About

Fred Adams is the Ta-You Wu Collegiate Professor of Physics and Director of the Leinweber Center for Theoretical Physics at the University of Michigan. He works in the general area of theoretical astrophysics with a focus on the study of star formation and cosmology. He is internationally recognized for his work on the radiative signature of the star formation process, the dynamics of circumstellar disks, and the theory of the initial mass function for forming stars. His recent research includes star formation in clusters, studies of extra-solar planetary systems, and the environmental effects of clusters on planetary systems. In the field of cosmology, he has studied aspects of the inflationary universe, cosmological phase transitions, magnetic monopoles, cosmic rays, the cosmic background radiation, galactic halos of dark matter, and the possible fine-tuning of the universe. Adams holds a Ph.D. from the University of California at Berkeley (1988) and a B.S. from Iowa State University (1983).

Research topics

• Physics
• Astronomy
• Astrophysics
• Astrobiology

Selected publications

• Architectures of Planetary Systems II: Trends with Host Star Mass and Metallicity

  Open MIND · 2026-02-03

  preprintSenior author

  The current census of planetary systems displays a wide range of architectures. Extending earlier work, this paper investigates the correlation between our classification framework for these architectures and host stellar properties. Specifically, we explore how planetary system properties depend on stellar mass and stellar metallicity. This work confirms previously detected trends that jovian planets are less prevalent for low-mass and low-metallicity stars. We also find new, but expected trends such as that the total mass in planets increases with stellar mass, and that observed planetary system masses show an upper limit that is roughly consistent with expectations from the stability of circumstellar disks. We tentatively identify potential unique trends in the host stars of super-puffs and hot jupiters and a possible subdivision of the class of hot jupiter systems. In general, we find that system architectures are not overly dependent on host star properties.

  DOI

• The source code and example notebook of Probabilistic Spectral Reconstruction of Trans-Neptunian Objects from Sparse Photometry: A Framework for Taxonomy, Survey Optimization, and Outlier Detection

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-27

  otherOpen access

  JWST TNO Spectra Reconstructor from NIR Photometry A machine learning framework for reconstructing full Trans-Neptunian Object (TNO) spectra from sparse JWST NIRCam photometry. Overview This project provides tools to bridge the gap between sparse photometric observations and high-resolution spectral models. By leveraging a PCA-based manifold of known TNO spectra (from the DisCoTNO survey), we can reconstruct full reflectance spectra (0.75–5.1 µm) and estimate taxonomic classifications for objects with limited data, such as Neptune Trojans. Key Components Spectral Manifold: A PCA-based representation of spectral shapes that captures ~99% of variance in TNO reflectance. Reconstruction Model: Uses AutoGluon (TabularPredictor) with quantile regression to predict PCA components from filter photometry. Uncertainty Estimation: Employs correlated Monte Carlo sampling to generate spectral realizations and 95% confidence intervals. Taxonomic Classifier: An AutoGluon-based classifier that identifies object types (e.g., bowl, cliff, methanol-rich) from predicted PCA components. Filter Integration: Comprehensive handling of JWST NIRCam filter throughput curves and bandwidths.

  PublisherDOI

• Probabilistic Spectral Reconstruction of Trans-Neptunian Objects from Sparse Photometry: A Framework for Taxonomy, Survey Optimization, and Outlier Detection

  arXiv (Cornell University) · 2026-04-26

  preprintOpen access

  Near-infrared (near-IR) spectroscopy provides critical constraints on the surface composition of trans-Neptunian objects (TNOs), but spectroscopic observations remain limited compared to broadband photometry. We develop a probabilistic latent-space framework to quantify how much spectral information is retained in sparse photometric measurements. Using a principal component representation trained on a sample of near-IR spectra, we model the spectral manifold of TNOs and perform Bayesian inference in this reduced space to reconstruct full spectra from photometry while propagating uncertainties. Leave-one-out cross-validation demonstrates that the dominant modes of spectral variability are low-dimensional: 4 to 5 principal components capture the structure relevant for taxonomic classification, while 8-10 components improve spectral reconstruction fidelity and uncertainty calibration. For most objects, the reconstructed spectra achieve empirical credible-interval coverage of 95 percent across wavelength. This suggests the diversity of near-IR spectral shapes is governed by structured, correlated surface processes rather than stochastic variation. Practically, we apply this framework to survey optimization, quantifying the information content of JWST/NIRCam filters to identify optimal configurations (e.g., F090W, F115W, F410M, F460M) for TNO taxonomy. Additionally, we demonstrate the pipeline's capability to detect and reconstruct rare spectral types, such as the peculiar Neptune Trojans 2006 RJ103 and 2011 SO277, by allowing constraining photometry to select low-probability intermediate models from the continuous topological manifold. Ultimately, this framework bridges the gap between sparse photometry and spectroscopy, providing a statistically rigorous tool to map the compositional structure of minor planets in upcoming large-scale surveys.

  PublisherDOI

• The source code and example notebook of Probabilistic Spectral Reconstruction of Trans-Neptunian Objects from Sparse Photometry: A Framework for Taxonomy, Survey Optimization, and Outlier Detection

  Zenodo (CERN European Organization for Nuclear Research) · 2026-04-27 · 1 citations

  otherOpen access

  JWST TNO Spectra Reconstructor from NIR Photometry A machine learning framework for reconstructing full Trans-Neptunian Object (TNO) spectra from sparse JWST NIRCam photometry. Overview This project provides tools to bridge the gap between sparse photometric observations and high-resolution spectral models. By leveraging a PCA-based manifold of known TNO spectra (from the DisCoTNO survey), we can reconstruct full reflectance spectra (0.75–5.1 µm) and estimate taxonomic classifications for objects with limited data, such as Neptune Trojans. Key Components Spectral Manifold: A PCA-based representation of spectral shapes that captures ~99% of variance in TNO reflectance. Reconstruction Model: Uses AutoGluon (TabularPredictor) with quantile regression to predict PCA components from filter photometry. Uncertainty Estimation: Employs correlated Monte Carlo sampling to generate spectral realizations and 95% confidence intervals. Taxonomic Classifier: An AutoGluon-based classifier that identifies object types (e.g., bowl, cliff, methanol-rich) from predicted PCA components. Filter Integration: Comprehensive handling of JWST NIRCam filter throughput curves and bandwidths.

  PublisherDOI

• Architectures of Planetary Systems II: Trends with Host Star Mass and Metallicity

  ArXiv.org · 2026-02-03

  articleOpen accessSenior author

  The current census of planetary systems displays a wide range of architectures. Extending earlier work, this paper investigates the correlation between our classification framework for these architectures and host stellar properties. Specifically, we explore how planetary system properties depend on stellar mass and stellar metallicity. This work confirms previously detected trends that jovian planets are less prevalent for low-mass and low-metallicity stars. We also find new, but expected trends such as that the total mass in planets increases with stellar mass, and that observed planetary system masses show an upper limit that is roughly consistent with expectations from the stability of circumstellar disks. We tentatively identify potential unique trends in the host stars of super-puffs and hot jupiters and a possible subdivision of the class of hot jupiter systems. In general, we find that system architectures are not overly dependent on host star properties.

  PublisherOA PDF

• Probabilistic Spectral Reconstruction of Trans-Neptunian Objects from Sparse Photometry: A Framework for Taxonomy, Survey Optimization, and Outlier Detection

  ArXiv.org · 2026-04-26

  articleOpen access

  Near-infrared (near-IR) spectroscopy provides critical constraints on the surface composition of trans-Neptunian objects (TNOs), but spectroscopic observations remain limited compared to broadband photometry. We develop a probabilistic latent-space framework to quantify how much spectral information is retained in sparse photometric measurements. Using a principal component representation trained on a sample of near-IR spectra, we model the spectral manifold of TNOs and perform Bayesian inference in this reduced space to reconstruct full spectra from photometry while propagating uncertainties. Leave-one-out cross-validation demonstrates that the dominant modes of spectral variability are low-dimensional: 4 to 5 principal components capture the structure relevant for taxonomic classification, while 8-10 components improve spectral reconstruction fidelity and uncertainty calibration. For most objects, the reconstructed spectra achieve empirical credible-interval coverage of 95 percent across wavelength. This suggests the diversity of near-IR spectral shapes is governed by structured, correlated surface processes rather than stochastic variation. Practically, we apply this framework to survey optimization, quantifying the information content of JWST/NIRCam filters to identify optimal configurations (e.g., F090W, F115W, F410M, F460M) for TNO taxonomy. Additionally, we demonstrate the pipeline's capability to detect and reconstruct rare spectral types, such as the peculiar Neptune Trojans 2006 RJ103 and 2011 SO277, by allowing constraining photometry to select low-probability intermediate models from the continuous topological manifold. Ultimately, this framework bridges the gap between sparse photometry and spectroscopy, providing a statistically rigorous tool to map the compositional structure of minor planets in upcoming large-scale surveys.

  PublisherOA PDF

• Further constraints on Jupiter's primordial structure

  arXiv (Cornell University) · 2025-12-03

  preprintOpen accessSenior author

  The primordial structure of Jupiter remains uncertain, yet it holds vital clues on the planet's formation and early evolution. Recent work used dynamical constraints from Jupiter's inner moons to determine its primordial state, thereby providing a novel, formation-era anchor point for interior modeling. Building on this approach, we combine these dynamical constraints with thermal evolution simulations to investigate which primordial structures are consistent with present-day Jupiter. We present 4,250 evolutionary models of the planetary structure, including compositional mixing and helium phase separation, spanning a broad range of initial entropies and composition profiles. We find that Jupiter's present-day structure is best explained by a warm ($4.98_{-2.57}^{+3.00}\, \mathrm{k_B\, m_u^{-1}}$), metal-rich dilute core inherited from formation. To simultaneously satisfy constraints on Jupiter's primordial spin, however, its envelope must have been significantly warmer ($9.32_{-0.58}^{+0.48}\, \mathrm{k_B\, m_u^{-1}}$) at the time of disk dispersal. We determine Jupiter's primordial radius to be $1.89_{-0.49}^{+0.40}\, \mathrm{R_J}$. These results provide new constraints on Jupiter's formation, suggesting that most heavy elements were accreted early during runaway gas accretion, and placing bounds on the energy dissipated during the accretion shock.

  PublisherOA PDFDOI

• Onset of CN Emission in 3I/ATLAS: Evidence for Strong Carbon-Chain Depletion

  ArXiv.org · 2025-09-01

  preprintOpen access

  Interstellar objects provide a direct window into the environmental conditions around stars other than the Sun. The recent discovery of 3I/ATLAS, a new interstellar comet, offers a unique opportunity to investigate the physical and chemical properties of interstellar objects and to compare them with those of comets in our own Solar System. In this Letter we present the results of a 10-night spectroscopic and photometric monitoring campaign with the 2.4 m Hiltner and 1.3 m McGraw-Hill telescopes at the MDM Observatory. The campaign was conducted between August 8 and 17 while 3I/ATLAS was inbound at heliocentric distances of 3.2 - 2.9 au. Our observations captured the onset of optical gas activity. Nightly spectra reveal a weak CN emission feature in the coma of 3I/ATLAS, absent during the first nights but steadily strengthening thereafter. We measure a CN production rate of $Q$(CN)$\sim6\times$10$^{24}$ s$^{-1}$, towards the lower end of activity observed in Solar System comets. Simultaneous photometry also indicates a small but measurable increase in the coma's radial profile and increasing $r$-band $Afρ$ with values in the order of $\sim300$ cm. We derived a gas-to-dust production ratio of $\log Q (\mathrm{CN})/Afρ\sim22.4$. Our upper limit on the C$_2$-to-CN ratio ($\log Q(\mathrm{C}_2)/Q(\mathrm{CN})\lesssim-0.8$) indicates that 3I/ATLAS is a strongly carbon-chain depleted comet. Further observations of 3I/ATLAS are required to verify the apparent carbon-chain depletion and to explore whether such composition represents a recurring trait of the interstellar comet population.

  PublisherOA PDFDOI

• The TESS Ten Thousand Catalog: 10,001 Uniformly Vetted and Validated Eclipsing Binary Stars Detected in Full-frame Image Data by Machine Learning and Analyzed by Citizen Scientists

  The Astrophysical Journal Supplement Series · 2025-08-01 · 8 citations

  articleOpen access

  Abstract The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in full-frame image mode with a time resolution of 200 s to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries, which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1–82 of the TESS full-frame image data searching for eclipsing binary stars using a neural network that identified ∼1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ∼60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10,001 uniformly vetted and validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ∼900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (≈21″).

  PublisherDOI

• The TESS Ten Thousand Catalog: 10,001 uniformly-vetted and -validated Eclipsing Binary Stars detected in Full-Frame Image data by machine learning and analyzed by citizen scientists

  ArXiv.org · 2025-06-05

  preprintOpen access

  The Transiting Exoplanet Survey Satellite (TESS) has surveyed nearly the entire sky in Full-Frame Image mode with a time resolution of 200 seconds to 30 minutes and a temporal baseline of at least 27 days. In addition to the primary goal of discovering new exoplanets, TESS is exceptionally capable at detecting variable stars, and in particular short-period eclipsing binaries which are relatively common, making up a few percent of all stars, and represent powerful astrophysical laboratories for deep investigations of stellar formation and evolution. We combed Sectors 1-82 of TESS Full-Frame Image data searching for eclipsing binary stars using a neural network that identified ~1.2 million stars with eclipse-like features. Of these, we have performed an in-depth analysis on ~60,000 targets using automated methods and manual inspection by citizen scientists. Here we present a catalog of 10001 uniformly-vetted and -validated eclipsing binary stars that passed all our ephemeris and photocenter tests, as well as complementary visual inspection. Of these, 7936 are new eclipsing binaries while the remaining 2065 are known systems for which we update the published ephemerides. We outline the detection and analysis of the targets, discuss the properties of the sample, and highlight potentially interesting systems. Finally, we also provide a list of ~900,000 unvetted and unvalidated targets for which the neural network found eclipse-like features with a score higher than 0.9, and for which there are no known eclipsing binaries within a sky-projected separation of a TESS pixel (~21 arcsec).

  PublisherOA PDFDOI

Frequent coauthors

• Juliette Becker

  58 shared

• Marco Fatuzzo

  41 shared

• D. W. Gerdes

  University of Michigan–Ann Arbor

  40 shared

• Kevin J. Napier

  37 shared

• Gregory Laughlin

  Yale University

  36 shared

• Anthony M. Bloch

  University of Michigan–Ann Arbor

  36 shared

• Larissa Markwardt

  University of Michigan–Ann Arbor

  32 shared

• Hsing Wen Lin

  29 shared

Education

• PhD, Physics

  University of California Berkeley

  1988