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Register a new userSPICA spectroscopic cosmological surveys to unravel galaxy evolution
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The main energy-generating mechanisms in galaxies are black hole (BH) accretion and star formation (SF) and the interplay of these processes is driving the evolution of galaxies. MIR/FIR spectroscopy are able to distinguish between BH accretion and SF, as it was shown in the past by infrared spectroscopy from the space by the Infrared Space Observatory and Spitzer. Spitzer and Herschel spectroscopy together can trace the AGN and the SF components in galaxies, with extinction free lines, almost only in the local Universe, except for a few distant objects. One of the major goals of the study of galaxy evolution is to understand the history of the luminosity source of galaxies along cosmic time. This goal can be achieved with far-IR spectroscopic cosmological surveys. SPICA in combination with ground based large single dish submillimeter telescopes, such as CCAT, will offer a unique opportunity to do this. We use galaxy evolution models linked to the observed MIR-FIR counts (including Herschel) to predict the number of sources and their IR lines fluxes, as derived from observations of local galaxies. A shallow survey in an area of 0.5 square degrees, with a typical integration time of 1 hour per pointing, will be able to detect thousands of galaxies in at least three emission lines, using SAFARI, the far-IR spectrometer onboard of SPICA.
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We develop a novel method to extract key cosmological information, which is primarily carried by the baryon acoustic oscillations (BAO) and redshift space distortions (RSD), from spectroscopic galaxy surveys, based on a joint principal component analysis (PCA) and Karhunen-Lo`eve (KL) data compression scheme. Comparing to the traditional methods using the multipoles or wedges of the galaxy correlation functions, we find that our method is able to extract the key information more efficiently, with a better control of the potential systematics, which manifests it as a powerful tool for clustering analysis for ongoing and forthcoming galaxy surveys.
We perform forecasts for how baryon acoustic oscillation (BAO) scale and redshift-space distortion (RSD) measurements from future spectroscopic emission line galaxy (ELG) surveys such as Euclid are degraded in the presence of spectral line misidentification. Using analytic calculations verified with mock galaxy catalogs from log-normal simulations we find that constraints are degraded in two ways, even when the interloper power spectrum is modeled correctly in the likelihood. Firstly, there is a loss of signal-to-noise ratio for the power spectrum of the target galaxies, which propagates to all cosmological constraints and increases with contamination fraction, $f_c$. Secondly, degeneracies can open up between $f_c$ and cosmological parameters. In our calculations this typically increases BAO scale uncertainties at the 10-20% level when marginalizing over parameters determining the broadband power spectrum shape. External constraints on $f_c$, or parameters determining the shape of the power spectrum, for example from cosmic microwave background (CMB) measurements, can remove this effect. There is a near-perfect degeneracy between $f_c$ and the power spectrum amplitude for low $f_c$ values, where $f_c$ is not well determined from the contaminated sample alone. This has the potential to strongly degrade RSD constraints. The degeneracy can be broken with an external constraint on $f_c$, for example from cross-correlation with a separate galaxy sample containing the misidentified line, or deeper sub-surveys.
The Square Kilometre Array (SKA) will conduct the biggest spectroscopic galaxy survey ever, by detecting the 21cm emission line of neutral hydrogen (HI) from around a billion galaxies over 3/4 of the sky, out to a redshift of z~2. This will allow the redshift-space matter power spectrum, and corresponding dark energy observables, to be measured with unprecedented precision. In this paper, we present an improved model of the HI galaxy number counts and bias from semi-analytic simulations, and use it to calculate the expected yield of HI galaxies from surveys with a variety of Phase 1 and 2 SKA configurations. We illustrate the relative performance of the different surveys by forecasting errors on the radial and transverse scales of the baryon acoustic oscillation (BAO) feature, finding that the full billion galaxy survey with SKA2 will deliver the largest dark energy figure of merit of any current or future large-scale structure survey.
[Abridged] We consider how galaxy clustering data, from Mpc to Gpc scales, from upcoming large scale structure surveys, such as Euclid and DESI, can provide discriminating information about the bispectrum shape arising from a variety of inflationary scenarios. Through exploring in detail the weighting of shape properties in the calculation of the halo bias and halo mass function we show how they probe a broad range of configurations, beyond those in the squeezed limit, that can help distinguish between shapes with similar large scale bias behaviors. We assess the impact, on constraints for a diverse set of non-Gaussian shapes, of galaxy clustering information in the mildly non-linear regime, and surveys that span multiple redshifts and employ different galactic tracers of the dark matter distribution. Fisher forecasts are presented for a Euclid-like spectroscopic survey of H$alpha$-selected emission line galaxies (ELGs) using recent revisions of the expected H$alpha$ luminosity function, and a DESI-like survey, of luminous red galaxies (LRGs) and [O-II] doublet-selected ELGs, in combination with Planck-like CMB temperature and polarization data. While ELG samples provide better probes of shapes that are divergent in the squeezed limit, LRG constraints, centered below $z<1$, yield stronger constraints on shapes with scale-independent large-scale halo biases, such as the equilateral template. The ELG and LRG samples provide complementary degeneracy directions for distinguishing between different shapes. If the Gaussian galaxy bias is constrained to better than a percent level, such as can be determined from the galaxy bispectrum or weak lensing, then the LSS and CMB data could provide complementary constraints that will enable differentiation of bispectra with distinct theoretical origins but with similar large scale, squeezed-limit properties.
We present a deep machine learning (ML)-based technique for accurately determining $sigma_8$ and $Omega_m$ from mock 3D galaxy surveys. The mock surveys are built from the AbacusCosmos suite of $N$-body simulations, which comprises 40 cosmological volume simulations spanning a range of cosmological models, and we account for uncertainties in galaxy formation scenarios through the use of generalized halo occupation distributions (HODs). We explore a trio of ML models: a 3D convolutional neural network (CNN), a power-spectrum-based fully connected network, and a hybrid approach that merges the two to combine physically motivated summary statistics with flexible CNNs. We describe best practices for training a deep model on a suite of matched-phase simulations and we test our model on a completely independent sample that uses previously unseen initial conditions, cosmological parameters, and HOD parameters. Despite the fact that the mock observations are quite small ($sim0.07h^{-3},mathrm{Gpc}^3$) and the training data span a large parameter space (6 cosmological and 6 HOD parameters), the CNN and hybrid CNN can constrain $sigma_8$ and $Omega_m$ to $sim3%$ and $sim4%$, respectively.
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