Qi Feng

I will start as an assistant professor at the University of Utah in January 2024. Check out my new website here.

Research

I am a member of the VERITAS and CTA Collaborations. We detect the Cherenkov light from very-high-energy gamma rays using the VERITAS telescope array (shown in the background image). The energies of these gamma rays (and cosmic-ray particles) are so high that we can image the long, elliptical track they leave in the atmosphere. It is like watching shooting stars, except Cherenkov flashes last only for a few nanoseconds, and are seen by us 400 times every second.

We are also building a prototype next generation telescope, the prototype Schwarzschild-Couder telescope (pSCT), at the gamma-ray group at Barnard College / Columbia University. This is the first telescope in the world with both primary and secondary mirrors segmented. Its novel design is motivated by the need of the next-generation Cherenkov Telescope Array (CTA) observatory. The SCT design, if implemented in the CTA installation, has the potential to improve significantly both the x-ray angular resolution and the off-axis sensitivity of the observatory, reaching nearly the theoretical limit of the technique. I made significant contribution to the initial alignment of the pSCT optical system, as well as to the first detection of the Crab Nebula with the pSCT.

I use different machine learning algorithms (e.g. boosted decision trees and convolutional networks) for event classifications and regression in gamma-ray astronomical data with Python (Feng & Lin 2016). Recently, I started to work with Ari Brill, Bryan Kim, Daniel Nieto, and Brian Humensky on the development of a machine learning Python package, CTLearn, which can handle data from all telescope types in the CTA array, as well as the VERITAS telescope. We train ResNet, MobileNet, and CNN-RNN models to separate gamma-ray events from cosmic-ray events.

In 2016, a team of people in the VERITAS Collaboration including myself started a citizen science project called Muon Hunter to facilitate research like mine. You can help us obtain a high-quality training data set, and contribute to a good machine learning model.

With the VERITAS data, I try to understand the nature of a collection of variable sources. One type of the variable sources that we detect is the active regions near the central supermassive blackholes in some galaxies, called blazars. I study the fastest variabilities observed from blazars at the highest energies, to learn about their physics and geometry.

More recently, I became interested in the hypothetical death cry from black holes born at very early universe (primordial black holes). Hawking predicted radiation from black holes, which means black holes are slowly losing energy and becoming smaller. Eventually, they get so small that they disappear, before which a tiny burst of energetic particles are produced. I work with Dr. Simon Archambault and Prof. David Hanna at McGill on constraining the rate density of the evaporation of primordial black holes using VERITAS data.