arXiv subjects

Sujatha Ramakrishnan

Publications and source records attributed to Sujatha Ramakrishnan.

7 recordsLinked to original sources

arXiv2025

Halo Lightcones with Optimised Orientation and Interpolation in Cosmological Simulations -- an application to mock H$α$ selected galaxies

A critical step to create realistic mock catalogs that support large-scale photometric and spectroscopic sky surveys is the production of cosmological simulations that accurately model the survey observables taking into account the redshift-dependent galaxy formation and evolution processes. Here we develop an efficient framework, HOLCon (Halo Optimised Lightcone Constructor), for post-facto construction of dark matter halo lightcones from simulations and use them to generate a mock galaxy catalogue. HOLCon includes a module to optimise the lightcone's orientation within the simulation box, minimising repeated structures when the survey volume exceeds a single box -- a common challenge in modern surveys. A linear interpolation scheme tracks the evolution of halo properties across snapshots. Applied to the publicly available Uchuu simulation, we construct a lightcone of 50 ${\rm deg}^2$ and extending up to $z = 10$, providing representative coverage of deep fields of Stage IV surveys. We validate the lightcone for cosmological applications by comparing the dark matter halo clustering in the lightcone with those from the original simulation snapshots. Subsequently, we make the galaxy-halo connection on the lightcone with a redshift extended version of the SciPIC algorithm producing a comprehensive set of descriptive galaxy attributes. HOLCon leverages Dask, a scalable parallel computing pythonic framework for fast construction of dark matter halo lightcones enabling rapid creation of multiple statistical realizations essential for robust cosmological inference. The produced galaxy mock makes predictions for clustering of H$α$ emitters, making it a useful cosmology resource.

astro-ph.CO↗

arXiv2025

The multi-dimensional halo assembly bias can be preserved when enhancing halo properties with HALOSCOPE

Over $90$\% of dark matter haloes in cosmological simulations have unresolved properties. This can hinder the dynamical range of simulations and result in systematic biases when modelling cosmological tracers. We aim to more precisely determine unresolved structural and dynamical halo properties while preserving the correlations with environment and halo assembly bias found in simulations. We have developed HALOSCOPE, a machine learning technique that uses multi-variate conditional probability distribution functions. This method ensures that correlations among various halo properties, as well as their dependence on the local environment, are preserved. In this work, we trained HALOSCOPE with a high-resolution (HR) simulation and used it to better determine the properties (concentration, spin, and two shape parameters) of unresolved dark matter haloes in an eight times lower resolution simulation. HALOSCOPE is able to recover the multi-dimensional halo assembly bias, that is, the correlations of different combinations of halo properties with the large-scale environment, measured in the HR simulation. This is achieved by including the linear halo-by-halo bias and tidal anisotropy in the set of input training parameters. HALOSCOPE, by design, also recovers the joint distribution of the halo properties. To study how resolution effects propagate into the clustering of model galaxies, we generated catalogues of central galaxies using two implementations of the assembly bias in a halo occupation distribution model. The clustering of central model galaxies is improved by a factor of three at $0.009<k ({\rm Mpc}^{-1}h)<0.6$ when the unresolved haloes are enhanced with HALOSCOPE. HALOSCOPE can improve the accuracy of cosmological tracer catalogues produced with approximate methods when many realisations are needed.

astro-ph.CO↗

arXiv2024

An improved Halo Occupation Distribution prescription from UNITsim H_alpha Emission Line Galaxies: conformity and modified radial profile

Emission line galaxies (ELGs) are targeted by the new generation of spectroscopic surveys to make unprecedented measurements in cosmology from their distribution. Accurately interpreting this data requires understanding the imprints imposed by the physics of galaxy formation and evolution on galaxy clustering. In this work we utilize a semi-analytical model of galaxy formation (SAGE) to explore the necessary components for accurately reproducing the clustering of ELGs. We focus on developing a Halo Occupation Distribution (HOD) prescription able to reproduce the clustering of SAGE galaxies. Typically, HOD models assume that satellite and central galaxies of a given type are independent events. We investigate the need for conformity, i.e. whether the average satellite occupation depends on the existence of a central galaxy of a given type. Incorporating conformity into HOD models is crucial for reproducing the clustering in the reference galaxy sample. Another aspect we investigate is the radial distribution of satellite galaxies within haloes. The traditional density profile models, NFW and Einasto profiles, fail to accurately replicate the small-scale clustering measured for SAGE satellite galaxies. To overcome this limitation, we propose a generalization of the NFW profile, thereby enhancing our understanding of galaxy clustering.

astro-ph.CO↗

arXiv2022

Properties beyond mass for unresolved haloes across redshift and cosmology using correlations with local halo environment

The structural and dynamic properties of the dark matter halos, though an important ingredient in understanding large-scale structure formation, require more conservative particle resolution than those required by halo mass alone in a simulation. This reduces the parameter space of the simulations, more severely for high-redshift and large-volume mocks which are required by the next-generation large sky surveys. Here, we incorporate redshift and cosmology dependence into an algorithm that assigns accurate halo properties such as concentration, spin, velocity, and spatial distribution to the sub-resolution haloes in a simulation. By focusing on getting the right correlations with halo mass and local tidal anisotropy $α$ measured at $4 \times$ halo radius, our method will also recover the correlations of these small scale structural properties with the large-scale environment, i.e., the halo assembly bias at all scales greater than $5 \times$ halo radius. We find that the distribution of halo properties is universal with redshift and cosmology. By applying the algorithm to a large volume simulation $(600 h^{-1}{\rm Mpc})$, we can access the $30-500$ particle haloes, thus gaining an order of magnitude in halo mass and two to three orders of magnitude in number density at $z=2-4$. This technique reduces the cost of mocks required for the estimation of covariance matrices, weak lensing studies, or any large-scale clustering analysis with less massive haloes.

astro-ph.CO↗

arXiv2021

Mock halo catalogs: assigning unresolved halo properties using correlations with local halo environment

Large-scale sky surveys require companion large volume simulated mock catalogs. To ensure precision cosmology studies are unbiased, the correlations in these mocks between galaxy properties and their large-scale environments must be realistic. Since galaxies are embedded in dark matter halos, an important first step is to include such correlations -- sometimes called assembly bias -- for dark matter halos. However, galaxy properties correlate with smaller scale physics in halos which large simulations struggle to resolve. We describe an algorithm which addresses and largely mitigates this problem. Our algorithm exploits the fact that halo assembly bias is unchanged as long as correlations between halo property $c$ and the intermediate-scale tidal environment $α$ are preserved. Therefore, knowledge of $α$ is sufficient to assign small-scale, otherwise unresolved properties to a halo in a way which preserves its large-scale assembly bias accurately. We demonstrate this explicitly for halo internal properties like formation history (concentration $c_{\rm 200b}$), shape $c/a$, dynamics $c_{v}/a_{v}$, velocity anisotropy $β$ and angular momentum (spin $λ$). Our algorithm increases a simulation's reach in halo mass and number density by an order of magnitude, with improvements in the bias signal as large as 45% for 30-particle halos, thus significantly reducing the cost of mocks for future weak lensing and redshift space distortion studies.

astro-ph.CO↗

arXiv2020

Separate Universe calibration of the dependence of halo bias on cosmic web anisotropy

We use the Separate Universe technique to calibrate the dependence of linear and quadratic halo bias $b_1$ and $b_2$ on the local cosmic web environment of dark matter haloes. We do this by measuring the response of halo abundances at fixed mass and cosmic web tidal anisotropy $α$ to an infinite wavelength initial perturbation. We augment our measurements with an analytical framework developed in earlier work which exploits the near-Lognormal shape of the distribution of $α$ and results in very high precision calibrations. We present convenient fitting functions for the dependence of $b_1$ and $b_2$ on $α$ over a wide range of halo mass for redshifts $0\leq z\leq1$. Our calibration of $b_2(α)$ is the first demonstration to date of the dependence of non-linear bias on the local web environment. Motivated by previous results which showed that $α$ is the primary indicator of halo assembly bias for a number of halo properties beyond halo mass, we then extend our analytical framework to accommodate the dependence of $b_1$ and $b_2$ on any such secondary property which has, or can be monotonically transformed to have, a Gaussian distribution. We demonstrate this technique for the specific case of halo concentration, finding good agreement with previous results. Our calibrations will be useful for a variety of halo model analyses focusing on galaxy assembly bias, as well as analytical forecasts of the potential for using $α$ as a segregating variable in multi-tracer analyses.

astro-ph.CO↗

arXiv2019

Cosmic web anisotropy is the primary indicator of halo assembly bias

The internal properties of dark matter haloes correlate with the large-scale halo clustering strength at fixed halo mass $-$ an effect known as assembly bias $-$ and are also strongly affected by the local, non-linear cosmic web. Characterising a halo's local web environment by its tidal anisotropy $α$ at scales $\sim4$ x the halo radius, we demonstrate that these multi-scale correlations represent two distinct statistical links: one between the internal property and $α$, and the other between $α$ and large-scale ( $>30h^{-1}$Mpc) halo bias $b_1$. We focus on scalar internal properties of haloes related to formation time (concentration $c_{\rm vir}$), shape (mass ellipsoid asphericity $c/a$), velocity dispersion structure (velocity ellipsoid asphericity $c_v/a_v$ and velocity anisotropy $β$) and angular momentum (dimensionless spin $λ$) in the mass range $8\times10^{11}< M_{\rm vir}/(h^{-1}M_\odot)<5\times10^{14}$. Using conditional correlation coefficients and other detailed tests, we show that the joint distribution of $α$, $b_1$ and any of the internal properties $c\in\{β,c_v/a_v,c/a,c_{\rm vir},λ\}$ is consistent with $p(α,b_1,c)\simeq p(α)p(b_1|α)p(c|α)$, at all but the largest masses. $\textit{Thus, the assembly bias trends $c-b_1$ reflect the two fundamental correlations $c-α$ and $b_1-α$.}$ Our results are unaffected by the exclusion of haloes with recent major merger events or splashback objects, although the latter are distinguished by the fact that $α$ does not explain their assembly bias trends. The overarching importance of $α$ provides a new perspective on the nature of assembly bias of distinct haloes, with potential ramifications for incorporating realistic assembly bias effects into mock catalogs of future large-scale structure surveys and for detecting galaxy assembly bias.

astro-ph.CO↗