arXiv subjects

Ofer Lahav

Publications and source records attributed to Ofer Lahav.

At least 37 records · Page 2Linked to original sources

arXiv2025

Beyond general relativity: probing gravity with gravitational redshifts

Despite the success of general relativity (GR), the unexplained nature of dark energy on cosmological scales leaves open the question of whether GR provides a complete description of gravity. This quest is further motivated by growing tensions among cosmological observations when interpreted within $Λ$CDM. Gravitational redshifts of cluster member galaxies probe cluster potentials on megaparsec scales directly, complementing conventional large-scale structure tests. Here, we investigate how redshift precision and survey design propagate into constraints on modified gravity using an end-to-end pipeline run on mock catalogues, focusing on mis-centring and spectroscopic completeness. We find that competitive measurements require wide-field spectroscopic cluster surveys explicitly designed to maximise the number of spectroscopically confirmed members per cluster, to enable high-purity stacking, and to control systematic effects.

astro-ph.CO↗

arXiv2025

Learning the Cosmic Web: Graph-based Classification of Simulated Galaxies by their Dark Matter Environments

We present a novel graph-based machine learning classifier for identifying the dark matter cosmic web environments of galaxies. Large galaxy surveys offer comprehensive statistical views of how galaxy properties are shaped by large-scale structure, but this requires robust classifications of galaxies' cosmic web environments. Using stellar mass-selected IllustrisTNG-300 galaxies, we apply a three-stage, simulation-based framework to link galaxies to the total (mainly dark) underlying matter distribution. Here, we apply the following three steps: First, we assign the positions of simulated galaxies to a void, wall, filament, or cluster environment using the T-Web classification of the underlying matter distribution. Second, we construct a Delaunay triangulation of the galaxy distribution to summarise the local geometric structure with ten graph metrics for each galaxy. Third, we train a graph attention network (GAT) on each galaxy's graph metrics to predict its cosmic web environment. For galaxies with stellar mass $\mathrm{>10^9 M_{\odot}}$, our GAT+ model achieves an accuracy of $85\,\%$, outperforming graph-agnostic multilayer perceptrons and graph convolutional networks. Our results demonstrate that graph-based representations of galaxy positions provide a powerful and physically meaningful way to infer dark matter environments. We plan to apply this simulation-based graph modelling to investigate how the properties of observed galaxies from the Dark Energy Spectroscopic Instrument (DESI) survey are influenced by their dark matter environments.

astro-ph.GA↗

arXiv2025

Baryon fraction from the BAO amplitude: a consistent approach to parameterizing perturbation growth

Galaxy clustering constrains the baryon fraction Omega_b/Omega_m through the amplitude of baryon acoustic oscillations and the suppression of perturbations entering the horizon before recombination. This produces a different pre-recombination distribution of baryons and dark matter. After recombination, the gravitational potential responds to both components in proportion to their mass, allowing robust measurement of the baryon fraction. This is independent of new-physics scenarios altering the recombination background (e.g. Early Dark Energy). The accuracy of such measurements does, however, depend on how baryons and CDM are modeled in the power spectrum. Previous template-based splitting relied on approximate transfer functions that neglected part of information. We present a new method that embeds an extra parameter controlling the balance between baryons and dark matter in the growth terms of the perturbation equations in the CAMB Boltzmann solver. This approach captures the baryonic suppression of CDM prior to recombination, avoids inconsistencies, and yields a clean parametrization of the baryon fraction in the linear power spectrum, separating out the simple physics of growth due to the combined matter potential. We implement this framework in an analysis pipeline using Effective Field Theory of Large-Scale Structure with HOD-informed priors and validate it against noiseless LCDM and EDE cosmologies with DESI-like errors. The new scheme achieves comparable precision to previous splitting while reducing systematic biases, providing a more robust way to baryon-fraction measurements. In combination with BBN constraints on the baryon density and Alcock-Paczynski estimates of the matter density, these results strengthen the use of baryon fraction measurements to derive a Hubble constant from energy densities, with future DESI and Euclid data expected to deliver competitive constraints.

astro-ph.CO↗

arXiv2025

Effect of local environment on Ly$α$ line profile in DESI/ODIN LAEs

Lyman-Alpha Emitters (LAEs) are star-forming galaxies with significant Ly$α$ emission and are often used as tracers of large-scale structure at high redshift. We explore the relationship between the Ly$α$ line profile and environmental density with spectroscopy from the Dark Energy Spectroscopic Instrument (DESI) of LAEs selected with narrow-band photometry through the One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey. We use LAE surface density maps in the N419 (z $\sim$ 2.45) and N501 (z $\sim$ 3.12) narrow bands to probe the relationship between local environmental density and the Ly$α$ line profile. In both narrow bands, we stack the LAE spectra in bins of environmental density and inside and outside of protocluster regions. The N501 data shows $\sim$15% higher Ly$α$ line luminosity for galaxies in protoclusters, suggesting increased star formation in these regions. However, the line luminosity is not appreciably greater in protocluster galaxies in the N419 band, suggesting a potential redshift evolution of this effect. The shape of the line profile itself does not vary with environmental density, suggesting that line shape changes are caused by local effects independent of a galaxy's environment. These data indicate a potential relationship between LAE local environmental density, ionized gas distribution, and Ly$α$ line luminosity.

astro-ph.GA↗

arXiv2025

Galaxy-Multiplet Clustering from DESI DR2

We present an efficient estimator for higher-order galaxy clustering using small groups of nearby galaxies, or multiplets. Using the Luminous Red Galaxy (LRG) sample from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2, we identify galaxy multiplets as discrete objects and measure their cross-correlations with the general galaxy field. Our results show that the multiplets exhibit stronger clustering bias as they trace more massive dark matter halos than individual galaxies. When comparing the observed clustering statistics with the mock catalogs generated from the N-body simulation AbacusSummit, we find that the mocks underpredict multiplet clustering despite reproducing the galaxy two-point auto-correlation reasonably well. This discrepancy indicates that the standard Halo Occupation Distribution (HOD) model is insufficient to describe the properties of galaxy multiplets, revealing the greater constraining power of this higher-order statistic on galaxy-halo connection and the possibility that multiplets are specific to additional assembly bias. We demonstrate that incorporating secondary biases into the HOD model improves agreement with the observed multiplet statistics, specifically by allowing galaxies to preferentially occupy halos in denser environments. Our results highlight the potential of utilizing multiplet clustering, beyond traditional two-point correlation measurements, to break degeneracies in models describing the galaxy-dark matter connection.

astro-ph.CO↗

arXiv2025

Quasars acting as Strong Lenses Found in DESI DR1

Quasars acting as strong gravitational lenses offer a rare opportunity to probe the redshift evolution of scaling relations between supermassive black holes and their host galaxies, particularly the $M_{\mathrm{BH}}$--$M_{\mathrm{host}}$ relation. Using these powerful probes, the mass of the host galaxy can be precisely inferred from the Einstein radius $θ_{\mathrm{E}}$. Using 812{,}118 quasars from DESI DR1 ($0.03 \leq z \leq 1.8$), we searched for quasars lensing higher-redshift galaxies by identifying background emission-line features in their spectra. To detect these rare systems, we trained a convolutional neural network (CNN) on mock lenses constructed from real DESI spectra of quasars and emission-line galaxies (ELGs), achieving a high classification performance (AUC = 0.99). We also trained a regression network to estimate the redshift of the background ELG. Applying this pipeline, we identified seven high-quality (Grade~A) lens candidates, each exhibiting a strong [O\,\textsc{ii}] doublet at a higher redshift than the foreground quasar; four candidates additionally show H$β$ and [O\,\textsc{iii}] emission. These results significantly expand the sample of quasar lens candidates beyond the twelve identified and three confirmed in previous work, and demonstrate the potential for scalable, data-driven discovery of quasars as strong lenses in upcoming spectroscopic surveys.

astro-ph.GA↗

arXiv2025

AT2025ulz and S250818k: Leveraging DESI spectroscopy in the hunt for a kilonova associated with a sub-solar mass gravitational wave candidate

On August 18th, 2025, the LIGO--Virgo--KAGRA collaboration reported a sub-threshold gravitational wave candidate detection consistent with a sub-solar-mass neutron star merger, denoted S250818k. An optical transient, AT2025ulz, was discovered within the localization region. AT2025ulz initially appeared to meet the expected behavior of kilonova (KN) emission, the telltale signature of a binary neutron star merger. The transient subsequently rebrightened after $\sim$\,$5$ days and developed spectral features characteristic of a Type IIb supernova. In this work, we analyze the observations of the host galaxy of AT2025ulz obtained by the Dark Energy Spectroscopic Instrument (DESI). From the DESI spectrum, we obtain a secure redshift of $z = 0.084840 \pm 0.000006$, which places the transient within $2σ$ of the gravitational wave distance and results in an integral overlap between the gravitational wave alert and the transient location of $\log_{10}\mathcal{I} \approx 3.9-4.2$. Our analysis of the host galaxy's spectral energy distribution reveals a star-forming, dusty galaxy with stellar mass ${\sim} 10^{10}~M_\odot$, broadly consistent with the population of both short gamma-ray bursts and core-collapse supernova host galaxies. We also present our follow-up of DESI-selected candidate host galaxies using the Fraunhofer Telescope at the Wendelstein Observatory, and show the promise of DESI for associating or rejecting candidate electromagnetic counterparts to gravitational wave alerts. These results emphasize the value of DESI's extensive spectroscopic dataset in rapidly characterizing host galaxies, enabling spectroscopic host subtraction, and guiding targeted follow-up.

astro-ph.HE↗

arXiv2025

Enhancing Multiplet Alignment Measurements with Imaging

We demonstrate that measurements of the gravitational tidal field made with spectroscopic redshifts can be improved with information from imaging surveys. The average orientation of small groups of galaxies, or "multiplets" is correlated with large-scale structure and is used to measure the direction of tidal forces. Previously, multiplet intrinsic alignment has been measured in DESI using galaxies that have spectroscopic redshifts. The DESI Legacy Imaging catalog can be used to supplement multiplet catalogs. Our findings show that galaxy positions from the imaging catalog produce a measurement similar to the measurements made with only spectroscopic data. This demonstrates that imaging can improve our signal-to-noise ratio for multiplet alignment in DESI.

astro-ph.CO↗

arXiv2025

The dark matter content of Milky Way dwarf spheroidal galaxies: Draco, Sextans and Ursa Minor

The Milky Way Survey of the Dark Energy Spectroscopic Instrument (DESI) has so far observed three classical dwarf spheroidal galaxies (dSphs): Draco, Sextans and Ursa Minor. Based on the observed line-of-sight velocities and metallicities of their member stars, we apply the axisymmetric Jeans Anisotropic Multi-Gaussian Expansion modeling (JAM) approach to recover their inner dark matter distributions. In particular, both the traditional single-population Jeans model and the multiple population chemodynamical model are adopted. With the chemodynamical model, we divide member stars of each dSph into metal-rich and metal-poor populations. The metal-rich populations are more centrally concentrated and dynamically colder, featuring lower velocity dispersion profiles than the metal-poor populations. We find a diversity of the inner density slopes $γ$ of dark matter halos, with the best constraints by single-population or chemodynamical models consistent with each other. The inner density slopes are $0.71^{+0.34}_{-0.35}$, $0.26^{+0.22}_{-0.12}$ and $0.33^{+0.20}_{-0.16}$ for Draco, Sextans and Ursa Minor, respectively. We also present the measured astrophysical J and D factors of the three dSphs. Our results indicate that the study of the dark matter content of dSphs through stellar kinematics is still subject to uncertainties behind both the methodology and the observed data, through comparisons with previous measurements and data sets.

astro-ph.GA↗

arXiv2025

Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measurements

Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. We analyze baryon acoustic oscillation (BAO) measurements from DESI Data Release 1 (DR1) and Data Release 2 (DR2), combined with Type Ia supernovae and a cosmic microwave background (CMB) distance prior. With the larger statistical power and wider redshift coverage of DR2, the preference for dynamical dark energy does not diminish relative to DR1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the dark-energy equation of state $w(z)$ varies with redshift. BAO data alone yield modest constraints, but in combination with independent supernova compilations and the CMB prior they strengthen the evidence for dynamics. Bayesian model comparison shows moderate support for departures from $Λ$CDM when multiple degrees of freedom in $w(z)$ are allowed, corresponding to $\approx3σ$ tension with $Λ$CDM (and higher for some data sets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of approaches. Possible systematics remain under study; forthcoming DESI, \emph{Euclid}, and next-generation CMB data will provide decisive tests.

astro-ph.CO↗

arXiv2025

Methods for robustly measuring the minimum spanning tree and other field level statistics from galaxy surveys

Field level statistics, such as the minimum spanning tree (MST), have been shown to be a promising tool for parameter inference in cosmology. However, applications to real galaxy surveys are challenging, due to the presence of small scale systematic effects and non-trivial survey selection functions. Since many field level statistics are 'hard-wired', the common practice is to forward model survey systematic effects to synthetic galaxy catalogues. However, this can be computationally demanding and produces results that are a product of cosmology and systematic effects, making it difficult to directly compare results from different experiments. We introduce a method for inverting survey systematic effects through a Monte Carlo subsampling technique where galaxies are assigned probabilities based on their galaxy weight and survey selection functions. Small scale systematic effects are mitigated through the addition of a point-process smoothing technique called jittering. The inversion technique removes the requirement for a computational and labour intensive forward modelling pipeline for parameter inference. We demonstrate that jittering can mask small scale theoretical uncertainties and survey systematic effects like fibre collisions and we show that Monte Carlo subsampling can remove the effects of survey selection functions. We outline how to measure field level statistics from future surveys.

astro-ph.CO↗

arXiv2025

The Spectroscopic Stage-5 Experiment

The existence, properties, and dynamics of the dark sectors of our universe pose fundamental challenges to our current model of physics, and large-scale astronomical surveys may be our only hope to unravel these long-standing mysteries. In this white paper, we describe the science motivation, instrumentation, and survey plan for the next-generation spectroscopic observatory, the Stage-5 Spectroscopic Experiment (Spec-S5). Spec-S5 is a new all-sky spectroscopic instrument optimized to efficiently carry out cosmological surveys of unprecedented scale and precision. The baseline plan for Spec-S5 involves upgrading two existing 4-m telescopes to new 6-m wide-field facilities, each with a highly multiplexed spectroscopic instrument capable of simultaneously measuring the spectra of 13,000 astronomical targets. Spec-S5, which builds and improves on the hardware used for previous cosmology experiments, represents a cost-effective and rapid approach to realizing a more than 10$\times$ gain in spectroscopic capability compared to the current state-of-the-art represented by the Dark Energy Spectroscopic Instrument project (DESI). Spec-S5 will provide a critical scientific capability in the post-Rubin and post-DESI era for advancing cosmology, fundamental physics, and astrophysics in the 2030s.

astro-ph.CO↗

arXiv2025

Enhancing DESI DR1 Full-Shape analyses using HOD-informed priors

We present an analysis of DESI Data Release 1 (DR1) that incorporates Halo Occupation Distribution (HOD)-informed priors into Full-Shape (FS) modeling of the power spectrum based on cosmological perturbation theory (PT). By leveraging physical insights from the galaxy-halo connection, these HOD-informed priors on nuisance parameters substantially mitigate projection effects in extended cosmological models that allow for dynamical dark energy. The resulting credible intervals now encompass the posterior maximum from the baseline analysis using gaussian priors, eliminating a significant posterior shift observed in baseline studies. In the $Λ$CDM framework, a combined DESI DR1 FS information and constraints from the DESI DR1 baryon acoustic oscillations (BAO)-including Big Bang Nucleosynthesis (BBN) constraints and a weak prior on the scalar spectral index-yields $Ω_{\rm m} = 0.2994\pm 0.0090$ and $σ_8 = 0.836^{+0.024}_{-0.027}$, representing improvements of approximately 4% and 23% over the baseline analysis, respectively. For the $w_0w_a$CDM model, our results from various data combinations are highly consistent, with all configurations converging to a region with $w_0 > -1$ and $w_a < 0$. This convergence not only suggests intriguing hints of dynamical dark energy but also underscores the robustness of our HOD-informed prior approach in delivering reliable cosmological constraints.

astro-ph.CO↗

arXiv2025

Finding Pegasus: Enhancing Unsupervised Anomaly Detection in High-Dimensional Data using a Manifold-Based Approach

Unsupervised machine learning methods are well suited to searching for anomalies at scale but can struggle with the high-dimensional representation of many modern datasets, hence dimensionality reduction (DR) is often performed first. In this paper we analyse unsupervised anomaly detection (AD) from the perspective of the manifold created in DR. We present an idealised illustration, "Finding Pegasus", and a novel formal framework with which we categorise AD methods and their results into "on manifold" and "off manifold". We define these terms and show how they differ. We then use this insight to develop an approach of combining AD methods which significantly boosts AD recall without sacrificing precision in situations employing high DR. When tested on MNIST data, our approach of combining AD methods improves recall by as much as 16 percent compared with simply combining with the best standalone AD method (Isolation Forest), a result which shows great promise for its application to real-world data.

cs.LG↗

arXiv2025

Psi-GAN: A power-spectrum-informed generative adversarial network for the emulation of large-scale structure maps across cosmologies and redshifts

Simulations of the dark matter distribution throughout the Universe are essential in order to analyse data from cosmological surveys. $N$-body simulations are computationally expensive, and many cheaper alternatives (such as lognormal random fields) fail to reproduce accurate statistics of the smaller, non-linear scales. In this work, we present \textsc{Psi-GAN} (\textbf{P}ower-\textbf{s}pectrum-\textbf{i}nformed \textbf{G}enerative \textbf{A}dversarial \textbf{N}etwork), a machine learning model which takes a two-dimensional lognormal dark matter density field and transforms it into a more realistic field. We construct \textsc{Psi-GAN} so that it is continuously conditional, and can therefore generate realistic realisations of the dark matter density field across a range of cosmologies and redshifts in $z \in [0, 3]$. We train \textsc{Psi-GAN} as a generative adversarial network on $2\,000$ simulation boxes from the Quijote simulation suite. We use a novel critic architecture that utilises the power spectrum as the basis for discrimination between real and generated samples. \textsc{Psi-GAN} shows agreement with $N$-body simulations over a range of redshifts and cosmologies, consistently outperforming the lognormal approximation on all tests of non-linear structure, such as being able to reproduce both the power spectrum up to wavenumbers of $1~h~\mathrm{Mpc}^{-1}$, and the bispectra of target $N$-body simulations to within ${\sim}5$ per cent. Our improved ability to model non-linear structure should allow more robust constraints on cosmological parameters when used in techniques such as simulation-based inference.

astro-ph.CO↗

arXiv2024

Probing the major driver of stellar population properties over sub-galaxy scales with SDSS MaNGA IFU spectroscopy

Thanks to Integral Field Unit survey data it is possible to explore in detail the link between the formation of the stellar content in galaxies and the drivers of evolution. Traditionally, scaling relations have connected galaxy-wide parameters such as stellar mass (M$_s$), morphology or average velocity dispersion ($σ$) to the star formation histories (SFHs). We study a high quality sample of SDSS-MaNGA spectra to test the possibility that sub-galaxy ($\sim$2\,kpc) scales are dominant, instead of galaxy-wide parameters. We find a strong correlation between local velocity dispersion and key line strengths that depend on the SFHs, allowing us to make the ansatz that this indicator - that maps the local gravitational potential - is the major driver of star formation in galaxies, whereas larger scales play a role of a secondary nature. Galactocentric distance has a weaker correlation, suggesting that the observed radial gradients effectively reflect local variations of velocity dispersion. In our quest for a cause, instead of a correlation, we contrast $σ$ with local stellar mass, that appears less correlated with population properties. We conclude that the inherently higher uncertainty in M$_s$ may explain its lower correlation with respect to $σ$, but the extra uncertainty needed for $σ$ to have similar correlations as M$_s$ is rather high. Therefore we posit local velocity dispersion as the major driver of evolution, a result that should be reproduced by hydrodynamical models at the proper resolution.

astro-ph.GA↗

arXiv2024

Nature versus nurture in galaxy formation: the effect of environment on star formation with causal machine learning

Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as correlations have primarily been established rather than the underlying causality. We address this challenge by applying the causal inference framework. Specifically, we tackle the fundamental open question of whether galaxy formation and evolution depends more on nature (i.e., internal processes) or nurture (i.e., external processes), by estimating the causal effect of environment on star-formation rate in the IllustrisTNG simulations. To do so, we develop a comprehensive causal model and employ cutting-edge techniques from epidemiology to overcome the long-standing problem of disentangling nature and nurture. We find that the causal effect is negative and substantial, with environment suppressing the SFR by a maximal factor of $\sim100$. While the overall effect at $z=0$ is negative, in the early universe, environment is discovered to have a positive impact, boosting star formation by a factor of $\sim10$ at $z\sim1$ and by even greater amounts at higher redshifts. Furthermore, we show that: (i) nature also plays an important role, as ignoring it underestimates the causal effect in intermediate-density environments by a factor of $\sim2$, (ii) controlling for the stellar mass at a snapshot in time, as is common in the literature, is not only insufficient to disentangle nature and nurture but actually has an adverse effect, though (iii) stellar mass is an adequate proxy of the effects of nature. Finally, this work may prove a useful blueprint for extracting causal insights in other fields that deal with dynamical systems with closed feedback loops, such as the Earth's climate.

astro-ph.GA↗

arXiv2024

Constraints on compact objects from the Dark Energy Survey five-year supernova sample

Gravitational lensing magnification of Type Ia supernovae (SNe Ia) allows information to be obtained about the distribution of matter on small scales. In this paper, we derive limits on the fraction $α$ of the total matter density in compact objects (which comprise stars, stellar remnants, small stellar groupings and primordial black holes) of mass $M > 0.03 M_{\odot}$ over cosmological distances. Using 1,532 SNe Ia from the Dark Energy Survey Year 5 sample (DES-SN5YR) combined with a Bayesian prior for the absolute magnitude $M$, we obtain $α< 0.12$ at the 95\% confidence level after marginalisation over cosmological parameters, lensing due to large-scale structure, and intrinsic non-Gaussianity. Similar results are obtained using priors from the cosmic microwave background, baryon acoustic oscillations and galaxy weak lensing, indicating our results do not depend on the background cosmology. We argue our constraints are likely to be conservative (in the sense of the values we quote being higher than the truth), but discuss scenarios in which they could be weakened by systematics of the order of $Δα\sim 0.04$

astro-ph.CO↗