From Photons to Photos: Observational Astronomy with CCDs

Summer research season is underway for me! This year, I am working with Mike Brown on an observational astronomy project. I’ll be using some of the data collected on the Keck Telescope last winter to study Jupiter’s Trojan asteroids.

Observational astronomy is probably what most people imagine when they picture an astronomer’s work, although the exact image in mind might be a bit outdated. Today’s astronomers are more likely sitting behind a computer screen than behind the eyepiece of a telescope, even in work that isn’t strictly computational or theoretical. That’s because astronomy, like many aspects of modern life, has gone digital.

Astronomers first recorded their observations on paper, by hand, until the invention of photography. By the early twentieth century, ground breaking discoveries, such as Edwin Hubble’s discovery of other galaxies and the expansion of the universe, were being made with the assistance of photographic plates. As photography evolved, so did astronomy. Today, digital cameras use sensors called CCDs to capture images, as do most telescopes. Now, astronomers can affix sensors to the foci of their telescopes in order to collect high-quality data.

How do CCDs capture astronomical images, assuming you have a telescope and a target for your observations? CCD stands for charge coupled device, a name that gives a hint of how it works. A CCD is sectioned into pixels, or bins, and when exposed to light, the bins gain a charge. The charge on each bin is proportional to the amount of light that it received, so the more charge each bin has, the more light it was exposed to, the brighter the area of the sky it observed. When the exposure is finished, the charge on each bin is read out and converted into a number (count) that represents how much charge built up in each bin. This transforms the image into an array of counts that represents how much light was detected in each pixel of the CCD.

Arrays, simple lists of numbers, are very easy for computers to store, transfer, and manipulate, so they are a useful format for astronomical data. Conversion of images to numbers just isn’t possible with sketches and photographic plates, and opens up new possibilities for handling data, since computers can very easily handle manipulating lists of that form. Some astronomers today work on “training” computers to perform automatic analyses of arrays, so computers can quickly accomplish basic tasks identifying variable stars or Kuiper Belt Objects. Such computer programs are useful, especially with the rise of large scale digital sky surveys that produce enormous quantities of data on a nightly basis.

A small part of a typical array might look like this. While useful to a computer, it’s very difficult for a human brain to figure out what’s going on without some help. In order to understand what’s going on, we can rearrange the numbers a bit. To make things even clearer, we can map count numbers to colors. I’ll pick greyscale, so that we can keep in mind that more counts corresponds to more light.

Unfortunately, CCDs, as powerful and useful as they are, do introduce their own biases into the data, so our image doesn’t look very clean right now. This problem is easy to correct, as long as you are prepared to encounter it. The CCD-introduced bias can be fixed by taking two specific types of pictures, known as darks and flats, which act like a control in a scientific experiment.

The first type of control picture, the dark, is necessary due to thermal noise in the CCD chip. Thermal noise is caused by the heat radiation from the sensor itself, since CCDs are sensitive to infrared light (heat). CCDs in telescopes are often cooled to low temperatures to reduce the effect of this noise, which is present as long as the instrument has a temperature, so it cannot be eliminated completely. To combat this problem, astronomers prepare a dark, which is an exposure of the CCD to a completely lightless environment, a bit like taking a picture with a camera that still has the lens cap attached. This way, the CCD is only exposed to the thermal noise originating from the instrument itself. Here is what a dark might look like:

The second type of image, the flat, is an image taken of a flat field of uniform light. This could be an evenly illuminated surface. Many astronomers will take flats during sunset when the evening sky is bright enough to wash out the background stars, but not so bright that the sensors are overloaded. Since we know the image should be evenly lit, the flat field allows astronomers to pick up systematic defects in the CCD. Due to tiny imperfections during manufacturing, some pixels may be more or less sensitive than average, or the telescope itself might have lens imperfections that concentrate light in different areas of the image. Flat field images let astronomers discover and correct for these effects. A typical flat might look like:

Now that we have our image, dark, and flat field, we can begin to process the data. First, we subtract the dark from the image of the object:

And from that image, we subtract the flat field, giving a nice, clear picture of our target object:

Now that we’ve done initial processing of the image to correct for bias, we can start to do more interesting analyses of the data. One very basic thing we can do is use this image to figure out how bright the object we are looking at is. We can sum up the counts that belong to the object to get a total brightness. In this case, the sum of the object counts is 550. But this number doesn’t mean very much on its own. The object might actually be quite dim, but appears bright because a long exposure was taken, and the CCD had more time to collect light. Or, we could have taken a very short exposure of a very bright object. So, we need to find a reference star of known brightness in our image, and measure that. If we know how bright the object appears compared to the reference star in our images, and we know how bright the reference star is, we can infer the brightness of the object.

If we have taken a picture of the same object in different filters, we can also create false-color images. Filters can be placed in the telescope aperture in order to restrict which wavelengths can pass through the telescope.  Using filters allows astronomers to choose which colors of light will reach the CCD and be counted. To make a false color image, astronomers combine images from two or more different filters. Each separate image is assigned a color according to which filter it was taken in (perhaps blue for ultraviolet light, green for visible light, and red for infrared light), then the images are combined into one.

False color images are useful because the color coding for each filter help draw attention to important differences between the individual images while still allowing astronomers to see the structure of the object in many different filters at once. In planetary science, for instance, different colors in an image might reflect differences in the composition of the surface of a planet, revealing regions of strikingly different geological histories across the whole planet. Images can also be combined to produce “true color” images using filters for different wavelengths of visible light in order to produce pictures that mimic closely what different astronomical objects would look like to human eyes. CCD technology has brought astronomy down to earth, quite literally, by producing images that reveal what the cosmos would look like, if only we could see it as well as our telescopes.

Cross Sectional Astronomy

My research this summer came to an end last week with a seminar I presented at along with many other students in Caltech's Summer Undergraduate Research Program. In addition to presenting my work with Monte Carlo simulations, I also attended talks given by other students doing research in astronomy and physics.

Many of the astronomy projects I learned about focused on creating software for recognizing and analyzing different astronomical phenomena, from variable stars to pulsars and contact binary systems. Many large-scale sky surveys, such as the Palomar Transient Factory and the Sloan Digital Sky Survey, produce a wealth of data on astronomical objects. Computers are often the best way to analyze the abundance of data produced by these surveys in order to identify interesting targets for follow-up study. But why do astronomers need these huge sky surveys and millions of target objects to study?

Analyzing how any population changes over time, whether it is a population of people, stars, or starfish, is a common problem in many areas of science. It can be a tricky problem too, especially when trying to tease out correlation and causation from subtle differences between subgroups of the population. There are two main study methodologies for dealing with this problem: longitudinal studies and cross sectional studies.

Longitudinal studies are the intuitive approach to learning how a population changes over time: just watch as the population (or more realistically, a random sample of the population) evolves naturally. It makes sense, but it's difficult in a lot of situations. For example, longitudinal studies of humans take dedication and decades of research. For phenomena with long lifespans, such as stars, this type of study is simply impossible--the stars vastly outlast human lives and even human civilizations!

Cross sectional studies instead study many individuals in the population at the same time. Each individual represents an individual in a slightly different stage of evolution, with slightly different characteristics; a random sample provided by nature. In humans, an example of a cross sectional study is gathering pictures of many different individuals at different ages in order to examine how appearance changes with age.

Since astronomers only have access to a snapshot of the universe as it appears today, cross sectional studies are what astronomers use to study populations of stars. The most famous example of a cross sectional study is the Hertzsprung-Russel diagram, a plot that correlates star surface temperatures (or colors) with their luminosities. The diagram shows stars in different stages of their evolution, from main sequence stars to red giants and white dwarves, along with stars in transitional states between these major milestones.With the diagram, we can trace the development of different types of stars, and how this development changes with different intrinsic properties of the star (mass turns out to be the most important property in determining the ultimate fate of a star).

There are some problems with the cross sectional approach. For example, age itself may correlate with the evolution of the population in question. In the human example, improving health as time goes on might manifest itself in physical differences, such as an increase in height, between generations that are not caused by the aging process itself. In astronomy, a star that is now nearing the end of its life formed in a quite different universe than a protostar that has just reached the main sequence. We know from theoretical models that the concentration of metals in the universe has increased with time as stars convert hydrogen and helium into heavier elements. Luckily, we can attempt to correct for these effects. Due to the finite speed of light and the vast size of the universe, by looking further and further away, we effectively look back in time. This can help us to determine how conditions were different for older stars when they formed, when compared to stars which are forming today.

Having a large sample size is important in a cross sectional study because it ensures that a representative sample is available and than no important features of the population will be missed. Cross sectional methods and large samples provided by surveys help astronomers to discover how stars age, correlate properties among different populations of stars, and provide experimental confirmation of hypotheses for many types of astronomical objects. There is still much to be learned about a variety of astronomical systems--stars, planets, and more.

Art and Science - Glassblowing

This week, I had the opportunity to visit a glassblowing studio. I discovered there was a lot of science involved in glassblowing, which seemed at first like a solidly artistic endeavor. Materials science in particular is important to understanding how glass will react in various circumstances, and knowing how materials respond to different conditions is vital to having control over the work being produced. For example, hot glass doesn't stick to cool steel, so in order to gather a blob of glass, you need to first heat a metal rod. Cold metal, however, works well as a surface for shaping glass. Fluid dynamics is also important. Molten glass turns out to have a honey-like consistency. It flows, but very slowly, and it droops in response to gravity if held still for too long. To prevent the glass from dripping onto the floor, the rod needs to be turned at all times.

A rather dramatic example of how science becomes relevant to glassblowing is the Prince Rupert's drop. Imagine taking a blob of glass and letting it drop into a bucket of water. The glass ends up forming an elongated teardrop shape with a very thin, twisted tail. These tails can be as thin as a human hair. Due tension created in the glass during rapid cooling, the bulb of the drop in incredibly strong. Smash it with a hammer, and it will not burst. However, breaking the thin tail causes the entire drop to burst, creating a fine white powder of glass particles.

I ended up making a heart-shaped paperweight. I still can't come up with a good metaphor for what it felt like to shape it that captures both the heat and malleability of molten glass.

We often tend to think of the craftsperson, painter, or sculptor as purely an artist. The skills for creating these media are acquired over years of experience. Over time, the artist tests different methods of creating a work in order to find out which techniques are the most effective. After a lifetime, this built up knowledge is vast, allowing an artist to deal with almost any situation they encounter. This body of knowledge, acquired empirically, is just like the body of knowledge most people imagine when they think of science. The artist is doing science when they discover a new method for working with their chosen material. By repeating the process over and over again, they can test the reliability of the effect and build a style. And when something goes wrong, the artist instinctively checks for what happened during the process, seeking out variables that changed the result of their experiment. An artist develops theories: cool glass quickly and it is fragile, like the Prince Rupert's drop, but cool it slowly and it is strong. Certain colors, when paired together or treated incorrectly change state and produce unexpected hues. The method used by artists to create and explore new techniques is science, and there is a lot we can learn from these artists' experiences.