Redshift Errors Research Articles

On the capability of high redshift kSZ measurement with galaxy surveys

The kinematic Sunyaev-Zel'dovich (kSZ) effect has been detected at z < 1 using various techniques and data sets. The ongoing and upcoming spectroscopic galaxy surveys such as DESI (Dark Energy Spectroscopic Instrument) and PFS (Prime Focus Spectrograph) will push the detection beyond z = 1, and therefore map the baryon distribution at high redshifts. Such detection can be achieved by both the kSZ stacking and tomography methods. While the two methods are theoretically equivalent, they differ significantly in the probed physics and scales, and required data sets. Taking the combination of PFS and ACT (Atacama Cosmology Telescope) as an example, we build mocks of kSZ and galaxies, quantify the kSZ detection S/N, and compare between the two methods. We segment the PFS galaxies into three redshift bins: 0.6 < z < 1.0, 1.0 < z < 1.6, and 1.6 < z < 2.4. For tomography method, our analysis reveals that the two higher redshift bins exhibit significantly higher S/N ratios, with values of 32 and 28, respectively, compared to the first redshift bin, which yielded an S/N of 8. This is attributed to not only the increasing of electron density with redshifts, but also the larger survey volume and the reduced non-linearity, facilitating velocity reconstruction at higher redshifts. Therefore, the capability of the PFS survey to measure high redshift kSZ effect stands as a substantial advantage over other spectroscopic surveys at lower redshift. The S/N of kSZ stacking largely depends on the number of galaxy groups available from another photometric survey. But in general, its S/N is lower than that of kSZ tomography, largely due to CMB instrument noise and error in galaxy group redshift. Incorporating next-generation CMB surveys like CMB-S4, characterized by significantly reduced instrument noise and improved angular resolution, is expected to enhance tomographic detection by a factor of ten and stacking detection by fivefold. This future high S/N detection holds the promise of not only providing precise constraints on the overall baryon abundance but also initiating a new insight into baryon distribution.

Measuring the Conditional Luminosity and Stellar Mass Functions of Galaxies by Combining the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 Data

In this investigation, we leverage the combination of the Dark Energy Spectroscopic Instrument (DESI) Legacy Imaging Surveys Data Release 9, Survey Validation 3, and Year 1 data sets to estimate the conditional luminosity functions and conditional stellar mass functions (CLFs and CSMFs) of galaxies across various halo mass bins and redshift ranges. To support our analysis, we utilize a realistic DESI mock galaxy redshift survey (MGRS) generated from a high-resolution Jiutian simulation. An extended halo-based group finder is applied to both MGRS catalogs and DESI observation. By comparing the r- and z-band luminosity functions (LFs) and stellar mass functions (SMFs) derived using both photometric and spectroscopic data, we quantified the impact of photometric redshift (photo-z) errors on the galaxy LFs and SMFs, especially in the low-redshift bin at the low-luminosity/mass end. By conducting prior evaluations of the group finder using MGRS, we successfully obtain a set of CLF and CSMF measurements from observational data. We find that at low redshift, the faint-end slopes of CLFs and CSMFs below ∼109 h −2 L ⊙ (or h −2 M ⊙) evince a compelling concordance with the subhalo mass functions. After correcting the cosmic variance effect of our local Universe following Chen et al., the faint-end slopes of the LFs/SMFs turn out to also be in good agreement with the slope of the halo mass function.

The FENIKS Survey: Multiwavelength Photometric Catalog in the UDS Field, and Catalogs of Photometric Redshifts and Stellar Population Properties

We present the construction of a deep multiwavelength point-spread-function-matched photometric catalog in the Ultra-Deep Survey (UDS) field following the final UKIDSS UDS release. The catalog includes photometry in 24 filters, from the MegaCam-uS 0.38 μm band to the Spitzer-IRAC 8 μm band, over ∼0.9 deg2 and with a 5σ depth of 25.3 AB in the K-band detection image. The catalog, containing ≈188,564 (136,235) galaxies at 0.2 < z < 8.0 with stellar mass log(M*/M⊙)>8 and K-band total magnitude K < 25.2 (24.3) AB, enables a range of extragalactic studies. We also provide photometric redshifts, corresponding redshift probability distributions, and rest-frame absolute magnitudes and colors derived using the template-fitting code eazy-py. Photometric redshift errors are less than 3%−4% at z < 4 across the full brightness range in the K band and stellar mass range 8<log(M*/M⊙)<12 . Stellar population properties (e.g., stellar mass, star formation rate, dust extinction) are derived from the modeling of the spectral energy distributions using the codes FAST and Dense Basis.

Galaxy Spectra neural Network (GaSNet). II. Using deep learning for spectral classification and redshift predictions

ABSTRACT The size and complexity reached by the large sky spectroscopic surveys require efficient, accurate, and flexible automated tools for data analysis and science exploitation. We present the Galaxy Spectra Network/GaSNet-II, a supervised multinetwork deep learning tool for spectra classification and redshift prediction. GaSNet-II can be trained to identify a customized number of classes and optimize the redshift predictions. Redshift errors are determined via an ensemble/pseudo-Monte Carlo test obtained by randomizing the weights of the network-of-networks structure. As a demonstration of the capability of GaSNet-II, we use 260k Sloan Digital Sky Survey spectra from Data Release 16, separated into 13 classes including 140k galactic, and 120k extragalactic objects. GaSNet-II achieves 92.4 per cent average classification accuracy over the 13 classes and mean redshift errors of approximately 0.23 per cent for galaxies and 2.1 per cent for quasars. We further train/test the pipeline on a sample of 200k 4MOST (4-metre Multi-Object Spectroscopic Telescope) mock spectra and 21k publicly released DESI (Dark Energy Spectroscopic Instrument) spectra. On 4MOST mock data, we reach 93.4 per cent accuracy in 10-class classification and mean redshift error of 0.55 per cent for galaxies and 0.3 per cent for active galactic nuclei. On DESI data, we reach 96 per cent accuracy in (star/galaxy/quasar only) classification and mean redshift error of 2.8 per cent for galaxies and 4.8 per cent for quasars, despite the small sample size available. GaSNet-II can process ∼40k spectra in less than one minute, on a normal Desktop GPU. This makes the pipeline particularly suitable for real-time analyses and feedback loops for optimization of Stage-IV survey observations.

Tomographic cross correlations between galaxy surveys and the CMB gravitational lensing potential. Effect of the redshift bin mismatch

Upcoming surveys of the large-scale structure of our Universe will employ a large coverage area of about half of the sky and will significantly increase the observational depth. With these surveys, we will be able to cross-correlate cosmic microwave background (CMB) gravitational lensing and galaxy surveys divided into narrow redshift bins to map the evolution of the cosmological parameters with redshift. We study the effect of the redshift bin mismatch of objects that is due to photometric redshift errors in tomographic cross-correlation measurements. We used the code FLASK to create Monte Carlo simulations of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) and Planck CMB lensing convergence. We simulated log-normal fields and divided galaxies into nine redshift bins with the Gaussian and modified Lorentzian photometric redshift errors. To estimate the parameters, we used angular power spectra of CMB lensing and galaxy density contrast fields and the maximum likelihood estimation method. We show that even with simple Gaussian errors with a standard deviation of $ the galaxy auto-power spectra in tomographic bins are offset by $2-15&lt;!PCT!&gt;$. The estimated cross-power spectra between galaxy clustering and CMB lensing are also biased, with smaller deviations $&lt;5&lt;!PCT!&gt;$. As a result, the $ $ parameter deviates between $0.2-1.2\ due to the redshift bin mismatch of the objects. We propose a computationally fast and robust method based on the scattering matrix approach to correct for the redshift bin mismatch of the objects. The estimates of the parameters in tomographic studies such as the linear galaxy bias, the cross-correlation amplitude, and $ $ are biased due to the redshift bin mismatch of the objects. The biases in these parameters are alleviated with our scattering matrix approach.

The miniJPAS survey: Maximising the photo-z accuracy from multi-survey datasets with probability conflation

We present a new method for obtaining photometric redshifts (photo-z) for sources observed by multiple photometric surveys using a combination (conflation) of the redshift probability distributions (PDZs) obtained independently from each survey. The conflation of the PDZs has several advantages over the usual method of modelling all the photometry together, including the modularity, speed, and accuracy of the results. Using a sample of galaxies with narrow-band photometry in 56 bands from J-PAS and deeper grίzy photometry from the Hyper-SuprimeCam Subaru Strategic program (HSC-SSP), we show that PDZ conflation significantly improves photo-z accuracy compared to fitting all the photometry or using a weighted average of point estimates. The improvement over J-PAS alone is particularly strong for i≳22 sources, which have low signal-to-noise ratios in the J-PAS bands. For the entire i&lt;22.5 sample, we obtain a 64% (45%) increase in the number of sources with redshift errors |Δz|&lt;0.003, a factor of 3.3 (1.9) decrease in the normalised median absolute deviation of the errors (σNMAD), and a factor of 3.2 (1.3) decrease in the outlier rate (η) compared to J-PAS (HSC-SSP) alone. The photo-z accuracy gains from combining the PDZs of J-PAS with a deeper broad-band survey such as HSC-SSP are equivalent to increasing the depth of J-PAS observations by ~ 1.2–1.5 mag. These results demonstrate the potential of PDZ conflation and highlight the importance of including the full PDZs in photo-z catalogues.

Analysis of the impact of broad absorption lines on quasar redshift measurements with synthetic observations

ABSTRACT Accurate quasar classifications and redshift measurements are increasingly important to precision cosmology experiments. Broad absorption line (BAL) features are present in 15–20 per cent of all quasars, and these features can introduce systematic redshift errors, and in extreme cases produce misclassifications. We quantitatively investigate the impact of BAL features on quasar classifications and redshift measurements with synthetic spectra that were designed to match observations by the Dark Energy Spectroscopic Instrument (DESI) survey. Over the course of 5 yr, DESI aims to measure spectra for 40 million galaxies and quasars, including nearly three million quasars. Our synthetic quasar spectra match the signal-to-noise ratio and redshift distributions of the first year of DESI observations, and include the same synthetic quasar spectra both with and without BAL features. We demonstrate that masking the locations of the BAL features decreases the redshift errors by about 1 per cent and reduces the number of catastrophic redshift errors by about 80 per cent. We conclude that identifying and masking BAL troughs should be a standard part of the redshift determination step for DESI and other large-scale spectroscopic surveys of quasars.

Tightly coupled INS/CNS/spectral redshift integrated navigation system with the aid of redshift error measurement

Tightly coupled INS/CNS/spectral redshift integrated navigation system with the aid of redshift error measurement

Weak lensing tomographic redshift distribution inference for the Hyper Suprime-Cam Subaru Strategic Program three-year shape catalogue

ABSTRACTWe present posterior sample redshift distributions for the Hyper Suprime-Cam Subaru Strategic Program Weak Lensing three-year (HSC Y3) analysis. Using the galaxies’ photometry and spatial cross-correlations, we conduct a combined Bayesian Hierarchical Inference of the sample redshift distributions. The spatial cross-correlations are derived using a subsample of Luminous Red Galaxies (LRGs) with accurate redshift information available up to a photometric redshift of z &amp;lt; 1.2. We derive the photometry-based constraints using a combination of two empirical techniques calibrated on spectroscopic and multiband photometric data that cover a spatial subset of the shear catalogue. The limited spatial coverage induces a cosmic variance error budget that we include in the inference. Our cross-correlation analysis models the photometric redshift error of the LRGs to correct for systematic biases and statistical uncertainties. We demonstrate consistency between the sample redshift distributions derived using the spatial cross-correlations, the photometry, and the posterior of the combined analysis. Based on this assessment, we recommend conservative priors for sample redshift distributions of tomographic bins used in the three-year cosmological Weak Lensing analyses.

Constraints on Dark Energy from the CSST Galaxy Clusters

We study the potential of the galaxy cluster sample expected from the Chinese Space Station Telescope (CSST) survey to constrain dark energy properties. By modeling the distribution of observed cluster mass for a given true mass to be log-normal and adopting a selection threshold in the observed mass M 200m ≥ 0.836 × 1014 h −1 M ⊙, we find about 4.1 × 105 clusters in the redshift range 0 ≤ z ≤ 1.5 can be detected by the CSST. We construct the Fisher matrix for the cluster number counts from CSST, and forecast constraints on dark energy parameters for models with constant (w 0CDM) and time dependent (w 0 w a CDM) equation of state. In the self-calibration scheme, the dark energy equation of state parameter w 0 of the w 0CDM model can be constrained to Δw 0 = 0.036. If w a is added as a free parameter, we obtain Δw 0 = 0.077 and Δw a = 0.39 for the w 0 w a CDM model, with a Figure of Merit for (w 0, w a ) of 68.99. Should we have perfect knowledge of the observable-mass scaling relation (“known SR” scheme), we would obtain Δw 0 = 0.012 for the w 0CDM model, and Δw 0 = 0.062 and Δw a = 0.24 for the w 0 w a CDM model. The dark energy Figure of Merit of (w 0, w a ) increases to 343.25. This indicates again the importance of calibrating the observable-mass scaling relation for optically selected galaxy clusters. By extending the maximum redshift of the clusters from to , the dark energy Figure of Merit for (w 0, w a ) increases to 89.72 (self-calibration scheme) and 610.97 (“known SR” scheme), improved by a factor of ∼1.30 and ∼1.78, respectively. We find that the impact of clusters’ redshift uncertainty on the dark energy constraints is negligible as long as the redshift error of clusters is smaller than 0.01, achievable by CSST. We also find that the bias in logarithm mass must be calibrated to be 0.30 or better to avoid significant dark energy parameter bias.

Cosmological constraints from HSC survey first-year data using deep learning

ABSTRACT We present cosmological constraints from the Subaru Hyper Suprime-Cam (HSC) first-year weak lensing shear catalogue using convolutional neural networks (CNNs) and conventional summary statistics. We crop 19 $3\times 3\, \mathrm{{deg}^2}$ sub-fields from the first-year area, divide the galaxies with redshift 0.3 ≤ z ≤ 1.5 into four equally spaced redshift bins, and perform tomographic analyses. We develop a pipeline to generate simulated convergence maps from cosmological N-body simulations, where we account for effects such as intrinsic alignments (IAs), baryons, photometric redshift errors, and point spread function errors, to match characteristics of the real catalogue. We train CNNs that can predict the underlying parameters from the simulated maps, and we use them to construct likelihood functions for Bayesian analyses. In the Λ cold dark matter model with two free cosmological parameters Ωm and σ8, we find $\Omega _\mathrm{m}=0.278_{-0.035}^{+0.037}$, $S_8\equiv (\Omega _\mathrm{m}/0.3)^{0.5}\sigma _{8}=0.793_{-0.018}^{+0.017}$, and the IA amplitude $A_\mathrm{IA}=0.20_{-0.58}^{+0.55}$. In a model with four additional free baryonic parameters, we find $\Omega _\mathrm{m}=0.268_{-0.036}^{+0.040}$, $S_8=0.819_{-0.024}^{+0.034}$, and $A_\mathrm{IA}=-0.16_{-0.58}^{+0.59}$, with the baryonic parameters not being well-constrained. We also find that statistical uncertainties of the parameters by the CNNs are smaller than those from the power spectrum (5–24 per cent smaller for S8 and a factor of 2.5–3.0 smaller for Ωm), showing the effectiveness of CNNs for uncovering additional cosmological information from the HSC data. With baryons, the S8 discrepancy between HSC first-year data and Planck 2018 is reduced from $\sim 2.2\, \sigma$ to $0.3\!-\!0.5\, \sigma$.

Velocity reconstruction with the cosmic microwave background and galaxy surveys

The kinetic Sunyaev Zel'dovich (kSZ) and moving lens effects, secondary contributions to the cosmic microwave background (CMB), carry significant cosmological information due to their dependence on the large-scale peculiar velocity field. Previous work identified a promising means of extracting this cosmological information using a set of quadratic estimators for the radial and transverse components of the velocity field. These estimators are based on the statistically anisotropic components of the cross-correlation between the CMB and a tracer of large scale structure, such as a galaxy redshift survey. In this work, we assess the challenges to the program of velocity reconstruction posed by various foregrounds and systematics in the CMB and galaxy surveys, as well as biases in the quadratic estimators. To do so, we further develop the quadratic estimator formalism and implement a numerical code for computing properly correlated spectra for all the components of the CMB (primary/secondary blackbody components and foregrounds) and a photometric redshift survey, with associated redshift errors, to allow for accurate forecasting. We create a simulation framework for generating realizations of properly correlated CMB maps and redshift binned galaxy number counts, assuming the underlying fields are Gaussian, and use this to validate a velocity reconstruction pipeline and assess map-based systematics such as masking. We highlight the most significant challenges for velocity reconstruction, which include biases associated with: modelling errors, characterization of redshift errors, and coarse graining of cosmological fields on our past light cone. Despite these challenges, the outlook for velocity reconstruction is quite optimistic, and we use our reconstruction pipeline to confirm that these techniques will be feasible with near-term CMB experiments and photometric galaxy redshift surveys.

Combining cosmic shear data with correlated photo-z uncertainties: constraints from DESY1 and HSC-DR1

An accurate calibration of the source redshift distribution p(z) is a key aspect in the analysis of cosmic shear data. This, one way or another, requires the use of spectroscopic or high-quality photometric samples. However, the difficulty to obtain colour-complete spectroscopic samples matching the depth of weak lensing catalogs means that the analyses of different cosmic shear datasets often use the same samples for redshift calibration. This introduces a source of statistical and systematic uncertainty that is highly correlated across different weak lensing datasets, and which must be accurately characterised and propagated in order to obtain robust cosmological constraints from their combination. In this paper we introduce a method to quantify and propagate the uncertainties on the source redshift distribution in two different surveys sharing the same calibrating sample. The method is based on an approximate analytical marginalisation of the p(z) statistical uncertainties and the correlated marginalisation of residual systematics. We apply this method to the combined analysis of cosmic shear data from the DESY1 data release and the HSC-DR1 data, using the COSMOS 30-band catalog as a common redshift calibration sample. We find that, although there is significant correlation in the uncertainties on the redshift distributions of both samples, this does not change the final constraints on cosmological parameters significantly. The same is true also for the impact of residual systematic uncertainties from the errors in the COSMOS 30-band photometric redshifts. Additionally, we show that these effects will still be negligible in Stage-IV datasets. Finally, the combination of DESY1 and HSC-DR1 allows us to constrain the “clumpiness” parameter to S 8 = 0.768+0.021 -0.017. This corresponds to a ∼√(2) improvement in uncertainties with respect to either DES or HSC alone.

Redshift requirements for cosmic shear with intrinsic alignment

Intrinsic alignment (IA) modelling and photometric redshift estimation are two of the main sources of systematic uncertainty in weak lensing surveys. We investigate the impact of redshift errors and their interplay with different IA models. Generally, errors on the mean δz and on the width σz of the redshift bins can both lead to biases in cosmological constraints. We find that such biases can, however, only be partially resolved by marginalizing over δz and σz . For Stage-III surveys, δz and σz cannot be well constrained due to limited statistics. The resulting biases are thus sensitive to prior volume effects. For Stage-IV surveys, we observe that marginalizing over the redshift parameters has an impact and reduces the bias. We derive requirements on the uncertainty of σz and δz for both Stage-III and Stage-IV surveys. We assume that the redshift systematic errors on S 8 should be less than half of the statistical errors, and the median bias should be smaller than 0.25σ. We find that the uncertainty on δz has to be ≲ 0.025 for the NLA IA model with a Stage-III survey. We find no requirement threshold for σz since the requirements are met even for our maximum prior width of 0.3. For the TATT IA model, the uncertainty on δz has to be ≲ 0.02 and the uncertainty on σz has to be ≲ 0.2. Current redshift precision of Stage-III surveys is therefore high enough to meet these requirements. For Stage-IV surveys, systematic effects will be more important due to the higher statistical precision. In this case, the uncertainty on δz has to be ≲ 0.005 and the uncertainty on σz should be ≲ 0.1, with no significant dependence on the IA model. This required high precision will be a challenge for the redshift calibration of these future surveys. Finally, we investigate whether the interplay between redshift systematics and IA modelling can explain the S 8-tension between cosmic shear results and CMB measurements. We find that this is unlikely to explain the current S 8-tension. The code that was used to conduct this analysis is publicly available.[refrigerator: https://cosmo-gitlab.phys.ethz.ch/cosmo_public/refrigerator.]

A Catalog of Candidate Double and Lensed Quasars from Gaia and WISE Data

Making use of strong correlations between closely separated multiple or double sources and photometric and astrometric metadata in Gaia Early Data Release 3 (EDR3), we generate a catalog of candidate double- and multiply imaged lensed quasars and active galactic nuclei (AGNs), comprising 3140 systems. It includes two partially overlapping parts: a sample of distant (redshifts mostly greater than 1) sources with perturbed data; and systems that have been resolved into separate components by Gaia at separations less than 2″. For the first part, which is roughly one-third of the published catalog, we synthesized 0.617 million redshifts using multiple machine-learning prediction and classification methods, using independent photometric and astrometric data from Gaia EDR3 and the Wide-field Infrared Survey Explorer, with accurate spectroscopic redshifts from the Sloan Digital Sky Survey (SDSS) as a training set. Using these synthetic redshifts, we estimate a 4.9% rate of interlopers with spectroscopic redshifts below 1 in this part of the catalog. Unresolved candidate double and dual AGNs and quasars are selected as sources with a marginally high BP/RP excess factor (phot_bp_rp_excess_factor), which is sensitive to source extent, limiting our search to high-redshift quasars. For the second part of the catalog, additional filters on measured parallax and near-neighbor statistics are applied to diminish the propagation of the remaining stellar contaminants. The estimated rate of the positives (double or multiple sources) is 98%, and the estimated rate of dual (physically related) quasars is greater than 54%. A few dozen serendipitously found objects of interest are discussed in more detail, including known and new lensed images, planetary nebulae, young IR stars of peculiar morphology, and quasars with catastrophic redshift errors in SDSS.

Tomographic Alcock–Paczynski method with redshift errors

ABSTRACT The tomographic Alcock–Paczynski (AP) method is a promising method that uses the redshift evolution of anisotropic clustering in redshift space to calibrate cosmology. It extends the applicable range of the AP method to substantially nonlinear scales, yielding very tight cosmological constraints. For future stage-IV slitless spectroscopic surveys, non-negligible redshift errors might reduce the advantage of the tomographic AP method by suppressing the resolution of the nonlinear structure along the line of sight. The present work studies how redshift errors propagate to cosmological parameters in tomographic AP analysis. We use a formula σz = σ(1 + z)α to model the redshift errors, with σ varying from 0.001 to 0.006 and α varying from 0.5 to 1.5. The redshift errors produce a signal of anisotropic clustering that is similar to a strong Finger-of-God effect, which smears out both the AP signal and the contamination caused by redshift-space distortions (RSD). For the target precision of the Chinese Space Station Telescope optical survey (σ ≲ 0.002), the decrement of constraining power on the dark energy equation of state is mild ($\lesssim 50{{\ \rm per\ cent}}$), and the suppression of RSD contamination leads to a smaller bias-to-signal ratio. Our results indicate that the tomographic AP method will remain a useful and complementary tool for analyses of future slitless spectroscopic surveys.

Cosmological inference from an emulator based halo model. I. Validation tests with HSC and SDSS mock catalogs

We present validation tests of emulator-based halo model method for cosmological parameter inference, assuming hypothetical measurements of the projected correlation function of galaxies, $w_{\rm p}(R)$, and the galaxy-galaxy weak lensing, $\Delta\!\Sigma(R)$, from the spectroscopic SDSS galaxies and the Hyper Suprime-Cam Year1 (HSC-Y1) galaxies. To do this, we use \textsc{Dark Emulator} developed in Nishimichi et al. based on an ensemble of $N$-body simulations, which is an emulation package enabling a fast, accurate computation of halo clustering quantities for flat-geometry $w$CDM cosmologies. Adopting the halo occupation distribution, the emulator allows us to obtain model predictions of $\Delta\!\Sigma$ and $w_{\rm p}$ for the SDSS-like galaxies at a few CPU seconds for an input set of parameters. We present performance and validation of the method by carrying out Markov Chain Monte Carlo analyses of the mock signals measured from a variety of mock catalogs that mimic the SDSS and HSC-Y1 galaxies. We show that the halo model method can recover the underlying true cosmological parameters to within the 68\% credible interval, except for the mocks including the assembly bias effect (although we consider the unrealistically-large amplitude of assembly bias effect). Even for the assembly bias mock, we demonstrate that the cosmological parameters can be recovered {\it if} the analysis is restricted to scales $R\gtrsim 10~h^{-1}{\rm Mpc}$. We also show that, by using a single population of source galaxies to infer the relative strengths of $\Delta\!\Sigma$ for multiple lens samples at different redshifts, the joint probes method allows for self-calibration of photometric redshift errors and multiplicative shear bias. Thus we conclude that the emulator-based halo model method can be safely applied to the HSC-Y1 dataset, achieving a precision of $\sigma(S_8)\simeq 0.04$.

Fisher forecasts for primordial non-Gaussianity from persistent homology

We study the information content of summary statistics built from the multi-scale topology of large-scale structures on primordial non-Gaussianity of the local and equilateral type. We use halo catalogs generated from numerical N-body simulations of the Universe on large scales as a proxy for observed galaxies. Besides calculating the Fisher matrix for halos in real space, we also check more realistic scenarios in redshift space. Without needing to take a distant observer approximation, we place the observer on a corner of the box. We also add redshift errors mimicking spectroscopic and photometric samples. We perform several tests to assess the reliability of our Fisher matrix, including the Gaussianity of our summary statistics and convergence. We find that the marginalized 1-σ uncertainties in redshift space are Δf NL loc ∼ 16 and Δf NL equi ∼ 41 on a survey volume of 1 (Gpc/h)3. These constraints are weakly affected by redshift errors. We close by speculating as to how this approach can be made robust against small-scale uncertainties by exploiting (non)locality.

Toward a direct measurement of the cosmic acceleration: The first preparation with FAST

Damped Lyman- α Absorber (DLA) of HI 21 cm system is an ideal probe to directly measure cosmic acceleration in real-time cosmology via Sandage–Loeb (SL) test. During short observations toward two DLAs in the commissioning progress of FAST, we manage to exhibit an HI 21 cm absorption feature from PKS1413+135 spectrum in one epoch with our highest resolution up to 100 Hz, preliminarily validating the frequency consistency under different resolutions and bandwidths. We make a Gaussian fitting to extract the spectral features, introduce two theoretical indicators to describe the fitted velocity uncertainty, and ultimately give a mean redshift and its constraint of z M = 0 . 24670045 ± 0 . 00000036 in accord with most literature. But our redshift error of the target is still three magnitudes higher than the level we can reach the drift signal. Though our first preparation has some flaws in time recording and diode settings, it still proves the correctness of our data process. Confined by limited observing time, we do not stretch FAST’s ability to obtain a better velocity constraint, so further researches are needed and in schedule. With fine sensitivity and improving spectral resolution, such observations in FAST could have reasonable possibility to explore cosmic acceleration in late time universe practically.

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

ABSTRACT 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 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.