Tom Abel’s research while affiliated with Kavli Institute for Particle Astrophysics and Cosmology, Stanford University and other places

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 kNN 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 kNN CDFs are equivalent to the volumes of intersections of $\ge k$ spheres around the data points. We present similar geometric interpretations for the kNN 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 kNN CDFs and Minkowski Functionals. We find that the CDFs and their derivatives and the Minkowski Functionals have nearly identical information content. However, kNN 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.

Environment plays a critical role in shaping the assembly of low-mass galaxies. Here, we use the U niverse M achine (UM) galaxy–halo connection framework and Data Release 3 of the Satellites Around Galactic Analogs (SAGA) Survey to place dwarf galaxy star formation and quenching into a cosmological context. UM is a data-driven forward model that flexibly parameterizes galaxy star formation rates (SFRs) using only halo mass and assembly history. We add a new quenching model to UM, tailored for galaxies with m ⋆ ≲ 10 ⁹ M ⊙ , and constrain the model down to m ⋆ ≳ 10 ⁷ M ⊙ using new SAGA observations of 101 satellite systems around Milky Way (MW)–mass hosts and a sample of isolated field galaxies in a similar mass range from the Sloan Digital Sky Survey. The new best-fit model, “UM-SAGA,” reproduces the satellite stellar mass functions, average SFRs, and quenched fractions in SAGA satellites while keeping isolated dwarfs mostly star-forming. The enhanced quenching in satellites relative to isolated field galaxies leads the model to maximally rely on halo assembly to explain the observed environmental quenching. Extrapolating the model down to m ⋆ ∼ 10 6.5 M ⊙ yields a quenched fraction of ≳30% for isolated field galaxies and ≳80% for satellites of MW-mass hosts at this stellar mass. Spectroscopic surveys can soon test this specific prediction to reveal the relative importance of internal feedback, cessation of mass and gas accretion, satellite-specific gas processes, and reionization for the evolution of faint low-mass galaxies.

Searches for primordial non-Gaussianity in cosmological perturbations are a key means of revealing novel primordial physics. However, robustly extracting signatures of primordial non-Gaussianity from non-linear scales of the late-time Universe is an open problem. In this paper, we apply k-Nearest Neighbour cumulative distribution functions, kNN-CDFs, to the quijote-png simulations to explore the sensitivity of kNN-CDFs to primordial non-Gaussianity. An interesting result is that for halo samples with $M_\mathrm{ h}\langle 10^{14}$ M$_\odot$ $h^{-1}$, the kNN-CDFs respond to equilateral PNG in a manner distinct from the other parameters. This persists in the galaxy catalogues in redshift space and can be differentiated from the impact of galaxy modelling, at least within the halo occupation distribution (HOD) framework considered here. kNN-CDFs are related to counts-in-cells and, through mapping a subset of the kNN-CDF measurements into the count-in-cells picture, we show that our results can be modelled analytically. A caveat of the analysis is that we only consider the HOD framework, including assembly bias. It will be interesting to validate these results with other techniques for modelling the galaxy–halo connection, e.g. (hybrid) effective field theory or semi-analytical methods.

In this fourth paper from the AGORA Collaboration, we study the evolution down to redshift z = 2 and below of a set of cosmological zoom-in simulations of a Milky Way mass galaxy by eight of the leading hydrodynamic simulation codes. We also compare this CosmoRun suite of simulations with dark matter-only simulations by the same eight codes. We analyze general properties of the halo and galaxy at z = 4 and 3, and before the last major merger, focusing on the formation of well-defined rotationally supported disks, the mass–metallicity relation, the specific star formation rate, the gas metallicity gradients, and the nonaxisymmetric structures in the stellar disks. Codes generally converge well to the stellar-to-halo mass ratios predicted by semianalytic models at z ∼ 2. We see that almost all the hydro codes develop rotationally supported structures at low redshifts. Most agree within 0.5 dex with the observed mass–metallicity relation at high and intermediate redshifts, and reproduce the gas metallicity gradients obtained from analytical models and low-redshift observations. We confirm that the intercode differences in the halo assembly history reported in the first paper of the collaboration also exist in CosmoRun , making the code-to-code comparison more difficult. We show that such differences are mainly due to variations in code-dependent parameters that control the time stepping strategy of the gravity solver. We find that variations in the early stellar feedback can also result in differences in the timing of the low-redshift mergers. All the simulation data down to z = 2 and the auxiliary data will be made publicly available.

The classical field approximation is widely used to better understand the predictions of ultralight dark matter. Here, we use the truncated Wigner approximation method to test the classical field approximation of ultralight dark matter. This method approximates a quantum state as an ensemble of independently evolving realizations drawn from its Wigner function. The method is highly parallelizable and allows the direct simulation of quantum corrections and decoherence times in systems many times larger than have been previously studied in reference to ultralight dark matter. Our study involves simulation of systems in 1, 2, and 3 spatial dimensions. We simulate three systems, the condensation of a Gaussian random field in three spatial dimensions, a stable collapsed object in three spatial dimensions, and the merging of two stable objects in two spatial dimensions. We study the quantum corrections to the classical field theory in each case. We find that quantum corrections grow exponentially during nonlinear growth with the timescale being approximately equal to the system dynamical time. In stable systems the corrections grow quadratically. We also find that the primary effect of quantum corrections is to reduce the amplitude of fluctuations on the de Broglie scale in the spatial density. Finally, we find that the timescale associated with decoherence due to gravitational coupling to baryonic matter is at least as fast as the quantum corrections due to gravitational interactions. These results are consistent with the predictions of the classical field theory being accurate.

We analyze and compare the satellite halo populations at z ∼ 2 in the high-resolution cosmological zoom-in simulations of a 10 ¹² M ⊙ target halo ( z = 0 mass) carried out on eight widely used astrophysical simulation codes ( Art-I , Enzo , Ramses , Changa , Gadget-3 , Gear , Arepo-t , and Gizmo ) for the AGORA High-resolution Galaxy Simulations Comparison Project. We use slightly different redshift epochs near z = 2 for each code (hereafter “ z ∼ 2”) at which the eight simulations are in the same stage in the target halo’s merger history. After identifying the matched pairs of halos between the CosmoRun simulations and the DMO simulations, we discover that each CosmoRun halo tends to be less massive than its DMO counterpart. When we consider only the halos containing stellar particles at z ∼ 2, the number of satellite galaxies is significantly fewer than that of dark matter halos in all participating AGORA simulations and is comparable to the number of present-day satellites near the Milky Way or M31. The so-called “missing satellite problem” is fully resolved across all participating codes simply by implementing the common baryonic physics adopted in AGORA and the stellar feedback prescription commonly used in each code, with sufficient numerical resolution (≲100 proper pc at z = 2). We also compare other properties such as the stellar mass–halo mass relation and the mass–metallicity relation. Our work highlights the value of comparison studies such as AGORA, where outstanding problems in galaxy formation theory are studied simultaneously on multiple numerical platforms.

We analyze the circumgalactic medium (CGM) for eight commonly-used cosmological codes in the AGORA collaboration. The codes are calibrated to use identical initial conditions, cosmology, heating and cooling, and star formation thresholds, but each evolves with its own unique code architecture and stellar feedback implementation. Here, we analyze the results of these simulations in terms of the structure, composition, and phase dynamics of the CGM. We show properties such as metal distribution, ionization levels, and kinematics are effective tracers of the effects of the different code feedback and implementation methods, and as such they can be highly divergent between simulations. This is merely a fiducial set of models, against which we will in the future compare multiple feedback recipes for each code. Nevertheless, we find that the large parameter space these simulations establish can help disentangle the different variables that affect observable quantities in the CGM, e.g., showing that abundances for ions with higher ionization energy are more strongly determined by the simulation’s metallicity, while abundances for ions with lower ionization energy are more strongly determined by the gas density and temperature.

We present the methodology for deriving accurate and reliable cosmological constraints from non-linear scales (<50 h−1 Mpc) with k-th nearest neighbor (kNN) statistics. We detail our methods for choosing robust minimum scale cuts and validating galaxy–halo connection models. Using cross-validation, we identify the galaxy–halo model that ensures both good fits and unbiased predictions across diverse summary statistics. We demonstrate that we can model kNNs effectively down to transverse scales of rp ∼ 3 h−1 Mpc and achieve precise and unbiased constraints on the matter density and clustering amplitude, leading to a 2% constraint on σ8. Our simulation-based model pipeline is resilient to varied model systematics, spanning simulation codes, halo finding, and cosmology priors. We demonstrate the effectiveness of this approach through an application to the Beyond-2p mock challenge. We propose further explorations to test more complex galaxy–halo connection models and tackle potential observational systematics.

Widefield surveys probe clustered scalar fields — such as galaxy counts, lensing potential, etc. — that are sensitive to different cosmological and astrophysical processes. Constraining such processes depends on the statistics that summarize the field. We explore the cumulative distribution function (CDF) as a summary of the galaxy lensing convergence field. Using a suite of N-body lightcone simulations, we show the CDFs’ constraining power is modestly better than the 2nd and 3rd moments, as CDFs approximately capture information from all moments. We study the practical aspects of applying CDFs to data, using the Dark Energy Survey (DES Y3) data as an example, and compute the impact of different systematics on the CDFs. The contributions from the point spread function and reduced shear approximation are $\lesssim 1\%$ of the total signal. Source clustering effects and baryon imprints contribute 1-10%. Enforcing scale cuts to limit systematics-driven biases in parameter constraints degrades these constraints a noticeable amount, and this degradation is similar for the CDFs and the moments. We detect correlations between the observed convergence field and the shape noise field at 13σ. The non-Gaussian correlations in the noise field must be modeled accurately to use the CDFs, or other statistics sensitive to all moments, as a rigorous cosmology tool.

Widefield surveys of the sky probe many clustered scalar fields -- such as galaxy counts, lensing potential, gas pressure, etc. -- that are sensitive to different cosmological and astrophysical processes. Our ability to constrain such processes from these fields depends crucially on the statistics chosen to summarize the field. In this work, we explore the cumulative distribution function (CDF) at multiple scales as a summary of the galaxy lensing convergence field. Using a suite of N-body lightcone simulations, we show the CDFs' constraining power is modestly better than that of the 2nd and 3rd moments of the field, as they approximately capture the information from all moments of the field in a concise data vector. We then study the practical aspects of applying the CDFs to observational data, using the first three years of the Dark Energy Survey (DES Y3) data as an example, and compute the impact of different systematics on the CDFs. The contributions from the point spread function are 2-3 orders of magnitude below the cosmological signal, while those from reduced shear approximation contribute $\lesssim 1\%$ to the signal. Source clustering effects and baryon imprints contribute $1-10\%$. Enforcing scale cuts to limit systematics-driven biases in parameter constraints degrades these constraints a noticeable amount, and this degradation is similar for the CDFs and the moments. We also detect correlations between the observed convergence field and the shape noise field at $13\sigma$. We find that the non-Gaussian correlations in the noise field must be modeled accurately to use the CDFs, or other statistics sensitive to all moments, as a rigorous cosmology tool.