Astrophysics Data Lab
I formed the Astrophysics Data Lab in 2020 to:
- Answer fundamental questions in astrophysics using the increasingly large datasets that are available now and in the future.
- Build the framework for translating machine learning methods to astrophysics.
- Develop innovative ways of using data for astrophysics.
- Help astronomers integrate new data-driven methods and practices into their work.
Please visit our website for more information about members of the Lab and more details on our work.
Related Publications
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Multi-Modal Masked Autoencoders for Learning Image-Spectrum Associations for Galaxy Evolution and Cosmology
Himes M., Krishnamurthy S., Lizarraga A., Saikrishnan S., Seenivasan V., Soriano J., Nian Wu Y., Do T. (2025)
arXiv:2510.22527 -
AquiLLM: a RAG Tool for Capturing Tacit Knowledge in Research Groups
Campbell C., Boscoe B., Do T. (2025)
arXiv:2508.05648 -
Probing Axions via Spectroscopic Measurements of S-stars at the Galactic Center
Bai Z., Cardoso V., Chen Y., Do T., Hees A., Xiao H., Xue X. (2025)
arXiv:2507.07482 -
Understanding Galaxy Morphology Evolution Through Cosmic Time via Redshift Conditioned Diffusion Models
Lizarraga A., Hanchen Jiang E., Nowack J., Li Y. Q., Nian Wu Y., Boscoe B., Do T. (2024)
arXiv:2411.18440 -
Using different sources of ground truths and transfer learning to improve the generalization of photometric redshift estimation
Soriano J., Saikrishnan S., Seenivasan V., Boscoe B., Singal J., Do T. (2024)
arXiv:2411.18054 -
The Galactic Center in Color: Measuring Extinction with High-proper-motion Stars
Haggard Z., Ghez A. M., Sakai S., Gautam A. K., Do T., Lu J. R., Hosek M., Morris M. R., Granados S. (2024)
The Astronomical Journal, 168, 166 -
Redshift Prediction with Images for Cosmology Using a Bayesian Convolutional Neural Network with Conformal Predictions
Jones E., Do T., Li Y. Q., Alfaro K., Singal J., Boscoe B. (2024)
The Astrophysical Journal, 974, 159 -
Using Galaxy Evolution as Source of Physics-Based Ground Truth for Generative Models
Li Y. Q., Do T., Jones E., Boscoe B., Alfaro K., Nguyen Z. (2024)
arXiv:2407.07229 -
Improving Photometric Redshift Estimation for Cosmology with LSST Using Bayesian Neural Networks
Jones E., Do T., Boscoe B., Singal J., Wan Y., Nguyen Z. (2024)
The Astrophysical Journal, , 130 -
Elements of effective machine learning datasets in astronomy
Boscoe B., Do T., Jones E., Li Y., Alfaro K., Ma C. (2022)
arXiv:2211.14401 -
Machine Learning Classification to Identify Catastrophic Outlier Photometric Redshift Estimates
Singal J., Silverman G., Jones E., Do T., Boscoe B., Wan Y. (2022)
The Astrophysical Journal, , 6 -
Photometric Redshifts for Cosmology: Improving Accuracy and Uncertainty Estimates Using Bayesian Neural Networks
Jones E., Do T., Boscoe B., Wan Y., Nguyen Z., Singal J. (2022)
arXiv:2202.07121
Machine Learning Reading Group
Machine learning is a topic that has risen in prominence recently as we get more and more data. We are seeing techniques from machine learning used more widely in astronomy.
The goal of this reading group is to become more familiar with topics in machine learning and its connections to statistical tools that are in use in Astronomy. The plan is to go through a couple of textbooks on machine learning and discuss the basic underlying principles and methods. Each week, members of the reading group would present a topic with associated code implementing the algorithm.
The reading group github page is at: https://github.com/UCLAMLRG. Some of the links to books and resources are to the right.
Links/Resources
- Deep Learning by Goodfellow, I.; Bengio, Y.; Courville, A.
- Python Data Science Handbook by VanderPlas, J.
- Hands-On Machine Learning with Scikit-Learn and TensorFlow by A. Geron. Github link