About

Adam Johnson is the Phil and Penny Knight Professor of Creative Writing and a Professor of English at Stanford University. He holds a B.A. in Journalism from Arizona State University, an M.A. and M.F.A. in Creative Writing (Fiction) from McNeese State University, and a Ph.D. in English from Florida State University. Johnson is an accomplished author, having written several books including Fortune Smiles, which won the National Book Award, and the novel The Orphan Master’s Son, which was awarded the Pulitzer Prize. His stories have been published in prominent outlets such as Esquire, GQ, Playboy, Harper's Magazine, Granta, The Paris Review, and have been recognized with awards like the Story Prize, The Sunday Times Short Story Award, and the Dayton Literary Peace Prize. His work has been translated into more than three-dozen languages. Johnson was born in South Dakota and is an enrolled member of the Cheyenne River Sioux Tribe. His teaching and research interests include the development of the novel, indigeneity, the oral tradition, counter narrative, trauma theory, and speculative fiction.

Research topics

• Physics
• Mathematics
• Computer Science
• Particle physics
• Nuclear physics
• Artificial Intelligence
• Atomic physics
• Algorithm
• Mathematical analysis
• Quantum mechanics

Selected publications

• CTN-004: Rubin Observatory Raw Data File Format

  DOE SLAC National Accelerator Laboratory Repository · 2025-12-30

  datasetOpen accessSenior author

  At the NSF-DOE Vera C. Rubin Observatory we write data from the LSSTCam and LATISS instruments using FITS format with one file written per detector. Here we discuss the layout of those FITS files and describe the FITS headers.

  PublisherDOI

• Photometric Redshift Estimation for Rubin Observatory Data Preview 1 with Redshift Assessment Infrastructure Layers (RAIL)

  ArXiv.org · 2025-10-08

  preprintOpen access

  We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep ugrizy coverage in the Extended Chandra Deep Field South (ECDFS) field and griz data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalog from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of $σ_{\rm NMAD} \sim 0.03$ and outlier fractions around 10% in the 6-band data, with performance degrading at faint magnitudes and z>1.2. The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution n(z). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at z>1.2. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.

  PublisherOA PDFDOI

• Comparison of Solar Energetic Particle Flux Measurements by CRaTER and GOES

  Space Weather · 2025-04-01

  articleOpen access

  Abstract The Cosmic Ray Telescope for the Effects of Radiation (CRaTER) aboard the Lunar Reconnaissance Orbiter spacecraft has been operating in lunar orbit since 2009. CRaTER data provide a long‐term record of the near‐Moon radiation environment that is relevant to human exploration. In considering requirements for operational instruments on crewed lunar missions that are anticipated in the next several years, the question arises whether real‐time measurements by existing assets such as Geostationary Operational Environment Satellite (GOES) are adequate for providing radiation warnings for the safety of astronauts on or near the Moon during Energetic Solar Particle Events (ESPEs). ESPEs are distinguished from more typical Solar Particle Events (SPEs) by their large fluxes of higher‐energy particles. Here we show direct comparisons of contemporaneous ESPE measurements made in the two locations to establish that the radiation environments in lunar space and in geostationary orbit (GEO) are sufficiently similar to allow the use of GOES as a warning system for crew on or near the Moon. The conclusion is based primarily on data from six ESPEs in the October 2021–October 2024 time frame in which the integral flux of protons with energies greater than 100 MeV exceeded 1 at the event's peak as measured by operational GOES satellites, and to a lesser extent on data from two earlier ground‐level events (GLEs) that are of particular interest. We report new methods for analyzing CRaTER data under high‐flux conditions and find good agreement with data reported by GOES‐16 and later satellites.

  PublisherOA PDFDOI

• Integrating the LSST camera

  2024-07-18 · 3 citations

  articleOpen access

  The LSST Camera is a complex, highly integrated instrument for the Vera C. Rubin Observatory. Now that the assembly is complete, we present the highlights of the LSST Camera assembly: successful installation of all Raft Tower Modules (RTM) into the cryostat, integration of the world’s largest lens with the camera body, and successful integration and testing of the shutter and filter exchange systems. While the integration of the LSST Camera is a story of success, there were challenges faced along the way which we present: component failures, late design changes, and facility infrastructure issues.

  PublisherOA PDFDOI

• Contributors

  Elsevier eBooks · 2024-11-15

  book-chapterOpen access
  PublisherDOI

• Space radiation measurements during the Artemis I lunar mission

  Nature · 2024-09-18 · 30 citations

  articleOpen access

  Abstract Space radiation is a notable hazard for long-duration human spaceflight 1 . Associated risks include cancer, cataracts, degenerative diseases 2 and tissue reactions from large, acute exposures 3 . Space radiation originates from diverse sources, including galactic cosmic rays 4 , trapped-particle (Van Allen) belts 5 and solar-particle events 6 . Previous radiation data are from the International Space Station and the Space Shuttle in low-Earth orbit protected by heavy shielding and Earth’s magnetic field 7,8 and lightly shielded interplanetary robotic probes such as Mars Science Laboratory and Lunar Reconnaissance Orbiter 9,10 . Limited data from the Apollo missions 11–13 and ground measurements with substantial caveats are also available 14 . Here we report radiation measurements from the heavily shielded Orion spacecraft on the uncrewed Artemis I lunar mission. At differing shielding locations inside the vehicle, a fourfold difference in dose rates was observed during proton-belt passes that are similar to large, reference solar-particle events. Interplanetary cosmic-ray dose equivalent rates in Orion were as much as 60% lower than previous observations 9 . Furthermore, a change in orientation of the spacecraft during the proton-belt transit resulted in a reduction of radiation dose rates of around 50%. These measurements validate the Orion for future crewed exploration and inform future human spaceflight mission design.

  PublisherOA PDFDOI

• Generative adversarial networks for scintillation signal simulation in EXO-200

  Journal of Instrumentation · 2023 · 6 citations

  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Abstract Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. The network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.

  PublisherOA PDFDOI

• Search for MeV electron recoils from dark matter in EXO-200

  Physical review. D/Physical review. D. · 2023 · 12 citations

  • Physics
  • Atomic physics
  • Nuclear physics

  We present a search for electron-recoil signatures from the charged-current absorption of fermionic dark matter using the EXO-200 detector. We report an average electron-recoil background rate of $6.8\ifmmode\times\else\texttimes\fi{}{10}^{4}\text{ }\text{ }\mathrm{cts}\text{ }{\mathrm{kg}}^{\ensuremath{-}1}\text{ }{\mathrm{yr}}^{\ensuremath{-}1}\text{ }{\mathrm{keV}}^{\ensuremath{-}1}$ above 4 MeV and find no statistically significant excess over our background projection. Using a total $^{136}\mathrm{Xe}$ exposure of 234.1 kg yr, we exclude new parameter space for the charged-current absorption cross section for dark matter masses between ${m}_{\ensuremath{\chi}}=2.6--11.6\text{ }\text{ }\mathrm{MeV}$ with a minimum of $6\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}51}\text{ }{\mathrm{cm}}^{2}$ at 8.3 MeV at the 90% confidence level.

  PublisherOA PDFDOI

• Search for Two-neutrino Double-Beta Decay of $^{136}\rm Xe$ to the $0^+_1$ excited state of $^{136}\rm Ba$ with the Complete EXO-200 Dataset

  arXiv (Cornell University) · 2023-03-02

  preprintOpen access

  A new search for two-neutrino double-beta ($2νββ$) decay of $^{136}\rm Xe$ to the $0^+_1$ excited state of $^{136}\rm Ba$ is performed with the full EXO-200 dataset. A deep learning-based convolutional neural network is used to discriminate signal from background events. Signal detection efficiency is increased relative to previous searches by EXO-200 by more than a factor of two. With the addition of the Phase II dataset taken with an upgraded detector, the median 90$\%$ confidence level half-life sensitivity of $2νββ$ decay to the $0^+_1$ state of $^{136}\rm Ba$ is $2.9 \times 10^{24}~\rm yr$ using a total $^{136}\rm Xe$ exposure of $234.1~\rm kg~yr$. No statistically significant evidence for $2νββ$ decay to the $0^+_1$ state is observed, leading to a lower limit of $T^{2ν}_{1/2}(0^+ \rightarrow 0^+_1) > 1.4\times10^{24}~\rm yr$ at 90$\%$ confidence level, improved by 70$\%$ relative to the current world's best constraint.

  PublisherOA PDFDOI

• Detecting HII Regions in the Outer Scutum-Centaurus Arm

  American Astronomical Society Meeting Abstracts · 2021-01-11

  article1st authorCorresponding
  Publisher

Frequent coauthors

• I. A. Grenier

  Centre National de la Recherche Scientifique

  2452 shared

• J. M. Casandjian

  Université Paris Cité

  2151 shared

• L. Tibaldo

  Université de Toulouse

  2078 shared

• E. Nuss

  Laboratoire Univers et Particules de Montpellier

  1686 shared

• J. Cohen-Tanugi

  Université Clermont Auvergne

  1675 shared

• F. Piron

  Laboratoire Univers et Particules de Montpellier

  1657 shared

• O. Reimer

  1625 shared

• A. Reimer

  1572 shared

Labs

• Creative WritingPI

Awards & honors

• Whiting Award
• Fellowship from the Guggenheim Foundation
• Fellowship from the National Endowment for the Arts
• Fellowship from the American Academy in Berlin
• National Book Award for Fortune Smiles