Astrophysicists are putting the Via Lactea II dark matter halo simulation to the test – and the results so far have them scratching their heads.
Physicists theorize that colliding dark matter particles would annihilate each other, producing gamma ray energy. Via Lactea II showed that some small dark matter lumps should have sufficient internal density to generate annihilations – and the resulting gamma rays – at faint but possibly detectable levels not associated with other sources.
The orbiting Fermi Large Area Telescope (LAT), launched in June 2008, is designed to detect those rays. Partially supported by the Department of Energy and NASA, the satellite scans the entire sky every three hours in search of gamma rays with energies ranging from 20 milli-electron-volts (MeV) to more than 300 giga-electron-volts (GeV). Besides dark matter collisions, exotic astrophysical phenomena like black holes and pulsars also generate these high-energy rays.
Via Lactea II predicted that by the end of the satellite’s lifetime of five to 10 years, it would accumulate enough exposure time that it would find a signal, says Michael Kuhlen, a postdoctoral researcher at the University of California, Berkeley, who helped create and run the model.
But after two years of looking, LAT has yet to find a gamma ray signal significant enough to stand out from background noise. It’s still early, Kuhlen adds. “Just because we haven’t seen anything doesn’t mean there is anything fundamentally wrong.”
Nonetheless, researchers are starting to consider what the absence of gamma radiation says about the nature of dark matter.
Notable program alum Amanda Randles models blood circulation — and is a role model for… Read More
Fruit flies, algorithms and light offer lessons in how complex organisms learn. Read More
An NYU fellow accelerates algorithms that run vital processes backward. Read More
Models of semiconductor defects point to improved qubit reliability. Read More
A Georgia Tech fellow models renewable energy and other problems with ever-changing unknowns. Read More
A UC Berkeley fellow applies machine learning to snowpack monitoring and more. Read More