An Astronomical Challenge
Summer Scholars tackle big data to explore galaxy evolution
Associate Professor of Physics and Astrophysics Danilo Marchesini, Ph.D. candidate Kalina Nedkova, and senior Erin Kado-Fong collaborate on their Summer Scholars project. (Alonso Nichols/Tufts University).
by Dana Guth, '17
Managing the output of astronomical data presents a challenge to researchers: currently, big data in the field of extragalactic astronomy require human collection and interpretation. But in the coming era of even bigger data, this sort of laborious process will not be efficient enough.
"Using modern computing techniques to extract and analyze astronomical data is becoming increasingly important as we enter the era of 'big data' astronomy," says rising senior Erin Kado-Fong, astrophysics major in the School of Arts and Sciences. This summer, Kado-Fong and Kalina Nedkova, a doctoral candidate in physics in the Graduate School of Arts and Sciences, are immersed in a project to create a suite of tools that will allow for the unsupervised extraction of photometric data for a large number of sources. Kado-Fong and Marchesini enlisted Nedkova's assistance in collecting data that would create a benchmark for the tools Fong is developing.
"Because light travels at a finite speed, light from galaxies that are farther away takes longer to reach us, allowing us to directly observe galaxies at different points of cosmic time," Kado-Fong explains. "These data allow us to flesh out the evolutionary paths of various types of galaxies.
Danilo Marchesini, an associate professor in the Department of Physics and Astronomy, is Kado-Fong and Nedkova's project advisor as part of Tufts Summer Scholars, a program that funds students who delve into their academic passions with the support of a professor. Nedkova received funding as a Tufts Graduate Summer Scholar, a new scholarship to encourage open collaboration across various Tufts schools and departments.
"In the past, many undergraduate Summer Scholars worked informally with graduate students in their departments or labs, and we wanted to offer further support in terms of finances and mentoring resources," says Graduate School of Arts and Sciences Associate Dean Sinaia Nathanson. "While in the lab, both students develop or deepen ongoing relationships, and we believe that they are stronger researchers and scholars because of this experience."
Using tools—some that Marchesini and his collaborators have developed and optimized, and others that have been publicly released—Kado-Fong is developing an automatic pipeline that applies all of the tools to large datasets without the need of human intervention. "Erin has also been improving these tools, as well as making the whole machinery computationally fast and adapting it to run on super-computers, such as the computing cluster at Tufts," says Marchesini. "This pipeline is applicable to large area surveys to construct catalogs with exquisite quality as quickly as possible."
Kado-Fong, Nedkova, and Professor Marchesini examine a mosaic of cosmic images in the lab. (Alonso Nichols/Tufts University).
While the current goal of the project is to apply these tools to today's available astronomical surveys, the team's work could also prepare for the arrival of the Large Synoptic Survey Telescope (LSST), a survey telescope to be released in the next decade that can process an unprecedented amount of astronomical data. With the capacity to map the entire visible sky in just a few nights, "the LSST will allow us to image millions of stars and galaxies using several different filters," notes Kado-Fong.
The research grew out of Kado-Fong's previous collaboration with Marchesini on an academic paper that explored images of galaxies in the early universe. "His research focuses on galaxy evolution and galaxies in the early universe, and my projects with him have been centered around these topics," she explains. She wanted to combine her previous work with a data-driven direction due to the inevitable release of the LSST, which is predicted to process fifteen terabytes of data every night.
Marchesini says Kado-Fong was a natural choice for a student research partner. As an undergraduate, Kado-Fong was enrolled in his graduate class, Stellar Structure and Evolution, and was conducting research in galaxy formation and evolution under his supervision.
"When that project was coming to a conclusion, says Marchesini, "we looked ahead. Erin had already worked with large data sets [studying stars at Harvard last summer]. Under my supervision, Erin learned how to reduce and analyze near-infrared spectra of distant ultra-massive galaxies and how to determine their properties, answering questions such as: How big are these galaxies? How old are their stars? Are they still forming new stars? Do they host actively growing super-massive black holes? Now, Erin is taking advantage of the expertise and skills she developed during the two research opportunities."
Nedkova is creating a mosaic of the images from data currently available from the Dark Energy Survey (DES), a collaborative effort to map millions of galaxies and find patterns in cosmic structure. Although her area of study has focused on physics, Nedkova likes the challenge of tackling big-data coding and computer science for this project. "I convert DES data to usable, science-quality images," says. "I look at the coordinates of each picture and place it where it belongs. It's useful because the images I produce will constitute the perfect test bench for Erin to assess her new tools."
Nedkova's first research project at Tufts "continues to be a valuable and rewarding experience," she says. "It has helped me develop many of the skills needed for research in astronomy. Erin is already familiar with a lot of astronomical tools, so collaborating with her was a great way to start my research career."
While the three researchers say the project is in the developmental stage, Kado-Fong notes that she's very proud of what they have accomplished together to date. "I love setting up infrastructure with a plan in mind," she says. "Code tends to get out of hand a little bit at a time, so it's really rewarding to see that the program I set up is robust enough to be flexible and adapt."
Looking ahead, Marchesini says the tools and infrastructure Kado-Fong has developed will be applied to the construction of multi-wavelength photometric catalogs from the NEWFIRM Medium-Band Survey II, funded by NASA through their Astrophysics Data Archive Program, and the project has received additional funding from the Massachusetts Space Grant Consortium. Some tools are being applied to ongoing and next generation surveys, such as the Hubble Frontier Fields dataset, aiming at exploring the first billion years of cosmic history and finding candidates of the first galaxies that formed in the Universe, which will be among the objects the James Webb Space Telescope will observe after its launch into space in 2018. "We expect the infrastructure to be fully developed and tested by the end of 2016," says Marchesini, "and will continue to be used for many years to come."