2018 Segrè Interns

Tuesday, July 10, 2018

Berkeley’s Emilio Segrè Internship is an opportunity for three undergraduate students to enhance their experimental research skills by improving some of the experiments in Physics 111B, the Advanced Experimentation Laboratory located in the Donald A. Glaser 111 Lab. Students start their eight-week internship programs in June and complete their tasks by the first week of August. 

The Emilio Segrè Internship honors Italian physicist Emilio Segrè, a student of Enrico Fermi, who emigrated to the United States and accepted a position at the University of California, Berkeley. Segrè’s work with Owen Chaimberlain on anti-protons awarded them the Nobel Prize in 1959. 

This year’s interns are Shannon Baucom, Matthew Chow, and Mike Lawrence, who've put together a short summary of what they're working on in the lab:

Collective Segre Intern Summary

Following analysis of student course feedback from the past few years, the interns have been working on clarifying and updating existing lab write ups, fixing known bugs in lab software (even redrafting some of the software completely), and increasing course support. Already this Summer, the Segre Interns have made improvements to course material and/or functionality of eight experiments in the 111B lab. These include the atomic force microscope, atomic physics, Brownian motion in cells, Hall Effect in semiconductors, gamma ray, muon lifetime, magneto optical trap, and pulsed nuclear magnetic resonance. Additionally, they have created many new resources from scratch. These resources include video tutorials for handling data in Python with accompanying Jupyter notebooks, “storyline” interactive resources, as well as better diagrams for many of the existing labs. On the 111A side of the lab, they have completed several hardware fixes.

Two examples of major improvements were the noise reduction in one of the the photomultiplier tubes (PMT) in the muon lifetime experiment, and the development of a resonance drift visualization software for the pulsed nuclear magnetic resonance (PNMR) experiment. Below, you can see the results of these improvements:

 

 

 

 

 

 

 

 

 

 

ABove is a LabVIEW scope trace of the signal (post amplification) out of the muon lifetime PMT from before (a.) and after (b.) fix. The large pulse in both graphs represents detection of a muon passing through the scintillation tank. As a charged particle (in this case the muon) passes through the scintillation tank, a pulse of photons is released. The pulse is then converted into a voltage and amplified by the PMT. Then the signal is passed to the DAQ which is controlled by a LabVIEW program. In the top graph, we see that there is a periodic noise (in the range of 700 kHz) that was picked in addition to the pulse. By turning up the voltage supply to the PMT (increasing multiplication factor of the dynodes) and reducing the post PMT amplification, the noise pickup was drastically reduced while preserving the muon detection signal.

 

 

 

 

 

 

 

 

 

 

 

 

Above is a graph produced by the drift visualization software for PNMR. This shows the “mixed down frequency,” which is the difference between the larmor frequency of the sample (0.1M glycerin) and the pulse frequency, plotted against time (Note: absolute value shown in the graph). The pulse frequency was kept constant, so change in the mixed down signal represents changes in the resonant frequency of the setup (and thus mostly a measurement of inconsistency of the magnetic field over time). The fluctuations in the graph above correspond to axial magnetic field strength fluctuations on the order of half a Gauss. This is the first time this measurement technique has been used for the 111 lab PNMR setup. 

 

 

 

 

 

 

 

 

 

 

 

 

 

On the course support end of enhancements, data shows that a large proportion of students coming into the 111 labs have been exposed to Python, while course support is traditionally only in MATLAB. Because of this, a significant amount of time has been spent creating parallel course support for Python. The plot above is one such example, in which a video tutorial walks students through manipulating and visualizing a week’s worth of [randomly generated] data at once using a Jupyter notebook. This is one of many ways that the lab is improving  -- to get students the right resources so they can focus on understanding the physics.

 

Visit the Donald A. Glaser 111 Lab website.

The Segrè Internship is made possible through the generosity of Arlene and Doug Giancoli.