §5.3).
: The error on the Galactocentric radius was determined analogously to . Because the closest point to the Galactic center along a given line-of-sight is the tangent point, the adopted is always a lower limit and the error can only be positive.
: The error on the height is estimated to be .
: The error on the radius of the cloud depends on the
uncertainties in the distance and angular size estimates and is
: The error on the mass is , where is given in Table 1.
5. Analysis of the Tangent Point Cloud Population
5.1. Simulated Halo Cloud Population
To constrain the spatial and kinematic properties of the observed tangent point cloud population we simulated a population of clouds to which were applied the same , and selection criteria as for the GASS pilot region clouds. The simulated clouds were randomly sampled from the following distribution:
where is the radial surface density distribution, is the exponential scale height and and are the cylindrical coordinates. is composed of independent radial bins of width kpc, spanning to kpc. The amplitude of each bin was optimized to best fit the observed longitude distribution of the tangent point clouds by minimizing the Kolmogorov-Smirnov (K-S) statistic (the maximum deviation between the cumulative distributions) using Powell’s algorithm (Press et al., 1992). We optimized the fits using three different initial estimates on . All converged on a similar solution and we adopted the mean of the three as the best fit to the data, which is shown in Figure 9.
Velocities of simulated clouds were based on a flat rotation curve where with a random velocity component drawn from a Gaussian of dispersion km s, which is discussed in detail in §5.2. We generated clouds in a half-galaxy (third and fourth quadrants), of which were within the Galactic coordinates of the GASS pilot region and also within the defined velocity range of the tangent point cloud sample. We then normalized this distribution to compare directly with the observed distribution. We performed K-S tests to estimate the quality of the fit between the observed and simulated distributions. Based on these tests, we find that the parameters of the simulated population reproduce the distribution of observed tangent point clouds well and are therefore good estimates for those of the intrinsic population. Results of the fits to the distributions are presented in §5.2–5.5 along with tests of other functional forms.
5.2. Cloud-to-Cloud Velocity Dispersion
We assume that a random cloud-to-cloud velocity dispersion () is responsible for the presence of clouds at forbidden velocities within the GASS pilot region. Simulations of the global cloud population were required to model the effects of these motions, which can cause clouds that are not located at tangent points to have velocities that are near or beyond . The cloud-to-cloud velocity dispersion can provide information on the expected scale height of the distribution and a better understanding of the formation mechanisms of the clouds. We find the distribution of the tangent point sample of clouds to be consistent with that derived from a Gaussian distribution of random velocities whose dispersion is km s, with a K-S test probability greater than 111For reference, a probability is roughly equivalent in confidence to a detection of a difference, i.e., no detectable difference in the distributions, corresponds to , and corresponds to .. These distributions are presented in Figure 10, where the simulated distribution is represented by a dashed line and the observed distribution by a solid line. Velocity dispersions of to km s also provide acceptable fits (K-S test probabilities greater than or equal to ), as do fits where the random velocity component is drawn from an exponential distribution rather than a Gaussian distribution (with a K-S test probability of for a scale velocity of km s). The implied kinematics of the cloud population based on this result are discussed in §6.1.
Estimating the uncertainties on our determined is extremely difficult. We expect these uncertainties to be coupled to errors introduced by the measurements of , which are difficult to untangle due to the possibilty that random motions can systematically offset the measured . Another possible source of uncertainty is streaming motions associated with spiral features. However, because was determined directly from H i and CO measurements rather than from a fitted rotation curve, streaming motions should already be reflected in the adopted .
5.3. Distances Errors
The simulations allow us to estimate the error in our assumption that clouds with km s are at the tangent point. We have calculated the fractional distance error of the simulated clouds as a function of deviation velocity within a km s wide bin (Figure 11). Clouds at increasingly forbidden velocities have smaller distance errors, i.e., are more likely to be near the tangent point; the degree to which this is true depends on the magnitude of . We also calculated the fractional distance error as a function of longitude and found that clouds with longitudes corresponding to the Galactocentric radii where few clouds are detected () have larger distance errors (this is expected because few clouds are intrinsically at these radii and therefore a larger fraction of the forbidden velocity clouds at these longitudes are likely interlopers from larger radii). We assume that the errors due to and longitude are independent. The adopted fractional distance error is the product of the fractional distance error due to (relative to the typical error of ), due to longitude (relative to the typical error) and the typical error itself. The fractional distance errors due to and longitude are taken to be the rms error for the simulated clouds within the same bin. We have confirmed that the fractional distance error of the simulated clouds does not significantly depend on their latitude. Based on the relative distance errors, we believe that our assumption that clouds with km s are located near their tangent point is reasonable.
5.4. Radial Distribution
The adopted radial distribution of the tangent point clouds along with that of the simulated population of clouds is shown in Figure 12. The apparent offset in the distributions results from the assumption that the forbidden velocity clouds are located at tangent points, which are always at the smallest value of along the line of sight.
Another useful quantity that can be extracted from the simulations is the mean radial surface density distribution of clouds within the GASS pilot region (Figure 9). Although clouds are observed at all longitudes within the GASS pilot region, the distribution is concentrated in Galactocentric radius and peaks at kpc. The error bars at kpc are significantly larger because clouds at these radii must have large random velocities in order to meet the sample criteria and therefore represent a small tail of the population. With a K-S test probability of , the simulated longitude distribution fits that of the observed distribution of the tangent point sample well (Figure 13a).
The number of clouds in a uniformly distributed population of tangent point clouds should monotonically decrease by a factor of between and , as demonstrated by Equation (A2) in Stil et al. (2006), where they show that the line-of-sight distance effectively surveyed over forbidden velocities is . We have overlayed the distribution of a uniform surface density population in Figure 13a and it is clear that it is in stark contrast to the centrally peaked longitude distribution we observe. We therefore conclude that the peaked radial distribution is real. In Figure 13b we present the longitude distributions of the simulated and observed clouds within the entire pilot region, i.e., at all km s. Even though the vast majority of these clouds were not used to constrain the simulated radial distribution, their longitude distribution is well reproduced. The tangent point sample therefore appears to be a fair representation of the entire GASS pilot region.
It is worth investigating whether our assumptions regarding the functional form of the velocity distribution affects the inferred radial distribution, i.e., could a distribution with more velocity outliers and a less strongly peaked radial distribution also fit the data? To test this we have performed the same optimization of the radial distribution while using an exponential velocity distribution. We find that the resulting radial profile is identical to within the errors, confirming the robustness of this result.
5.5. Vertical Distribution
The vertical distribution of both the tangent point and simulated cloud population is presented in Figure 14. Clouds have been detected throughout the entire range of latitudes covered by the GASS pilot region, up to corresponding heights of kpc. However, there are very few clouds at because identification of clouds was extremely difficult close to and within the Galactic plane, except in cases where clouds were observed at large forbidden velocities. Because of this incompleteness at low latitudes, we compare the latitude distributions of simulated and observed clouds with and find that they are consistent for vertical scale heights between pc, with K-S test probabilities of at least . As incompleteness may still be a problem at , we also compare the height distributions for only clouds at and find acceptable fits for scale heights between and pc, both with probabilities greater than . Based on these comparisons we conclude that the population is best represented with a exponential scale height of pc. A distribution with pc provides an equally good fit the the data, which is not surprising given that the exponential and distributions differ primarily near pc where we cannot constrain the fit.
Although incomplete, the combination of surface density, mean mass and scale height of the clouds gives a rough estimate of the vertical distribution of H i contributed by the cloud population. The mid-plane H i number density can be estimated by , where is the mean cloud H i mass and is the H i atom mass. We find that the clouds are responsible for , by H i number density, of the exponential component of the H i layer in Dickey & Lockman (1990) and have a very similar scale height.
We note that there is an asymmetry in the number of observed clouds above and below the disk at pc and an excess of clouds at large positive latitudes. Possible explanations for these are discussed in §6.
5.6. Physical Size and Mass
Cloud radii, , vary from pc to pc, with a median radius of pc, as can be seen in Figure 15a. The angular resolution of the telescope sets a lower limit on the observed cloud size. The maximum angular extent of the detected clouds suggests that roughly of the entire sample is unresolved in at least one dimension. Derived radii should therefore be thought of as upper limits. A histogram of the H i mass of the clouds at tangent points is presented in Figure 15b, demonstrating that the clouds range in size from hundreds to thousands of solar masses, with a median H i mass of . These values may be overestimates if confusion is significant.
5.7. Comparison of GASS Clouds to Lockman Clouds
Based on their angular sizes and location in the lower halo, the clouds detected in the GASS pilot region appear to be similar to those observed by Lockman (2002). We investigate this possibility further by comparing the properties of each distribution summarized in Table 3. The median is strikingly similar in both sets of data, which is not surprising if the clouds are part of the same population. The derived are also in agreement. The median of this sample is smaller than that of Lockman (2002) but this is most likely due to a selection effect, as the areas searched by Lockman (2002) tended to be further from the Galactic plane to avoid areas of confusion. The most obvious differences in the sample stem from the difference in the angular resolutions of each survey: for GASS data versus for the GBT data. This affects , , and , where and would naturally be lower for unresolved clouds, and would be larger. If confusion is important, would also be larger. Lockman (2002) estimated that of the clouds were unresolved while we have estimated here. The clouds observed by Lockman (2002) are much less massive, with approximately one third having , while none of the observed GASS clouds have masses that low. As the only differences in the observed properties are due to differences in the observations, these comparisons reveal that the clouds belong to the same population of clouds as those detected by Lockman (2002).
5.8. Observed Trends
There does not appear to be any correlation between the height of the clouds and , , or . However, as the data in Figure 16 suggest, there may be a trend between and , where clouds near the plane ( kpc) have a median FWHM of km s and a large dispersion, where as those at kpc have a median FWHM of km s and a smaller dispersion. One possible explanation for such a trend could be that the clouds at larger heights belong to a different population of clouds than those at lower heights. Another possibility is that if the clouds are in pressure equilibrium, the trend is reflecting pressure variations throughout the halo. Similar to our results, Lockman (2002) found that and are independent of , and also found evidence that clouds with more narrow linewidths lay closer to the plane.
As one might expect if a population of clouds has a narrow range of densities, the larger the radius of the cloud, the more massive it is. This trend is evident in Figure 17 and does not appear to be solely due to the H i mass sensitivity limit of the data. This limit is denoted by the curved line and is based on the minimum observable H i column density, in cm, where is the minimum observable (assumed to be ) and is the observed median .
Another apparent trend, as seen in Figure 18, is that the clouds with higher H i column densities appear to be at lower heights. This could be due to inclusion of unrelated diffuse emission with the clouds at lower heights if the background subtraction was not effective. It could also be explained by a scenario where each cloud was given the same kinetic energy from a formation process or via equipartition in the subsequent evolution of the cloud population. The higher mass clouds, which have higher , would then have preferentially lower velocities and would not reach heights as large as those reached by clouds with higher velocities. Figure 19 provides tentative support for this hypothesis, revealing that at kpc there are very few clouds with (note, however, that at these heights our statistics are poor). Lockman (2002) did not find a correlation between the H i column density or mass with height, but this could be due to the limited vertical range of his data. Similarly, a correlation between the H i mass of a cloud and its deviation velocity is suggested in Figure 20; in particular, the more massive clouds may have lower deviation velocities. If the more massive clouds have lower random motions, they could have lower typical deviation velocities. This would be consistent with a scenario in which clouds evolve to an equipartition of energy or in which each cloud is given a similar initial kick, for example, from similar supernovae explosions. This could also explain why all of the most massive clouds are seen closer to the plane (Figure 19): if they have smaller initial velocities, they would not move as far into the halo before falling back towards the plane. At this stage, however, we can not exclude the possibility that at lower heights confusion is affecting the determined H i column density and mass of the clouds. We discuss the kinematics of the cloud population further in §6.1.
6. The Origin and Nature of Halo Clouds
6.1. Kinematics of Halo Cloud Population
The velocity dispersion of the cloud population is likely a remnant of a common formation process, such as violent supernovae explosions. If the population is in equilibrium, the vertical distribution and velocity dispersion are linked via the potential. By using the simulated population of clouds, we found that the vertical distribution is best fit with an exponential scale height of pc (§5.5) and the distribution is best fit with a Gaussian dispersion of km s (§5.2). However, assuming a vertical force using the mass model of Kalberla et al. (2007) at kpc, we derive an exponential scale height of pc for an isothermal population of clouds with km s. To produce our observed scale height of pc within this potential, the cloud-to-cloud velocity dispersion would have to be km s. The difference between the required and that observed may be due to the lack of clouds observable in the disk; if a large number of clouds within the disk have gone undetected, the scale height could be lower than pc, and could therefore be explained by the observed velocity dispersion. It is also possible that the distribution cannot be explained by a single component, but this is not obvious in the current data (we will address this possibility in a subsequent paper). However, the magnitude of this difference suggests that the clouds do not belong to an equilibrium population and their heights must, in part, result from processes that do not increase the velocity dispersion, such as uniformly expanding H i shells, instead of bursts of energy from areas of active star formation generating random “kicks.” The clouds could have also originated above the disk, as in a galactic fountain model.
Similar clouds have been detected at forbidden velocities within the Galactic disk using data from the VLA Galactic Plane Survey, suggesting that the clouds are not restricted to the halo (Stil et al., 2006). Those cloud diameters are much smaller than the ones derived here (they have diameters pc), likely due to the higher spatial resolution of the VLA data. Based on their models, Stil et al. (2006) find that a vertical Gaussian half-width at half-maximum (HWHM) larger than kpc (equivalent to an exponential scale height of kpc) best fits their data, but given that they only surveyed within , this is likely to be strongly affected by the lack of coverage at high latitudes, especially because we observe similar clouds up to kpc and Lockman (2002) has observed clouds up to kpc. Stil et al. (2006) find a lower limit to the HWHM of the clouds to be pc (exponential scale height of pc), which is consistent with the value we derive and also inconsistent with the value expected for the derived velocity dispersion.
It is worth noting that there is evidence that similar clouds may also be abundant in the outer Galaxy (Stanimirović et al., 2006). It is not yet certain whether they belong to the same population of clouds, but with larger surveys of H i clouds in the lower halo of the Galaxy, such as those presented here and in the entire inner Galaxy within GASS, along with those underway by the Galactic Arecibo L-Band Focal Plane Array Consortium and other groups, properties such as and the spatial distribution can be determined more accurately and will therefore help constrain the kinematics and formation mechanisms of the clouds.
6.2. Halo Clouds and Spiral Structure
As discussed in §5.4, the surface density of halo clouds is not uniformly distributed but instead peaks at a Galactocentric radius of kpc and the population is concentrated in radius. This confined nature suggests that the halo clouds are related to the spiral structure of the Galaxy. Although distances to spiral arms are currently not well constrained, the location of the peaked radial distribution of the clouds indicates that they may be related to the expanding “ kpc” arm (van Woerden et al., 1957; Rougoor & Oort, 1960), which is tangential to an observer’s line-of-sight at roughly (Bronfman 2008; see also Vallée 2008 who argues that this feature lies at and is the start of the Perseus arm, distinct from the kpc–Norma arm), corresponding to kpc. Also, it has been suggested that the kpc arm must be confined to an annulus of less than kpc in extent (Lockman, 1981), which corresponds well with the radial concentration of clouds. At this time, however, the possibility that these clouds are instead related to other Galactic structures such as the “ kpc molecular ring” (Jackson et al., 2004) cannot be ruled out. This “ring” is likely not a coherent structure but instead a complex region where multiple spiral arms originate (Vallée, 2008).
The apparent association between many of the H i clouds and filamentary structures within the GASS pilot region suggests that the clouds are related to star formation because such structures are common in areas of significant supernova activity or stellar winds (Dickey & Lockman, 1990). Figure 21 displays an integrated intensity map with many clouds that are clearly aligned along filaments and loops, reminiscent of the clouds observed to be associated with the cap of a superbubble by McClure-Griffiths et al. (2006). Recently, proper motion measurements of the molecular cloud NGC 281 West, which is associated with an H i loop, revealed that the cloud is moving away from the Galactic plane at km s (Sato et al., 2007). This velocity is similar to our observed velocity dispersion and further supports the scenario that the clouds are related to expanding shells. In this scenario, violent supernovae and stellar winds may have pushed H i from the disk up into the halo or the clouds may be fragments of H i shells (Mac Low et al., 1989; McClure-Griffiths et al., 2006), rather than a result of a standard galactic fountain. In the galactic fountain model hot gas rises from the disk, cools and condenses, then falls back to the plane (Shapiro & Field, 1976; Bregman, 1980). This would result in a more uniform radial distribution of clouds (Bregman, 1980), while we clearly observe a peak in the radial distribution of the clouds that may be associated with the kpc arm. If the halo clouds are related to star formation, the asymmetry in the number of detected clouds at low heights ( pc) could be a result of this, as any asymmetry in the structure of the ISM and the location of star forming regions may be reflected in the distribution of clouds, and there appears to be more filaments below than above the Galactic plane in the GASS pilot region.
If the clouds are related to spiral structure and star formation then we would expect to see a correlation between the radial surface density distribution of the H i clouds and that of Galactic H ii regions. We compare these distributions, along with the mass surface densities of H i and , in Figure 22. The mass surface densities have been averaged over the entire Galaxy and were derived by Dame (1993) using H i data from Dickey & Lockman (1990) and Burton & Gordon (1978), and data from Bronfman et al. (1988), whereas the halo cloud distribution from this study includes only the GASS pilot region. The H ii regions are taken from Paladini et al. (2004). There is no obvious relationship between the H i cloud and H ii region distributions or between the H i clouds and the H i and surface densities, providing conflicting evidence for the relationship between the H i clouds and current star formation. As the evaporation timescale for a cold cloud in a hot medium is expected to be much longer than other timescales associated with cloud evolution (Cowie & McKee, 1977; Nagashima et al., 2006), and if evaporation is the main disruptive mechanism, the clouds are long-lived. It is therefore possible that the clouds are tracing past rather than current star formation. With the present data we are only able to make a preliminary study of the relationship between the halo cloud distribution and spiral structure and star formation in the Galaxy. Future analysis of clouds over a larger range of longitudes will allow us to test the assertion that the clouds are related to star formation in greater detail.
6.3. Possible Association with High Velocity Cloud Complex L
An excess of clouds at large positive heights can be seen in the vertical distribution of the clouds (Figure 14); of the clouds at positive heights lie at kpc while none of the clouds below the plane are seen at such heights, and many of them have unusually large deviation velocities (Figure 7). This excess could have several possible origins including infalling gas, increased disk activity on one side of the disk that has resulted in outflowing gas reaching larger heights, or small number statistics. Given the proximity of high velocity cloud complex L to the halo clouds in the upper portion of the GASS pilot region, we compared the H i associated with each population to determine whether or not the presence of complex L could be responsible for the observed excess.
Complex L was first described by Wakker & van Woerden (1991) to have velocities ranging from km s, longitudes ranging from and latitudes ranging from , and they speculated that the clouds were part of a population that was related to a galactic fountain. Since then, H distance limits have been determined for complex L, placing it within the Galactic halo at heights of kpc from the plane and heliocentric distances of to kpc (Weiner et al., 2001; Putman et al., 2003).
In Figure 23 we display a region of the GASS data that encompasses both the upper GASS pilot region, outlined by the solid black lines, and the lower velocity gas of complex L. We have overlaid circles on high velocity clouds associated with complex L as catalogued by Wakker & van Woerden (1991), regardless of their velocity. The spatial morphology of the H i at velocities where gas is detected in complex L suggests that there is a connection between the clouds in the GASS pilot region and those in complex L, in the form of a filamentary structure that is contiguous in velocity and connects complex L with the disk. Also, with the assumption that the halo clouds at large positive latitudes are located at tangent points, their heights are between kpc with distances kpc, which place them in the vicinity of the lower height estimates of complex L. These correlations suggest that complex L may have similar origins as the clouds presented here and it may be responsible for the observed excess of clouds at large positive latitudes.
6.4. Stability of Halo Clouds
The amount of mass required for a spherical cloud to be gravitationally bound is , where is the radius of the cloud, is the linewidth of its velocity profile and is the gravitational constant. For a cloud with pc and km s, the median observed values, this would require a mass on the order of . As none of the detected clouds have masses this large, they are either pressure confined or transitory.
Because turbulence also contributes to the linewidths, an upper limit for the thermal pressure within the clouds is , in units of K cm, where is the average number density and is the thermal temperature. However, as is a lower limit and is an upper limit, and and are uncorrelated, we are unable to put any constraints on the pressure of these clouds with the present data. It is possible that pressure changes are responsible for the observed trend in Figure 16, which tentatively shows that clouds further from the disk of the Galaxy have larger linewidths. If the clouds are in pressure equilibrium, their pressures could provide us with insight into the pressure structure of the halo, which is currently not well understood.
We have detected over H i clouds in the lower halo of the Galaxy in the Galactic All-Sky Survey pilot region. These clouds have a median peak brightness temperature of K, a median velocity width of km s, and angular sizes . As these clouds follow Galactic rotation, a subset was selected that is likely to be located at tangent points of the inner Galaxy, allowing us to determine their distances and therefore their sizes and masses. The tangent point clouds have radii on the order of pc and a median H i mass of . The properties of these clouds suggest that they belong to the same population of clouds discovered by Lockman (2002).
We simulated the population of clouds to constrain their random cloud-to-cloud velocity dispersion, , and spatial distribution. We found that km s, but if the clouds were left to evolve in the Galactic potential without any disruptions, these random motions would produce a scale height of pc, which is inconsistent with our derived scale height of pc. This suggests that the clouds do not belong to an equilibrium population. We detected clouds throughout the entire GASS pilot region, up to the latitude boundaries (). Few clouds were observed at low latitudes due to confusion, which may have resulted in an underestimate of the number of clouds at low heights, and therefore an overestimate in the derived scale height.
Our large, homogeneously-selected sample has allowed us to determine the spatial distribution of these halo clouds for the first time and has revealed that although clouds were observed at all longitudes within the GASS pilot region, they do not appear to be uniformly distributed but instead are concentrated in radius, peaking at kpc. We analyzed this distribution and suggest that the clouds are related to the spiral structure of the Galaxy. In particular, the peak in the radial distribution is suggestive of a relation to the kpc arm. This relation to a specific spiral feature remains speculative until further analysis using a larger sample of clouds and better constrained spiral structure models can be performed. It is therefore unlikely that the halo clouds are a result of a standard galactic fountain, as radial enhancements would not be expected in this scenario (Bregman, 1980). Instead, it appears that the clouds may be directly related to areas of active star formation, in the form of fragmenting H i shells and H i gas that has been pushed into the halo. This is further supported by the appearance of numerous clouds related to filaments and loops, whose structures may have resulted from stellar winds and supernovae (Dickey & Lockman, 1990). However, a comparison between the radial surface density distribution of H i clouds and H ii regions provides conflicting evidence: if clouds are related to areas of star formation, a relationship between the clouds and H ii regions would be expected but is not observed.
The morphology of H i at large positive latitudes in the GASS pilot region suggests that some of the clouds may be related to high velocity cloud complex L, whose lower height estimates of kpc and distance estimates of kpc place it in the vicinity of the clouds presented here. If they are related, it is possible that whatever process is responsible for the halo clouds, e.g., star formation, is also responsible for complex L. This is further supported by recent observations of IVCs, which suggest that the IVCs are related to energetic events in the Galactic disk and that they are likely linked to spiral structure (Kerton et al., 2006). The majority of those IVCs have km s, implying a Galactic origin, and we suggest that they are similar to the cloud population presented here.
A future study of halo clouds within the entire inner Galaxy observed by the Galactic All-Sky Survey will provide a much larger sample of tangent point clouds, enabling more complete statistics for distribution studies and a better understanding of Galactic structure. These studies will include comparisons between the distribution of halo clouds and that of tracers of star formation, such as H ii and , and will allow us to better constrain the origin of these clouds.
- Barnes et al. (2001) Barnes, D. G. et al. 2001, MNRAS, 322, 486
- Bregman (1980) Bregman, J. N. 1980, ApJ, 236, 577
- Bronfman (2008) Bronfman, L. 2008, Ap&SS, 313, 81
- Bronfman et al. (1988) Bronfman, L., Cohen, R. S., Alvarez, H., May, J., & Thaddeus, P. 1988, ApJ, 324, 248
- Burton & Gordon (1978) Burton, W. B., & Gordon, M. A. 1978, A&A, 63, 7
- Cowie & McKee (1977) Cowie, L. L., & McKee, C. F. 1977, ApJ, 211, 135
- Dame (1993) Dame, T. M. 1993, in American Institute of Physics Conference Series, Vol. 278, Back to the Galaxy, ed. S. S. Holt & F. Verter, 267
- de Avillez (2000) de Avillez, M. A. 2000, MNRAS, 315, 479
- Dickey & Lockman (1990) Dickey, J. M., & Lockman, F. J. 1990, ARA&A, 28, 215
- Ferrière (2001) Ferrière, K. M. 2001, Reviews of Modern Physics, 73, 1031
- Heiles (1967) Heiles, C. 1967, ApJS, 15, 97
- Heiles (1979) —. 1979, ApJ, 229, 533
- Heiles (1990) —. 1990, ApJ, 354, 483
- Houck & Bregman (1990) Houck, J. C., & Bregman, J. N. 1990, ApJ, 352, 506
- Jackson et al. (2004) Jackson, J. M., Simon, R., Shah, R., Rathborne, J., Heyer, M. H., Clemens, D. P., & Bania, T. M. 2004, in Astronomical Society of the Pacific Conference Series, Vol. 317, Milky Way Surveys: The Structure and Evolution of our Galaxy, ed. D. Clemens, R. Shah, & T. Brainerd, 49
- Kalberla et al. (2005) Kalberla, P. M. W., Burton, W. B., Hartmann, D., Arnal, E. M., Bajaja, E., Morras, R., & Pöppel, W. G. L. 2005, A&A, 440, 775
- Kalberla et al. (2007) Kalberla, P. M. W., Dedes, L., Kerp, J., & Haud, U. 2007, A&A, 469, 511
- Kerr & Lynden-Bell (1986) Kerr, F. J., & Lynden-Bell, D. 1986, MNRAS, 221, 1023
- Kerton et al. (2006) Kerton, C. R., Knee, L. B. G., & Schaeffer, A. J. 2006, AJ, 131, 1501
- Koo et al. (1992) Koo, B.-C., Heiles, C., & Reach, W. T. 1992, ApJ, 390, 108
- Koo & McKee (1992) Koo, B.-C., & McKee, C. F. 1992, ApJ, 388, 93
- Lockman (1981) Lockman, F. J. 1981, ApJ, 245, 459
- Lockman (1984) —. 1984, ApJ, 283, 90
- Lockman (2002) —. 2002, ApJ, 580, L47
- Luna et al. (2006) Luna, A., Bronfman, L., Carrasco, L., & May, J. 2006, ApJ, 641, 938
- Mac Low et al. (1989) Mac Low, M.-M., McCray, R., & Norman, M. L. 1989, ApJ, 337, 141
- McClure-Griffiths & Dickey (2007) McClure-Griffiths, N. M., & Dickey, J. M. 2007, ApJ, 671, 427
- McClure-Griffiths et al. (2002) McClure-Griffiths, N. M., Dickey, J. M., Gaensler, B. M., & Green, A. J. 2002, ApJ, 578, 176
- McClure-Griffiths et al. (2005) McClure-Griffiths, N. M., Dickey, J. M., Gaensler, B. M., Green, A. J., Haverkorn, M., & Strasser, S. 2005, ApJS, 158, 178
- McClure-Griffiths et al. (2006) McClure-Griffiths, N. M., Ford, A., Pisano, D. J., Gibson, B. K., Staveley-Smith, L., Calabretta, M. R., Dedes, L., & Kalberla, P. M. W. 2006, ApJ, 638, 196
- Morras et al. (2000) Morras, R., Bajaja, E., Arnal, E. M., & Pöppel, W. G. L. 2000, A&AS, 142, 25
- Nagashima et al. (2006) Nagashima, M., Inutsuka, S.-i., & Koyama, H. 2006, ApJ, 652, L41
- Norman & Ikeuchi (1989) Norman, C. A., & Ikeuchi, S. 1989, ApJ, 345, 372
- Paladini et al. (2004) Paladini, R., Davies, R. D., & DeZotti, G. 2004, MNRAS, 347, 237
- Press et al. (1992) Press, W. H., Teukolsky, S. A., Vetterling, W. T., & Flannery, B. P. 1992, Numerical recipes in C. The art of scientific computing (Cambridge: University Press, —c1992, 2nd ed.)
- Putman et al. (2003) Putman, M. E., Bland-Hawthorn, J., Veilleux, S., Gibson, B. K., Freeman, K. C., & Maloney, P. R. 2003, ApJ, 597, 948
- Putman et al. (2002) Putman, M. E. et al. 2002, AJ, 123, 873
- Richter et al. (2001a) Richter, P., Savage, B. D., Wakker, B. P., Sembach, K. R., & Kalberla, P. M. W. 2001a, ApJ, 549, 281
- Richter et al. (2001b) Richter, P., Sembach, K. R., Wakker, B. P., Savage, B. D., Tripp, T. M., Murphy, E. M., Kalberla, P. M. W., & Jenkins, E. B. 2001b, ApJ, 559, 318
- Rougoor & Oort (1960) Rougoor, G. W., & Oort, J. H. 1960, Proceedings of the National Academy of Science, 46, 1
- Sato et al. (2007) Sato, M. et al. 2007, PASJ, 59, 743
- Shapiro & Field (1976) Shapiro, P. R., & Field, G. B. 1976, ApJ, 205, 762
- Stanimirović et al. (2006) Stanimirović, S. et al. 2006, ApJ, 653, 1210
- Staveley-Smith et al. (1996) Staveley-Smith, L. et al. 1996, Publications of the Astronomical Society of Australia, 13, 243
- Stil et al. (2006) Stil, J. M. et al. 2006, ApJ, 637, 366
- Tomisaka & Ikeuchi (1988) Tomisaka, K., & Ikeuchi, S. 1988, ApJ, 330, 695
- Vallée (2008) Vallée, J. P. 2008, AJ, 135, 1301
- van Woerden et al. (1957) van Woerden, H., Rougoor, W., & Oort, J. 1957, Comptes rendus de l’Académie des sciences, 244, 1691
- Wakker (1991) Wakker, B. P. 1991, A&A, 250, 499
- Wakker & van Woerden (1991) Wakker, B. P., & van Woerden, H. 1991, A&A, 250, 509
- Wakker et al. (2008) Wakker, B. P., York, D. G., Wilhelm, R., Barentine, J. C., Richter, P., Beers, T. C., Ivezić, Ž., & Howk, J. C. 2008, ApJ, 672, 298
- Weiner et al. (2001) Weiner, B. J., Vogel, S. N., & Williams, T. B. 2001, in Astronomical Society of the Pacific Conference Series, Vol. 240, Gas and Galaxy Evolution, ed. J. E. Hibbard, M. Rupen, & J. H. van Gorkom, 515
- Williams (1973) Williams, D. R. W. 1973, A&AS, 8, 505
|aaUncertainties in are K.||bbUncertainties in the maximum angular extents are dominated by background levels surrounding the cloud and are assumed to be of the estimated values.||ccMass uncertainties are dominated by the interactive process used in mass determination and are assumed to be of the estimated values.||Notes|
|(deg)||(deg)||(km s)||(K)||(km s)||( cm)||( )||( kpc)|
Note. – Observed properties of H i clouds in the GASS pilot
region. The complete version of Table 1 is available in
the electronic edition of the Astrophysical Journal. We provide this
sample as a guide to its content. Descriptions of each property are
presented in §3.2. Clouds that have been
cataloged elsewhere in the literature are noted by the following labels:
1: detected by Putman et al. (2002) and
2: detected by Wakker & van Woerden (1991).
Although the clouds detected elsewhere do not necessarily have the exact Galactic coordinates and as listed here, it is likely that they are the same cloud and that the differences are a result of observational constraints. Also, we note that Morras et al. (2000) detected H i in some areas of these clouds but such detections were not identified as individual objects.
|aaAlong a given line-of-sight, the smallest Galactocentric radius possible is at the tangent point. If the cloud is not located at the tangent point it must be further away from the center and the error on must be positive.|
|(deg)||(deg)||(km s)||(km s)||(kpc)||(kpc)||(kpc)||(pc)||()|