Dark matter and neutrinos. Build detector and analyze.

We work at the intersection between Cosmology and Particle Physics, specifically related to dark matter and neutrinos. The nature of dark matter is one of the biggest questions in modern physics. We build and analyze detectors that aim to discover particle dark matter. Furthermore, the same technology can be used to study the nature of the neutrinos through a process called neutrinoless double-beta decay. The Rice group has been a part of the XENON collaboration since its inception building xenon time-projection chambers in the Gran Sasso Laboratory in Italy. This includes developing this technology with XENON10, demonstrating the dark-matter technology in XENON100, the current leader XENON1T, and the future XENONnT. The interests in the group range from developing the high-voltage electrodes to operations to developing the software used for acquiring, processing, and analyzing the experimental data (leveraging leading Big Data technologies in our cyberinfrastructure).

Picture XENON1T

Christopher Tunnell

Prof. Christopher Tunnell

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 2016 Breakthrough Prize.(Publication)

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Petr Chaguine

Prof. Petr Chaguine

Research Assistant Professor of Physics and Astronomy. He is a world-leading on the large electrodes, leading these efforts for XENON. Dr. Chaguine will soon be 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)

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Junji Naganoma

Dr. Junji Naganoma

Researcher. Based at the Gran Sasso Laboratory as the XENON1T Operations Manager. Dr Naganoma's day-to-day job is to quite literally to lead operations of the most sensitive dark matter experiment and serve as a nexus point between all aspects of the experimental hardware. (Publications)

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Sophia Andaloro

Sophia Andaloro

PhD Student. First year student working with Petr on building XENONnT, while training up on both cosmology and machine learning.

Shixiao Liang

Shixiao Liang

PhD Student. First year student working on position reconstruction (a.k.a inverse problem) and estimations using machine learning.

Linoy Kotler

Linoy Kotler

PhD Student. First year coming in with an astro background.

Alejandro Oranday

Alejandro Oranday

Undergraduate. A Rice SURF Fellow working on understanding how manifold learning can be applied to XENON1T data to understand detector response and position reconstruction.

Former members include Prof. Uwe Oberlack (U. Mainz) and Prof Marc Schumann (U. Freiburg).


XENON. The experiments.

The XENON1T and XENONnT experiments constitute the core experimental activities of the group (More general information here). XENON1T is our current experiment and the most sensitive such detector in the world. It is a 2-tonne cryogenic liquid-xenon time-projection chamber located under a mountain in the Gran Sasso National Laboratory. Dark matter produced in the early Universe permeates our Milky Way and shoots through the Earth as though it was more transparent than the clearest glass. Our detector tries to measure faint interactions with this dark matter. Such an interaction will produce photons and electrons that we can subsequently measure with a suitably specialized detector using single-photon photosensors digitized at 10 nanoseconds. These dark matter interactions produce signals that are buried under other types of background. Therefore, we built a purely software trigger data acquisitionn, specialized software for the signal processing, event reconstruction, and data analysis to search for these single interactions in petabytes of data.

Tunnell with XENON1T

From bits to Science. Cyberinfrastructure.

One of the main interests of the group is 'eScience' / cyberinfrastructure. Modern day astroparticle experiments are only possible due to how the information revolution has enabled us to extract science from previously unmanagable datasets. Within dark matter, we are particularly interested in the software technologies that must be developed to do the science we want. For example, within the NEST collaboration on microphysics, we developed the nestpy package. We developed tools within XENON1T that are now used by many such detectors for software triggers, signal processing, event reconstruction (pax), data analysis (hax), data selection (lax), data handling (cax), and much more. We develop such tools by looking to other communities, especially the Big Data technology realm, to determine which problems have been solved in one domain and not the other. This means that we were able to leverage for example the Scientific Python Stack and numba (a just-in-time Python compiler) to enable petabyte scale analysis and data acquisition in pure Python. The benefits in this -- beyond training -- are a compact flexible refactorable experimental stack. In the future, we are exploring technologies with other communities to determine what technologies can revolutionize our next detector XENONnT. This includes signal processing and event reconstruction in Python at 100 MB/s/core (strax), new data flow models relying on cloud services and NodeJS UI (snax), and machine learning techniques such as manifold learning to solve long-standing analysis challenges with this detector. This list is not complete, but our code is public so feel free to browse!

Our successful model has been to collaborate with companies and other experiments to develop novel solutions that push the frontiers of computational physics, with the goal of understanding how new technologies may be used to our scientific advantage. This puts us in a unique niche within our field.

Computer cluster Example XENON1T waveform

Want to join? Current openings.

The group is looking to expand at all levels. For questions, feel free to reach out by email.

Postdoctoral researchers: We are currently looking for postdoctoral researchers to work on computational astroparticle physics.

  • The group is directly hiring, where you should contact us with your CV.
  • D2K Postdoctoral Fellowships in data science information here (requires informally contacting our group first).
  • Rice Academy Fellowship information here (requires informally contacting our group first).

Graduate students: We have openings. A thesis is a personalized product that finds an overlap between the intersts of the supervisor, student, and experiment. If you are an existing Rice student, please swing by my office. Otherwise, email.

Bachelors students: Projects related to dark matter data analysis, machine learning, and scientific software development are available. Until a full list is posted here, please reach out to us if curious.

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 astroparticle@rice.edu.


Need advice? Consulting.

Many of the computational problems that we work on have analogs in other fields. If you are dealing with large amounts of sensor data, we may be able to help.

  • Academic: If you have an interesting computational problem that could lead to a joint article or other academic output, please contact us.
  • Government: No conditions.
  • Industry: Rice is a non-profit. Collaborations with us are welcome that result in, for example, donations to help us financially support students such thatt we built Data Science and Machine Learning expertise in the Houston area.

If interested, contact us by email.