arXiv subjects

Zarija Lukić

Publications and source records attributed to Zarija Lukić.

At least 37 records · Page 2Linked to original sources

arXiv2023

Convergence of small scale Ly$α$ structure at high-$z$ under different reionization scenarios

The Ly$α$ forest (LAF) at $z>5$ probes the thermal and reionization history of the intergalactic medium (IGM) and the nature of dark matter, but its interpretation requires comparison to cosmological hydrodynamical simulations. At high-$z$, convergence of these simulations is more exacting since transmission is dominated by underdense voids that are challenging to resolve. With evidence mounting for a late end to reionization, small structures down to the sub-kpc level may survive to later times than conventionally thought due to the reduced time for pressure smoothing to impact the gas, further tightening simulation resolution requirements. We perform a suite of simulations using the Eulerian cosmological hydrodynamics code Nyx, spanning domain sizes of 1.25-10 $h^{-1}$ Mpc and 5-80 $h^{-1}$ kpc cells, and explore the interaction of these variables with the timing of reionization on the properties of the matter distribution and the simulated LAF at $z=5.5$. In observable Ly$α$ power, convergence within 10% is achieved for $k< 0.1$ s/km, but larger $k$ shows deviation of up to 20 percent. While a later reionization retains more small structure in the density field, because of the greater thermal broadening there is little difference in the convergence of LAF power between early ($z=9$) and later ($z=6$) reionizations. We conclude that at $z\sim5.5$, resolutions of 10 kpc are necessary for convergence of LAF power at $k<0.1$ s/km, while higher-$k$ modes require higher resolution, and that the timing of reionization does not significantly impact convergence given realistic photoheating.

astro-ph.CO↗

arXiv2023

Snowmass2021 Cosmic Frontier White Paper: Dark Matter Physics from Halo Measurements

The non-linear process of cosmic structure formation produces gravitationally bound overdensities of dark matter known as halos. The abundances, density profiles, ellipticities, and spins of these halos can be tied to the underlying fundamental particle physics that governs dark matter at microscopic scales. Thus, macroscopic measurements of dark matter halos offer a unique opportunity to determine the underlying properties of dark matter across the vast landscape of dark matter theories. This white paper summarizes the ongoing rapid development of theoretical and experimental methods, as well as new opportunities, to use dark matter halo measurements as a pillar of dark matter physics.

hep-ph↗

arXiv2022

Modeling the Lyman-$α$ forest with Eulerian and SPH hydrodynamical methods

We compare two state-of-the-art numerical codes to study the overall accuracy in modeling the intergalactic medium and reproducing Lyman-$α$ forest observables for DESI and high-resolution data sets. The codes employ different approaches to solving both gravity and modeling the gas hydrodynamics. The first code, Nyx, solves the Poisson equation using the Particle-Mesh (PM) method and the Euler equations using a finite volume method. The second code, \CRKHACC, uses a Tree-PM method to solve for gravity, and an improved Lagrangian smoothed particle hydrodynamics (SPH) technique, where fluid elements are modeled with particles, to treat the intergalactic gas. We compare the convergence behavior of the codes in flux statistics as well as the degree to which the codes agree in the converged limit. We find good agreement overall with differences being less than observational uncertainties, and a particularly notable $\lesssim$1\% agreement in the 1D flux power spectrum. This agreement was achieved by applying a tessellation methodology for reconstructing the density in \CRKHACC instead of using an SPH kernel as is standard practice. We show that use of the SPH kernel can lead to significant and unnecessary biases in flux statistics; this is especially prominent at high redshifts, $z \sim 5$, as the Lyman-$α$ forest mostly comes from lower-density regions which are intrinsically poorly sampled by SPH particles.

astro-ph.CO↗

arXiv2022

Snowmass2021 Computational Frontier White Paper: Cosmological Simulations and Modeling

Powerful new observational facilities will come online over the next decade, enabling a number of discovery opportunities in the "Cosmic Frontier", which targets understanding of the physics of the early universe, dark matter and dark energy, and cosmological probes of fundamental physics, such as neutrino masses and modifications of Einstein gravity. Synergies between different experiments will be leveraged to present new classes of cosmic probes as well as to minimize systematic biases present in individual surveys. Success of this observational program requires actively pairing it with a well-matched state-of-the-art simulation and modeling effort. Next-generation cosmological modeling will increasingly focus on physically rich simulations able to model outputs of sky surveys spanning multiple wavebands. These simulations will have unprecedented resolution, volume coverage, and must deliver guaranteed high-fidelity results for individual surveys as well as for the cross-correlations across different surveys. The needed advances are as follows: (1) Development of scientifically rich and broadly-scoped simulations, which capture the relevant physics and correlations between probes (2) Accurate translation of simulation results into realistic image or spectral data to be directly compared with observations (3) Improved emulators and/or data-driven methods serving as surrogates for expensive simulations, constructed from a finite set of full-physics simulations (4) Detailed and transparent verification and validation programs for both simulations and analysis tools. (Abridged)

astro-ph.IM↗

arXiv2021

A Fundamental Test for Galaxy Formation Models: Matching the Lyman-$α$ Absorption Profiles of Galactic Halos over Three Decades in Distance

Galaxy formation depends critically on the physical state of gas in the circumgalactic medium (CGM) and its interface with the intergalactic medium (IGM), determined by the complex interplay between inflows from the IGM and outflows from supernovae or AGN feedback. The average Lyman-alpha (Ly-a) absorption profile around galactic halos represents a powerful tool to probe their gaseous environments. We compare predictions from Illustris and Nyx hydrodynamical simulations with the observed absorption around foreground quasars, damped Ly-a systems, and Lyman-break galaxies. We show how large-scale BOSS and small-scale quasar pair measurements can be combined to precisely constrain the absorption profile over three decades in transverse distance 20kpc$\lesssim b\lesssim$20Mpc. Far from galaxies $\gtrsim2$Mpc, the simulations converge to the same profile and provide a reasonable match to the observations. This asymptotic agreement arises because the $Λ$CDM model successfully describes the ambient IGM, and represents a critical advantage of studying the mean absorption profile. However, significant differences between the simulations, and between simulations and observations are present on scales 20kpc$\lesssim b\lesssim$2Mpc, illustrating the challenges of accurately modeling and resolving galaxy formation physics. It is noteworthy that these differences are observed as far out as $\sim2$Mpc, indicating that the `sphere-of-influence' of galaxies could extend to approximately $\sim7$ times the halo virial radius ($\sim100$kpc). Current observations are very precise on these scales and can thus strongly discriminate between different galaxy formation models. We demonstrate that the Ly-a absorption profile is primarily sensitive to the underlying temperature-density relationship of diffuse gas around galaxies, and argue that it thus provides a fundamental test of galaxy formation models.

astro-ph.GA↗

arXiv2021

Improving IGM temperature constraints using wavelet analysis on high-redshift quasars

The thermal state of the intergalactic medium (IGM) contains vital information about the epoch of reionization, one of the most transformative yet poorly understood periods in the young universe. This thermal state is encoded in the small-scale structure of Lyman-$α$ (Ly$α$) absorption in quasar spectra. The 1D flux power spectrum measures the average small-scale structure along quasar sightlines. At high redshifts, where the opacity is large, averaging mixes high signal-to-noise ratio transmission spikes with noisy absorption troughs. Wavelet amplitudes are an alternate statistic that maintains spatial information while quantifying fluctuations at the same spatial frequencies as the power spectrum, giving them the potential to more sensitively measure the small-scale structure. Previous Ly$α$ forest studies using wavelet amplitude probability density functions (PDFs) used limited spatial frequencies and neglected strong correlations between PDF bins and across wavelets scales, resulting in sub-optimal and unreliable parameter inference. Here we present a novel method for performing statistical inference using wavelet amplitude PDFs that spans the full range of spatial frequencies probed by the power spectrum and that fully accounts for these correlations. We applied this procedure to realistic mock data drawn from a simple thermal model parameterized by the temperature at mean density, $T_0$, and find that wavelets deliver 1$σ$ constraints on $T_0$ that are on average 7% more sensitive at $z=5$ (12% at $z=6$) than those from the power spectrum. We consider the possibility of combing wavelet PDFs with the power, but find that this does not lead to improved sensitivity.

astro-ph.CO↗

arXiv2021

Fast, high-fidelity Lyman $α$ forests with convolutional neural networks

Full-physics cosmological simulations are powerful tools for studying the formation and evolution of structure in the universe but require extreme computational resources. Here, we train a convolutional neural network to use a cheaper N-body-only simulation to reconstruct the baryon hydrodynamic variables (density, temperature, and velocity) on scales relevant to the Lyman-$α$ (Ly$α$) forest, using data from Nyx simulations. We show that our method enables rapid estimation of these fields at a resolution of $\sim$20kpc, and captures the statistics of the Ly$α$ forest with much greater accuracy than existing approximations. Because our model is fully-convolutional, we can train on smaller simulation boxes and deploy on much larger ones, enabling substantial computational savings. Furthermore, as our method produces an approximation for the hydrodynamic fields instead of Ly$α$ flux directly, it is not limited to a particular choice of ionizing background or mean transmitted flux.

astro-ph.CO↗

arXiv2021

HyPhy: Deep Generative Conditional Posterior Mapping of Hydrodynamical Physics

Generating large volume hydrodynamical simulations for cosmological observables is a computationally demanding task necessary for next generation observations. In this work, we construct a novel fully convolutional variational auto-encoder (VAE) to synthesize hydrodynamic fields conditioned on dark matter fields from N-body simulations. After training the model on a single hydrodynamical simulation, we are able to probabilistically map new dark matter only simulations to corresponding full hydrodynamical outputs. By sampling over the latent space of our VAE, we can generate posterior samples and study the variance of the mapping. We find that our reconstructed field provides an accurate representation of the target hydrodynamical fields as well as a reasonable variance estimates. This approach has promise for the rapid generation of mocks as well as for implementation in a full Bayesian inverse model of observed data.

astro-ph.CO↗

arXiv2021

Self-Supervised Representation Learning for Astronomical Images

Sky surveys are the largest data generators in astronomy, making automated tools for extracting meaningful scientific information an absolute necessity. We show that, without the need for labels, self-supervised learning recovers representations of sky survey images that are semantically useful for a variety of scientific tasks. These representations can be directly used as features, or fine-tuned, to outperform supervised methods trained only on labeled data. We apply a contrastive learning framework on multi-band galaxy photometry from the Sloan Digital Sky Survey (SDSS) to learn image representations. We then use them for galaxy morphology classification, and fine-tune them for photometric redshift estimation, using labels from the Galaxy Zoo 2 dataset and SDSS spectroscopy. In both downstream tasks, using the same learned representations, we outperform the supervised state-of-the-art results, and we show that our approach can achieve the accuracy of supervised models while using 2-4 times fewer labels for training.

astro-ph.IM↗

arXiv2021

Simulating intergalactic gas for DESI-like small scale Lymanα forest observations

Measurements of the Ly$α$ forest based on large numbers of quasar spectra from sky surveys such as SDSS/eBOSS accurately probe the distribution of matter on small scales and thus provide important constraints on several ingredients of the cosmological model. A main summary statistic derived from those measurements is the one-dimensional power spectrum, P1D, of the Ly$α$ absorption. However, model predictions for P1D rely on expensive hydrodynamical simulations of the intergalactic medium, which was the limiting factor in previous analyses. Datasets from upcoming surveys such as DESI will push observational accuracy near the 1%-level and probe even smaller scales. This observational push mandate seven more accurate simulations as well as more careful exploration of parameter space. In this work we evaluate the robustness and accuracy of simulations and the statistical framework used to constrain cosmological parameters. We present a comparison between the grid-based simulation code Nyx and SPH-based code Gadget in the context ofP1D. In addition, we perform resolution and box-size convergence tests using Nyx code. We use a Gaussian process emulation scheme to reduce the number of simulations required for exploration of parameter space without sacrificing the model accuracy. We demonstrate the ability to produce unbiased parameter constraints in an end-to-end inference test using mock eBOSS- and DESI-like data, and we advocate for the usage of adaptive sampling schemes as opposed to using a fixed Latin hypercube design.

astro-ph.CO↗

arXiv2021

Estimating Galactic Distances From Images Using Self-supervised Representation Learning

We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as well as an application-specific augmentation accounting for galactic dust. We find that the resulting visual representations of galaxy images are semantically useful and allow for fast similarity searches, and can be successfully fine-tuned for the task of redshift estimation. We show that (1) pretraining on a large corpus of unlabeled data followed by fine-tuning on some labels can attain the accuracy of a fully-supervised model which requires 2-4x more labeled data, and (2) that by fine-tuning our self-supervised representations using all available data labels in the Main Galaxy Sample of the Sloan Digital Sky Survey (SDSS), we outperform the state-of-the-art supervised learning method.

astro-ph.IM↗

arXiv2019

CosmoGAN: creating high-fidelity weak lensing convergence maps using Generative Adversarial Networks

Inferring model parameters from experimental data is a grand challenge in many sciences, including cosmology. This often relies critically on high fidelity numerical simulations, which are prohibitively computationally expensive. The application of deep learning techniques to generative modeling is renewing interest in using high dimensional density estimators as computationally inexpensive emulators of fully-fledged simulations. These generative models have the potential to make a dramatic shift in the field of scientific simulations, but for that shift to happen we need to study the performance of such generators in the precision regime needed for science applications. To this end, in this work we apply Generative Adversarial Networks to the problem of generating weak lensing convergence maps. We show that our generator network produces maps that are described by, with high statistical confidence, the same summary statistics as the fully simulated maps.

astro-ph.IM↗

arXiv2019

The Power Spectrum of the Lyman-$α$ Forest at z < 0.5

We present new measurements of the flux power-spectrum P(k) of the $z<0.5$ HI Lyman-$α$ forest spanning scales k ~ 0.001-0.1 s/km. These results were derived from 65 far ultraviolet quasar spectra (resolution R~18000) observed with the Cosmic Origin Spectrograph (COS) on board the Hubble Space Telescope. The analysis required careful masking of all contaminating, coincident absorption from HI and metal-line transitions of the Galactic interstellar medium and intervening absorbers as well as proper treatment of the complex COS line-spread function. From the P(k) measurements, we estimate the HI photoionization rate ($Γ_{\rm HI}$) in the z<0.5 intergalactic medium. Our results confirm most of the previous $Γ_{\rm HI}$ estimates. We conclude that previous concerns of a photon underproduction crisis are now resolved by demonstrating that the measured $Γ_{\rm HI}$ can be accounted for by ultraviolet emission from quasars alone. In a companion paper, we will present constraints on the thermal state of the $z<0.5$ intergalactic medium from the P(k) measurements presented here.

astro-ph.CO↗

arXiv2019

Inflation and Dark Energy from spectroscopy at $z > 2$

The expansion of the Universe is understood to have accelerated during two epochs: in its very first moments during a period of Inflation and much more recently, at $z < 1$, when Dark Energy is hypothesized to drive cosmic acceleration. The undiscovered mechanisms behind these two epochs represent some of the most important open problems in fundamental physics. The large cosmological volume at $2 < z < 5$, together with the ability to efficiently target high-$z$ galaxies with known techniques, enables large gains in the study of Inflation and Dark Energy. A future spectroscopic survey can test the Gaussianity of the initial conditions up to a factor of ~50 better than our current bounds, crossing the crucial theoretical threshold of $σ(f_{NL}^{\rm local})$ of order unity that separates single field and multi-field models. Simultaneously, it can measure the fraction of Dark Energy at the percent level up to $z = 5$, thus serving as an unprecedented test of the standard model and opening up a tremendous discovery space.

astro-ph.CO↗

arXiv2018

New Constraints on IGM Thermal Evolution from the Lyα Forest Power Spectrum

We determine the thermal evolution of the intergalactic medium (IGM) over $3\, \mathrm{Gyr}$ of cosmic time $1.8<z<5.4$ by comparing measurements of the Lyα forest power spectrum to a suite of $\sim70$ hydrodynamical simulations. We conduct Bayesian inference of IGM thermal parameters using an end-to-end forward modeling framework whereby mock spectra generated from our simulation grid are used to build a custom emulator which interpolates the power spectrum between thermal grid points. The temperature at mean density $T_0$ rises steadily from $T_0\sim 6000\, \mathrm{K}$ at $z=5.4$, peaks at $14000\, \mathrm{K}$ for $z\sim 3.4$, and decreases at lower redshift reaching $T_0\sim 7000\, \mathrm{K}$ by $z\sim1.8$. This evolution provides conclusive evidence for photoionization heating resulting from the reionization of He II, as well as the subsequent cooling of the IGM due to the expansion of the Universe after all reionization events are complete. Our results are broadly consistent with previous measurements of thermal evolution based on a variety of approaches, but the sensitivity of the power spectrum, the combination of high precision BOSS measurements of large-scale modes ($k\lesssim 0.02\, \mathrm{s/km}$) with our recent determination of the small-scale power, our large grid of models, and our careful statistical analysis allow us to break the well known degeneracy between the temperature at mean density $T_0$ and the slope of the temperature density relation $γ$ that has plagued previous analyses. At the highest redshifts $z\geq5$ we infer lower temperatures than expected from the standard picture of IGM thermal evolution leaving little room for additional smoothing of the Lyα forest by free streaming of warm dark matter.

astro-ph.CO↗

arXiv2018

A Detection of $z$~2.3 Cosmic Voids from 3D Lyman-$α$ Forest Tomography in the COSMOS Field

We present the most distant detection of cosmic voids ($z \sim 2.3$) and the first detection of three-dimensional voids in the Lyman-$α$ forest. We used a 3D tomographic map of the absorption with effective comoving spatial resolution of $2.5\,h^{-1}\mathrm{Mpc}$ and volume of $3.15\times 10^5\,h^{-3}\mathrm{Mpc}^3$, which was reconstructed from moderate-resolution Keck-I/LRIS spectra of 240 background Lyman-break galaxies and quasars in a $0.16\,\mathrm{deg}^2$ footprint in the COSMOS field. Voids were detected using a spherical overdensity finder calibrated from hydrodynamical simulations of the intergalactic medium. This allows us to identify voids in the IGM corresponding to voids in the underlying matter density field, yielding a consistent volume fraction of voids in both data (19.5%) and simulations (18.2%). We fit excursion set models to the void radius function and compare the radially-averaged stacked profiles of large voids ($r > 5$ $h^{-1}$ Mpc) to stacked voids in mock observations and the simulated density field. Comparing with 432 coeval galaxies with spectroscopic redshifts in the same volume as the tomographic map, we find that the tomography-identified voids are underdense in galaxies by 5.95$σ$ compared to random cells.

astro-ph.CO↗

arXiv2018

Quantitative Constraints on the Reionization History from the IGM Damping Wing Signature in Two Quasars at z > 7

During reionization, neutral hydrogen in the intergalactic medium (IGM) imprints a damping wing absorption feature on the spectrum of high-redshift quasars. A detection of this signature provides compelling evidence for a significantly neutral Universe, and enables measurements of the hydrogen neutral fraction $x_{\rm HI}(z)$ at that epoch. Obtaining reliable quantitative constraints from this technique, however, is challenging due to stochasticity induced by the patchy inside-out topology of reionization, degeneracies with quasar lifetime, and the unknown unabsorbed quasar spectrum close to rest-frame Ly$α$. We combine a large-volume semi-numerical simulation of reionization topology with 1D radiative transfer through high-resolution hydrodynamical simulations of the high-redshift Universe to construct models of quasar transmission spectra during reionization. Our state-of-the-art approach captures the distribution of damping wing strengths in biased quasar halos that should have reionized earlier, as well as the erosion of neutral gas in the quasar environment caused by its own ionizing radiation. Combining this detailed model with our new technique for predicting the quasar continuum and its associated uncertainty, we introduce a Bayesian statistical method to jointly constrain the neutral fraction of the Universe and the quasar lifetime from individual quasar spectra. We apply this methodology to the spectra of the two highest redshift quasars known, ULAS J1120+0641 and ULAS J1342+0928, and measured volume-averaged neutral fractions $\langle x_{\rm HI} \rangle(z=7.09)=0.48^{+0.26}_{-0.26}$ and $\langle x_{\rm HI} \rangle(z=7.54)=0.60^{+0.20}_{-0.23}$ (posterior medians and 68% credible intervals) when marginalized over quasar lifetimes of $10^3 \leq t_{\rm q} \leq 10^8$ years.

astro-ph.CO↗

arXiv2018

DESCQA: An Automated Validation Framework for Synthetic Sky Catalogs

The use of high-quality simulated sky catalogs is essential for the success of cosmological surveys. The catalogs have diverse applications, such as investigating signatures of fundamental physics in cosmological observables, understanding the effect of systematic uncertainties on measured signals and testing mitigation strategies for reducing these uncertainties, aiding analysis pipeline development and testing, and survey strategy optimization. The list of applications is growing with improvements in the quality of the catalogs and the details that they can provide. Given the importance of simulated catalogs, it is critical to provide rigorous validation protocols that enable both catalog providers and users to assess the quality of the catalogs in a straightforward and comprehensive way. For this purpose, we have developed the DESCQA framework for the Large Synoptic Survey Telescope Dark Energy Science Collaboration as well as for the broader community. The goal of DESCQA is to enable the inspection, validation, and comparison of an inhomogeneous set of synthetic catalogs via the provision of a common interface within an automated framework. In this paper, we present the design concept and first implementation of DESCQA. In order to establish and demonstrate its full functionality we use a set of interim catalogs and validation tests. We highlight several important aspects, both technical and scientific, that require thoughtful consideration when designing a validation framework, including validation metrics and how these metrics impose requirements on the synthetic sky catalogs.

astro-ph.IM↗