After the Big Bang—How Did Galaxies Form and Evolve?
Danilo Marchesini, PhD, joined the Tufts Department of Physics and Astronomy in 2009. He earned his PhD in astrophysics from the SISSA/International School for Advanced Studies, Trieste, Italy, and did postdoctoral work in the Department of Astronomy at Yale University. Marchesini seeks to understand how galaxies formed after the Big Bang and how they have evolved since their formation. He is interested in understanding the physical processes responsible for shaping the earliest galaxies into what we see today in the “local universe,” which consists of the galaxies within 500 million light years of our own Milky Way.
Marchesini and his research group use very large, powerful telescopes, some fitted with custom-built filters, to image different regions of the sky. They recently discovered a group of very massive galaxies that formed when the universe was relatively young, just 1.5 billion years old. The Big Bang is thought to have occurred 13.7 billion years ago. “We’re looking back in time 12.2 billion years,” he says. “That means the light we’re receiving now from that galaxy left it 12.2 billion years ago.” These young, massive galaxies display characteristics that appear to contradict the generally accepted model of galaxy formation, which is that galaxies formed through merging and assembly of smaller components. “How was this galaxy able to make so many stars in such a short time?” asks Marchesini. “What will be the evolution of that galaxy from that point on? What will be the descendent of this galaxy in the local universe, and what are the physical processes that make this galaxy turn into a massive galaxy in the local universe today?”
The first step in seeking answers to these questions is to verify the current data, which is based on the modeling of spectral energy distributions and has large uncertainties. Marchesini is collaborating with several groups to verify these massive galaxies spectroscopically, both in the optical and near infrared wavelengths. “I need to use the biggest telescopes around because these are faint galaxies,” says Marchesini, who travels to telescopes in Chile and Hawaii to take his measurements. A very large radio telescope called ALMA, the Atacama Large Millimeter/submillimeter Array, recently built in northern Chile through an international collaboration, will provide the observational capabilities to further verify the properties of these early massive galaxies. The ALMA should give Marchesini the data he needs to determine whether the extreme luminosity of these early massive galaxies in the infrared range is due to massive ongoing star formation or to active galactic nuclei (AGN). “So it could be either of those two things,” says Marchesini. “I currently prefer the interpretation requiring AGNs, the active supermassive black holes, because if these galaxies are already so massive and they’re forming thousands of stars every year, they’re going to become monsters within the following few hundred million years or so; they will be more massive than the most massive galaxies we see now in the local universe, and that’s a contradiction. There are ways of getting out of this contradiction; for example, they could have a mechanism (perhaps the feedback from the AGN itself) to shut off star formation very quickly so that they don’t build that many stars any more in the following few hundred million years. But the ALMA facility will allow me to discriminate between these scenarios in a better way.”
Marchesini enjoys interdisciplinary research and welcomes collaborations. “When you’re collaborating, you’re giving something and you’re getting something, and it’s in that exchange that both people involved are growing,” he says. His primary area for collaboration involves the specialized software programs that he uses to analyze astronomical images. The software could be used in any discipline that seeks to analyze an image quantitatively, whether the image is of the sky, or the earth, or a bacterium under a microscope. “The software can be easily adjusted to quantify an image of a cell or of a distribution of cells,” he says. “You can also measure the light distribution in a cell, and from that distribution derive the shape and size of the cell.”
Collaborations with computer scientists, mathematicians, and statisticians could also be productive. Marchesini would like to make some of his software programs public, but in order to do so he would need to work with a computer scientist who could make them more user friendly. Computer scientists could also help him solve specific problems. “What takes me a month would probably take a computer scientist a day because they know the techniques and they know what to do to solve a specific coding issue,” he says. “Mathematicians might have much more powerful techniques to model a set of data when you have so many correlated variables. It could be fun to collaborate with mathematicians or statisticians to try to find the best approach to tackle these problems.”
For more information, please go to http://cosmos2.phy.tufts.edu/~danilo.