arXiv subjects

Georgios Valogiannis

Publications and source records attributed to Georgios Valogiannis.

At least 19 recordsLinked to original sources

arXiv2026

Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group

Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely available textbook at https://deeplearning4astro.com, curated from the NASA Cosmic Origins Artificial Intelligence and Machine Learning Science and Technology Interest Group (AI/ML STIG) lecture series. The book collects 23 chapters by 17 lecturers across six parts, moving from computational foundations and deep-learning architectures through generative modeling, simulation-based inference, reinforcement learning, and large-language-model agents to the practice of AI-laden science. Many include executable notebooks using astronomical data.

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arXiv2026

On the potential for inhomogeneities to mimic an evolving dark energy

In this work we explore the ability of inhomogeneities to result in an apparent dynamical evolution of dark energy. The idea that inhomogeneities may alter the expansion history of the Universe is not a new one. However, with the current excitement surrounding the inferred time-evolution of the equation of state of dark energy by the Dark Energy Spectroscopic Instrument (DESI), combined with Cosmic Microwave Background (CMB) and supernovae observations, it is worth revisiting. We use numerical relativity simulations of large-scale structure formation combined with nonlinear general-relativistic ray tracing to infer dark energy parameters for synthetic observers. We adopt a simplified set-up to roughly mimic the observational properties of the DESI plus supernovae and CMB constraints. In our small sample of 20 observers, we find one who infers parameters consistent with the DESI values at 2-$σ$ significance. While it is rare in our limited sample size, we show that it is at least possible for observers to infer significant non-LCDM parameters when their universe is well-described by a cosmological constant on average.

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arXiv2026

Large-Scale Structure Probes of the Post-Inflationary Axiverse

We study the cosmology of axions in the post-inflationary scenario, where random initial conditions and the ensuing string-domain-wall network generate an isocurvature power spectrum. Axions radiated from strings behave as warm, wave-like dark matter: when they constitute the full dark matter abundance, free streaming sets the strongest bounds on their mass. For subdominant fractions, despite being warm, they still lead to an overall enhancement of structure growth in the dominant component, seeded by the axion white-noise fluctuations. We search for this effect using the ultraviolet luminosity function (UVLF) of galaxies at $z=4$-$10$, probing $k\simeq0.5$-$10\,\mathrm{Mpc}^{-1}$. Combining the UVLF analysis with Lyman-$α$ and CMB data yields the leading cosmological limits on post-inflationary axion dark matter, sensitive to tiny fractions for $m_a\lesssim10^{-21}\,\mathrm{eV}$. As a byproduct, we obtain new constraints on generic white-noise power spectra from the UVLF. These results apply broadly to scenarios that generate similar isocurvature perturbations, linking early-universe field dynamics to high-redshift structure formation.

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arXiv2026

Probing Physics Beyond the Standard Model through Combined Analyses of Next-Generation Type Ia Supernova, CMB, and BAO Surveys

Observations of Type Ia supernovae (\sne), which probe the late Universe, together with baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB), which probe the intermediate and early epochs, provide complementary constraints on the expansion history of the Universe. In this work, we forecast constraints on dark energy and other extensions to the standard cosmological model by combining the SNIa sample expected from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), data from current and forthcoming CMB surveys, and BAO measurements from the Dark Energy Spectroscopic Instrument (DESI). For the CMB, we use temperature, polarization, and lensing power spectra ($TT/EE/TE/ϕϕ$) from South Pole Telescope, the planned Advanced Simons Observatory, and a CMB-S4-like experiment. We derive constraints on $Λ{\rm CDM}$ and its extensions involving the dark energy equation of state parameters $(w_{0}, w_{a})$ and the sum of neutrino masses $\sum m_ν$, using a Markov Chain Monte Carlo (MCMC) sampling framework. We find that the LSST Year-3 SNIa sample can improve upon the DES Year-5 dark energy constraints by a factor of $\times2-\times2.5$, with the gains driven primarily by the significantly higher SNIa density in the LSST sample. Similarly, DESI-DR3 shows up to a $\times1.8$ improvement on dark energy parameters over DR2, driven largely by the substantial increase in low-redshift sample. Combining CMB with LSST-Y3-SNIa and DESI-DR3-BAO yields $σ(w_{0}) = 0.028$ and $σ(w_{a}) = 0.11$ for $w_{0} w_{a} {\rm CDM}$ cosmology with the results being largely independent of the CMB dataset. The constraints weaken by 10%-30% when freeing $\sum m_ν$ and spatial curvature. Moreover, the joint analysis of the three datasets can enable a $2-3σ$ detection of $\sum m_ν$.

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arXiv2026

Setting SAIL: Leveraging Scientist-AI-Loops for Rigorous Visualization Tools

Scientists across all disciplines share a common challenge: the divide between their theoretical knowledge and the specialized skills and time needed to build interactive tools to communicate this expertise. While large language models (LLMs) offer unparalleled acceleration in code generation, they frequently prioritize functional syntax over scientific accuracy, risking visually convincing but scientifically invalid results. This work advocates the Scientist-AI-Loop (SAIL), a framework designed to harness this speed without compromising rigor. By separating domain logic from code syntax, SAIL enables researchers to maintain strict oversight of scientific concepts and constraints while delegating code implementation to AI. We illustrate this approach through two open-source, browser-based astrophysics tools: an interactive gravitational lensing visualization and a large-scale structure formation sandbox, both publicly available. Our methodology condensed development to mere days while maintaining scientific integrity. We specifically address failure modes where AI-generated code neglects phenomenological boundaries or scientific validity. While cautioning that research-grade code requires stringent protocols, we demonstrate through two examples that SAIL provides an effective code generation workflow for outreach, teaching, professional presentations, and early-stage research prototyping. This framework contributes to a foundation for the further development of AI-assisted scientific software.

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arXiv2026

Constraining Power of Wavelet vs. Power Spectrum Statistics for CMB Lensing and Weak Lensing with Learned Binning

We present forecasts for constraints on the matter density ($Ω_m$) and the amplitude of matter density fluctuations at 8h$^{-1}$Mpc ($σ_8$) from CMB lensing convergence maps and galaxy weak lensing convergence maps. For CMB lensing convergence auto statistics, we compare the angular power spectra ($C_\ell$'s) to the wavelet scattering transform (WST) coefficients. For CMB lensing convergence $\times$ galaxy weak lensing convergence statistics, we compare the cross angular power spectra to wavelet phase harmonics (WPH). This work also serves as the first application of WST and WPH to these probes. For CMB lensing convergence, we find that WST and $C_\ell$'s yield similar constraints in forecasts for all surveys considered in this work. When CMB lensing convergence is crossed with galaxy weak lensing convergence projected from $\textit{Euclid}$ Data Release 2 (DR2), we find that WPH outperforms cross-$C_\ell$'s by factors between $2.2$ and $3.4$ for individual parameter constraints. To compare these different summary statistics, we develop a novel learned binning approach. This method compresses summary statistics while maintaining interpretability. We find this leads to improved constraints compared to more naive binning schemes for our wavelet-based statistics, but not for $C_\ell$'s. By learning the binning and measuring constraints on distinct data sets, our method is robust to overfitting by construction.

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arXiv2025

Cosmological constraints from the Minkowski functionals of the BOSS CMASS galaxy sample

For the first time, we develop a simulation-based model for the Minkowski functionals (MFs) of large-scale structure, which allows us to extract the full information available from the MFs (including both the Gaussian and non-Gaussian part), and apply it to the BOSS DR12 CMASS galaxy sample. Our model is based on high-fidelity mock galaxy catalogs constructed from the \textsc{Abacus}\textsc{Summit} simulations using the halo occupation distribution (HOD) framework, which include the redshift-space distortions and Alcock-Paczynski distortions, incorporate survey realism, including survey geometry and veto masks, and account for angular plus radial selection effects. The cosmological and HOD parameter dependence of the MFs is captured with a neural network emulator trained from the galaxy mocks with various cosmological and HOD parameters. To benchmark the constraining power of the MFs, we also train an emulator for the galaxy 2-point correlation function (2PCF) using the same pipeline. Having validated our approach through successful parameter recovery tests on both internal and external mocks, including non-HOD forward models of the halo-galaxy connection, we apply our forward model to analyze the CMASS data in the redshift range $0.45<z<0.58$. We find the MFs provide stronger constraints on the cosmological parameters than the 2PCF. The combination of the two gives $ω_{\rm cdm}=0.1172^{+0.0020}_{-0.0023}$, $σ_8=0.783\pm 0.026$, and $n_s=0.966^{+0.019}_{-0.015}$, which are tighter by a factor of 2.0, 1.9, and 1.6 than the 2PCF alone. The derived constraint $fσ_8=0.453 \pm 0.016$ is also improved by a factor of 1.9, compared to the 2PCF, and agrees well with Planck 2018 predictions and other results from a series of studies in the literature.

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arXiv2025

Precise Cosmological Constraints from BOSS Galaxy Clustering with a Simulation-Based Emulator of the Wavelet Scattering Transform

We perform a reanalysis of the BOSS CMASS DR12 galaxy dataset using a simulation-based emulator for the Wavelet Scattering Transform (WST) coefficients. Moving beyond our previous works, which laid the foundation for the first galaxy clustering application of this estimator, we construct a neural net-based emulator for the cosmological dependence of the WST coefficients and the 2-point correlation function multipoles, trained from the state-of-the-art suite of \textsc{AbacusSummit} simulations combined with a flexible Halo Occupation Distribution (HOD) galaxy model. In order to confirm the accuracy of our pipeline, we subject it to a series of thorough internal and external mock parameter recovery tests, before applying it to reanalyze the CMASS observations in the redshift range $0.46<z<0.57$. We find that a joint WST + 2-point correlation function likelihood analysis allows us to obtain marginalized 1$σ$ errors on the $Λ$CDM parameters that are tighter by a factor of $2.5-6$, compared to the 2-point correlation function, and by a factor of $1.4-2.5$ compared to the WST-only results. This corresponds to a competitive $0.9\%$, $2.3\%$ and $1\%$ level of determination for parameters $ω_c$, $σ_8$ $\&$ $n_s$, respectively, and also to a $0.7\%$ $\&$ $2.5 \%$ constraint on derived parameters h and $f(z)σ_8(z)$, in agreement with the \textit{Planck} 2018 results. Our results reaffirm the constraining power of the WST and highlight the exciting prospect of employing higher-order statistics in order to fully exploit the power of upcoming Stage-IV spectroscopic observations.

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arXiv2025

Probing massive neutrinos and modified gravity with redshift-space morphologies and anisotropies of large-scale structure

Strong degeneracy exists between some modified gravity (MG) models and massive neutrinos because the enhanced structure growth produced by modified gravity can be suppressed due to the free-streaming massive neutrinos. Previous works showed this degeneracy can be broken with non-Gaussian or velocity information. Therefore in this work, we focus on the large-scale structure (LSS) in redshift space and investigate for the first time the possibility of using the non-Gaussian information and velocity information captured by the 3D scalar Minkowski functionals (MFs) and the 3D Minkowski tensors (MTs) to break this degeneracy. Based on the Quijote and Quijote-MG simulations, we find the imprints on redshift space LSS left by the Hu-Sawicki $f(R)$ gravity can be discriminated from those left by massive neutrinos with these statistics. With the Fisher information formalism, we first show how the MTs extract information with their perpendicular and parallel elements for both low- and high-density regions; then we compare constraints from the power spectrum monopole and MFs in real space with those in redshift space, and investigate how the constraining power is further improved with anisotropies captured by the quadrupole and hexadecapole of the power spectrum and the MTs; finally, we combine the power spectrum multipoles with MFs plus MTs and find the constraints from the power spectrum multipoles on $Ω_{\mathrm{m}}, h, σ_8$, $M_ν$, and $f_{R_0}$ can be improved, because they are complemented with non-Gaussian information, by a factor of 3.4, 3.0, 3.3, 3.3, and 1.9 on small scales ($k_{\rm{max}}=0.5~h\rm{Mpc}^{-1},\ R_G=5~h^{-1}\rm{Mpc}$), and 2.8, 2.2, 3.4, 3.4, and 1.5 on large scales ($k_{\rm{max}}=0.25~h\rm{Mpc}^{-1},\ R_G=10~h^{-1}\rm{Mpc}$).

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arXiv2024

Towards unveiling the large-scale nature of gravity with the wavelet scattering transform

We present the first application of the Wavelet Scattering Transform (WST) in order to constrain the nature of gravity using the three-dimensional (3D) large-scale structure of the universe. Utilizing the Quijote-MG N-body simulations, we can reliably model the 3D matter overdensity field for the f(R) Hu-Sawicki modified gravity (MG) model down to $k_{\rm max}=0.5$ h/Mpc. Combining these simulations with the Quijote $ν$CDM collection, we then conduct a Fisher forecast of the marginalized constraints obtained on gravity using the WST coefficients and the matter power spectrum at redshift z=0. Our results demonstrate that the WST substantially improves upon the 1$σ$ error obtained on the parameter that captures deviations from standard General Relativity (GR), yielding a tenfold improvement compared to the corresponding matter power spectrum result. At the same time, the WST also enhances the precision on the $Λ$CDM parameters and the sum of neutrino masses, by factors of 1.2-3.4 compared to the matter power spectrum, respectively. Despite the overall reduction in the WST performance when we focus on larger scales, it still provides a relatively $4.5\times$ tighter 1$σ$ error for the MG parameter at $k_{\rm max}=0.2$ h/Mpc, highlighting its great sensitivity to the underlying gravity theory. This first proof-of-concept study reaffirms the constraining properties of the WST technique and paves the way for exciting future applications in order to perform precise large-scale tests of gravity with the new generation of cutting-edge cosmological data.

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arXiv2024

Scrutinizing the Primordial Black Holes Interpretation of PTA Gravitational Waves and JWST Early Galaxies

Recent observations have granted to us two unique insights into the early universe: the presence of a low-frequency stochastic gravitational wave background detected by the NANOGrav and Pulsar Timing Array (PTA) experiments and the emergence of unusually massive galaxy candidates at high redshifts reported by the James Webb Space Telescope (JWST). In this letter, we consider the possibility that both observations have a common origin, namely primordial black holes (PBHs) in the mass range between $10^{6}~M_{\odot}$ and $10^{13}~M_{\odot}$. While superheavy PBHs act as seeds for accelerated galaxy formation capable of explaining the JWST extreme galaxies, they can also form binary mergers that source gravitational waves which can be potentially identified as the PTA signal. The analysis is performed taking into account the constraints on the relevant region of the PBH parameter space including the novel bound imposed by the Ultraviolet Luminosity Function of galaxies observed by the Hubble Space Telescope. We conclude that PTA's and JWST's interpretations in terms of PBH binary mergers and Poissonian gas of PBHs, respectively, are strongly excluded.

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arXiv2023

SUNBIRD: A simulation-based model for full-shape density-split clustering

Combining galaxy clustering information from regions of different environmental densities can help break cosmological parameter degeneracies and access non-Gaussian information from the density field that is not readily captured by the standard two-point correlation function (2PCF) analyses. However, modelling these density-dependent statistics down to the non-linear regime has so far remained challenging. We present a simulation-based model that is able to capture the cosmological dependence of the full shape of the density-split clustering (DSC) statistics down to intra-halo scales. Our models are based on neural-network emulators that are trained on high-fidelity mock galaxy catalogues within an extended-$Λ$CDM framework, incorporating the effects of redshift-space, Alcock-Paczynski distortions and models of the halo-galaxy connection. Our models reach sub-percent level accuracy down to $1\,h^{-1}{\rm Mpc}$ and are robust against different choices of galaxy-halo connection modelling. When combined with the galaxy 2PCF, DSC can tighten the constraints on $ω_{\rm cdm}$, $σ_8$, and $n_s$ by factors of 2.9, 1.9, and 2.1, respectively, compared to a 2PCF-only analysis. DSC additionally puts strong constraints on environment-based assembly bias parameters. Our code is made publicly available on Github.

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arXiv2023

Cosmological constraints from density-split clustering in the BOSS CMASS galaxy sample

We present a clustering analysis of the BOSS DR12 CMASS galaxy sample, combining measurements of the galaxy two-point correlation function and density-split clustering down to a scale of $1\,h^{-1}{\rm Mpc}$. Our theoretical framework is based on emulators trained on high-fidelity mock galaxy catalogues that forward model the cosmological dependence of the clustering statistics within an extended-$Λ$CDM framework, including redshift-space and Alcock-Paczynski distortions. Our base-$Λ$CDM analysis finds $ω_{\rm cdm} = 0.1201\pm 0.0022$, $σ_8 = 0.792\pm 0.034$, and $n_s = 0.970\pm 0.018$, corresponding to $fσ_8 = 0.462\pm 0.020$ at $z \approx 0.525$, which is in agreement with Planck 2018 predictions and various clustering studies in the literature. We test single-parameter extensions to base-$Λ$CDM, varying the running of the spectral index, the dark energy equation of state, and the density of massless relic neutrinos, finding no compelling evidence for deviations from the base model. We model the galaxy-halo connection using a halo occupation distribution framework, finding signatures of environment-based assembly bias in the data. We validate our pipeline against mock catalogues that match the clustering and selection properties of CMASS, showing that we can recover unbiased cosmological constraints even with a volume 84 times larger than the one used in this study.

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arXiv2023

$\texttt{Hi-COLA}$: Fast, approximate simulations of structure formation in Horndeski gravity

We introduce $\texttt{Hi-COLA}$, a code designed to run fast, approximate $\textit{N}$-body simulations of non-linear structure formation in reduced Horndeski gravity. Given an input Lagrangian, $\texttt{Hi-COLA}$ dynamically constructs the appropriate field equations and consistently solves for the cosmological background, linear growth, and screened fifth force of that theory. Hence $\texttt{Hi-COLA}$ is a general, adaptable, and useful tool that allows the mildly non-linear regime of many Horndeski theories to be investigated for the first time, at low computational cost. In this work, we first describe the screening approximations and simulation setup of $\texttt{Hi-COLA}$ for theories with Vainshtein screening. We validate the code against traditional $\textit{N}$-body simulations for cubic Galileon gravity, finding $2.5\%$ agreement up to $k_{\rm max}=1.2~h/{\rm Mpc}$. To demonstrate the flexibility of $\texttt{Hi-COLA}$, we additionally run the first simulations of an extended shift-symmetric gravity theory. We use the consistency and modularity of $\texttt{Hi-COLA}$ to dissect how the modified background, linear growth, and screened fifth force all contribute to departures from $Λ$CDM in the non-linear matter power spectrum. $\texttt{Hi-COLA}$ can be found at https://github.com/Hi-COLACode/Hi-COLA .

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arXiv2022

Going Beyond the Galaxy Power Spectrum: an Analysis of BOSS Data with Wavelet Scattering Transforms

We perform the first application of the wavelet scattering transform (WST) to actual galaxy observations, through a WST analysis of the BOSS DR12 CMASS dataset. We included the effects of redshift-space anisotropy, non-trivial survey geometry, systematic weights, and the Alcock-Paczynski distortion effect, following the commonly adopted steps for the power spectrum analysis. In order to capture the cosmological dependence of the WST, we use galaxy mocks obtained from the state-of-the-art ABACUSSUMMIT simulations, tuned to match the anisotropic correlation function of the BOSS CMASS sample in the redshift range $0.46<z<0.60$. Using our model for the WST coefficients, as well as for the first 2 multipoles of the galaxy power spectrum, that we use as reference, we perform a likelihood analysis of the CMASS data. We obtain the posterior probability distributions of 4 cosmological parameters, $\{ω_b,ω_c,n_s,σ_8\}$, as well as the Hubble constant, derived from a fixed value of the angular size of the sound horizon at last scattering measured by the Planck satellite, all of which are marginalized over the 7 nuisance parameters of the Halo Occupation Distribution model. The WST is found to deliver a substantial improvement in the values of the predicted $1σ$ errors compared to the regular power spectrum, which are tighter by a factor of $3-5$ in the case of flat and uninformative priors and by a factor of $3-8$, when a Big Bang Nucleosynthesis prior is applied on the value of $ω_b$. Our results are investigative and subject to certain approximations, which we discuss in the text.

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arXiv2022

Galaxy Cluster Statistics in Modified Gravity Cosmologies

If the accelerated expansion of the universe is due to a modification of general relativity at late times, it is likely that the growth of structure on large scales would also display deviations from the standard cosmology. We investigate the statistics of the distribution of galaxy cluster-sized halos as a probe of gravity. We analyze the output of several matched N-body simulations with the same initial conditions and expansion histories but using both DGP and f (R) gravity with various parameters. From each simulation we extract the cluster mass function, power spectrum, and mean pairwise velocity at redshifts 1, 0.3, and 0. All three statistics display systematic differences between gravity theories. The mean pairwise velocity provides an important consistency test for any posited departure from general relativity suggested by measurements of the power spectrum and cluster mass function. Upcoming microwave background experiments, including Simons Observatory and CMB- S4, will detect tens of thousands of galaxy clusters via the thermal Sunyaev-Zeldovich effect, probe their masses with lensing of the microwave background, and potentially measure velocities using the transverse lensing effect or the kinematic Sunyaev-Zeldovich effect. These cluster measurements promise to be a substantial probe of modified gravity.

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arXiv2022

Towards an Optimal Estimation of Cosmological Parameters with the Wavelet Scattering Transform

Optimal extraction of the non-Gaussian information encoded in the Large-Scale Structure (LSS) of the universe lies at the forefront of modern precision cosmology. We propose achieving this task through the use of the Wavelet Scattering Transform (WST), which subjects an input field to a layer of non-linear transformations that are sensitive to non-Gaussianity in spatial density distributions through a generated set of WST coefficients. In order to assess its applicability in the context of LSS surveys, we apply the WST on the 3D overdensity field obtained by the Quijote simulations, out of which we extract the Fisher information in 6 cosmological parameters. It is subsequently found to deliver a large improvement in the marginalized errors on all parameters, ranging between $1.2-4\times$ tighter than the corresponding ones obtained from the regular 3D cold dark matter + baryon power spectrum, as well as a $50 \%$ improvement over the neutrino mass constraint given by the marked power spectrum. Through this first application on 3D cosmological fields, we demonstrate the great promise held by this novel statistic and set the stage for its future application to actual galaxy observations.

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arXiv2022

Constraints on $f(R)$ and nDGP Modified Gravity Model Parameters with Cluster Abundances and Galaxy Clustering

We present forecasted cosmological constraints from combined measurements of galaxy cluster abundances from the Simons Observatory and galaxy clustering from a DESI-like experiment on two well-studied modified gravity models, the chameleon-screened $f(R)$ Hu-Sawicki model and the nDGP braneworld Vainshtein model. A Fisher analysis is conducted using $σ_8$ constraints derived from thermal Sunyaev-Zel'dovich (tSZ) selected galaxy clusters, as well as linear and mildly non-linear redshift-space 2-point galaxy correlation functions. We find that the cluster abundances drive the constraints on the nDGP model while $f(R)$ constraints are led by galaxy clustering. The two tracers of the cosmological gravitational field are found to be complementary, and their combination significantly improves constraints on the $f(R)$ in particular in comparison to each individual tracer alone. For a fiducial model of $f(R)$ with $\text{log}_{10}(f_{R0})=-6$ and $n=1$ we find combined constraints of $σ(\text{log}_{10}(f_{R0}))=0.48$ and $σ(n)=2.3$, while for the nDGP model with $n_{\text{nDGP}}=1$ we find $σ(n_{\text{nDGP}})=0.087$. Around a fiducial General Relativity (GR) model, we find a $95\%$ confidence upper limit on $f(R)$ of $f_{R0}\leq5.68\times 10^{-7}$. Our results present the exciting potential to utilize upcoming galaxy and CMB survey data available in the near future to discern and/or constrain cosmic deviations from GR.

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