Andreas Zoglauer, Staff Researcher

Closed (1) Preparing for the next 100-day stratospheric balloon flight of the Compton Spectrometer and Imager (COSI)

Closed. This professor is continuing with Spring 2019 apprentices on this project; no new apprentices needed for Fall 2019.

COSI, the Compton Spectrometer and Imager, is a NASA-funded balloon-borne gamma-ray telescope, observing Galactic nucleosynthesis, Galactic positron annihilation, as well as the most violent events in our Universe (supernovae, neutron star mergers) and the most extreme environments (pulsars, black holes). COSI had a very successful 46-day balloon flight in 2016 starting from Wanaka, New Zealand, once around Antarctica, and landing in the Atacama Desert in Peru. Currently, we are preparing our instrument for our next flight, which might happen as early as spring 2019. This provides multiple opportunities to get involved with various aspects of preparing a telescope for flight readiness.

Currently the following tasks are available:
(1) Modify the existing image deconvolution code, which creates all-sky maps for COSI, to utilize GPUs.
(2) Create a data base of all existing (and future) measurements and observations.
(3) Optimize our existing simulation code to run faster on the cori supercomputer (you will get access to the supercomputer).

As part of the COSI team, you will have the opportunity to gain experience with a state-of-the-art gamma-ray detector system and its data analysis pipeline.


Day-to-day supervisor for this project: Clio Sleator, Graduate Student

Qualifications: Required: A basic understanding of physics (e.g. physics 7 series courses); Familiarity with the Linux/Unix environment; Great organization and communication skills; Punctuality and reliability; Programming experience for software tasks (Python or C++)

Weekly Hours: to be negotiated

Off-Campus Research Site: While you can work from anywhere you want, the weekly meetings will be held at the Space Sciences Laboratory - take the Hill shuttle from the mining circle and exit at the last stop. You can find us in the Addition building (the eastern most building) in room 136 (Andreas Zoglauer) and room 137 (Clio Sleator, Hadar Lazar).

Related website: http://cosi.ssl.berkeley.edu

Closed (2) Improving the data-analysis pipeline of the COSI telescope with machine learning

Closed. This professor is continuing with Spring 2019 apprentices on this project; no new apprentices needed for Fall 2019.

COSI, the Compton Spectrometer and Imager, is a NASA-funded balloon-borne gamma-ray telescope, observing Galactic nucleosynthesis, Galactic positron annihilation as well as the most violent events in our Universe (supernovae, neutron star mergers) and the most extreme environments (pulsars, black holes). COSI had a very successful 46-day balloon flight in 2016 starting from Wanaka, NZ, once around Antarctica, and landing in the Atacama Desert in Peru. Currently the analysis of this data is ongoing as well as the preparations for the next COSI flight in spring 2020.

This project is centered on improving the data analysis pipeline of COSI by applying the latest machine learning tools to individual segments of the pipeline. The available machine learning topics are:
(1) Improve an existing approach for the localization of the gamma-ray interactions in our Germanium detector with a neural network.
(2) Identify background events such as Earth Albedo events, incompletely absorbed events, wrongly reconstructed events and internal radioactive decays using machine learning on simulated training data sets.
(3) Approximate the imaging response using a deep neural network.

All topics are novel and should - if we get it to work - ultimately lead to published papers.


As a member of this URAP team, you can pick one machine learning topic and apply either the TensorFlow or the TMVA machine learning library to it. The ultimate goal is to improve upon the currently existing reference implementations with new, cutting-edge machine learning approaches. You can either work alone or in a team on your topic. The data sets containing the test and training data will be provided.

The concrete learning goals depend on your chosen topic. In general, they include:
(1) Learn about the data analysis of a modern NASA-sponsored telescope, COSI
(2) Learn how to use a machine learning toolkit: TensorFlow or TMVA
(3) Learn how to apply these toolkits to the data of a modern telescope
(4) Learn how to use a supercomputer (depending on what you are doing: either UC Berkeley's savio or NERSC's cori)

Since this is a multi-year project, we prefer students who want to work with us for more than 1 semester. The time spent on the project should be on average at least 6 hours per week, more strongly preferred. Weekly meetings will be held at BIDS, the Berkeley Institute for Data Science (190 Doe Library).


Qualifications: Data 8 or equivalent; proficiency in Python or C++; prior experience with machine learning (e.g. (deep) neural networks, (random forests of) (boosted) decision trees, support vector machines) and with a common machine learning library recommended (e.g. scikit-learn, tensorflow, TMVA); familiarity with the Linux/Unix environment, bash, and git; interest in Physics and Astrophysics; great organization and communication skills; punctuality and reliability; talent for multitasking and balancing this project with your normal classes.

Weekly Hours: to be negotiated

Related website: http://cosi.ssl.berkeley.edu
Related website: https://github.com/zoglauer/bids-discovery

Open (3) Pioneering new Data-Analysis Techniques for the next generation of gamma-ray space telescope with Machine Learning

Open. Apprentices needed for the fall semester. Enter your application on the web beginning August 21st. The deadline to apply is Tuesday, September 3rd at 9 AM.

The next generation of gamma-ray space telescopes aims to open a new window into gamma-ray astronomy with unprecedented angular resolution and sensitivity. The goals of these new telescopes range from trying to detect dark matter, to better understand the element formation in our Galaxy, to identifying the physical processes at work in the extreme conditions around black holes.

However, to unlock their full potential, we need to develop new data analysis techniques. This project is centered on improving the existing (experimental) data-analysis pipeline with the latest machine learning techniques. The available topics include:
(1) Apply machine learning to better classify the event type which happened in the telescope: Compton scattering, pair creation, charged particle, nuclear decay.
(2) Use machine learning to better identify the start location and the path of charged particles in the tracker.
(3) Near real-time localization of gamma-ray bursts.

All topics are novel and should - if we get it work - ultimately lead to published papers.

As a member of this URAP team, you can pick one machine learning topic and apply either the TensorFlow or the TMVA machine learning library to it. The ultimate goal is to improve upon the currently existing reference implementations with new, cutting-edge machine learning approaches. You can either work alone or in a team on your topic. The data sets containing the test and training data will be provided.

The concrete learning goals depend on your chosen topic. In general, they include:
(1) Learn about the data analysis of a future gamma-ray telescopes
(2) Learn how to use a machine learning toolkit: TensorFlow or TMVA
(3) Learn how to apply these toolkits to the data of a modern telescope
(4) Learn how to use a supercomputer (depending on what you are doing: either UC Berkeley's savio or NERSC's cori)

Since this is a multi-year project, we prefer students who want to work with us for more than 1 semester. The time spent on the project should be on average at least 6 hours per week, more strongly preferred. Weekly meetings will be held at BIDS, the Berkeley Institute for Data Science (190 Doe Library).

Qualifications: Interest in astrophysics (in particular dark matter, supernovae, black holes); proficiency in Python or C++; prior experience with machine learning (e.g. (deep) neural networks, (random forests of) (boosted) decision trees, support vector machines) and with a common machine learning library strongly preferred (e.g. scikit-learn, tensorflow, TMVA); familiarity with the Linux/Unix environment, bash, and git; great organization and communication skills; punctuality and reliability; talent for multitasking and balancing this project with your normal classes.

Weekly Hours: to be negotiated
Related website: https://github.com/zoglauer/bids-discovery