Constraining Cosmology from Modeling Strong Gravitational Lenses on Multiple GPU Nodes
Saul Perlmutter, Professor
Physics
Applications for Fall 2024 are closed for this project.
Strong gravitational lenses are very rare occurrences and are a powerful tool in studying dark matter and dark energy, two mysterious entities that together account for 95% of the energy in the universe. The strong lensing team works on a range of projects with state-of-the-art computation/ML/AI techniques. We have found ~5000 strong gravitational lenses in a large imaging survey, the DESI Legacy Surveys (covering the entire extragalactic sky, http://legacysurvey.org/), using three of the best neural network architectures. We have increased the number of known lenses by an order of magnitude! Our Hubble Space Telescope program provides images with exquisite details about these systems. This allows us to construct detailed models of these lenses in order to better understand dark matter and dark energy, and measure the expansion rate of the universe (H0). To this end, we developed a fast GPU-accelerated lens modeling code (the fastest in the world! https://ui.adsabs.harvard.edu/abs/2022ApJ...935...49G/abstract) and have apply it to our systems with Hubble images (first ever in the world; one paper in internal review, and more to come!). DESI (https://www.desi.lbl.gov/) is currently measuring redshifts for all 5000 of our lensing candidates (we already have spectra for 2000 systems in hand; one paper in internal review and more to come). Also, our paper (https://arxiv.org/abs/2408.10320) on this beautiful lensing system (with 7 background lensed sources!) has been accepted for publication -- we are now constructing a more detailed lens model using GIGA-Lens to measure cosmological parameters.
Finally, as of yesterday (Aug 22, 2024), we have successfully run GIGA-Lens using 8 GPU nodes, or 32 GPUs (!), for the first time in the world. This is an unprecedented time to work in strong lensing to address some of the most fundamental questions in cosmology and physics. Our team has all the requisite data and tools to constrain cosmological parameters in a competitive way for the first time ever!
The strong lensing team has also been highly successful in training undergraduate researchers (here's one example: https://www.nersc.gov/news-publications/nersc-news/nersc-center-news/2022/nersc-honors-early-career-researchers-with-2022-achievement-awards/). Approximately 20 URAP students have worked with us since 2019. Many of them are now in top Physics or Astrophysics PhD programs around the nation, some of whom continue to work with our team!
Role: The student will focus on one or both of the following:
1. Strong lens modeling using Hubble images on multiple GPUs. This can lead to one or more publications on measuring the mass distribution of the lensing galaxies and/or measurements of cosmological parameters (characterizing properties of dark matter and dark energy) based on lens modeling results.
2. Analyzing DESI spectra for our lens candidates: we want to measure the star formation rates to estimate the rates of lensed supernovae. This will lead to an independent and competitive way of addressing the tension in the H0 measurements.
For both areas, we expect multiple papers.
Qualifications: Proficiency in Python coding a necessity. Experience with numpy/scipy/matplotlib/pandas and TensorFlow/JAX/PyTorch highly desirable, and background in ML/AI a plus.
Preferred but not required (and the order is not so important):
1) experience with parallel (and distributed) computing (we use supercomputers at NERSC, https://www.nersc.gov/, both CPUs and GPUs);
2) experience with training neural nets on GPUs (we train on Google Colab and NERSC);
3) experience with HDF5/PyTables;
4) knowledge in gravitational lensing, or even some experience in modeling gravitational lensing systems;
5) experience with any of the following: modeling galaxy light profiles, image coaddition, and transient detection by image subtraction;
6) spectroscopic observations, data reduction, and fitting for spectral absorption and emission lines;
7) experience with web development.
Day-to-day supervisor for this project: Dr. Xiaosheng Huang, Staff Researcher
Hours: 9-11 hrs
Off-Campus Research Site: Lawrence Berkeley National Lab, Building 50. This project can also be carried out remotely with regular Zoom meetings.
Related website: https://www.youtube.com/watch?v=BdpgOhxEyMs
Related website: https://www.youtube.com/watch?v=BdpgOhxEyMs