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Saee Dhawalikar

Publications and source records attributed to Saee Dhawalikar.

7 recordsLinked to original sources

Filaments within filaments: unraveling the hierarchy of the cosmic web

Cosmic filaments are the most striking feature of the hierarchical cosmic web. Yet, their own sub-structure remains unexplored, unlike the well-known, universal halo/sub-halo hierarchy. We use a hierarchical reconstruction scheme to identify sub-filaments within larger filaments. Examining differences in the radial phase-space profiles of parent and sub-filaments, we argue that the latter constitute a statistically distinct class of structures. Establishing the existence of a universal filament hierarchy must then account for these differences, with implications for cosmology and dark matter.

astro-ph.CO↗

Evolution of Neutron Star Environment in the Galactic Halo : Implications for Dark Matter Accretion

Neutron stars (NS) are one of the indirect detection probes for dark matter (DM). The presence of DM is known to affect the observable properties of NS. Theoretical calculations for various equations of state of a DM admixed NS lead to estimates of the DM mass in a NS of the order of $10^{-2} M_{\odot}$. On the other hand, simplistic estimates of the amount of DM that is accreted on to the NS in the Solar neighborhood, over its age, suggest that this number is of the order $10^{-14} M_{\odot}$. Various studies have addressed this non-agreement theoretically by explaining the mechanisms leading to higher fraction than expected from smooth spherically symmetric accretion. In this work, we attempt to assess the role of the dynamic DM environmental density in the Galactic halo to explain possible enhancement in DM accretion. We consider a high resolution N-body simulation and method of Voronoi tessellation to calculate local DM density around putative NS locations in a statistically representative sample of Milky Way analogues. We infer that the dynamics of substructure may enhance the DM mass in NS by about a factor 2 as compared to the baseline, spherically symmetric expectation. Environmental effects therefore cannot explain the orders of magnitude discrepancy between equation of state and accretion based estimates of DM admixed in NS.

astro-ph.CO↗

Cosmic Velocity Flows: from Theory to Observations

The Large-Scale Structure (LSS) of the Universe forms a complex network of nodes, filaments, sheets, and voids known as the Cosmic Web. As current and upcoming galaxy surveys increasingly probe the quasi-linear and non-linear regimes of structure formation, understanding its geometry and dynamics is essential for precision cosmology. In particular, cosmic filaments, which channel matter across the web, are central to these dynamical processes. This thesis develops numerical tools to study the non-linear cosmic web. First, the Sahyadri suite of high-resolution cosmological $N$-body simulations is introduced, providing a framework for precision studies of LSS and its cosmological dependence. A calibration framework for filament reconstruction is then developed using controlled filament realizations, enabling systematic investigation of reconstruction biases. The effects of filament curvature and reconstruction noise on inferred filament properties are quantified, and a novel Fourier-space smoothing approach is introduced to improve profile recovery. The thesis further presents Skeletor, a Voronoi-based filament finder that identifies filamentary structures directly from discrete tracers while explicitly incorporating the hierarchical nature of the cosmic web. A novel framework for classifying sub-filamentary structure is developed. Applying these tools to cosmological simulations reveals distinct properties of filament substructure and provides a detailed view of filament phase space, including coherent inflows, multistreaming, and caustic-like features. Together, these developments provide a framework for studying the geometry, hierarchy, and dynamics of the non-linear cosmic web. More broadly, they contribute to the ongoing effort to build a physically motivated understanding of the non-linear cosmic web beyond traditional measures of clustering.

astro-ph.CO↗

Sahyadri: A simulation suite for the cosmology dependence of the Cosmic Web

We present Sahyadri, a suite of cosmological $N$-body simulations designed to enable precision studies of the low-redshift Universe with next-generation spectroscopic surveys. Sahyadri includes systematic variations of four cosmological parameters around Planck 2018 constraints, with seed-matched initial conditions enabling cosmological parameter derivatives. It is planned to ultimately extend to six parameters. Each simulation evolves $2048^3$ particles in a periodic box of side length $200$ $h^{-1}$ Mpc, yielding a particle mass of $m_{\rm{p}} = 8.1 \times 10^{7}\,h^{-1}\,M_{\odot}$ in the fiducial Planck 2018 cosmology. This resolution enables robust identification of dark matter halos down to $M_{\rm min} = 3.2 \times 10^{9}$ $h^{-1}$ $M_\odot$, which represents a factor of $\sim$25 improvement over the AbacusSummit suite, and is over two orders of magnitude better than the Quijote and Aemulus suites. We estimate that approximately 40% of DESI BGS galaxies at redshift $z < 0.15$ - roughly 1.6 million objects - reside in halos accessible to Sahyadri but beyond the reach of existing parameter-varying simulation suites. We demonstrate Sahyadri's capabilities through measurements of the matter power spectrum, halo mass function and power spectrum, and beyond 2-point statistics such as the Voronoi volume function and $k^{\rm th}$ nearest neighbour statistics, showing excellent agreement with theoretical predictions and significant sensitivity to $Ω_{\rm m}$ variations. We implement a custom compression scheme reducing storage requirements by a factor of $\sim$3 while maintaining sub-percent clustering accuracy. Key data products are publicly available.

astro-ph.CO↗

Stabilizing simulation-based cosmological Fisher forecasts: a case study using the Voronoi volume function

Forecasting cosmological constraints from halo-based statistics often suffers from instability in derivative estimates, especially when the number of simulations is limited. This instability reduces the reliability of Fisher forecasts and machine learning based approaches that use derivatives. We introduce a general framework that addresses this challenge by stabilizing the input statistic and then systematically identifying the optimal subset of summary statistics that maximizes cosmological information while simultaneously minimizing the instability of predicted constraints. We demonstrate this framework using the halo mass function as well as the Voronoi volume function (VVF), a summary statistic that captures beyond two-point clustering information. Applying our two-step procedure -- random sub-sampling followed by optimization -- improves the constraining power by up to a factor of 4, while also enhancing the stability of the forecasts across realizations. As surveys like Euclid, DESI, and LSST push toward tighter constraints, the ability to produce stable and accurate theoretical predictions is essential. Our results suggest that new summary statistics such as the VVF, combined with careful data curation and stabilization strategies, can play a key role in next-generation precision cosmology.

astro-ph.CO↗

Towards unbiased recovery of cosmic filament properties: the role of spine curvature and optimized smoothing

Cosmic filaments, the most prominent features of the cosmic web, possibly hold untapped potential for cosmological inference. While it is natural to expect the structure of filaments to show universality similar to that seen in dark matter halos, the lack of agreement between different filament finders on what constitutes a filament has hampered progress on this topic. We initiate a programme to systematically investigate and uncover possible universal features in the phase space structure of cosmic filaments, by generating particle realizations of mock filaments with $\textit{a priori}$ known properties. Using these, we identify an important source of bias in the extraction of radial density profiles, which occurs when the local curvature $κ$ of the spine exceeds a threshold determined by the filament thickness. This bias exists even for perfectly determined spines, thus affecting $\textit{all}$ filament finders. We show that this bias can be nearly eliminated by simply discarding the regions with the highest $κ$, with little loss of precision. An additional source of bias is the noise generated by the filament finder when identifying the spine, which depends on both the finder algorithm as well as intrinsic properties of the individual filament. We find that, to mitigate this bias, it is essential not only to smooth the estimated spine, but to $\textit{optimize}$ this smoothing separately for each filament. We propose a novel optimization based on minimizing the estimated filament thickness, along with Fourier space smoothing. We implement these techniques using two tools, $\texttt{FilGen}$ which generates mock filaments and $\texttt{FilAPT}$ which analyses and processes them. We expect these tools to be useful in calibrating the performance of filament finders, thereby enabling searches for filament universality.

astro-ph.CO↗

The driving mode of shock-driven turbulence

Turbulence in the interstellar medium (ISM) is crucial in the process of star formation. Shocks produced by supernova explosions, jets, radiation from massive stars, or galactic spiral-arm dynamics are amongst the most common drivers of turbulence in the ISM. However, it is not fully understood how shocks drive turbulence, in particular whether shock driving is a more solenoidal(rotational, divergence-free) or a more compressive (potential, curl-free) mode of driving turbulence. The mode of turbulence driving has profound consequences for star formation, with compressive driving producing three times larger density dispersion, and an order of magnitude higher star formation rate than solenoidal driving. Here, we use hydrodynamical simulations of a shock inducing turbulent motions in a structured, multi-phase medium. This is done in the context of a laser-induced shock, propagating into a foam material, in preparation for an experiment to be performed at the National Ignition Facility (NIF). Specifically, we analyse the density and velocity distributions in the shocked turbulent medium, and measure the turbulence driving parameter $b=(σ^{2 Γ}_{ρ/\langle ρ\rangle}-1)^{1/2} (1-σ_{ρ\langle ρ\rangle}^{-2})^{-1/2}\mathcal{M}^{-1}Γ^{-1/2}$ with the density dispersion $σ_{ρ/ \langle ρ\rangle}$, the turbulent Mach number $\mathcal{M}$, and the polytropic exponent $Γ$. Purely solenoidal and purely compressive driving correspond to $b \sim 1/3$ and $b \sim 1$, respectively. Using simulations in which a shock is driven into a multi-phase medium with structures of different sizes and $Γ< 1$, we find $b \sim 1$ for all cases, showing that shock-driven turbulence is consistent with strongly compressive driving.

astro-ph.GA↗