I received my B.S. in Physics with a specialization in Astrophysics from the University of California, Irvine. As an undergraduate I joined the Kirkby lab. My undergraduate research worked with all-sky images that were taken at Kitt Peak National Observatory. I developed a convolutional neural network (CNN) to identify clouds that were moving towards and over the Mayall telescope. The results of this work can be seen in my repo kpno-allsky.
As a graduate student I have remained in the Kirkby lab at UCI, where my current research focuses on using deep learning to develop data analysis pipelines. As part of the Kirkby lab I am also a part of both the Dark Energy Spectroscopic Instrument (DESI) survey and the Legacy Survey of Space and Time (LSST) / Dark Energy Science Collaboration (DESC).
As part of my work in the DESC I participated in the tomographic binning challenge which produced a paper that was published in the Open Journal of Astrophysics (see below). My submission designed an entirely unique clustering algorithm and is outlined in a jupyter notebook in my fork of the tomographic challenge repo. Feel free to check out the jupyter notebook.
I am a DESI builder, and primarily contribute to the data systems and data pipeline development.
My first project aimed to use deep learning to identify and flag cosmic rays that appear in spectroscopic images taken for the DESI Survey. Additionally I wrote and maintain the desipoint website, which is a web based displayer for the DESI project that displays the current pointing of the telescope as well as a variety of other useful parameters.
Current work focuses on improving the Convolutional Neural Network (CNN) QuasarNet so that it can better identify quasars using DESI spectroscopic data. As part of this work I wrote the entirely numpy-based implementation of QuasarNet, QuasarNP, which gets run as part of the spectroscopic reduction pipeline for the DESI Survey. Recently we have implemented an active learning pipeline that uses visual inspection to label spectra which QuasarNET finds most useful to have labeled. This work has produced a nearly 5% improvement in purity compared to the prior weights file. These results will be included in DESI's Y3 public data release, date TBD. A paper is in preparation for publication in 2025.
In 2023 I was awarded the DOE Office of Science Graduate Student Research Fellowship, and worked for 4 months at Lawrence Berkeley Lab with Dr. Stephen Bailey on Non-negative matrix factorization (NMF) and applications to the DESI pipeline. While there I derived a variant of NMF we named Nearly-NMF, which introduces weighting to standard NMF in a way that correctly handles the presence of some negative data, compared to "standard" NMF which requires all input data to be factorized to be strictly non-negative. A paper outlining this method was published in Late 2024. Further work outside the SCGSR program has continued to apply the Nearly-NMF algorithm to real DESI data for possible inclusion in the data processing pipeline.
I have 5 days of in person observation experience at the Mayall 4-meter telescope, where I did work commissioning the DESI spectrographs. I have since completed an additional 20 days of remote observing on the Mayall 4-meter. I also have observing experience at Lick Observatory as part of the 2020/21 Burbidge Observational Astronomy Workshop.
First author publications are denoted by red
Dethe, T., Gill, H., Green, D., Greensweight, A., Gutierrez, L., He, M., Tajima, T., & Yang, K. ‘Causality and dispersion relations’. American Journal of Physics 87, no. 4 (April 2019): 279–90. doi:10.1119/1.5092679.
Zuntz, J., Lanusse, F., Malz, A. I., Wright, A. H., Slosar, A., Abolfathi, B., ... Green, D. ... & Mao, Y. Y. ‘The LSST-DESC 3x2pt Tomography Optimization Challenge’. (October 2021) The Open Journal of Astrophysics 4, 13.
Pat, F., Juneau, S., Böhm, V., ... Green, D., Myers, A. 'Reconstructing and Classifying SDSS DR16 Galaxy Spectra with Machine-Learning and Dimensionality Reduction Algorithms.' (2022) arXiv: 2211.11783
DESI Collaboration, incl. Green, D. ‘Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument’. (November 2022) The Astronomical Journal 164, 207.
Guy, J., Bailey, S., ... Green, D. ..., Zou, H. ‘The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument’. (April 2023) The Astronomical Journal 165, 144.
DESI Collaboration incl. Green, D. 'The Early Data Release of the Dark Energy Spectroscopic Instrument.' (2023) arXiv: 2306.06308
DESI Collaboration incl. Green, D. 'Validation of the Scientific Program for the Dark Energy Spectroscopic Instrument.' (January 2024). The Astronomical Journal, 167, 62
Green, D. & Bailey, S. (2024). Algorithms for Non-Negative Matrix Factorization on Noisy Data With Negative Values. IEEE Transactions on Signal Processing
Green, D. & Kirkby, D. (2024). Using Active Learning to Improve Quasar Identification for the DESI Spectra Processing Pipeline. In Prep.
Green, D. (2021, February 26). Deep Learning for Cosmic Ray Identification desi-data Telecon, Online.
Green, D. (2021, April 15). Automated Classification of Quasar Targets in DESI. DESI Research Forum, Online.
Green, D. (2022, June 24). The Future of QuasarNP DESI Collaboration Meeting June 2022, Online