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Estimating rates of COVID-19 infection and associated mortality is challenging due to uncertainties in case ascertainment. We perform a counterfactual time series analysis on overall mortality data from towns in Italy, comparing the population mortality in 2020 with previous years, to estimate mortality from COVID-19. We find that the number of COVID-19 deaths in Italy in 2020 until September 9 was 59,000–62,000, compared to the official number of 36,000. The proportion of the population that died was 0.29% in the most affected region, Lombardia, and 0.57% in the most affected province, Bergamo. Combining reported test positive rates from Italy with estimates of infection fatality rates from the Diamond Princess cruise ship, we estimate the infection rate as 29% (95% confidence interval 15–52%) in Lombardy, and 72% (95% confidence interval 36–100%) in Bergamo.

Our universe is homogeneous and isotropic, and its perturbations obey translation and rotation symmetry. In this work we develop Translation and Rotation Equivariant Normalizing Flow (TRENF), a generative Normalizing Flow (NF) model which explicitly incorporates these symmetries, defining the data likelihood via a sequence of Fourier space-based convolutions and pixel-wise nonlinear transforms. TRENF gives direct access to the high dimensional data likelihood p(x|y) as a function of the labels y, such as cosmological parameters. In contrast to traditional analyses based on summary statistics, the NF approach has no loss of information since it preserves the full dimensionality of the data. On Gaussian random fields, the TRENF likelihood agrees well with the analytical expression and saturates the Fisher information content in the labels y. On nonlinear cosmological overdensity fields from N-body simulations, TRENF leads to significant improvements in constraining power over the standard power spectrum summary statistic. TRENF is also a generative model of the data, and we show that TRENF samples agree well with the N-body simulations it trained on, and that the inverse mapping of the data agrees well with a Gaussian white noise both visually and on various summary statistics: when this is perfectly achieved the resulting p(x|y) likelihood analysis becomes optimal. Finally, we develop a generalization of this model that can handle effects that break the symmetry of the data, such as the survey mask, which enables likelihood analysis on data without periodic boundaries., Comment: 11 pages, 10 figures. Submitted to MNRAS. Comments welcome

pocoMC is a Python package for accelerated Bayesian inference in astronomy and cosmology. The code is designed to sample efficiently from posterior distributions with non-trivial geometry, including strong multimodality and non-linearity. To this end, pocoMC relies on the Preconditioned Monte Carlo algorithm which utilises a Normalising Flow in order to decorrelate the parameters of the posterior. It facilitates both tasks of parameter estimation and model comparison, focusing especially on computationally expensive applications. It allows fitting arbitrary models defined as a log-likelihood function and a log-prior probability density function in Python. Compared to popular alternatives (e.g. nested sampling) pocoMC can speed up the sampling procedure by orders of magnitude, cutting down the computational cost substantially. Finally, parallelisation to computing clusters manifests linear scaling., Comment: 6 pages, 1 figure. Submitted to JOSS. Code available at https://github.com/minaskar/pocomc

Massive neutrinos suppress the growth of cosmic structure on small, non-linear, scales. It is thus often proposed that using statistics beyond the power spectrum can tighten constraints on the neutrino mass by extracting additional information from these non-linear scales. We study the information content regarding neutrino mass at the field level, quantifying how much of this information arises from the difference in non-linear evolution between a cosmology with 1 fluid (CDM) and 2 fluids (CDM + neutrinos). We do so by running two $N$-body simulations, one with and one without massive neutrinos; both with the same phases, and matching their linear power spectrum at a given, low, redshift. This effectively isolates the information encoded in the linear initial conditions from the non-linear cosmic evolution. We demonstrate that for $k \lesssim 1\,h/{\rm Mpc}$, and for a single redshift, there is negligible difference in the real-space CDM field between the two simulations. This suggests that all the information regarding neutrino mass is in the linear power spectrum set by the initial conditions. Thus any probe based on the CDM field alone will have negligible constraining power beyond that which exists at the linear level over the same range of scales. Consequently, any probe based on the halo field will contain little information beyond the linear power. We find similar results for the matter field responsible for weak lensing. We also demonstrate that there may be much information beyond the power spectrum in the 3d matter field, however, this is not observable in modern surveys via dark matter halos or weak lensing. Finally, we show that there is additional information to be found in redshift space., Comment: 10 pages, 8 figures

We update Halo Zeldovich Perturbation Theory (HZPT), an analytic model for the two-point statistics of dark matter, to describe halo and galaxy clustering, and galaxy-matter cross-correlation on nonlinear scales. The model correcting Zeldovich has an analytic Fourier transform, and therefore is valid in both configuration space and Fourier space. The model is accurate at the $2\%$-level or less for $P_{mm}$ (k < 1 h/Mpc), $P_{hm}$ (k < 1 h/Mpc), $P_{hh}$ (k < 2 h/Mpc), $P_{gm}$ (k < 1 h/Mpc), $P_{gg}$ (k < 1 h/Mpc), $��_{mm}$ (r > 1 Mpc/h), $��_{hm}$ (r > 2 Mpc/h), $��_{hh}$ (r > 2 Mpc/h), $��_{gm}$ (r > 1 Mpc/h), $��_{gg}$ (r > 2 Mpc/h), for LRG-like mock galaxies. We show that the HZPT model for matter correlators can account for the effects of a wide range of baryonic feedback models and provide extended dark matter models which are of $1\% ~(3\%)$ accuracy for k < 10 (8) h/Mpc. We explicitly model the non-perturbative features of halo exclusion for the halo-halo and galaxy-galaxy correlators, as well as the presence of satellites for galaxy-matter and galaxy-galaxy correlation functions. We perform density estimation using N-body simulations and a wide range of HOD galaxy mocks to obtain correlations of model parameters with the cosmological parameters $��_{m}$ and $��_{8}$. HZPT can provide a fast, interpretable, and analytic model for combined-probe analyses of redshift surveys using scales well into the non-linear regime., 46 pages, 21 figures, (including appendices). To be submitted to JCAP

In experiments where one searches a large parameter space for an anomaly, one often finds many spurious noise-induced peaks in the likelihood. This is known as the look-elsewhere effect, and must be corrected for when performing statistical analysis. This paper introduces a method to calibrate the false alarm probability (FAP), or $p$-value, for a given dataset by considering the heights of the highest peaks in the likelihood. In the simplest form of self-calibration, the look-elsewhere-corrected $\chi^2$ of a physical peak is approximated by the $\chi^2$ of the peak minus the $\chi^2$ of the highest noise-induced peak. Generalizing this concept to consider lower peaks provides a fast method to quantify the statistical significance with improved accuracy. In contrast to alternative methods, this approach has negligible computational cost as peaks in the likelihood are a byproduct of every peak-search analysis. We apply to examples from astronomy, including planet detection, periodograms, and cosmology., Comment: 12 pages, 7 figures

We develop a method for planet detection in transit data, which is based on the matched filter technique, combined with the Gaussianization of the noise outliers. The method is based on Fourier transforms and is as fast as the existing methods for planet searches. The Gaussianized matched filter (GMF) method significantly outperforms the standard baseline methods in terms of the false positive rate, enabling planet detections at up to 30 per cent lower transit amplitudes. Moreover, the method extracts all the main planet transit parameters, amplitude, period, phase, and duration. By comparison to the state-of-the-art Gaussian process methods on both simulations and real data, we show that all the transit parameters are determined with an optimal accuracy (no bias and minimum variance), meaning that the GMF method can be used for both the initial planet detection and the follow-up planet parameter analysis.

When searching over a large parameter space for anomalies such as events, peaks, objects, or particles, there is a large probability that spurious signals with seemingly high significance will be found. This is known as the look-elsewhere effect and is prevalent throughout cosmology, (astro)particle physics, and beyond. To avoid making false claims of detection, one must account for this effect when assigning the statistical significance of an anomaly. This is typically accomplished by considering the trials factor, which is generally computed numerically via potentially expensive simulations. In this paper we develop a continuous generalization of the Bonferroni and Sidak corrections by applying the Laplace approximation to evaluate the Bayes factor, and in turn relating the trials factor to the prior-to-posterior volume ratio. We use this to define a test statistic whose frequentist properties have a simple interpretation in terms of the global $p$-value, or statistical significance. We apply this method to various physics-based examples and show it to work well for the full range of $p$-values, i.e. in both the asymptotic and non-asymptotic regimes. We also show that this method naturally accounts for other model complexities such as additional degrees of freedom, generalizing Wilks' theorem. This provides a fast way to quantify statistical significance in light of the look-elsewhere effect, without resorting to expensive simulations., 21 pages, 7 figures

We perform a counterfactual time series analysis on 2020 mortality data from towns in Italy using data from the previous five years as control. We find an excess mortality that is correlated in time with the official COVID-19 death rate, but exceeds it by a factor of at least 1.5. Our analysis suggests that there is a large population of predominantly older people that are missing from the official fatality statistics. We estimate that the number of cOvID-19 deaths in Italy is 49,000-53,000 as of May 9 2020, as compared to the official number of 33,000. The Population Fatality Rate (PFR) has reached 0.26% in the most affected region of Lombardia and 0.58% in the most affected province of Bergamo. These PFRs constitutes a lower bound to the Infection Fatality Rate (IFR). We combine the PFRs with the Test Positivity Ratio to derive the lower bound of 0.61% on the IFR for Lombardia. We further estimate the IFR as a function of age and find a steeper age dependence than previous studies: we find 17% of COVID-related deaths are attributed to the age group above 90, 7.5% to 80-89, declining to 0.04% for age 40-49 and 0.01% for age 30-39, the latter more than an order of magnitude lower than previous estimates. We observe that the IFR traces the Yearly Mortality Rate (YMR) above ages of 60 years, which can be used as a model to estimate the IFR for other populations and thus other regions in the world. We predict an IFR lower bound of 0.5% for NYC and that 27% of the total COVID-19 fatalities in NYC should arise from the population below 65 years. This is in agreement with the official NYC data and three times higher than the percentage observed in Lombardia. Combining the PFR with the Princess Diamond cruise ship IFR for ages above 70 we estimate the infection rates (IR) for regions in Italy. These peak in Lombardia at 26% (13%-47%, 95% c.l.), and for provinces in Bergamo at 69% (35%-100%, 95% c.I.). These estimates suggest that the number of infected people greatly exceeds the number of positive tests, e.g., by a factor of 35 in Lombardia.*

Fundamental plane of elliptical galaxies can be used to predict the intrinsic size of galaxies and has a number of plausible application to study cosmology and galaxy physics. We present a detailed analysis of the fundamental plane of the SDSS-III BOSS LOWZ and CMASS galaxies. For the standard fundamental plane, we find a strong redshift evolution for the mean residual and show that it is primarily driven by the redshift evolution of the surface brightness of the galaxies. After correcting for the redshift evolution, the FP residuals are strongly correlated with the galaxy properties and some observational systematics. We show that the variations in the FP between the central and satellite galaxies, that have been observed in the literature, can primarily be explained by the correlation of the FP with the galaxy luminosity. We also measure the cross correlations of the FP residuals with the galaxy density field. The amplitude of the cross correlations depends on the galaxy properties and environment with brighter and redder galaxies showing stronger correlation. In general, galaxies in denser environments (higher galaxy bias ) show stronger correlations. We also compare FP amplitude with the amplitudes of intrinsic alignments of galaxy shapes (IA), finding the two to be correlated. Finally, using the FP residuals we also study the impact of intrinsic alignments on the constraint of growth rate using redshift space distortions. We do not observe any significant trends in measurements of the growth rate $f$ as function of the amplitude of FP-density correlations, resulting in null detection of the effects of IA on the RSD measurements., 28 pages, 17+ figures. Main results in Fig. 2, 3, 4, 8, 13, 17. Submitted to MNRAS

Exoplanet Transit Timing Variations (TTVs) caused by gravitational forces between planets can be used to determine planetary masses and orbital parameters. Most of the observed TTVs are small and sinusoidal in time, leading to degeneracies between the masses and orbital parameters. Here we report a TTV analysis of Kepler-90g and Kepler-90h, which exhibit large TTVs up to 25 hours. With optimization, we find a unique solution which allows us to constrain all of the orbital parameters. The best fit masses for Kepler-90g and 90h are $15.0^{+0.9}_{-0.8}$ $M_{\bigoplus}$ (Earth mass) and $203^{+5}_{-5}M_{\bigoplus}$, respectively, with Kepler-90g having an unusually low apparent density of $0.15\pm 0.05\, {\rm g\,cm^{-3}}$. The uniqueness of orbital parameter solution enables a long-term dynamical integration, which reveals that although their periods are close to 2:3 orbital resonance, they are not locked in resonance, and the configuration is stable over billions of years. The dynamical history of the system suggests that planet interactions are able to raise the eccentricities and break the resonant lock after the initial formation., Comment: 12 pages, 5 figures

Galaxy-galaxy strong gravitational lenses have become a popular probe of dark matter (DM) by providing a window into structure formation on the smallest scales. In particular, the convergence power spectrum of subhalos within lensing galaxies has been suggested as a promising observable to study DM. However, the distances involved in strong-lensing systems are vast, and we expect the relevant volume to contain line-of-sight (LOS) halos that are not associated with the main lens. We develop a formalism to calculate the effect of LOS halos as an effective convergence power spectrum. The multi-lens plane equation couples the angular deflections of consecutive lens planes, but by assuming that the perturbations due to the LOS halos are small, we show that they can be projected onto the main-lens plane as effective subhalos. We test our formalism by simulating lensing systems using the full multi-plane lens equation and find excellent agreement. We show how the relative contribution of LOS halos and subhalos depends on the source and lens redshift, as well as the assumed halo and subhalo mass functions. For a fiducial system with fraction of DM halo mass in substructure $f_{\rm sub}=0.4\%$ for subhalo masses $[10^5-10^8]\rm{M}_{\odot}$, the interloper contribution to the power spectrum is at least several times greater than that of subhalos for source redshifts $z_s\gtrsim0.5$. Furthermore, it is likely that for the SLACS and BELLS lenses the interloper contribution dominates: $f_{\rm sub}\gtrsim2\%$ ($4\%$) is needed for subhalos to dominate in SLACS (BELLS), which is higher than current upper bounds on $f_{\rm sub}$ for our mass range. Since the halo mass function is better understood from first principles, the dominance of interlopers in galaxy-galaxy lenses with high-quality imaging can be seen as a significant advantage when translating this observable into a constraint on DM., Comment: 24 pages, 8 figures, Matches accepted version, v3: Fixed a typo in Eq. C4 in the appendix. No changes in the derivations

The goal of generative models is to learn the intricate relations between the data to create new simulated data, but current approaches fail in very high dimensions. When the true data generating process is based on physical processes these impose symmetries and constraints, and the generative model can be created by learning an effective description of the underlying physics, which enables scaling of the generative model to very high dimensions. In this work we propose Lagrangian Deep Learning (LDL) for this purpose, applying it to learn outputs of cosmological hydrodynamical simulations. The model uses layers of Lagrangian displacements of particles describing the observables to learn the effective physical laws. The displacements are modeled as the gradient of an effective potential, which explicitly satisfies the translational and rotational invariance. The total number of learned parameters is only of order 10, and they can be viewed as effective theory parameters. We combine N-body solver FastPM with LDL and apply them to a wide range of cosmological outputs, from the dark matter to the stellar maps, gas density and temperature. The computational cost of LDL is nearly four orders of magnitude lower than the full hydrodynamical simulations, yet it outperforms it at the same resolution. We achieve this with only of order 10 layers from the initial conditions to the final output, in contrast to typical cosmological simulations with thousands of time steps. This opens up the possibility of analyzing cosmological observations entirely within this framework, without the need for large dark-matter simulations., Comment: 10 pages, 6 figures

We present FlowPM, a Particle-Mesh (PM) cosmological N-body code implemented in Mesh-TensorFlow for GPU-accelerated, distributed, and differentiable simulations. We implement and validate the accuracy of a novel multi-grid scheme based on multiresolution pyramids to compute large scale forces efficiently on distributed platforms. We explore the scaling of the simulation on large-scale supercomputers and compare it with corresponding python based PM code, finding on an average 10x speed-up in terms of wallclock time. We also demonstrate how this novel tool can be used for efficiently solving large scale cosmological inference problems, in particular reconstruction of cosmological fields in a forward model Bayesian framework with hybrid PM and neural network forward model. We provide skeleton code for these examples and the entire code is publicly available at https://github.com/modichirag/flowpm., Comment: 14 pages, 17 figures. Code provided at https://github.com/modichirag/flowpm

We study the covariance properties of real space correlation function estimators -- primarily galaxy-shear correlations, or galaxy-galaxy lensing -- using SDSS data for both shear catalogs and lenses (specifically the BOSS LOWZ sample). Using mock catalogs of lenses and sources, we disentangle the various contributions to the covariance matrix and compare them with a simple analytical model. We show that not subtracting the lensing measurement around random points from the measurement around the lens sample is equivalent to performing the measurement using the lens density field instead of the lens over-density field. While the measurement using the lens density field is unbiased (in the absence of systematics), its error is significantly larger due to an additional term in the covariance. Therefore, this subtraction should be performed regardless of its beneficial effects on systematics. Comparing the error estimates from data and mocks for estimators that involve the over-density, we find that the errors are dominated by the shape noise and lens clustering, that empirically estimated covariances (jackknife and standard deviation across mocks) are consistent with theoretical estimates, and that both the connected parts of the 4-point function and the super-sample covariance can be neglected for the current levels of noise. While the trade-off between different terms in the covariance depends on the survey configuration (area, source number density), the diagnostics that we use in this work should be useful for future works to test their empirically-determined covariances., Comment: 18 pages. Summary in figure 7 and table 1. Matches accepted version in MNRAS

We present an efficient implementation of Wiener filtering of real-space linear field and optimal quadratic estimator of its power spectrum Band-powers. We first recast the field reconstruction into an optimization problem, which we solve using quasi-Newton optimization. We then recast the power spectrum estimation into the field marginalization problem, from which we obtain an expression that depends on the field reconstruction solution and a determinant term. We develop a novel simulation based method for the latter. We extend the simulations formalism to provide the covariance matrix for the power spectrum. We develop a flexible framework that can be used on a variety of cosmological fields and present results for a variety of test cases, using simulated examples of projected density fields, projected shear maps from galaxy lensing, and observed Cosmic Microwave Background (CMB) temperature anisotropies, with a wide range of map incompleteness and variable noise. For smaller cases where direct numerical inversion is possible, we show that our solution matches that created by direct Wiener Filtering at a fraction of the overall computation cost. Even more significant reduction of computational is achieved by this implementation of optimal quadratic estimator due to the fast evaluation of the Hessian matrix. This technique allows for accurate map and power spectrum reconstruction with complex masks and nontrivial noise properties., Comment: 23 pages, 14 figures

We develop a statistical analysis model of Kepler star flux data in the presence of planet transits, non-Gaussian noise, and star variability. We first develop a model for Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue these components need to be modeled together if optimal signal is to be extracted from the data. For the star variability model we develop an optimal Gaussian process analysis using a Fourier based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the star variability than the standard spline method, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than the standard spline method.

Generating mocks for future sky surveys requires large volumes and high resolutions, which is computationally expensive even for fast simulations. In this work we try to develop numerical schemes to calibrate various halo and matter statistics in fast low resolution simulations compared to high resolution N-body and hydrodynamic simulations. For the halos, we improve the initial condition accuracy and develop a halo finder "relaxed-FOF", where we allow different linking length for different halo mass and velocity dispersions. We show that our relaxed-FoF halo finder improves the common statistics, such as halo bias, halo mass function, halo auto power spectrum in real space and in redshift space, cross correlation coefficient with the reference halo catalog, and halo-matter cross power spectrum. We also incorporate the potential gradient descent (PGD) method into fast simulations to improve the matter distribution at nonlinear scale. By building a lightcone output, we show that the PGD method significantly improves the weak lensing convergence tomographic power spectrum. With these improvements FastPM is comparable to the high resolution full N-body simulation of the same mass resolution, with two orders of magnitude fewer time steps. These techniques can be used to improve the halo and matter statistics of FastPM simulations for mock catalogs of future surveys such as DESI and LSST., 19 pages, 13 figures

Author(s): Schlegel, David J; Kollmeier, Juna A; Aldering, Greg; Bailey, Stephen; Baltay, Charles; Bebek, Christopher; BenZvi, Segev; Besuner, Robert; Blanc, Guillermo; Bolton, Adam S; Bouri, Mohamed; Brooks, David; Buckley-Geer, Elizabeth; Cai, Zheng; Crane, Jeffrey; Dey, Arjun; Doel, Peter; Fan, Xiaohui; Ferraro, Simone; Font-Ribera, Andreu; Gutierrez, Gaston; Guy, Julien; Heetderks, Henry; Huterer, Dragan; Infante, Leopoldo; Jelinsky, Patrick; Johns, Matthew; Karagiannis, Dionysios; Kent, Stephen M; Kim, Alex G; Kneib, Jean-Paul; Kronig, Luzius; Konidaris, Nick; Lahav, Ofer; Lampton, Michael L; Lang, Dustin; Leauthaud, Alexie; Liguori, Michele; Linder, Eric V; Magneville, Christophe; Martini, Paul; Mateo, Mario; McDonald, Patrick; Miller, Christopher J; Moustakas, John; Myers, Adam D; Mulchaey, John; Newman, Jeffrey A; Nugent, Peter E; Palanque-Delabrouille, Nathalie; Padmanabhan, Nikhil; Piro, Anthony L; Poppett, Claire; Prochaska, Jason X; Pullen, Anthony R; Rabinowitz, David; Ramirez, Solange; Rix, Hans-Walter; Ross, Ashley J; Samushia, Lado; Schaan, Emmanuel; Schubnell, Michael; Seljak, Uros; Seo, Hee-Jong; Shectman, Stephen A; Silber, Joseph; Simon, Joshua D; Slepian, Zachary; Soares-Santos, Marcelle; Tarle, Greg; Thompson, Ian; Valluri, Monica; Wechsler, Risa H; White, Martin; Wilson, Michael J; Yeche, Christophe; Zaritsky, Dennis | Abstract: MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at 2

We present constraints on local primordial non-Gaussianity (PNG), parametrized through $f^{\rm loc}_{\rm NL}$, using the Sloan Digital Sky Survey IV extended Baryon Oscillation Spectroscopic Survey Data Release 14 quasar sample. We measure and analyze the anisotropic clustering of the quasars in Fourier space, testing for the scale-dependent bias introduced by primordial non-Gaussianity on large scales. We derive and employ a power spectrum estimator using optimal weights that account for the redshift evolution of the PNG signal. We find constraints of $-51, Comment: 28 pages, 12 figures. Comments welcome!

著者人数: 152名(所属. 宇宙航空研究開発機構宇宙科学研究所(JAXA)(ISAS): 羽澄, 昌史; 堂谷, 忠靖; 長谷部, 孝; 今田, 大皓; 満田, 和久; 坂井, 真一郎; 関本, 裕太郎; Tomida, T.; 辻本, 匡弘; Yamamoto, R.; 山崎, 典子), Accepted: 2019-01-18, 資料番号: SA1180402000

Calibrating photometric redshift errors in weak lensing surveys with external data is extremely challenging. We show that both Gaussian and outlier photo-z parameters can be self-calibrated from the data alone. This comes at no cost for the neutrino masses, curvature and dark energy equation of state $w_0$, but with a 65% degradation when both $w_0$ and $w_a$ are varied. We perform a realistic forecast for the Vera Rubin Observatory (VRO) Legacy Survey of Space and Time (LSST) 3x2 analysis, combining cosmic shear, projected galaxy clustering and galaxy - galaxy lensing. We confirm the importance of marginalizing over photo-z outliers. We examine a subset of internal cross-correlations, dubbed "null correlations", which are usually ignored in 3x2 analyses. Despite contributing only $\sim$ 10% of the total signal-to-noise, these null correlations improve the constraints on photo-z parameters by up to an order of magnitude. Using the same galaxy sample as sources and lenses dramatically improves the photo-z uncertainties too. Together, these methods add robustness to any claim of detected new Physics, and reduce the statistical errors on cosmology by 15% and 10% respectively. Finally, including CMB lensing from an experiment like Simons Observatory or CMB-S4 improves the cosmological and photo-z posterior constraints by about 10%, and further improves the robustness to systematics. To give intuition on the Fisher forecasts, we examine in detail several toy models that explain the origin of the photo-z self-calibration. Our Fisher code LaSSI (Large-Scale Structure Information), which includes the effect of Gaussian and outlier photo-z, shear multiplicative bias, linear galaxy bias, and extensions to $\Lambda$CDM, is publicly available at https://github.com/EmmanuelSchaan/LaSSI ., Comment: Accepted in JCAP on 10/08/2020. Code publicly available at https://github.com/EmmanuelSchaan/LaSSI

We present cosmological parameter constraints based on a joint modeling of galaxy-lensing cross correlations and galaxy clustering measurements in the SDSS, marginalizing over small-scale modeling uncertainties using mock galaxy catalogs, without explicit modeling of galaxy bias. We show that our modeling method is robust to the impact of different choices for how galaxies occupy dark matter halos and to the impact of baryonic physics (at the $\sim2\%$ level in cosmological parameters) and test for the impact of covariance on the likelihood analysis and of the survey window function on the theory computations. Applying our results to the measurements using galaxy samples from BOSS and lensing measurements using shear from SDSS galaxies and CMB lensing from Planck, with conservative scale cuts, we obtain $S_8\equiv\left(\frac{\sigma_8}{0.8228}\right)^{0.8}\left(\frac{\Omega_m}{0.307}\right)^{0.6}=0.85\pm0.05$ (stat.) using LOWZ $\times$ SDSS galaxy lensing, and $S_8=0.91\pm0.1$ (stat.) using combination of LOWZ and CMASS $\times$ Planck CMB lensing. We estimate the systematic uncertainty in the galaxy-galaxy lensing measurements to be $\sim6\%$ (dominated by photometric redshift uncertainties) and in the galaxy-CMB lensing measurements to be $\sim3\%$, from small scale modeling uncertainties including baryonic physics., Comment: Published in MNRAS

The nature of dark matter (DM) remains unknown despite very precise knowledge of its abundance in the universe. An alternative to new elementary particles postulates DM as made of macroscopic compact halo objects (MACHO) such as black holes formed in the very early universe. Stellar-mass primordial black holes (PBHs) are subject to less robust constraints than other mass ranges and might be connected to gravitational-wave signals detected by the Laser Interferometer Gravitational-Wave Observatory (LIGO). New methods are therefore necessary to constrain the viability of compact objects as a DM candidate. Here we report bounds on the abundance of compact objects from gravitational lensing of type Ia supernovae (SNe). Current SNe datasets constrain compact objects to represent less than 35.2% (Joint Lightcurve Analisis) and 37.2% (Union 2.1) of the total matter content in the universe, at 95% confidence-level. The results are valid for masses larger than $\sim 0.01M_\odot$ (solar-masses), limited by the size SNe relative to the lens Einstein radius. We demonstrate the mass range of the constraints by computing magnification probabilities for realistic SNe sizes and different values of the PBH mass. Our bounds are sensitive to the total abundance of compact objects with $M \lesssim 0.01M_\odot$ and complementary to other observational tests. These results are robust against cosmological parameters, outlier rejection, correlated noise and selection bias. PBHs and other MACHOs are therefore ruled out as the dominant form of DM for objects associated to LIGO gravitational wave detections. These bounds constrain early-universe models that predict stellar-mass PBH production and strengthen the case for lighter forms of DM, including new elementary particles., 16 pages, 9 figures. Discussion on finite SNe size and outliers expanded. Version accepted for publication in PRL

We develop a new method to constraint primordial non-Gaussianities of the local kind using unclustered tracers of the Large Scale Structure. We show that in the limit of low noise, zero bias tracers yield large improvement over standard methods, mostly due to vanishing sampling variance. We propose a simple technique to construct such a tracer, using environmental information obtained from the original sample, and validate our method with N-body simulations. Our results indicate that $\sigma_{f_{\mathrm{NL}}^{\mathrm{loc}}}\simeq1$ can be reached using only information on a single tracer of sufficiently high number density., Comment: 6 pages, 3 figures

We present a method to reconstruct the initial conditions of the universe using observed galaxy positions and luminosities under the assumption that the luminosities can be calibrated with weak lensing to give the mean halo mass. Our method relies on following the gradients of forward model and since the standard way to identify halos is non-differentiable and results in a discrete sample of objects, we propose a framework to model the halo position and mass field starting from the non-linear matter field using Neural Networks. We evaluate the performance of our model with multiple metrics. Our model is more than $95\%$ correlated with the halo-mass fields up to $k\sim 0.7 {\rm h/Mpc}$ and significantly reduces the stochasticity over the Poisson shot noise. We develop a data likelihood model that takes our modeling error and intrinsic scatter in the halo mass-light relation into account and show that a displaced log-normal model is a good approximation to it. We optimize over the corresponding loss function to reconstruct the initial density field and develop an annealing procedure to speed up and improve the convergence. We apply the method to halo number densities of $\bar{n} = 2.5\times 10^{-4} -10^{-3}({\rm h/Mpc})^3$, typical of current and future redshift surveys, and recover a Gaussian initial density field, mapping all the higher order information in the data into the power spectrum. We show that our reconstruction improves over the standard reconstruction. For baryonic acoustic oscillations (BAO) the gains are relatively modest because BAO is dominated by large scales where standard reconstruction suffices. We improve upon it by $\sim 15-20\%$ in terms of error on BAO peak as estimated by Fisher analysis at $z=0$. We expect larger gains will be achieved when applying this method to the broadband linear power spectrum reconstruction on smaller scales., Comment: 33 pages, 15 figures

Fast N-body PM simulations with a small number of time steps such as FastPM or COLA have been remarkably successful in modeling the galaxy statistics, but their lack of small scale force resolution and long time steps cannot give accurate halo matter profiles or matter power spectrum. High resolution N-body simulations can improve on this, but lack baryonic effects, which can only be properly included in hydro simulations. Here we present a scheme to calibrate the fast simulations to mimic the precision of the hydrodynamic simulations or high resolution N-body simulations. The scheme is based on a gradient descent of either effective gravitational potential, which mimics the short range force, or of effective enthalpy, which mimics gas hydrodynamics and feedback. The scheme is fast and differentiable, and can be incorporated as a post-processing step into any simulation. It gives very good results for the matter power spectrum for several of the baryonic feedback and dark matter simulations, and also gives improved dark matter halo profiles. The scheme is even able to find the large subhalos, and increase the correlation coefficient between the fast simulations and the high resolution N-body or hydro simulations. It can also be used to add baryonic effects to the high resolution N-body simulations. While the method has free parameters that can be calibrated on various simulations, they can also be viewed as astrophysical nuisance parameters describing baryonic effects that can be marginalized over during the data analysis. In this view these parameters can be viewed as an efficient parametrization of baryonic effects., Comment: 20 pages, 11 figures

We present measurements of $E_G$, a probe of gravity from large-scale structure, using BOSS LOWZ and CMASS spectroscopic samples, with lensing measurements from SDSS (galaxy lensing) and Planck (CMB lensing). Using SDSS lensing and the BOSS LOWZ sample, we measure $\langle{E_G}\rangle=0.40^{+0.05}_{-0.04}$ (stat), $\pm 0.026$ (systematic), consistent with the predicted value from the Planck $\Lambda$CDM model, $E_G=0.46$. Using CMB lensing, we measure $\langle{E_G}\rangle=0.46^{+0.08}_{-0.09}$ (stat) for LOWZ (statistically consistent with galaxy lensing and Planck predictions) and $\langle{E_G}\rangle=0.39^{+0.05}_{-0.05}$ (stat) for the CMASS sample, consistent with the Planck prediction of $E_G=0.40$ given the higher redshift of the sample. We also study the redshift evolution of $E_G$ by splitting the LOWZ sample into two samples based on redshift, with results being consistent with model predictions. We estimate systematic uncertainties on the above $\langle{E_G}\rangle$ numbers to be $\sim 6$% (when using galaxy-galaxy lensing) or $\sim 3$% (when using CMB lensing), subdominant to the quoted statistical errors. These systematic error budgets are dominated by observational systematics in galaxy-galaxy lensing and by theoretical modeling uncertainties, respectively. We do not estimate observational systematics in galaxy-CMB lensing cross correlations., Comment: Matches accepted version in MNRAS

We compute the dipole of the galaxy correlation function at 1-loop in perturbation theory by including all the relevant relativistic contributions. This provides a description and understanding of what the dipole truly measures, in particular in relation to the gravitational redshift effect in Large Scale Structure. In order to develop this perturbative approach we have computed for the first time the relevant relativistic corrections to third order in perturbation theory, including the corresponding non-linear galaxy bias model. This perturbative approach agrees on a wide range of scales with good accuracy with previous numerical results based on geodesic light tracing in N-body simulations. Previous claims of gravitational redshift detection may have neglected several relativistic effects which are comparable with the amplitude of gravitational redshift around 10 Mpc/h scales and which complicate the gravitational redshift interpretation of the measurement., Comment: 27 pages, 7 figures, version accepted for publication in JCAP

The lensing convergence measurable with future CMB surveys like CMB-S4 will be highly correlated with the clustering observed by deep photometric large scale structure (LSS) surveys such as the LSST, with cross-correlation coefficient as high as 95\%. This will enable use of sample variance cancellation techniques to determine cosmological parameters, and use of cross-correlation measurements to break parameter degeneracies. Assuming large sky overlap between CMB-S4 and LSST, we show that a joint analysis of CMB-S4 lensing and LSST clustering can yield very tight constraints on the matter amplitude $\sigma_8(z)$, halo bias, and $f_\mathrm{NL}$, competitive with the best stage IV experiment predictions, but using complementary methods, which may carry different and possibly lower systematics. Having no sky overlap between experiments degrades the precision of $\sigma_8(z)$ by a factor of 20, and that of $f_\mathrm{NL}$ by a factor of 1.5 to 2. Without CMB lensing, the precision always degrades by an order of magnitude or more, showing that a joint analysis is critical. Our results also suggest that CMB lensing in combination with LSS photometric surveys is a competitive probe of the evolution of structure in the redshift range $z\simeq 1-7$, probing a regime that is not well tested observationally. We explore predictions against other surveys and experiment configurations, finding that wide patches with maximal sky overlap between CMB and LSS surveys are most powerful for $\sigma_8(z)$ and $f_\mathrm{NL}$., Comment: 25+8 pages; added references, more details on LSST assumptions, and new section VIII on catastrophic redshift errors; agrees with published version

We present a new model for the redshift-space power spectrum of galaxies and demonstrate its accuracy in modeling the monopole, quadrupole, and hexadecapole of the galaxy density field down to scales of $k = 0.4 \ h\mathrm{Mpc}^{-1}$. The model describes the clustering of galaxies in the context of a halo model and the clustering of the underlying halos in redshift space using a combination of Eulerian perturbation theory and $N$-body simulations. The modeling of redshift-space distortions is done using the so-called distribution function approach. The final model has 13 free parameters, and each parameter is physically motivated rather than a nuisance parameter, which allows the use of well-motivated priors. We account for the Finger-of-God effect from centrals and both isolated and non-isolated satellites rather than using a single velocity dispersion to describe the combined effect. We test and validate the accuracy of the model on several sets of high-fidelity $N$-body simulations, as well as realistic mock catalogs designed to simulate the BOSS DR12 CMASS data set. The suite of simulations covers a range of cosmologies and galaxy bias models, providing a rigorous test of the level of theoretical systematics present in the model. The level of bias in the recovered values of $f \sigma_8$ is found to be small. When including scales to $k = 0.4 \ h\mathrm{Mpc}^{-1}$, we find 15-30\% gains in the statistical precision of $f \sigma_8$ relative to $k = 0.2 \ h\mathrm{Mpc}^{-1}$ and a roughly 10-15\% improvement for the perpendicular Alcock-Paczynski parameter $\alpha_\perp$. Using the BOSS DR12 CMASS mocks as a benchmark for comparison, we estimate an uncertainty on $f \sigma_8$ that is $\sim$10-20\% larger than other similar Fourier-space RSD models in the literature that use $k \leq 0.2 \ h\mathrm{Mpc}^{-1}$, suggesting that these models likely have a too-limited parametrization., Comment: Submitted to JCAP

Current and future galaxy surveys cover a large fraction of the entire sky with a significant redshift range, and the recent theoretical development shows that general relativistic effects are present in galaxy clustering on very large scales. This trend has renewed interest in the wide angle effect in galaxy clustering measurements, in which the distant-observer approximation is often adopted. Using the full wide-angle formula for computing the redshift-space correlation function, we show that compared to the sample variance, the deviation in the redshift-space correlation function from the simple Kaiser formula with the distant-observer approximation is negligible in galaxy surveys such as the SDSS, Euclid and the BigBOSS, if the theoretical prediction from the Kaiser formula is properly averaged over the survey volume. We also find corrections to the wide-angle formula and clarify the confusion in literature between the wide angle effect and the velocity contribution in galaxy clustering. However, when the FKP method is applied, substantial deviations can be present in the power spectrum analysis in future surveys, due to the non-uniform distribution of galaxy pairs., 17 pages, 11 figures, accepted for publication in MNRAS

We investigate the effect of primordial non-Gaussianity of the local f_NL type on the auto- and cross-power spectrum of dark matter haloes using simulations of the LCDM cosmology. We perform a series of large N-body simulations of both positive and negative f_NL, spanning the range between 10 and 100. Theoretical models predict a scale-dependent bias correction ��b(k,f_NL) that depends on the linear halo bias b(M). We measure the power spectra for a range of halo mass and redshifts covering the relevant range of existing galaxy and quasar populations. We show that auto and cross-correlation analyses of bias are consistent with each other. We find that for low wavenumbers with k1.5. We show that a scale-independent bias correction improves the comparison between theory and simulations on smaller scales, where the scale-dependent effect rapidly becomes negligible. The current limits on f_NL from Slosar et al. (2008) come mostly from very large scales k, 13 pages, 10 figures. (v2): substantial changes. added a physically motivated scale-independent bias correction which improves significantly the agreement with the simulations (v3): matches published version

著者人数: 152名（所属. 宇宙航空研究開発機構宇宙科学研究所(JAXA)(ISAS): 長谷部, 孝; 堂谷, 忠靖; 羽澄, 昌史; 今田, 大皓; 満田, 和久; 坂井, 真一郎; 関本, 裕太郎; Tomida, T.; 辻本, 匡弘; Yamamoto, R.; 山崎, 典子), Accepted: 2018-04-07, 資料番号: SA1180209000

Measurements of line-of-sight dependent clustering via the galaxy power spectrum's multipole moments constitute a powerful tool for testing theoretical models in large-scale structure. Recent work shows that this measurement, including a moving line-of-sight, can be accelerated using Fast Fourier Transforms (FFTs) by decomposing the Legendre polynomials into products of Cartesian vectors. Here, we present a faster, optimal means of using FFTs for this measurement. We avoid redundancy present in the Cartesian decomposition by using a spherical harmonic decomposition of the Legendre polynomials. Consequently, our method is substantially faster: a given multipole of order $\ell$ requires only $2\ell+1$ FFTs rather than the $(\ell+1)(\ell+2)/2$ FFTs of the Cartesian approach. For the hexadecapole ($\ell = 4$), this translates to $40\%$ fewer FFTs, with increased savings for higher $\ell$. The reduction in wall-clock time enables the calculation of finely-binned wedges in $P(k,\mu)$, obtained by computing multipoles up to a large $\ell_{\rm max}$ and combining them. This transformation has a number of advantages. We demonstrate that by using non-uniform bins in $\mu$, we can isolate plane-of-sky (angular) systematics to a narrow bin at $\mu \simeq 0$ while eliminating the contamination from all other bins. We also show that the covariance matrix of clustering wedges binned uniformly in $\mu$ becomes ill-conditioned when combining multipoles up to large values of $\ell_{\rm max}$, but that the problem can be avoided with non-uniform binning. As an example, we present results using $\ell_{\rm max}=16$, for which our procedure requires a factor of 3.4 fewer FFTs than the Cartesian method, while removing the first $\mu$ bin leads only to a 7% increase in statistical error on $f \sigma_8$, as compared to a 54% increase with $\ell_{\rm max}=4$., Comment: Submitted to JCAP