Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2609.35757

Can Fingers of God be Resummed?

Shi-Fan Chen·Alejandro Aviles
View original source ↗Read source document ↗Export metadata ↓

Abstract

Fingers of God (FoGs), Doppler shifts due to the small-scale virial motions of galaxies, are one of the most significant challenges for analytic models of galaxy clustering in redshift surveys. In the effective field theory approach, the characteristic scale of these shifts introduces a new, large length scale, limiting the reach of the theory. This has motivated many phenomenological models of FoGs in the literature where their effect is approximated by a single damping function of the line-of-sight wavenumber $λ=kμ$, effectively resumming powers of $λ$ due to single-point contractions of small-scale velocities. The goal of this work is to investigate whether such a resummation can be rigorously constructed. We show that common damping models can be understood as the mean-field limit of the single-particle characteristic function (SPCF) of FoG velocities, but that the environmental dependence of the SPCF generates equally large corrections that break the simple damping form. We develop the long-wavelength theory of the SPCF, showing that it admits an expansion in anisotropic bias operators closely related to those derived for selection effects, but with $λ$-dependent coefficients satisfying selection rules. We estimate these coefficients using the halo model for DESI-like galaxies, showing that they make significant contributions at the level of the 2- and 3-point functions of galaxies. To answer the titular question: potentially, but at the cost of significantly expanding the operator basis and promoting free coefficients to free functions.

Explore related subjects

astro-ph.CO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shi-Fan Chen, Alejandro Aviles. 2026-09-28. Can Fingers of God be Resummed?. https://arxiv.org/abs/2609.35757

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Euclid Quick Data Release (Q1). The first Euclid view of Planck galaxy protocluster candidates at cosmic noon

[ABRIGED ABSTRACT] A large catalogue of candidate galaxy protoclusters with high star-formation rates was produced by the Planck collaboration. We search, in the first data release (Q1) of the Euclid survey, for the visible and infrared counterparts of the Planck galaxy protocluster candidates expected to be above $z > 1.5$. Eight of them are in Euclid Q1. Our goal is to investigate the optical nature of these overdensities previously detected in the submillimetre wavelength range. We search for overdensities using the DETECTIFz algorithm, an overdensity finder based on Delaunay tessellation that uses photometric redshift probability distributions through Monte Carlo simulations. Focusing our search on the eight high-star forming Planck protocluster candidates, we find that two of them have one Euclid counterpart, and six have between two and four Euclid counterparts, which amounts to a total of 20 Euclid counterparts. These Euclid counterparts lie at photometric redshifts $1.4<z_{\rm ph} < 2.7$ and 12 of them also have partial Herschel coverage. All detections have also been confirmed by at least one other independent protocluster detection algorithm. We study the colours, derived stellar masses and star-formation rates (SFRs) of the detected member galaxies of those protocluster candidate counterparts. We also estimate the total stellar masses, SFRs, and the halo mass lower limits for all Euclid protocluster candidates. We find that in the dark matter halo mass ($M_{\rm h}$) / redshift plane, these Planck and Euclid overdense regions lie in the region $12.6 <\log_{10} (M_{\rm h}/M_\odot)< 13.4$, $1.4<z< 2.7$. This means that the halos of our objects are expected to have experienced a transition between cold flows in hot media to accretion of hot material.

astro-ph.CO↗
arXiv2026

CNN+FoF: application of deep learning to the identification of dark matter haloes

We present a deep-learning-based approach for identifying dark matter haloes in cosmological N-body simulations. Our framework consists of a volumetric Convolutional Neural Network to classify individual simulation particles as either halo or non-halo members, followed by a highly optimised and parallelised Friends-of-Friends clustering algorithm that groups the classified halo members into distinct haloes. The training data comprise simulations generated using GADGET-4, with labels obtained with the ROCKSTAR halo finder. Our models incorporate two main halo mass definitions, $M_{200\mathrm{b}}$ and $M_{\text{vir}}$, with similar performance. For haloes defined by the ROCKSTAR $M_{200\mathrm{b}}$ criterion, the classification network demonstrated stable performance across multiple simulation resolutions. For the highest resolution, it achieved over $98\%$ across all primary performance metrics when identifying halo particles. Furthermore, the FoF algorithm yielded halo catalogues with a purity generally exceeding $99\%$ and a stable completeness of $94\%$ for masses above $5\times10^{11} \, M_\odot$. Our pipeline recovered the centre-of-mass positions, velocities and halo masses with high fidelity, yielding a halo mass function consistent to within $5\%$ of the reference while faithfully reconstructing the internal density profiles. The primary objective of this study is to offer a faster and scalable alternative to conventional halo finders, achieving a speed-up of approximately one order of magnitude relative to ROCKSTAR. While traditional N-body simulations remain dominated by the time required to solve gravitational dynamics, this post-processing acceleration becomes essential when coupled with fast field-level emulators, offering a promising pathway towards efficient, end-to-end forward modelling in modern simulation-based inference.

astro-ph.CO↗
arXiv2026

Impact of Stochastic Pop~III X-ray Binaries on the Cosmological 21-cm Signal

High-mass X-ray binaries are one of the primary drivers of the 21-cm signal from Cosmic Dawn and Reionization, playing a leading role in the thermal history of the intergalactic medium. In traditional semi-numerical simulations, a deterministic scaling relation between the total X-ray luminosity of high-mass X-ray binaries, $L_{\rm X}$, and star formation rate (SFR) is usually adopted. However, this assumption is inaccurate for high-redshift low-SFR regions hosting few sources. The spatial variation in the number and luminosity of these sources is expected to enhance fluctuations in the Cosmic Dawn 21-cm signal. Here we quantify this effect by introducing a stochastic $L_{\rm X}$ model sampled from a power-law X-ray luminosity function. Implementing this in \texttt{21cmSPACE}, a large-scale simulation framework of Cosmic Dawn and Reionization, and adopting a soft X-ray spectral energy distribution ($β=1.5$, $E_{\rm min}=0.1\,\mathrm{keV}$) that leads to a relatively strong stochastic signature, we find that the stochasticity leads to enhanced fluctuations in X-ray heating rate fields, and affects the 21-cm power spectrum on small scales ($k>0.3~ \mathrm{cMpc^{-1}}$). The impact of stochasticity on the global 21-cm signal and on the large-scale power spectrum is found to be negligible. Our results suggest these effects will remain undetected by the upcoming Square Kilometer Array. However, large-scale lunar-based experiments may be sensitive to the signatures of stochastic X-ray heating at $z\sim 25$. Quantifying these corrections is a vital step toward robust 21-cm modeling and ensuring that future precision data interpretation is free from astrophysical biases.

astro-ph.CO↗