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Register a new userFisher matrix for multiple tracers: all you can learn from large-scale structure without assuming a model
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The galaxy power spectrum is one of the central quantities in cosmology. It contains information about the primordial inflationary process, the matter clustering, the baryon-photon interaction, the effects of gravity, the galaxy-matter bias, the cosmic expansion, the peculiar velocity field, etc.. Most of this information is however difficult to extract without assuming a specific cosmological model, for instance $Lambda$CDM and standard gravity. In this paper we explore instead how much information can be obtained that is independent of the cosmological model, both at background and linear perturbation level. We determine the full set of model-independent statistics that can be constructed by combining two redshift bins and two distinct tracers. We focus in particular on the statistics $r(k,z_1,z_2)$, defined as the ratio of $fsigma_8(z)$ at two redshift shells, and we show how to estimate it with a Fisher matrix approach. Finally, we forecast the constraints on $r$ that can be achieved by future galaxy surveys, and compare it with the standard single-tracer result. We find that $r$ can be measured with a precision from 3 to 11%, depending on the survey. Using two tracers, we find improvements in the constraints up to a factor of two.
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We show how to obtain constraints on $beta=f/b$, the ratio of the matter growth rate and the bias that quantifies the linear redshift-space distortions, that are independent of the cosmological model, using multiple tracers of large-scale structure. For a single tracer the uncertainties on $beta$ are constrained by the uncertainties in the amplitude and shape of the power spectrum, which is limited by cosmic variance. However, for two or more tracers this limit does not apply, since taking the ratio of power spectra cosmic variance cancels out, and in the linear (Kaiser) approximation one measures directly the quantity $(1+ beta_1 mu^2)^2/(1+ beta_2 mu^2)^2$, where $mu$ is the angle of a given mode with the line of sight. We provide analytic formulae for the Fisher matrix for one and two tracers, and quantify the signal-to-noise ratio needed to make effective use of the multiple-tracer technique. We also forecast the errors on $beta$ for a survey like Euclid.
In this paper, we consider the problem of learning models with a latent factor structure. The focus is to find what is possible and what is impossible if the usual strong factor condition is not imposed. We study the minimax rate and adaptivity issues in two problems: pure factor models and panel regression with interactive fixed effects. For pure factor models, if the number of factors is known, we develop adaptive estimation and inference procedures that attain the minimax rate. However, when the number of factors is not specified a priori, we show that there is a tradeoff between validity and efficiency: any confidence interval that has uniform validity for arbitrary factor strength has to be conservative; in particular its width is bounded away from zero even when the factors are strong. Conversely, any data-driven confidence interval that does not require as an input the exact number of factors (including weak ones) and has shrinking width under strong factors does not have uniform coverage and the worst-case coverage probability is at most 1/2. For panel regressions with interactive fixed effects, the tradeoff is much better. We find that the minimax rate for learning the regression coefficient does not depend on the factor strength and propose a simple estimator that achieves this rate. However, when weak factors are allowed, uncertainty in the number of factors can cause a great loss of efficiency although the rate is not affected. In most cases, we find that the strong factor condition (and/or exact knowledge of number of factors) improves efficiency, but this condition needs to be imposed by faith and cannot be verified in data for inference purposes.
We compare Baryonic Acoustic Oscillation (BAO) and Redshift Space Distortion (RSD) measurements from recent galaxy surveys with their Fisher matrix based predictions. Measurements of the position of the BAO signal lead to constraints on the comoving angular diameter distance $D_{M}$ and the Hubble distance $D_{H}$ that agree well with their Fisher matrix based expectations. However, RSD-based measurements of the growth rate $f sigma_{8}$ do not agree with the predictions made before the surveys were undertaken, even when repeating those predictions using the actual survey parameters. We show that this is due to a combination of effects including degeneracies with the geometric parameters $D_{M}$ and $D_{H}$, and optimistic assumptions about the scale to which the linear signal can be extracted. We show that measurements using current data and large-scale modelling techniques extract an equivalent amount of signal to that in the linear regime for $k < 0.08 ,h,{rm Mpc}^{-1}$, remarkably independent of the sample properties and redshifts covered.
We describe the pitfalls encountered in deducing from classical double radio source observables (luminosity, spectral index, redshift and linear size) the essential nature of how these objects evolve. We discuss the key role played by hotspots in governing the energy distribution of the lobes they feed, and subsequent spectral evolution. We present images obtained using the new 74 MHz receivers on the VLA and discuss constraints which these enforce on models of the backflow and ages in classical doubles.
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.
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