arXiv subjects

Anik Halder

Publications and source records attributed to Anik Halder.

16 recordsLinked to original sources

arXiv2026

pop-cosmos: Galaxy size evolution across structural and star-formation classifications in COSMOS-Web

Galaxy sizes are correlated with stellar mass and redshift, as characterised by size scaling relations. The inferred forms of these scaling relations are sensitive to how galaxies are classified -- either by their star formation activity (e.g. specific star-formation rate, sSFR) or by their morphology markers (e.g. bulge-to-total ratio, S\'{e}rsic index). We combine stellar mass and sSFR estimates from pop-cosmos (a generative model trained on COSMOS2020 Spitzer IRAC $\textit{Ch.1} <26$) with size and morphology measurements from COSMOS-Web, obtaining $99,369$ galaxies. By investigating the size-mass and the size-redshift relations, we show that: (i) the sSFR/morphology splits give quantitatively different slopes, intercepts, and intrinsic scatter behaviour; (ii) intrinsic scatter depends on structural morphology but not on sSFR, which constrains the galaxy-halo connection; (iii) the quiescent and bulge-dominated size-mass relations both show double-power law breaks, but at different pivot masses, indicating that quenching and structural transformation occur on different time-scales; (iv) the morphology-dependent trends are only recoverable from space-based imaging. Further, the quiescent pivot mass $M_{\ast} \sim 10^{10.7}~\mathrm{M}_{\odot}$ coincides with the mass scale at which AGN (infrared torus) bolometric luminosity fraction peaks in transitioning galaxies, while the bulge-dominated pivot mass $M_{\ast} \sim 10^{11.1}~\mathrm{M}_{\odot}$ coincides with the halo mass at which AGN-driven baryonic redistribution peaks, tracing the interval over which AGN feedback ramps from quenching onset to structural transformation.

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arXiv2026

pop-cosmos: Disentangling galaxy properties from observables using data-driven approaches

The physical processes that shape a galaxy's spectrum are strongly degenerate in observations, obscuring which processes act independently. Leveraging the pop-cosmos generative galaxy population model, we investigate how many independent degrees of freedom the rest-frame optical SED contains. We use a $\beta$-variational autoencoder (VAE) to compress a 16-parameter stellar population synthesis (SPS) description into a disentangled latent representation interpreted through mutual information (MI). We find that five independent dimensions suffice, corresponding to stellar mass, recent star formation, dust, and two degrees of freedom in the ionization state of the gas. Stellar metallicity and stellar age are not among these primary drivers; their spectral effects are distributed across the others rather than independently encoded. By tying each dimension to specific spectral features, this decomposition breaks the star-formation--dust--metallicity degeneracies that limit broadband photometry, and recovers the physical conditions of the gas in typical star-forming galaxies more cleanly than the line-ratio diagnostics in standard use.

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arXiv2026

Thermostats, Not Engines: A New Picture of Halo Gas Regulation

We propose that black hole feedback regulates gas in massive halos by establishing an entropy ceiling; the resulting buoyant gas migrates to the virial radius with no additional energy input required. The FLAMINGO simulations support this picture: at the virial radius, outflow entropy is mass-independent for isotropic thermal feedback but depends on the solid angle of directly heated gas for jet feedback. Above a critical halo mass $M_\rm{crit} \approx 10^{13.5\text{--}14}\, M_\odot$, virial shocks overwhelm the ceiling, predicting rejuvenation of star formation in the most massive galaxies, supported by new low-redshift evidence from star formation rates and morphologies.

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arXiv2026

pop-cosmos: Forward modeling KiDS-1000 redshift distributions using realistic galaxy populations

The accuracy of the cosmological constraints from Stage~IV galaxy surveys will be limited by how well the galaxy redshift distributions can be inferred. We have addressed this challenging problem for the Kilo-Degree Survey (KiDS) cosmic shear sample by developing a forward-modeling framework with two main ingredients: (1) the \texttt{pop-cosmos} generative model for the evolving galaxy population, calibrated on \textit{Spitzer} IRAC $\textit{Ch.\,1}<26$ galaxies from COSMOS2020; and (2) a data model for noise and selection, machine-learned from the SURFS-based KiDS-Legacy-Like Simulations (SKiLLS). Applying KiDS tomographic binning to our synthetic photometric data, we infer redshift distributions in each of five bins directly from the population and data models, bypassing the need for spectroscopic reweighting. Keeping the data model fixed, we compare results using two different galaxy population models: \texttt{pop-cosmos}; and \texttt{shark}, the semi-analytic galaxy formation model used in SKiLLS. In the first ($0.1<z<0.3$) and last ($0.9<z<1.2$) tomographic bins we find systematic differences in the mean redshifts of $\Delta z\sim0.05$-$0.1$, comparable to the reported uncertainties from spectroscopic reweighting methods. This work paves the way for accurate redshift distribution calibration for Stage~IV surveys directly through forward modeling, thus providing an independent cross-check on spectroscopic-based calibrations which avoids their selection biases and incompleteness. We will use the \texttt{pop-cosmos} redshift distributions in an upcoming full KiDS cosmology reanalysis.

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arXiv2026

pop-cosmos: Redshifts and physical properties of KiDS-1000 galaxies

Principled Bayesian inference of galaxy properties has not previously been performed for wide-area weak lensing surveys with millions of sources. We address this gap by applying the pop-cosmos generative model to perform spectral energy distribution (SED) fitting for 4 million KiDS-1000 galaxies. Calibrated on deep COSMOS2020 photometric data, pop-cosmos specifies a physically-motivated prior over the galaxy population up to $z \simeq 6$ in stellar population synthesis (SPS) parameter space. Using the Speculator SPS emulator with GPU-accelerated MCMC sampling, we perform full posterior inference at 8.2 GPU seconds per galaxy, obtaining joint constraints on galaxy redshifts and physical properties. We validate photometric redshifts against $\sim\!185,\!000$ KiDS galaxies cross-matched to DESI DR1 spectroscopic samples, achieving low bias ($2\times10^{-3}$), scatter ($\sigma_{\mathrm{MAD}}=0.03$), and outlier fraction (3.2%) for the Bright Galaxy Survey, with comparable performance (bias $3\times10^{-2}$, $\sigma_{\mathrm{MAD}}=0.05$, 1.0% outliers) for luminous red galaxies (LRGs). Within the LRG sample, we identify massive, dusty, star-forming contaminants at $z \simeq 0.4$ satisfying standard colour selections for quenched populations. We infer trends in stellar mass, star formation, metallicity, and dust across five tomographic redshift bins consistent with established scaling relations. Using specific star formation rate constraints, we identify $\sim$7% of KiDS-1000 galaxies as quenched, versus 37% implied by conservative colour cuts. This enables the construction of weak lensing samples defined by physical properties while mitigating intrinsic alignment systematics and preserving statistical power. Our analysis validates pop-cosmos out-of-sample, establishing it as a scalable approach for galaxy evolution and cosmological analyses with photometric surveys.

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arXiv2025

$\texttt{SBi3PCF:}$ Simulation-based inference with the integrated 3PCF

We present $\texttt{SBi3PCF}$, a simulation-based inference (SBI) framework for analysing a higher-order weak lensing statistic, the integrated 3-point correlation function (i3PCF). Our approach forward-models the cosmic shear field using the $\texttt{CosmoGridV1}$ suite of N-body simulations, including a comprehensive set of systematic effects such as intrinsic alignment, baryonic feedback, photometric redshift uncertainty, shear calibration bias, and shape noise. Using this, we have produced a set of DES Y3-like synthetic measurements for 2-point shear correlation functions $\xi_{\pm}$ (2PCFs) and i3PCFs $\zeta_{\pm}$ across 6 cosmological and 11 systematic parameters. Having validated these measurements against theoretical predictions and thoroughly examined for potential systematic biases, we have found that the impact of source galaxy clustering and reduced shear on the i3PCF is negligible for Stage-III surveys. Furthermore, we have tested the Gaussianity assumption for the likelihood of our data vector and found that while the sampling distribution of the 2PCF can be well approximated by a Gaussian function, the likelihood of the combined 2PCF + i3PCF data vector including filter sizes of $90'$ and larger can deviate from this assumption. Our SBI pipeline employs masked autoregressive flows to perform neural likelihood estimation and is validated to give statistically accurate posterior estimates. On mock data, we find that including the i3PCF yields a substantial $63.8\%$ median improvement in the figure of merit for $\Omega_m - \sigma_8 - w_0$. These findings are consistent with previous works on the i3PCF and demonstrate that our SBI framework can achieve the accuracy and realism needed to analyse the i3PCF in wide-area weak lensing surveys.

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arXiv2025

Bye binormal: analysing the joint PDF of galaxy density and weak lensing convergence

At any given scale, 3$\times$2-point statistics extract only three numbers from the joint distribution of the cosmic matter density and galaxy density fluctuations: their variances and their covariance. It is well known that the full shape of the PDF of those fluctuations contains significantly more information than can be accessed through these three numbers. But the study of the PDF of cosmic density fluctuations in real observational data is still in its infancy. Here we present \verb|CosMomentum|, a public software toolkit for calculating theoretical predictions for the full shape of the joint distribution of a line-of-sight projected tracer density and the gravitational lensing convergence. We demonstrate that an analysis of this full shape of the PDF can indeed disentangle complicated tracer bias and stochasticity relations from signatures of cosmic structure growth. Our paper also provides back-drop for an upcoming follow-up study, which prepares PDF analyses for application to observational data by incorporating the impact of realistic weak lensing systematics.

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arXiv2025

pop-cosmos: Insights from generative modeling of a deep, infrared-selected galaxy population

We present an extension of the pop-cosmos model for the evolving galaxy population up to redshift $z\sim6$. The model is trained on distributions of observed colors and magnitudes, from 26-band photometry of $\sim420,000$ galaxies in the COSMOS2020 catalog with Spitzer IRAC $\textit{Ch. 1}<26$. The generative model includes a flexible distribution over 16 stellar population synthesis (SPS) parameters, and a depth-dependent photometric uncertainty model, both represented using score-based diffusion models. We use the trained model to predict scaling relationships for the galaxy population, such as the stellar mass function, star-forming main sequence, and gas-phase and stellar metallicity vs. mass relations, demonstrating reasonable-to-excellent agreement with previously published results. We explore the connection between mid-infrared emission from active galactic nuclei (AGN) and star-formation rate, finding high AGN activity for galaxies above the star-forming main sequence at $1\lesssim z\lesssim 2$. Using the trained population model as a prior distribution, we perform inference of the redshifts and SPS parameters for 429,669 COSMOS2020 galaxies, including 39,588 with publicly available spectroscopic redshifts. The resulting redshift estimates exhibit minimal bias ($\text{median}[\Delta_z]=-8\times10^{-4}$), scatter ($\sigma_\text{MAD}=0.0132$), and outlier fraction ($6.19\%$) for the full $0<z<6$ spectroscopic compilation. These results establish that pop-cosmos can achieve the accuracy and realism needed to forward-model modern wide--deep surveys for Stage IV cosmology. We publicly release pop-cosmos software, mock galaxy catalogs, and COSMOS2020 redshift and SPS parameter posteriors.

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arXiv2025

Cosmological feedback from a halo assembly perspective

The impact of feedback from galaxy formation on cosmological probes is typically quantified in terms of the suppression of the matter power spectrum in hydrodynamical compared to gravity-only simulations. In this paper, we instead study how baryonic feedback impacts halo assembly histories and thereby imprints on cosmological observables. We investigate the sensitivity of the thermal Sunyaev-Zel'dovich effect (tSZ) power spectrum, X-ray number counts, weak lensing and kinetic Sunyaev-Zel'dovich (kSZ) stacked profiles to halo populations as a function of mass and redshift. We then study the imprint of different feedback implementations in the FLAMINGO suite of cosmological simulations on the assembly histories of these halo populations, as a function of radial scale. We find that kSZ profiles target lower-mass halos ($M_{200\mathrm{m}}\sim 10^{13.1}~\mathrm{M}_\odot$) compared to all other probes considered ($M_{200\mathrm{m}}\sim 10^{15}~\mathrm{M}_\odot$). Feedback is inefficient in high-mass clusters with $\sim 10^{15} \, \mathrm{M}_\odot$ at $z=0$, but was more efficient at earlier times in the same population, with a $\sim 5$-$10\%$ effect on mass at $2 2$). These findings are tied together by noting that, regardless of redshift, feedback most efficiently redistributes baryons when halos reach a mass of $M_{\rm 200m} \simeq {10^{12.8}}\,\mathrm{M}_{\odot}$ and ceases to have any significant effect by the time $M_{\rm 200m} \simeq {10^{15}}\,\mathrm{M}_{\odot}$. We put forward strategies for minimizing sensitivity of lensing analyses to baryonic feedback, and for exploring baryonic resolutions to the unexpectedly low tSZ power in cosmic microwave background observations.

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arXiv2024

C3NN: Cosmological Correlator Convolutional Neural Network -- an interpretable machine learning tool for cosmological analyses

Modern cosmological research in large scale structure has witnessed an increasing number of applications of machine learning methods. Among them, Convolutional Neural Networks (CNNs) have received substantial attention due to their outstanding performance in image classification, cosmological parameter inference and various other tasks. However, many models which make use of CNNs are criticized as "black boxes" due to the difficulties in relating their outputs intuitively and quantitatively to the cosmological fields under investigation. To overcome this challenge, we present the Cosmological Correlator Convolutional Neural Network (C3NN) -- a fusion of CNN architecture with the framework of cosmological N-point correlation functions (NPCFs). We demonstrate that the output of this model can be expressed explicitly in terms of the analytically tractable NPCFs. Together with other auxiliary algorithms, we are able to open the "black box" by quantitatively ranking different orders of the interpretable convolution outputs based on their contribution to classification tasks. As a proof of concept, we demonstrate this by applying our framework to a series of binary classification tasks using Gaussian and Log-normal random fields and relating its outputs to the analytical NPCFs describing the two fields. Furthermore, we exhibit the model's ability to distinguish different dark energy scenarios ($w_0=-0.95$ and $-1.05$) using N-body simulated weak lensing convergence maps and discuss the physical implications coming from their interpretability. With these tests, we show that C3NN combines advanced aspects of machine learning architectures with the framework of cosmological NPCFs, thereby making it an exciting tool with the potential to extract physical insights in a robust and explainable way from observational data.

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arXiv2023

Making the leap I: Modelling the reconstructed lensing convergence PDF from cosmic shear with survey masks and systematics

The last few years have seen the development of a promising theoretical framework for statistics of the cosmic large-scale structure -- the theory of large deviations (LDT) for modelling weak-lensing one-point statistics in the mildly non-linear regime. The goal of this series of papers is to make the leap and lay out the steps to perform an actual data analysis with this theoretical tool. Building upon the LDT framework, in this work (Paper I) we demonstrate how to accurately model the Probability Distribution Function (PDF) of a reconstructed Kaiser-Squires convergence field under a realistic mask, that of the third data release of the Dark Energy Survey (DES). We also present how weak lensing systematics and higher-order lensing corrections due to intrinsic alignments, shear biases, photo-$z$ errors and baryonic feedback can be incorporated in the modelling of the reconstructed convergence PDF. In an upcoming work (Paper II) we will then demonstrate the robustness of our modelling through simulated likelihood analyses, the final step required before applying our method to actual data.

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arXiv2023

Beyond 3$\times$2-point cosmology: the integrated shear and galaxy 3-point correlation functions

We present the integrated 3-point correlation functions (3PCF) involving both the cosmic shear and the galaxy density fields. These are a set of higher-order statistics that describe the modulation of local 2-point correlation functions (2PCF) by large-scale features in the fields, and which are easy to measure from galaxy imaging surveys. Based on previous works on the shear-only integrated 3PCF, we develop the theoretical framework for modelling 5 new statistics involving the galaxy field and its cross-correlations with cosmic shear. Using realistic galaxy and cosmic shear mocks from simulations, we determine the regime of validity of our models based on leading-order standard perturbation theory with an MCMC analysis that recovers unbiased constraints of the amplitude of fluctuations parameter $A_s$ and the linear and quadratic galaxy bias parameters $b_1$ and $b_2$. Using Fisher matrix forecasts for a DES-Y3-like survey, relative to baseline analyses with conventional 3$\times$2PCFs, we find that the addition of the shear-only integrated 3PCF can improve cosmological parameter constraints by $20-40\%$. The subsequent addition of the new statistics introduced in this paper can lead to further improvements of $10-20\%$, even when utilizing only conservatively large scales where the tree-level models are valid. Our results motivate future work on the galaxy and shear integrated 3PCFs, which offer a practical way to extend standard analyses based on 3$\times$2PCFs to systematically probe the non-Gaussian information content of cosmic density fields.

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arXiv2023

Cosmology from the integrated shear 3-point correlation function: simulated likelihood analyses with machine-learning emulators

The integrated shear 3-point correlation function $\zeta_{\pm}$ measures the correlation between the local shear 2-point function $\xi_{\pm}$ and the 1-point shear aperture mass in patches of the sky. Unlike other higher-order statistics, $\zeta_{\pm}$ can be efficiently measured from cosmic shear data, and it admits accurate theory predictions on a wide range of scales as a function of cosmological and baryonic feedback parameters. Here, we develop and test a likelihood analysis pipeline for cosmological constraints using $\zeta_{\pm}$. We incorporate treatment of systematic effects from photometric redshift uncertainties, shear calibration bias and galaxy intrinsic alignments. We also develop an accurate neural-network emulator for fast theory predictions in MCMC parameter inference analyses. We test our pipeline using realistic cosmic shear maps based on $N$-body simulations with a DES Y3-like footprint, mask and source tomographic bins, finding unbiased parameter constraints. Relative to $\xi_{\pm}$-only, adding $\zeta_{\pm}$ can lead to $\approx 10-25\%$ improvements on the constraints of parameters like $A_s$ (or $\sigma_8$) and $w_0$. We find no evidence in $\xi_{\pm} + \zeta_{\pm}$ constraints of a significant mitigation of the impact of systematics. We also investigate the impact of the size of the apertures where $\zeta_{\pm}$ is measured, and of the strategy to estimate the covariance matrix ($N$-body vs. lognormal). Our analysis solidifies the strong potential of the $\zeta_{\pm}$ statistic and puts forward a pipeline that can be readily used to improve cosmological constraints using real cosmic shear data.

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arXiv2022

Response approach to the integrated shear 3-point correlation function: the impact of baryonic effects on small scales

The integrated shear 3-point correlation function $\zeta_{\pm}$ is a higher-order statistic of the cosmic shear field that describes the modulation of the 2-point correlation function $\xi_{\pm}$ by long-wavelength features in the field. Here, we introduce a new theoretical model to calculate $\zeta_{\pm}$ that is accurate on small angular scales, and that allows to take baryonic feedback effects into account. Our model builds on the realization that the small-scale $\zeta_{\pm}$ is dominated by the nonlinear matter bispectrum in the squeezed limit, which can be evaluated accurately using the nonlinear matter power spectrum and its first-order response functions to density and tidal field perturbations. We demonstrate the accuracy of our model by showing that it reproduces the small-scale $\zeta_{\pm}$ measured in simulated cosmic shear maps. The impact of baryonic feedback enters effectively only through the corresponding impact on the nonlinear matter power spectrum, thereby permitting to account for these astrophysical effects on $\zeta_{\pm}$ similarly to how they are currently accounted for on $\xi_{\pm}$. Using a simple idealized Fisher matrix forecast for a DES-like survey we find that, compared to $\xi_{\pm}$, a combined $\xi_{\pm}\ \&\ \zeta_{\pm}$ analysis can lead to improvements of order $20-40\%$ on the constraints of cosmological parameters such as $\sigma_8$ or the dark energy equation of state parameter $w_0$. We find similar levels of improvement on the constraints of the baryonic feedback parameters, which strengthens the prospects for cosmic shear data to obtain tight constraints not only on cosmology but also on astrophysical feedback models. These are encouraging results that motivate future works on the integrated shear 3-point correlation function towards applications to real survey data.

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arXiv2021

The PDF perspective on the tracer-matter connection: Lagrangian bias and non-Poissonian shot noise

We study the connection of matter density and its tracers from the PDF perspective. One aspect of this connection is the conditional expectation value $\langle \delta_{\mathrm{tracer}}|\delta_m\rangle$ when averaging both tracer and matter density over some scale. We present a new way to incorporate a Lagrangian bias expansion of this expectation value into standard frameworks for modelling the PDF of density fluctuations and counts-in-cells statistics. Using N-body simulations and mock galaxy catalogs we confirm the accuracy of this expansion and compare it to the more commonly used Eulerian parametrization. For halos hosting typical luminous red galaxies, the Lagrangian model provides a significantly better description of $\langle \delta_{\mathrm{tracer}}|\delta_m\rangle$ at second order in perturbations. A second aspect of the matter-tracer connection is shot-noise, \ie the scatter of tracer density around $\langle \delta_{\mathrm{tracer}}|\delta_m\rangle$. It is well known that this noise can be significantly non-Poissonian and we validate the performance of a more general, two-parameter shot-noise model for different tracers and simulations. Both parts of our analysis are meant to pave the way for forthcoming applications to survey data.

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arXiv2021

The integrated 3-point correlation function of cosmic shear

We present the integrated 3-point shear correlation function $i\zeta_{\pm}$ -- a higher-order statistic of the cosmic shear field -- which can be directly estimated in wide-area weak lensing surveys without measuring the full 3-point shear correlation function, making this a practical and complementary tool to 2-point statistics for weak lensing cosmology. We define it as the 1-point aperture mass statistic $M_{\mathrm{ap}}$ measured at different locations on the shear field correlated with the corresponding local 2-point shear correlation function $\xi_{\pm}$. Building upon existing work on the integrated bispectrum of the weak lensing convergence field, we present a theoretical framework for computing the integrated 3-point function in real space for any projected field within the flat-sky approximation and apply it to cosmic shear. Using analytical formulae for the non-linear matter power spectrum and bispectrum, we model $i\zeta_{\pm}$ and validate it on N-body simulations within the uncertainties expected from the sixth year cosmic shear data of the Dark Energy Survey. We also explore the Fisher information content of $i\zeta_{\pm}$ and perform a joint analysis with $\xi_{\pm}$ for two tomographic source redshift bins with realistic shape-noise to analyse its power in constraining cosmological parameters. We find that the joint analysis of $\xi_{\pm}$ and $i\zeta_{\pm}$ has the potential to considerably improve parameter constraints from $\xi_{\pm}$ alone, and can be particularly useful in improving the figure of merit of the dynamical dark energy equation of state parameters from cosmic shear data.

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