Data-Intensive Astroparticle Physics
We work at intersection of astroparticle physics and new computational science methods. We are founding members of the XENON100, XENON1T, and XENONnT dark matter experiments, where XENON1T is the most sensitive dark matter detector ever built (as of 2021) and XENONnT is its upgradate. These experiments are some of the most sensitive ever built to exotic new physics, where we made measurements ranging from the discover of double-electron capture (halflife longer than Universe) to indications of exotic new physics through electron recoils. However, XENON and similar detectors are ideal test cases for new machine learning algorithms and data-science algorithms that can operate at scales of 10s of petabytes while using this sensitive understood detector to fundamentally rethink how data analysis and machine learning are used for discovery. Our group's interested and scope is best seen by the activities of our people. We pride ourselves on creating a new generation of computational particle physicists who can excel at both particle physics and machine learning.
Prof. Christopher Tunnell
Group leader. Assistant Professor of Physics and Astronomy and Computer Science. Computational astroparticle physicist and former XENON1T analysis coordinator specialized in data analysis, data acquisition, and cyberinfrastructure. Hired through the Rice Data Science Initiative. His background includes neutrino (solar, reactor, sterile, beam), collider, and accelerator physics. However, his unifying interest is cyberinfrastructure: how we handle, reconstruct, and analyze the petabytes of data created by such instruments. Recipient of the 2021 NSF CAREER award and 2016 Breakthrough Prize.(Publication)
Prof. Petr Chaguine
Research Associate Professor of Physics and Astronomy (on leave since Sept 2021). He is an expert on the large electrodes, leading these efforts for XENON. Dr. Chaguine is the XENONnT commissioning manager, responsible for getting our upgrade XENONnT build and taking science data. Beyond XENON, as a long established experimental-particle-physics career, bringing this detector knowledge to the hunt of dark matter. (Publications)
Dr. Aaron Higuera
Research Scientists in Physics and Astronomy. Background in neutrino physics includes, neutrino cross section, BSM physics with neutrinos from reactors and underground physics with neutrino detectors, particularly interested in neutrionless double-beta decay. DIDACTS. (Publications). Joined 2020.
Dr. Christina (Tina) Peters
Permanently Visiting Postdoctoral Researcher from the group of Prof. Hagit Shatkay (University of Delaware's Department of Computer and Information Sciences). Background in astrophysics. Dr Peters currently is studying the use of Bayesian Graphical Models to the problem of reconstruction in particle physics, where this method allows machine learning to quantify per-event uncertainty. DIDACTS. Joined 2020.
PhD Student in Physics and Astronomy. Second year. National Nuclear Security Administration Stockpile Stewardship Graduate Fellow. NSF GRF Awardee 2020. Works on XENONnT, NEST, and the frontiers of machine learning. Joined 2019.
PhD Student in Physics and Astronomy. Second year student working within DIDACTS to push the frontiers of reconstruction using graphs, with the science target of dark matter and neutrinoless double-beta decay. Joined 2019.
First-year PhD Student in Physics and Astronomy working on an unsupervised learning project with the statistics department. Fulbright Fellow 2020. Joined 2020.
PhD Student in Physics and Astronomy working with on a project with us from the statistics department on self-organizing maps for unsupervised learning. Joined 2020.
Undergraduate in Physics and Astronomy and a 2021 Rice SURF Fellow. Working to develop simulations and analysis of a novel optomechanical dark matter detector with the Windchime collaboration. Joined 2021.
Undergraduate in Computer Science. Working on web development related to Identity and Access Management. Joined 2020.
Junji Naganoma (XENON1T Operations Manager and Run Coordinator, left July 2020 to work at publisher).
2020: Bo Zheng (Masters CS, IRIS-HEP Fellow on pyhf), Xiongfeng Song (Masters CS, IRIS-HEP Fellow on SkyhookDM, 2020), Shuaicheng "Sam" Li, (Research Software Engineer, Bachelors ECE 2020), Dr. Venkat Roy (Postdoc Ph.D visiting scientist from INSPIRE Lab at Rutgers). Diep Hoang (CS Undergraduate COVID Community Vunerability Indexing). Yingfan Chen (CS undergrad, web data visualization). Yiyang 'Skylar' Xu (CS undergrad, SCIMMA and SNEWS). Chloe Liebenthal (Physics undergraduate, NEST).
2019--2021: Alejandro 'Alex' Oranday (Senior thesis and undergraduate research, now Indiana Bloomington)
2020--2021: Mirella Vassilev (Senior thesis, now Stanford)
Want to join? Current openings.
The group is looking to expand at all levels. For questions, feel free to reach out to Prof Tunnell by email (Note: Until pandemic is over, you will most likely get short responses).
Postdoctoral researchers: We are currently looking for postdoctoral researchers to work on computational astroparticle physics. The current two topics for a postdoc would be either gravitational dark-matter detection as part of Windchime, or advancing computational methods at XENONnT to enable double-beta decay for Darwin.
- The group has money to hire, so send your CV to discuss posibilities
- Rice Academy Fellowship information here (requires informally contacting our group first).
Graduate students: For people applying in Fall 2021, we expect 1 opening for a student in either Physics and Astronomy, or Computer Science. Please apply to the respective program as our group is not involved in admissions decisions.
Rice Bachelors students: Projects related to dark matter data analysis, machine learning, and scientific software development are available. All projects require Python knowledge or willingness to learn. Please reach out to us if curious. (Due to the pandemic, we have reduced supervision capacity so will reduce the number of students we accept to 2 or 3 for summer 2021.)
- Gravitational detection of dark matter using optomechanical sensors, track finding (requires learning machine learning)
- Calculating photon yields of particle interactions in XENONnT from first principles by simulating particle-atom interactions. (requires QM)
- Working on the high throughput data pipeline of XENONnT, probably requiring learning about NodeJS web frameworks as well
External funding: Numerous funding opportunities exist for young scientists within the US and abroad (ERC, NWO, DAAD-RISE). If you considering applying to some program where you would like the astroparticle group to be your sponsor or intend to work with us, then please reach out to us at firstname.lastname@example.org. (Due to the pandemic, we do not have the supervision capacity for undergraduates from other Universities or anybody else starting out with research.)