Roughly two to three billion years after the Big Bang came “Cosmic Noon,” which astrophysicists have discovered was a crucial period for a particular class of galaxies, Lyman Alpha Emitters (LAEs), then experiencing their first significant wave of star formation.
A Rutgers University-New Brunswick researcher led the new study, which the team believes will provide essential insights into the universe’s history, helping to explain how galaxies grow and evolve. Their work combined precise imaging with machine learning algorithms to explore what may have been a pinnacle of galaxy development.
Lyman Alpha Emitters
Due to their active star formation, Lyman Alpha Emitters shine brightly as their ultraviolet emissions turn visible due to the universe’s expansion. From Earth, this “Lyman Alpha” light is readily observable, even across great distances. The LAEs are 12 billion light years away, with the light now reaching scientists, offering a window into a remote past and providing clues to understanding the early universe.
“LAEs have been identified as progenitors of typical present-day galaxies like our own Milky Way,” said lead author Nicole Firestone, a National Science Foundation Graduate Research Fellow in the Department of Physics and Astronomy at the School of Arts and Sciences. “Now that we know when they first formed their stars, we have discovered our own galaxy’s ‘origin story,’ unlocking one of the mysteries of creation.”
Milky Way Origins
Understanding the history of the Milky Way galaxy has been a major driver of astronomers’ interest in ancient, distant galaxies. The time delay it takes light to reach Earth from these remote regions of space allows scientists to gain a firsthand look at star formation. Eventually, the LAEs will begin to look like our own galaxy.
“Until now, it remained an open question whether we had looked far enough back in time to find the starting points for the Milky Way and galaxies like it,” said co-author Eric Gawiser, who also led the research team behind the new finding. “Now we know the answer to that question is ‘Yes!’”
Understanding Star Formation
One question about LAEs has long puzzled scientists. Are they witnessing these galaxies’ initial periods of star formation, or might the galaxies be much older, experiencing punctuated periods of star formation activity that first occurred much earlier? Determining which scenario is occurring would be a major benchmark for understanding galactic evolution.
“For the very first time, we have been able to definitively show that most LAEs are experiencing their first major starburst at the time of observation and only have very young stars,” Firestone said.
Performing the Lyman Alpha Emitter Analysis
The ODIN project sky survey, utilizing the Dark Energy Camera at the Cerro Tololo Inter-American Observatory in Chile, provided the team with its imaging data. The Dark Energy Camera is capable of viewing a large area of the sky while recording highly specialized images of remote regions. In these images, LAEs show up as much more brilliant than objects emitting visible light.
The team used machine learning to analyze these bright points, determining their physical properties. Through this analysis, which included the rate of star formation over time, the scientists compiled the histories of the LAEs. Gawiser himself developed this methodology with a former grad student, Kartheik Iyer.
Their work uncovered that 95% of LAEs observed were at their pinnacle of star formation activity. This settles the debate, proving that LAEs showcase their galaxy’s early development, clarifying the galaxy formation timeline.
“This discovery helps us understand what our own Milky Way galaxy looked like when it first started forming stars,” Gawiser said.
The paper “ODIN: Star Formation Histories Reveal Formative Starbursts Experienced by Lyα-emitting Galaxies at Cosmic Noon” appeared on June 04, 2025, in Nature Astronomy.
Ryan Whalen covers science and technology for The Debrief. He holds an MA in History and a Master of Library and Information Science with a certificate in Data Science. He can be contacted at ryan@thedebrief.org, and follow him on Twitter @mdntwvlf.
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