arXiv subjects

Michael Walther

Publications and source records attributed to Michael Walther.

At least 19 recordsLinked to original sources

arXiv2026

A Measurement of the Thermal and Ionization State of the IGM at $z < 0.5$

We apply a machine-learning-based inference method that exploits the joint Doppler parameter-column density (b-NHI) distribution from Lya forest decomposition to measure the thermal and ionization state of the intergalactic medium (IGM) in four redshift bins spanning z = 0.06 to 0.48, using 82 archival quasar spectra from the Cosmic Origin Spectrograph (COS) on board Hubble Space Telescope (HST). Our results show that the low-z IGM (z < 0.5) is extremely hot and nearly isothermal, with log(T0/K) = 4.45 (+0.08 / -0.12) [T0 = 28183 (+5700 / -6804) K] and gamma = 1.06 (+0.13 / -0.09) at z = 0.1. This temperature lies approx 7sigma (and 7 times) above the canonical prediction (log T0 approx 3.60, i.e. T0 ~ 4000 K, with gamma ~ 1.6 at z = 0), where the IGM is expected to have cooled long after He II reionization. We also measure the hydrogen photoionization rate to be log (GammaHI/s^-1) = -13.70 (+0.10 / -0.08) at z = 0.1, which is about approx 4sigma below the range predicted by current UV-background synthesis models (approx -13.3). To investigate the discrepancy between these high temperatures and theoretical models, we assess the impact of small-scale turbulence. By exploring a parameter grid in turbulent velocity (vtur) and GammaHI, we find that a standard IGM thermal and ionization state combined with unresolved turbulence of vtur simeq 15 km s^-1 can successfully reproduce the observed line widths at z = 0.1. Comparisons with high-resolution Space Telescope Imaging Spectrograph (STIS) expanded data indicate that the observed line widths are unlikely to be caused by instrumental resolution effects. Our findings suggest that either new heating mechanisms or unresolved turbulence are required to explain the unexpectedly broad Lya lines observed in the low-z IGM.

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arXiv2025

Ly{\alpha}NNA II: Field-level inference with noisy Ly{\alpha} forest spectra

Deep learning (DL) has been shown to outperform traditional, human-defined summary statistics of the Ly{\alpha} forest in constraining key astrophysical and cosmological parameters owing to its ability to tap into the realm of non-Gaussian information. An understanding of the impact of nuisance effects such as noise on such field-level frameworks, however, still remains elusive. In this work we conduct a systematic investigation into the efficacy of DL inference from noisy Ly{\alpha} forest spectra. Building upon our previous, proof-of-concept framework (Nayak et al. 2024) for pure spectra, we constructed and trained a ResNet neural network using labeled mock data from hydrodynamical simulations with a range of noise levels to optimally compress noisy spectra into a novel summary statistic that is exclusively sensitive to the power-law temperature-density relation of the intergalactic medium. We fit a Gaussian mixture surrogate with 23 components through our labels and summaries to estimate the joint data-parameter distribution for likelihood free inference, in addition to performing inference with a Gaussian likelihood. The posterior contours in the two cases agree well with each other. We compared the precision and accuracy of our posterior constraints with a combination of two human defined summaries (the 1D power spectrum and PDF of the Ly{\alpha} transmission) that have been corrected for noise, over a wide range of continuum-to-noise ratios (CNR) in the likelihood case. We found a gain in precision in terms of posterior contour area with our pipeline over the said combination of 65% (at a CNR of 20 per 6 km/s) to 112% (at 200 per 6 km/s). While the improvement in posterior precision is not as large as in the noiseless case, these results indicate that DL still remains a powerful tool for inference even with noisy, real-world datasets.

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arXiv2025

DESI DR1 Ly{\alpha} 1D power spectrum: The Fast Fourier Transform estimator measurement

We present the one-dimensional Lyman-$\alpha$ forest power spectrum measurement derived from the data release 1 (DR1) of the Dark Energy Spectroscopic Instrument (DESI). The measurement of the Lyman-$\alpha$ forest power spectrum along the line of sight from high-redshift quasar spectra provides information on the shape of the linear matter power spectrum, neutrino masses, and the properties of dark matter. In this work, we use a Fast Fourier Transform (FFT)-based estimator, which is validated on synthetic data in a companion paper. Compared to the FFT measurement performed on the DESI early data release, we improve the noise characterization with a cross-exposure estimator and test the robustness of our measurement using various data splits. We also refine the estimation of the uncertainties and now present an estimator for the covariance matrix of the measurement. Furthermore, we compare our results to previous high-resolution and eBOSS measurements. In another companion paper, we present the same DR1 measurement using the Quadratic Maximum Likelihood Estimator (QMLE). These two measurements are consistent with each other and constitute the most precise one-dimensional power spectrum measurement to date, while being in good agreement with results from the DESI early data release.

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arXiv2025

Dynamical Dark Energy in light of the DESI DR2 Baryonic Acoustic Oscillations Measurements

Understanding whether cosmic acceleration arises from a cosmological constant or a dynamical component is a central goal of cosmology, and the Dark Energy Spectroscopic Instrument (DESI) enables stringent tests with high-precision distance measurements. We analyze baryon acoustic oscillation (BAO) measurements from DESI Data Release 1 (DR1) and Data Release 2 (DR2), combined with Type Ia supernovae and a cosmic microwave background (CMB) distance prior. With the larger statistical power and wider redshift coverage of DR2, the preference for dynamical dark energy does not diminish relative to DR1. Using both a shape-function reconstruction and non-parametric approaches with a Horndeski-motivated correlation prior, we find that the dark-energy equation of state $w(z)$ varies with redshift. BAO data alone yield modest constraints, but in combination with independent supernova compilations and the CMB prior they strengthen the evidence for dynamics. Bayesian model comparison shows moderate support for departures from $\Lambda$CDM when multiple degrees of freedom in $w(z)$ are allowed, corresponding to $\approx3\sigma$ tension with $\Lambda$CDM (and higher for some data sets). Despite methodological differences, our results are consistent with companion DESI papers, underscoring the complementarity of approaches. Possible systematics remain under study; forthcoming DESI, \emph{Euclid}, and next-generation CMB data will provide decisive tests.

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arXiv2024

Emulating the Lyman-Alpha forest 1D power spectrum from cosmological simulations: New models and constraints from the eBOSS measurement

We present the Lyssa suite of high-resolution cosmological simulations of the Lyman-$\alpha$ forest designed for cosmological analyses. These 18 simulations have been run using the Nyx code with $4096^3$ hydrodynamical cells in a 120 Mpc ($\sim$ 81Mpc/h) comoving box and individually provide sub-percent level convergence of the Lyman-$\alpha$ forest 1d flux power spectrum. We build a Gaussian process emulator for the Lyssa simulations in the lym1d likelihood framework to interpolate the power spectrum at arbitrary parameter values. We validate this emulator based on leave-one-out tests and based on the parameter constraints for simulations outside of the training set. We also perform comparisons with a previous emulator, showing a percent level accuracy and a good recovery of the expected cosmological parameters. Using this emulator we derive constraints on the linear matter power spectrum amplitude and slope parameters $A_{\mathrm{Ly}\alpha}$ and $n_{\mathrm{Ly}\alpha}$. While the best-fit Planck $\Lambda$CDM model has $A_{\mathrm{Ly}\alpha}=8.79$ and $n_{\mathrm{Ly}\alpha}=-2.363$, from DR14 eBOSS data we find that $A_{\mathrm{Ly}\alpha}<7.6$ (95\% CI) and $n_{\mathrm{Ly}\alpha}=-2.369 \pm 0.008$. The low value of $A_{\mathrm{Ly}\alpha}$, in tension with Planck, is driven by the correlation of this parameter with the mean transmission of the Lyman-$\alpha$ forest. This tension disappears when imposing a well-motivated external prior on this mean transmission, in which case we find $A_{\mathrm{Ly}\alpha}=9.8\pm1.1$ in accordance with Planck.

astro-ph.CO↗

arXiv2024

Towards learning digital twin: case study on an anisotropic non-ideal rotor system

In the manufacturing industry, the digital twin (DT) is becoming a central topic. It has the potential to enhance the efficiency of manufacturing machines and reduce the frequency of errors. In order to fulfill its purpose, a DT must be an exact enough replica of its corresponding physical object. Nevertheless, the physical object endures a lifelong process of degradation. As a result, the digital twin must be modified accordingly in order to satisfy the accuracy requirement. This article introduces the novel concept of "learning digital twin (LDT)," which concentrates on the temporal behavior of the physical object and highlights the digital twin's capacity for lifelong learning. The structure of a LDT is first described. Then, in-depth descriptions of various algorithms for implementing each component of a LDT are provided. The proposed LDT is validated on the simulated degradation process of an anisotropic non-ideal rotor system.

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arXiv2023

Measurements of the Thermal and Ionization State of the Intergalactic Medium during the Cosmic Afternoon

We perform the first measurement of the thermal and ionization state of the intergalactic medium (IGM) across 0.9 < z < 1.5 using 301 \lya absorption lines fitted from 12 HST STIS quasar spectra, with a total pathlength of \Delta z=2.1. We employ the machine-learning-based inference method that uses joint b-N distributions obtained from \lyaf decomposition. Our results show that the HI photoionization rates, \Gamma, are in good agreement with the recent UV background synthesis models, with \log (\Gamma/s^{-1})={-11.79}^{0.18}_{-0.15}, -11.98}^{0.09}_{-0.09}, and {-12.32}^{0.10}_{-0.12} at z=1.4, 1.2, and 1 respectively. We obtain the IGM temperature at the mean density, T_0, and the adiabatic index, \gamma, as [\log (T_0/K), \gamma]= [{4.13}^{+0.12}_{-0.10}, {1.34}^{+0.10}_{-0.15}], [{3.79}^{+0.11}_{-0.11}, {1.70}^{+0.09}_{-0.09}] and [{4.12}^{+0.15}_{-0.25}, {1.34}^{+0.21}_{-0.26}] at z=1.4, 1.2 and 1 respectively. Our measurements of T_0 at z=1.4 and 1.2 are consistent with the expected trend from z<3 temperature measurements as well as theoretical expectations that, in the absence of any non-standard heating, the IGM should cool down after HeII reionization. Whereas, our T_0 measurements at z=1 show unexpectedly high IGM temperature. However, because of the relatively large uncertainty in these measurements of the order of \Delta T_0~5000 K, mostly emanating from the limited redshift path length of available data in these bins, we can not definitively conclude whether the IGM cools down at z<1.5. Lastly, we generate a mock dataset to test the constraining power of future measurement with larger datasets. The results demonstrate that, with redshift pathlength \Delta z \sim 2 for each redshift bin, three times the current dataset, we can constrain the T_0 of IGM within 1500K. Such precision would be sufficient to conclusively constrain the history of IGM thermal evolution at z < 1.5.

astro-ph.CO↗

arXiv2023

Searching for the Imprints of AGN Feedback on the Lyman Alpha Forest Around Luminous Red Galaxies

We explore the potential of using the low-redshift Lyman-$\alpha$ (Ly$\alpha$) forest surrounding luminous red galaxies (LRGs) as a tool to constrain active galactic nuclei (AGN) feedback models. Our analysis is based on snapshots from the Illustris and IllustrisTNG simulations at a redshift of $z=0.1$. These simulations offer an ideal platform for studying the influence of AGN feedback on the gas surrounding galaxies, as they share the same initial conditions and underlying code but incorporate different feedback prescriptions. Both simulations show significant impacts of feedback on the temperature and density of the gas around massive halos. Following our previous work, we adjusted the UV background in both simulations to align with the observed number density of Ly$\alpha$ lines ($\rm dN/dz$) in the intergalactic medium and study the Ly$\alpha$ forest around massive halos hosting LRGs, at impact parameters ($r_{\perp}$) ranging from 0.1 to 100 pMpc. Our findings reveal that $\rm dN/dz$, as a function of $r_{\perp}$, is approximately 1.5 to 2 times higher in IllustrisTNG compared to Illustris up to $r_{\perp}$ of $\sim 10$ pMpc. To further assess whether existing data can effectively discern these differences, we search for archival data containing spectra of background quasars probing foreground LRGs. Through a feasibility analysis based on this data, we demonstrate that ${\rm dN/dz} (r_{\perp})$ measurements can distinguish between feedback models of IllustrisTNG and Illustris with a precision exceeding 12$\sigma$. This underscores the potential of ${\rm dN/dz} (r_{\perp})$ measurements around LRGs as a valuable benchmark observation for discriminating between different feedback models.

astro-ph.GA↗

arXiv2023

Ly$\alpha$NNA: A Deep Learning Field-level Inference Machine for the Lyman-$\alpha$ Forest

The inference of astrophysical and cosmological properties from the Lyman-$\alpha$ forest conventionally relies on summary statistics of the transmission field that carry useful but limited information. We present a deep learning framework for inference from the Lyman-$\alpha$ forest at field-level. This framework consists of a 1D residual convolutional neural network (ResNet) that extracts spectral features and performs regression on thermal parameters of the IGM that characterize the power-law temperature-density relation. We train this supervised machinery using a large set of mock absorption spectra from Nyx hydrodynamic simulations at $z=2.2$ with a range of thermal parameter combinations (labels). We employ Bayesian optimization to find an optimal set of hyperparameters for our network, and then employ a committee of 20 neural networks for increased statistical robustness of the network inference. In addition to the parameter point predictions, our machine also provides a self-consistent estimate of their covariance matrix with which we construct a pipeline for inferring the posterior distribution of the parameters. We compare the results of our framework with the traditional summary (PDF and power spectrum of transmission) based approach in terms of the area of the 68% credibility regions as our figure of merit (FoM). In our study of the information content of perfect (noise- and systematics-free) Ly$\alpha$ forest spectral data-sets, we find a significant tightening of the posterior constraints -- factors of 10.92 and 3.30 in FoM over power spectrum only and jointly with PDF, respectively -- that is the consequence of recovering the relevant parts of information that are not carried by the classical summary statistics.

astro-ph.CO↗

arXiv2023

The Impact of the WHIM on the IGM Thermal State Determined from the Low-$z$ Lyman-$\alpha$ Forest

At $z \lesssim 1$, shock heating caused by large-scale velocity flows and possibly violent feedback from galaxy formation, converts a significant fraction of the cool gas ($T\sim 10^4$ K) in the intergalactic medium (IGM) into warm-hot phase (WHIM) with $T >10^5$K, resulting in a significant deviation from the previously tight power-law IGM temperature-density relationship, $T=T_0 (\rho / {\bar{\rho}})^{\gamma -1}$. This study explores the impact of the WHIM on measurements of the low-$z$ IGM thermal state, $[T_0,\gamma]$, based on the $b$-$N_{H I}$ distribution of the Lyman-$\alpha$ forest. Exploiting a machine learning-enabled simulation-based inference method trained on Nyx hydrodynamical simulations, we demonstrate that [$T_0$, $\gamma$] can still be reliably measured from the $b$-$N_{H I}$ distribution at $z=0.1$, notwithstanding the substantial WHIM in the IGM. To investigate the effects of different feedback, we apply this inference methodology to mock spectra derived from the IllustrisTNG and Illustris simulations at $z=0.1$. The results suggest that the underlying $[T_0,\gamma]$ of both simulations can be recovered with biases as low as $|\Delta \log(T_0/\text{K})| \lesssim 0.05$ dex, $|\Delta \gamma | \lesssim 0.1$, smaller than the precision of a typical measurement. Given the large differences in the volume-weighted WHIM fractions between the three simulations (Illustris 38\%, IllustrisTNG 10\%, Nyx 4\%) we conclude that the $b$-$N_{H I}$ distribution is not sensitive to the WHIM under realistic conditions. Finally, we investigate the physical properties of the detectable Lyman-$\alpha$ absorbers, and discover that although their $T$ and $\Delta$ distributions remain mostly unaffected by feedback, they are correlated with the photoionization rate used in the simulation.

astro-ph.CO↗

arXiv2023

Optimal 1D Ly$\alpha$ Forest Power Spectrum Estimation -- III. DESI early data

The one-dimensional power spectrum $P_{\mathrm{1D}}$ of the Ly$\alpha$ forest provides important information about cosmological and astrophysical parameters, including constraints on warm dark matter models, the sum of the masses of the three neutrino species, and the thermal state of the intergalactic medium. We present the first measurement of $P_{\mathrm{1D}}$ with the quadratic maximum likelihood estimator (QMLE) from the Dark Energy Spectroscopic Instrument (DESI) survey early data sample. This early sample of $54~600$ quasars is already comparable in size to the largest previous studies, and we conduct a thorough investigation of numerous instrumental and analysis systematic errors to evaluate their impact on DESI data with QMLE. We demonstrate the excellent performance of the spectroscopic pipeline noise estimation and the impressive accuracy of the spectrograph resolution matrix with two-dimensional image simulations of raw DESI images that we processed with the DESI spectroscopic pipeline. We also study metal line contamination and noise calibration systematics with quasar spectra on the red side of the Ly$\alpha$ emission line. In a companion paper, we present a similar analysis based on the Fast Fourier Transform estimate of the power spectrum. We conclude with a comparison of these two approaches and implications for the upcoming DESI Year 1 analysis.

astro-ph.CO↗

arXiv2023

The Dark Energy Spectroscopic Instrument: One-dimensional power spectrum from first Lyman-$\alpha$ forest samples with Fast Fourier Transform

We present the one-dimensional Lyman-$\alpha$ forest power spectrum measurement using the first data provided by the Dark Energy Spectroscopic Instrument (DESI). The data sample comprises $26,330$ quasar spectra, at redshift $z > 2.1$, contained in the DESI Early Data Release and the first two months of the main survey. We employ a Fast Fourier Transform (FFT) estimator and compare the resulting power spectrum to an alternative likelihood-based method in a companion paper. We investigate methodological and instrumental contaminants associated to the new DESI instrument, applying techniques similar to previous Sloan Digital Sky Survey (SDSS) measurements. We use synthetic data based on log-normal approximation to validate and correct our measurement. We compare our resulting power spectrum with previous SDSS and high-resolution measurements. With relatively small number statistics, we successfully perform the FFT measurement, which is already competitive in terms of the scale range. At the end of the DESI survey, we expect a five times larger Lyman-$\alpha$ forest sample than SDSS, providing an unprecedented precise one-dimensional power spectrum measurement.

astro-ph.CO↗

arXiv2023

Can the Low Redshift Lyman Alpha Forest Constrain AGN Feedback Models?

We investigate the potential of low-redshift Lyman alpha (Ly$\alpha$) forest for constraining active galactic nuclei (AGN) feedback models by analyzing the Illustris and IllustrisTNG simulation at z=0.1. These simulations are ideal for studying the impact of AGN feedback on the intergalactic medium (IGM) as they share initial conditions with significant differences in the feedback prescriptions. Both simulations reveal that the IGM is significantly impacted by AGN feedback. Specifically, feedback is stronger in Illustris and results in reducing cool baryon fraction to 23% relative to 39% in IllustrisTNG. However, when comparing various statistics of Ly$\alpha$ forest such as 2D and marginalized distributions of Doppler widths and H I column density, line density, and flux power spectrum with real data, we find that most of these statistics are largely insensitive to the differences in feedback models. This lack of sensitivity arises because of the fundamental degeneracy between the fraction of cool baryons and the H I photoionization rate ($\Gamma_{\rm HI}$) as their product determines the optical depth of the Ly$\alpha$ forest. Since the $\Gamma_{\rm HI}$ cannot be precisely predicted from first principles, it needs to be treated as a nuisance parameter adjusted to match the observed Ly$\alpha$ line density. After adjusting $\Gamma_{\rm HI}$, the distinctions in the considered statistics essentially fade away. Only the Ly$\alpha$ flux power spectrum at small spatial scales exhibits potentially observable differences, although this may be specific to the relatively extreme feedback model employed in Illustris. Without independent constraints on either $\Gamma_{\rm HI}$ or cool baryon fraction, constraining AGN feedback with low-redshift Ly$\alpha$ forest will be very challenging.

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arXiv2022

Optimal 1D Ly$α$ Forest Power Spectrum Estimation -- II. KODIAQ, SQUAD & XQ-100

We measure the 1D Ly$\,α$ power spectrum $P_\mathrm{1D}$ from Keck Observatory Database of Ionized Absorption toward Quasars (KODIAQ), The Spectral Quasar Absorption Database (SQUAD) and XQ-100 quasars using the optimal quadratic estimator. We combine KODIAQ and SQUAD at the spectrum level, but perform a separate XQ-100 estimation to control its large resolution corrections in check. Our final analysis measures $P_\mathrm{1D}$ at scales $k<0.1\,$s$\,$km$^{-1}$ between redshifts $z=$ 2.0 -- 4.6 using 538 quasars. This sample provides the largest number of high-resolution, high-S/N observations; and combined with the power of optimal estimator it provides exceptional precision at small scales. These small-scale modes ($k\gtrsim 0.02\,$s$\,$km$^{-1}$), unavailable in Sloan Digital Sky Survey (SDSS) and Dark Energy Spectroscopic Instrument (DESI) analyses, are sensitive to the thermal state and reionization history of the intergalactic medium, as well as the nature of dark matter. As an example, a simple Fisher forecast analysis estimates that our results can improve small-scale cut off sensitivity by more than a factor of 2.

astro-ph.CO↗

arXiv2022

The effect of quasar redshift errors on Lyman-$α$ forest correlation functions

Using synthetic Lyman-$α$ forests from the Dark Energy Spectroscopic Instrument (DESI) survey, we present a study of the impact of errors in the estimation of quasar redshift on the Lyman-$α$ correlation functions. Estimates of quasar redshift have large uncertainties of a few hundred $\text{km s}^{-1}\,$ due to the broadness of the emission lines and the intrinsic shifts from other emission lines. We inject Gaussian random redshift errors into the mock quasar catalogues, and measure the auto-correlation and the Lyman-$α$-quasar cross-correlation functions. We find a smearing of the BAO feature in the radial direction, but changes in the peak position are negligible. However, we see a significant unphysical correlation for small separations transverse to the line of sight which increases with the amplitude of the redshift errors. We interpret this contamination as a result of the broadening of emission lines in the measured mean continuum, caused by quasar redshift errors, combined with the unrealistically strong clustering of the simulated quasars on small scales.

astro-ph.CO↗

arXiv2022

Measuring the thermal and ionization state of the low-$z$ IGM using likelihood free inference

We present a new approach to measure the power-law temperature density relationship $T=T_0 (ρ/ \barρ)^{γ-1}$ and the UV background photoionization rate $Γ_{\rm HI}$ of the IGM based on the Voigt profile decomposition of the Ly$α$ forest into a set of discrete absorption lines with Doppler parameter $b$ and the neutral hydrogen column density $N_{\rm HI}$. Previous work demonstrated that the shape of the $b$-$N_{\rm HI}$ distribution is sensitive to the IGM thermal parameters $T_0$ and $γ$, whereas our new inference algorithm also takes into account the normalization of the distribution, i.e. the line-density d$N$/d$z$, and we demonstrate that precise constraints can also be obtained on $Γ_{\rm HI}$. We use density-estimation likelihood-free inference (DELFI) to emulate the dependence of the $b$-$N_{\rm HI}$ distribution on IGM parameters trained on an ensemble of 624 Nyx hydrodynamical simulations at $z = 0.1$, which we combine with a Gaussian process emulator of the normalization. To demonstrate the efficacy of this approach, we generate hundreds of realizations of realistic mock HST/COS datasets, each comprising 34 quasar sightlines, and forward model the noise and resolution to match the real data. We use this large ensemble of mocks to extensively test our inference and empirically demonstrate that our posterior distributions are robust. Our analysis shows that by applying our new approach to existing Ly$α$ forest spectra at $z\simeq 0.1$, one can measure the thermal and ionization state of the IGM with very high precision ($σ_{\log T_0} \sim 0.08$ dex, $σ_γ\sim 0.06$, and $σ_{\log Γ_{\rm HI}} \sim 0.07$ dex).

astro-ph.CO↗

arXiv2022

Snowmass2021 Cosmic Frontier White Paper: Prospects for obtaining Dark Matter Constraints with DESI

Despite efforts over several decades, direct-detection experiments have not yet led to the discovery of the dark matter (DM) particle. This has led to increasing interest in alternatives to the Lambda CDM (LCDM) paradigm and alternative DM scenarios (including fuzzy DM, warm DM, self-interacting DM, etc.). In many of these scenarios, DM particles cannot be detected directly and constraints on their properties can ONLY be arrived at using astrophysical observations. The Dark Energy Spectroscopic Instrument (DESI) is currently one of the most powerful instruments for wide-field surveys. The synergy of DESI with ESA's Gaia satellite and future observing facilities will yield datasets of unprecedented size and coverage that will enable constraints on DM over a wide range of physical and mass scales and across redshifts. DESI will obtain spectra of the Lyman-alpha forest out to z~5 by detecting about 1 million QSO spectra that will put constraints on clustering of the low-density intergalactic gas and DM halos at high redshift. DESI will obtain radial velocities of 10 million stars in the Milky Way (MW) and Local Group satellites enabling us to constrain their global DM distributions, as well as the DM distribution on smaller scales. The paradigm of cosmological structure formation has been extensively tested with simulations. However, the majority of simulations to date have focused on collisionless CDM. Simulations with alternatives to CDM have recently been gaining ground but are still in their infancy. While there are numerous publicly available large-box and zoom-in simulations in the LCDM framework, there are no comparable publicly available WDM, SIDM, FDM simulations. DOE support for a public simulation suite will enable a more cohesive community effort to compare observations from DESI (and other surveys) with numerical predictions and will greatly impact DM science.

astro-ph.CO↗

arXiv2022

First measurement of the correlation between cosmic voids and the Lyman-$\alpha$ forest

We report the first detection at a median redshift $z = 2.49$ of large-scale matter flows around cosmic voids. Voids are identified within a tomographic map of large-scale Lyman-$\alpha$ (Ly$\alpha$) transmissions, built from the eBOSS Ly$\alpha$ forest sample in the SDSS Stripe 82 field. We measure the imprint of flows around voids, known as redshift-space distortions (RSD), with a statistical significance of $8\,\sigma$. The observed quadrupole of the void-forest cross-correlation is described by a linear RSD model. The derived RSD parameter of the Ly$\alpha$ forest around voids is $\beta = 1.21 \pm 0.18$. Our model accounts for the tomographic effect induced by the Ly$\alpha$ data being located along parallel quasar lines of sight. This work presents a novel approach to observing the growth of cosmic structures at redshifts currently inaccessible to galaxy surveys.

astro-ph.CO↗