A detail of the 18% isosurface around Orion taken from the third version of the solar neighbourhood map. Oriented so that the direction to the galactic centre is at the top.
As I mentioned in my previous blog post, I have created a new version (v. 3) of the solar neighbourhood map. This uses colours taken from the 2MASS catalog and has simpler controls (dust is always turned on and the views always show a 70% bright star isosurface). You can select several different hot star isosurface densities as well as three different label schemes.
The brightest stars now have labels if you select the isosurface or stars label options.
The new dust overlay is taken from figure 3 (top) in this preprint:
Three-dimensional mapping of the local interstellar medium with composite data,
Capitanio, Letizia; Lallement, Rosine; Vergely, Jean Luc; Elyajouri, Meriem; Monreal-Ibero, Ana
You can read detailed documentation by clicking on the Help link at the upper right of the map system, which can be found here.
Density isosurfaces of major OB star concentrations in the solar neighbourhood . Oriented so that the direction to the galactic centre is at the top.
Gaia DR1, which includes the TGAS parallax data set, provides no colours for its stars. Colour data is essential in order to produce maps of the hot OB stars, which mark the young star formation regions in our galaxy.
Colours will be provided in the next Gaia release, but for now, the second version of my solar neighbourhood map takes its colour information from the Tycho-2 catalog, which makes sense because TGAS is itself based on stars from that catalog.
However, I noticed that many professional astronomers publishing on Gaia were using colours from the near-infrared 2MASS catalog instead. Eric Mamajek has put up an invaluable colour reference table, which converts colours from many catalogs into star temperatures and spectral types. Mamajek's table includes the 2MASS colours.
Using the Tycho-2 colours with my usual filters (err/plx < 0.2, absolute magnitude brighter than 1.5) extracts 3400 OB stars from TGAS. However, using the 2MASS colours extracts 5800 OB stars - more than a 70% increase!
I suspect 2MASS colours are more accurate because near infrared colours are less likely to be distorted by dust than visual light colours.
I am now working on a third version of my map that combines the 2MASS-derived hot star colours with the latest solar neighbourhood dust map. I expect the map will be done before the end of the month. However, I already have some interesting results.
Perhaps the most important result is a structure analysis of the density isosurfaces for the hot OB stars. What I've done is compute a density isosurface for each integer percentage from 10% to 99% as well as generate statistics and a list of contained stars for each connected region inside each isosurface.
There are many isosurfaces and thousands of connected regions within them, but not all are equally important.
If we start with the low density 10% isosurface and watch what happens to each connected region within it as the density increases, we see that each region fragments into smaller and denser regions. The lower density regions contain a nested sequence of higher density regions, much like Russian matryoshka dolls.
Most of the time, the fragments are small. But occasionally a large fragment breaks off. The enclosure diagram below shows each fragment with 20 or more stars and the less dense enclosing region that it fragments from. Hovering over a circle should show the name I have given to the region.
There are many interesting features visible in this enclosure chart. I'll look at some of these in a future post. For now, I want to show what happens when I map these large fragments. The map at the beginning of this article shows the fragments denser than 12% with at least 20 stars. Other than Orion X, which is from the
Bouy, H., and J. Alves. "Cosmography of OB stars in the solar neighbourhood." Astronomy & Astrophysics 584 (2015): A26.
article, the others are named after a contained bright star, cluster or association. The prominent Wishing Well region is named after the common name for its largest cluster, NGC 3532.
The map looks very promising, but it has one disappointing feature. Many of the elongated structures are oriented so that they point towards the Sun.
You can see this here:
Elongated structures like these are a common artefact in Milky Way maps and are sometimes jokingly called "fingers of God" because of the way the galaxy appears to point at our minor G2 class main sequence star. Often the cause is dust, which blocks our view in some places but has gaps that allow us to see in certain directions for a long distance. It is possible that dust is a cause here too, but it also seems likely that the problem is due to errors in parallax measurement.
A poster displayed last week at an European astronomy conference in Prague by Larreina, Alves, Bouy, et.al. observes that "Due to errors in parallax many [TGAS] structures appear elongated towards the Sun".
We can hope that with better calibration and more observations, these "fingers of God" will be eliminated or at least significantly reduced in Gaia DR2, due out in April 2018.
This image shows dust (reddish brown), hot star clouds (blue) and bright star clouds (green) in the solar neighbourhood within 650 parsecs (2100 ly). The direction to the galactic centre is at the top.
My first map of the solar neighbourhood, released in February 2017, had a number of limitations and a couple of significant errors. This second attempt fills in some gaps and corrects the known errors.
This map version also includes 32 thousand "beacon" stars - bright stars contained in the dense star clouds within 650 parsecs or about 2100 light years. The region names in this version are based on star clusters or extremely bright stars contained within the region.
Filling in missing data
The TGAS data set released as part of Gaia DR1 is known to be missing many stars. Not only are stars missing in certain underscanned directions, but Gaia is missing high proper motion stars, many relatively bright stars, and stars that are very blue or very red. Unfortunately many of these stars are the ones that are needed to construct a map, especially within a few hundred parsecs.
In this map version I have used the older Hipparcos data set to add missing bright stars within about 300 parsecs. This is a useful stop gap until Gaia DR2 is released in April 2018.
In my previous map I used the Tycho-2 database to determine star colours as the Gaia-determined colours will not be available until DR2. However I did not consider the colour errors given in the Tycho-2 catalog and as a result, many of the stars I included in my "hot" star map are in fact of unknown temperature. In this version of the map, I have excluded all stars with a colour error greater than 0.1 and, further, at the suggestion of Gaia scientist Ronald Drimmel, I have excluded all stars with a relative magnitude dimmer than magnitude 11 as dimmer Tycho-2 stars are known to have dubious colour values.
In my previous map I did not take into account the fact that the Tycho-2 catalog is itself very incomplete, especially for stars dimmer than relative magnitude 11. After I filtered out the stars dimmer than relative magnitude 11 to deal with the colour errors, I realised that this also imposed a limit on the absolute magnitudes of the stars I used to construct a map. Here is the graph showing the absolute magnitudes of stars with relative magnitude 11 out to various distances:
After some analysis I decided to make the second version of the map out to 650 parsecs. In order to ensure that my selection of stars was even reasonably complete out to that distance, a look at this chart showed that I needed to filter my list to stars brighter than absolute magnitude 1.5 (or so, making the limit slightly more restrictive than the chart allowed for some dust obscuration as well).
Adding missing stars from Hipparcos expanded my star list, but filtering out dim stars considerably restricted it. My new map is constructed using about 140 thousand bright stars (1.5 absolute magnitude or brighter) including about 3400 hot stars (colour index < -0.02, corresponding to O and B class stars).
This second map version looks quite different from the first version. The first version showed three separate star concentrations or stellar continents. The second version fills in the gaps between these continents. There are now two major star concentrations, which I have called the Northern and Southern stars. I will look at these star concentrations in more detail in a future blog post.
The map application
There is much else I want to show including 3D meshes, animations etc., but this blog post has gone on long enough so it is time for the link to the map application itself:
I've added more configuration options to add different densities of bright and hot isosurfaces, turn on or off the dust clouds, and display different kinds of labels. You can read more about the map controls by clicking on the Help link at the bottom of the control area on the upper right.
Also note that at the highest zoom level you can hover over individual stars for names and distances above and below the galactic plane, and click on individual stars for more details. Click on the Help link below the map controls for more information. The JSON loading of the star data may slow down less powerful devices - I am looking into speeding that up as well as tweaking the display to work better with mobile devices.
I am confident that this map is more accurate than the first version because the hot star densities now correspond closely to the Hipparcos map shown in the paper:
Bouy, H., and J. Alves. "Cosmography of OB stars in the solar neighbourhood." Astronomy & Astrophysics 584 (2015): A26.
Compare the dense hot star clouds shown in this image from the second version of my map:
Many of the structures line up after a 90 degree rotation.
Having said that, I can see some more improvement possibilities so there may be a third map version before Gaia DR2 in April 2018 allows for a much better map of a large part of the galaxy. Exciting times ahead!
Flocks of birds, schools of fish and concentrations of stars can all be mapped using isosurfaces.
The TGAS (Tycho-Gaia Astrometric Solution) data set released as part of Gaia DR1 in September 2016 contains positions and parallaxes for 2057050 stars, about 85% of the 2432906 stars in the Tycho-2 catalog. This is the first time that parallaxes (and hence distance estimates) have been available for such a large number of stars.
Nevertheless, TGAS contains a tiny fraction of the hundreds of millions of star parallaxes that will be released as part of Gaia DR2, currently scheduled for April 2018.
How can we construct a map from such an enormous amount of information?
We know already that stars are not distributed randomly but are found in vast concentrations, much like flocks of birds or schools of fish on Earth. These concentrations range in scale from systems with multiple stars, clusters, and associations all the way up to spiral arms. There is structure at every scale. One important tool to map this stellar distribution is isosurfaces of constant star density.
Isosurfaces are the three-dimensional equivalent of the isolines on a topographic elevation map.
Isosurfaces fit inside each other like complex Russian nesting dolls.
An algorithm for converting the point set of a scalar field with a given value into a surface mesh was invented by William E. Lorensen and Harvey E. Cline and published in 1987. The marching cubes algorithm is most often used to convert MRI scans into images of tissues and bones for medical diagnosis and research. It has many other uses for scalar fields, including the analysis of stellar distribution.
We can think of the isosurfaces generated by the marching cubes algorithm as the three dimensional equivalent of the isolines used in topographic maps to show lines of constant elevation. In an elevation map, lines representing higher elevation appear inside lines representing lower elevation. The sequence of isolines of increasing elevation define a mountain peak. Similarly, a sequence of isosurfaces representing increasing star density represents a "star peak".
One difficulty in visualizing isosurfaces is that they are three-dimensional structures and representing a sequence of isosurfaces enclosing each other is often confusing and difficult to interpret - a bit like a series of very complex Russian nesting dolls. We can use translucent surfaces to represent a few enclosed surfaces as in this image:
However, for a larger sequence of isosurfaces, an animation that shows less dense isosurfaces fading or evaporating to reveal denser surfaces inside is often clearer.
Converting the Gaia parallax data into density isosurfaces is straightforward. First I select the most accurate parallax data with error/parallax < 0.2. There are about a million stars in the TGAS data set with this parallax accuracy. A second step is required because simply computing density surfaces of this data set does not give a map of the solar neighbourhood! Instead it returns a sequence of nested ellipsoids. This is because an unfiltered analysis is limited by what Gaia can see and Gaia sees more stars at shorter distances than further away. What we need to do to produce a map is to select stars that Gaia can see out to a reasonable distance of 600-800 parsecs. So we need intrinsically bright stars to construct a map.
After some experimenting, I found two TGAS subsets of the low error stars that generate reasonable maps. The first is the 400 thousand "bright" stars with absolute magnitude <= 3 (remember that the brighter the star, the lower the magnitude value). The second is the 20 thousand "hot" stars with colour index <= 0. Except for some very dim but hot white dwarfs close to the Sun, the hot stars are essentially a subset of the bright stars in the TGAS data set.
For each of these "bright" and "hot" star sets, I convert the parallax and positions into (x,y,z) coordinates and then count the stars in each 2x2x2 parsec bin.
In order to produce reasonably smooth isosurfaces, I then used a gaussian smoothing function (usually with a sigma of 15 parsecs) to produce a scalar field defined within a cube with a radius of 800 parsecs from the Sun. The scalar density field can then be converted into isosurface meshes using the marching cubes algorithm mentioned above.
You can see an animation showing the lower density bright isosurfaces fading into higher density surfaces below. The bright star isosurfaces dissolve from the low density 20% isosurface by 5% increments until they reach the high density 95% bright star isosurface. (The percentages used to define isosurfaces are always a percentage of the maximum density found in the data set. So a 40% isosurface contains all the stars with a density that is 40% or more of the maximum value.)
In this animation, the Sun is at the centre and the direction to the galactic nucleus (0° galactic longitude) is at the top.
A similar animation for hot isosurfaces is here:
Bright versus hot stars
Because the hot stars are essentially a subset of the bright stars, it is tempting to think that the regions within the bright star isosurfaces always contain a hot star core, much like human tissue is supported by a bony skeleton, but this is not the case! There are significant bright star concentrations that contain no hot star cores.
As I explained in my previous blog post, the dense hot star concentrations within the TGAS data are found largely within three major regions, or stellar continents. The bright stars, on the other hand, form a ring around the local bubble. As a result, there are large concentrations of bright stars found between the hot star continents, especially in the hot star gaps I've nicknamed the Strait of Centaurus and the Gulf of Auriga.
I've created a few animations to make the differences between the bright star and hot star concentrations clearer.
The first example shows the above bright star dissolve animation (this time in green) and within this dissolve, I have included the 40% hot star isosurface (in blue). You can see how the blue hot star isosurface is embedded within the green bright star isosurfaces, but also how there are bright stars located where there is no hot star core.
Here is a similar example, except that this time the bright star dissolve reveals the 50% hot star isosurface:
And the 60% hot star isosurface:
And the 70% hot star isosurface:
The difference between the distribution of the hot and bright stars tells us that mapping stars in the galaxy is not as straightforward as mapping land on the Earth. Depending upon the stars we select, many maps are possible.