About

Tom Abel is a Professor of Particle Physics and Astrophysics and of Physics at Stanford University. His research explores all of cosmic history using ab initio supercomputer calculations. He has demonstrated from first principles that the very first luminous objects in the universe are very massive stars and has developed novel numerical algorithms utilizing adaptive-mesh-refinement simulations that capture over 14 orders of magnitude in length and time scales. Abel's work includes showing how the first stars and galaxies form and influence subsequent cosmic evolution. He has pioneered numerical algorithms to study collisionless fluids such as dark matter, as well as astrophysical and terrestrial plasmas. Additionally, he has designed bespoke summary statistics for describing spatial clustering based on fast nearest neighbor searches. His recent work involves creating digital twins of astronomical objects and the universe as a whole, leveraging advances in machine learning and artificial intelligence through the Center for Decoding the Universe. Abel has served as the director of the Kavli Institute for Particle Astrophysics and Cosmology and was Division Director at SLAC from 2013 to 2018.

Research topics

• Statistics
• Mathematics
• Quantum mechanics
• Physics
• Statistical physics

Selected publications

• The Hubble tension: How magnetic fields could help solve one of the universe’s biggest mysteries

  2026-02-09

  article
  PublisherDOI

• Itô tracers: continuous-trajectory Lagrangian particles for Eulerian hydrodynamics

  arXiv (Cornell University) · 2026-04-24

  preprintOpen accessSenior author

  Lagrangian tracer particles have long been used to track the history of individual gas parcels in hydrodynamical codes. Particles advected by the cell-centered velocity carry no representation of underlying numerical diffusion, and thus exhibit systematic bias. The Monte-Carlo (MC) tracer resolves this with discrete probabilistic cell-to-cell, flux-based jumps, at the cost of trajectories that are discontinuous in time. We introduce the Itô tracer, a continuous-time Lagrangian particle with moments matched to the advection, diffusion, and dispersion of the gas. A subgrid-scale variant (SGS-Itô) replaces the numerical diffusion with a Smagorinsky--Lilly turbulent diffusivity, illustrating that the form of the diffusion matters less than its magnitude. We validate these methods with a 1D square-pulse advection test and 3D decaying turbulence at $σ_{\rm rms} = 15\,c_{\rm s}$. We compare the different tracer particle methods using several statistical tests. Itô tracers largely reproduce or improve upon MC tracers statistics across column-density maps, joint density histograms, log-density-ratio PDFs, and density power spectra. In the turbulence test, Itô tracers improve the correlation between tracers and gas over the MC tracers by >3\%, and reduce the width of the log-density ratio PDF by nearly 50\%. Relative to classical tracers, these improvements are $\gtrsim$30\% and 230\%, respectively. Because Itô tracers follow a stochastic differential equation, the method maps onto other continuous-trajectory Lagrangian processes (e.g. dust grains, charged particles, cosmic rays), admits variance-reduction techniques, higher-order integrators, and GPU-friendly implementations -- all of which are unavailable to discrete-jump schemes.

  PublisherDOI

• La tension de Hubble : peut-on résoudre l’un des plus grands mystères de l’univers grâce aux champs magnétiques ?

  2026-03-13

  article
  PublisherDOI

• The AGORA High-resolution Galaxy Simulations Comparison Project. X. Formation and Evolution of Galaxies at the High-redshift Frontier

  The Astrophysical Journal · 2026-03-27

  articleOpen access

  Abstract Recent observations from the James Webb Space Telescope have revealed unexpectedly luminous galaxies, exhibiting stellar masses and luminosities significantly higher than predicted by theoretical models at Cosmic Dawn. In this study, we present a suite of cosmological zoomed-in simulations targeting high-redshift ( z ≥ 10) galaxies with dark matter halo masses in the range 10 10 –10 11 M ⊙ at z = 10, using state-of-the-art galaxy formation simulation codes ( Enzo , Ramses , Changa , Gadget-3 , Gadget-4 , and Gizmo ). This study aims to evaluate the convergence of the participating codes and their reproducibility of high-redshift galaxies with the galaxy formation model calibrated at relatively low redshift, without additional physics for high-redshift environments. The subgrid physics follows the AGORA CosmoRun framework, with adjustments to resolution and initial conditions to emulate similar physical environments in the early Universe. The participating codes show consistent results for key galaxy properties (e.g., stellar mass), but also reveal notable differences (e.g., metallicity), indicating that galaxy properties at high redshifts are highly sensitive to the feedback implementation of the simulation. Massive halos ( M halo ≥5 × 10 10 M ⊙ at z = 10) succeed in reproducing observed stellar masses, metallicities, and UV luminosities at 10 ≤ z ≤ 12 without requiring additional subgrid physics, but tend to underpredict those properties at higher redshift. We also find that varying the dust-to-metal ratio modestly affects UV luminosity of simulated galaxies, whereas the absence of dust significantly enhances it. In future work, higher-resolution simulations will be conducted to better understand the formation and evolution of galaxies at Cosmic Dawn.

  PublisherDOI

• Itô tracers: continuous-trajectory Lagrangian particles for Eulerian hydrodynamics

  ArXiv.org · 2026-04-24

  articleOpen accessSenior author

  Lagrangian tracer particles have long been used to track the history of individual gas parcels in hydrodynamical codes. Particles advected by the cell-centered velocity carry no representation of underlying numerical diffusion, and thus exhibit systematic bias. The Monte-Carlo (MC) tracer resolves this with discrete probabilistic cell-to-cell, flux-based jumps, at the cost of trajectories that are discontinuous in time. We introduce the Itô tracer, a continuous-time Lagrangian particle with moments matched to the advection, diffusion, and dispersion of the gas. A subgrid-scale variant (SGS-Itô) replaces the numerical diffusion with a Smagorinsky--Lilly turbulent diffusivity, illustrating that the form of the diffusion matters less than its magnitude. We validate these methods with a 1D square-pulse advection test and 3D decaying turbulence at $σ_{\rm rms} = 15\,c_{\rm s}$. We compare the different tracer particle methods using several statistical tests. Itô tracers largely reproduce or improve upon MC tracers statistics across column-density maps, joint density histograms, log-density-ratio PDFs, and density power spectra. In the turbulence test, Itô tracers improve the correlation between tracers and gas over the MC tracers by >3\%, and reduce the width of the log-density ratio PDF by nearly 50\%. Relative to classical tracers, these improvements are $\gtrsim$30\% and 230\%, respectively. Because Itô tracers follow a stochastic differential equation, the method maps onto other continuous-trajectory Lagrangian processes (e.g. dust grains, charged particles, cosmic rays), admits variance-reduction techniques, higher-order integrators, and GPU-friendly implementations -- all of which are unavailable to discrete-jump schemes.

  PublisherOA PDF

• Geometric Interpretations of the $k$-Nearest Neighbour Distributions

  ArXiv.org · 2025-02-11

  preprintOpen accessSenior author

  The $k$-Nearest Neighbour Cumulative Distribution Functions are measures of clustering for discrete datasets that are fast and efficient to compute. They are significantly more informative than the 2-point correlation function. Their connection to $N$-point correlation functions, void probability functions and Counts-in-Cells is known. However, the connections between the CDFs and other geometric and topological spatial summary statistics are yet to be fully explored in the literature. This understanding will be crucial to find optimally informative summary statistics to analyse data from stage 4 cosmological surveys. We explore quantitatively the geometric interpretations of the $k$NN CDF summary statistics. We establish an equivalence between the 1NN CDF at radius $r$ and the volume of spheres with the same radius around the data points. We show that higher $k$NN CDFs are equivalent to the volumes of intersections of $\ge k$ spheres around the data points. We present similar geometric interpretations for the $k$NN cross-correlation joint CDFs. We further show that the volume, or the CDFs, have information about the angles and arc lengths created at the intersections of spheres around the data points, which can be accessed through the derivatives of the CDF. We show this information is very similar to that captured by Germ Grain Minkowski Functionals. Using a Fisher analysis we compare the information content and constraining power of various data vectors constructed from the $k$NN CDFs and Minkowski Functionals. We find that the CDFs and their derivatives and the Minkowski Functionals have nearly identical information content. However, $k$NN CDFs are computationally orders of magnitude faster to evaluate. Finally, we find that there is information in the full shape of the CDFs, and therefore caution against using the values of the CDF only at sparsely sampled radii.

  PublisherOA PDFDOI

• The AGORA High-resolution Galaxy Simulations Comparison Project. VIII. Disk Formation and Evolution of Simulated Milky Way Mass Galaxy Progenitors at 1 &lt; <i>z</i> &lt; 5

  The Astrophysical Journal · 2025-11-28 · 3 citations

  articleOpen access

  Abstract We investigate how differences in the stellar feedback produce disks with different morphologies in Milky Way–like progenitors over 1 ≤ z ≤ 5, using eight state-of-the-art cosmological hydrodynamics simulation codes in the AGORA project. In three of the participating codes, a distinct, rotation-dominated inner core emerges with a formation timescale of ≲300 Myr, largely driven by a major merger event, while two other codes exhibit similar signs of wet compaction—gaseous shrinkage into a compact starburst phase—at earlier epochs. The remaining three codes show only weak evidence of wet compaction. Consequently, we divide the simulated galaxies into two groups: those with strong compaction signatures and those with weaker ones. Galaxies in these two groups differ in size, stellar age gradients, and disk-to-total mass ratios. Specifically, codes with strong wet compaction build their outer disks in an inside-out fashion, leading to negative age gradients, whereas codes with weaker compaction feature flat or positive age gradients caused primarily by outward stellar migration. Although the stellar half-mass radii of these two groups diverge at z ∼ 3, the inclusion of dust extinction brings their sizes and shapes in mock observations closer to each other and to observed galaxies. We attribute the observed morphological differences primarily to variations in the stellar feedback implementations—such as delayed cooling timescales, and feedback strengths—that regulate both the onset and duration of compaction. Overall, our results suggest that disk assembly at high redshifts is highly sensitive to the details of the stellar feedback prescriptions in simulations.

  PublisherDOI

• The <i>AGORA</i> High-Resolution Galaxy Simulations Comparison Project

  Astronomy and Astrophysics · 2025-05-13 · 7 citations

  articleOpen access

  Context. Satellite galaxies experience multiple physical processes when interacting with their host halos, often leading to the quenching of star formation. In the Local Group, satellite quenching has been shown to be highly efficient, affecting nearly all satellites except the most massive ones. While recent surveys study Milky Way-analogs to assess how representative our Local Group is, the dominant physical mechanisms behind satellite quenching in Milky Way-mass halos remain under debate. Aims. We analyze satellite quenching within the same Milky Way-mass halo simulated using various widely used astrophysical codes, each using different hydrodynamic methods and implementing different supernovae feedback recipes. The goal is to determine whether quenched fractions, quenching timescales, and the dominant quenching mechanisms are consistent across codes or if they show sensitivity to the specific hydrodynamic method and supernovae feedback physics employed. Methods. We used a subset of high-resolution cosmological zoom-in simulations of a Milky Way-mass halo from the multiple-code AGORA CosmoRun suite. Our analysis focuses on comparing satellite quenching across the different models and against observational data. We also analyzed the dominant mechanisms driving satellite quenching in each model. Results. We find that the quenched fraction is consistent with the latest SAGA Survey results within its 1 σ host-to-host scatter across all the models. Regarding quenching timescales, all the models reproduce the trend observed in the ELVES survey, Local Group observations, and previous simulations: The less massive the satellite, the shorter its quenching timescale. All of our models converge on the dominant quenching mechanisms: Strangulation halts cold gas accretion in all satellites, while ram pressure stripping is the predominant mechanism for gas removal, and it is particularly effective in satellites with M * &lt;10 8 M ⊙ . Nevertheless, the efficiency of the stripping mechanisms differs among the codes, showing a strong sensitivity to the different supernovae feedback implementations and/or hydrodynamic methods employed.

  PublisherDOI

• Cosmic recombination in the presence of primordial magnetic fields

  Journal of Cosmology and Astroparticle Physics · 2025-03-01 · 10 citations

  article

  Abstract Primordial magnetic fields (PMFs) may explain observations of magnetic fields on extragalactic scales. They are most cleanly constrained by measurements of cosmic microwave background radiation (CMB) anisotropies. Their effects on cosmic recombination may even be at the heart of the resolution of the Hubble tension. We present the most detailed analysis of the effects of PMFs on cosmic recombination to date. To this end we extend the public magneto-hydrodynamic code ENZO with a new cosmic recombination routine, Monte-Carlo simulations of Lyman- α photon transport, and a Compton drag term in the baryon momentum equation. The resulting code allows us, for the first time, to realistically predict the impact of PMFs on the cosmic ionization history and the clumping of baryons during cosmic recombination. Our results identify the importance of mixing of Lyman- α photons between overdense- and underdense- regions for small PMF strength. This mixing speeds up recombination beyond the speed-up due to clumping. We also investigate the effects of pecuilar flows on the recombination rate and find it to be small for small PMF strengths. For non-helical PMFs with a Batchelor spectrum we find a surprising dependency of results on ultra-violet magnetic modes. We further show that the increase in the ionization fraction at low redshift by hydrodynamic baryon heating due to PMF dissipation is completely compensated by the faster recombination from baryon clumping. The present study shall serve as a theoretical foundation for a future precise comparison of recombination with PMFs to CMB data.

  PublisherDOI

• Howard T. Odum's Contributions to Evolutionary Theory

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access1st authorCorresponding
  PublisherDOI

Recent grants

• Collaborative Research: Cosmological All-wavelength Radiative Transfer (CART)

  NSF · $243k · 2008–2012

• CAREER: Stars and Galaxies in the First Billion Years of the Universe

  NSF · $421k · 2004–2008

• Collaborative Research: Toward 3-dimensional, time-dependent, non-LTE radiation transport for cosmological simulations

  NSF · $85k · 2008–2012

Frequent coauthors

• John Wise

  441 shared

• Matthew Turk

  University of Illinois Urbana-Champaign

  412 shared

• Britton Smith

  320 shared

• Michael L. Norman

  University of California, San Diego

  92 shared

• Ji-hoon Kim

  Seoul National University

  75 shared

• Britton Smith

  75 shared

• Samuel Totorica

  73 shared

• Arka Banerjee

  68 shared