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M. S. Cagliari

Publications and source records attributed to M. S. Cagliari.

6 recordsLinked to original sources

Euclid preparation: Testing multi-field inflation with galaxy power spectrum and bispectrum

Primordial non-Gaussianity (PNG) is a powerful probe of the origin of cosmic structure. Stage-IV surveys like \Euclid will measure galaxy $2$- and $3$-point clustering at high signal-to-noise, whose exploitation requires robust joint analysis. We prepare for Euclid's spectroscopic sample by validating a redshift-space power-spectrum and bispectrum pipeline (one-loop $P_\ell$, tree-level $B_\ell$) on Euclid-like mocks from Abacus-PNG $N$-body simulations with Gaussian and local-PNG initial conditions, using a halo occupation distribution (HOD) tuned to Euclid Flagship 2. We stress-test analysis choices -- PNG-bias parametrisation, priors, and scale cuts -- and perform null tests without PNG. In a `prior-agnostic setup', detection of the dominant PNG term $\propto f_{\rm NL} \, b_ϕ$ in single redshift bins is difficult; nevertheless, the bispectrum provides constraints on other PNG combinations that partially lift degeneracies. We propose a physically motivated prior on $b_ϕ$ that yields unbiased $f_{\rm NL}$ while accounting for theory uncertainty, and determine scale cuts that give unbiased $Λ$CDM and $f_{\rm NL}$. With $V_{\rm eff}=16\,h^{-3}\,{\rm Gpc}^3$ across four snapshots ($0.8\le z\le1.7$), our likelihood analyses recover $<1σ$ bias in $f_{\rm NL}$ and $Λ$CDM. At fixed cuts, $B_\ell$ alone reduces $σ({f_{\rm NL}})$ by $\sim29$--$46\%$ relative to $P_\ell$, and joint power spectrum-bispectrum analysis tightens a further $\sim8$--$13\%$; the cumulative gain from $z=0.8$ to $1.7$ is $\sim2.3$ for the joint case. The bispectrum quadrupole is key. Our strongest results are at $z=1.7$: $1.9σ$ for $f_{\rm NL} \, b_ϕ$ (prior-agnostic) and $2.35σ$ for $f_{\rm NL}$ (prior-based). Joint analyses thus offer strong prospects for testing multi-field inflation, pending end-to-end validation in the full Euclid geometry with observational systematics.

astro-ph.CO↗

Euclid preparation. Galaxy power spectrum and bispectrum modelling

Higher-order correlation functions of the large-scale galaxy distribution offer access to information beyond that contained in standard 2-point statistics such as the power spectrum. In this work we assess this potential for the $\textit{Euclid}$ mission using synthetic catalogues of H$α$ galaxies based on the 54 $\, h^{-3} \, {\rm Gpc}^3$ Flagship I simulation, designed to reproduce the $\textit{Euclid}$ spectroscopic sample. We comprehensively validate the one-loop galaxy power spectrum and tree-level bispectrum predictions from perturbation theory in both real and redshift space. Assuming scale cuts consistent with our previous power spectrum study on the same catalogues, this modelling yields unbiased cosmological constraints for the bispectrum up to $k_{\rm max} = 0.15\,\, h \, {\rm Mpc}^{-1}$ in real space and $0.08 \, (0.1)\,\, h \, {\rm Mpc}^{-1}$ at the lowest (highest) redshift, corresponding to $z=0.9$ ($z=1.8$), for the monopole and quadrupole in redshift space using statistical uncertainties corresponding to the full simulation volume. With these scale cuts, adding bispectrum information to the power spectrum improves constraints on the amplitude of scalar perturbations and the matter density by up to 30 %, increasing the overall figure of merit for key cosmological parameters by a factor of about 2.5. Similar conclusions hold when statistical uncertainties are rescaled to a $\textit{Euclid}$-like volume, highlighting the importance of the bispectrum for fully exploiting the forthcoming $\textit{Euclid}$ data. Our analysis also provides the first detailed characterisation of the nonlinear bias model of H$α$ emitters, showing that bias relations calibrated on low-resolution \textit{N}-body simulations do not adequately describe the clustering of H$α$ galaxies at low redshift, whereas excursion-set and co-evolution relations for tidal biases remain accurate.

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Hierarchical summaries for primordial non-Gaussianities

The advent of Stage IV galaxy redshift surveys such as DESI and Euclid marks the beginning of an era of precision cosmology, with one key objective being the detection of primordial non-Gaussianities (PNG), potential signatures of inflationary physics. In particular, constraining the amplitude of local-type PNG, parameterised by $f_{\rm NL}$, with $σ_{f_{\rm NL}} \sim 1$, would provide a critical test of single versus multi-field inflation scenarios. While current large-scale structure and cosmic microwave background analyses have achieved $σ_{f_{\rm NL}} \sim 5$-$9$, further improvements demand novel data compression strategies. We propose a hybrid estimator that hierarchically combines standard $2$-point and $3$-point statistics with a field-level neural summary, motivated by recent theoretical work that shows that such a combination is nearly optimal, disentangling primordial from late-time non-Gaussianity. We employ PatchNet, a convolutional neural network that extracts small-scale information from sub-volumes (patches) of the halo number density field while large-scale information is retained via the power spectrum and bispectrum. Using Quijote-PNG simulations, we evaluate the Fisher information of this combined estimator across various redshifts, halo mass cuts, and scale cuts. Our results demonstrate that the inclusion of patch-based field-level compression always enhances constraints on $f_{\rm NL}$, reaching gains of $30$-$45\%$ at low $k_{\rm max}$ ($\sim 0.1 \, h \, \text{Mpc}^{-1}$), and capturing information beyond the bispectrum. This approach offers a computationally efficient and scalable pathway to tighten the PNG constraints from forthcoming survey data.

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Euclid: Testing photometric selection of emission-line galaxy targets

Multi-object spectroscopic galaxy surveys typically make use of photometric and colour criteria to select targets. Conversely, the Euclid NISP slitless spectrograph will record spectra for every source over its field of view. Slitless spectroscopy has the advantage of avoiding defining a priori a galaxy sample, but at the price of making the selection function harder to quantify. The Euclid Wide Survey aims at building robust statistical samples of emission-line galaxies with fluxes in the Halpha-NII complex brighter than 2e-16 erg/s/cm^2 and within 0.9<z<1.8. At faint fluxes, we expect significant contamination by wrongly measured redshifts, either due to emission-line misidentification or noise fluctuations, with the consequence of reducing the purity of the final samples. This can be significantly improved by exploiting Euclid photometric information to identify emission-line galaxies over the redshifts of interest. To this goal, we compare and quantify the performance of six machine-learning classification algorithms. We consider the case when only Euclid photometric and morphological measurements are used and when these are supplemented by ground-based photometric data. We train and test the classifiers on two mock galaxy samples, the EL-COSMOS and Euclid Flagship2 catalogues. Dense neural networks and support vector classifiers obtain the best performance, with comparable results in terms of the adopted metrics. When training on Euclid photometry alone, these can remove 87% of the sources that are fainter than the nominal flux limit or lie outside the range 0.9<z<1.8, a figure that increases to 97% when ground-based photometry is included. These results show how by using the photometric information available to Euclid it will be possible to efficiently identify and discard spurious interlopers, allowing us to build robust spectroscopic samples for cosmological investigations.

astro-ph.CO↗

Augmenting photometric redshift estimates using spectroscopic nearest neighbours

As a consequence of galaxy clustering, close galaxies observed on the plane of the sky should be spatially correlated with a probability that is inversely proportional to their angular separation. In principle, this information can be used to improve photometric redshift estimates when spectroscopic redshifts are available for some of the neighbouring objects. Depending on the depth of the survey, however, this angular correlation is reduced by chance projections. In this work, we implement a deep-learning model to distinguish between apparent and real angular neighbours by solving a classification task. We adopted a graph neural network architecture to tie together photometry, spectroscopy, and the spatial information between neighbouring galaxies. We trained and validated the algorithm on the data of the VIPERS galaxy survey, for which photometric redshifts based on spectral energy distribution are also available. The model yields a confidence level for a pair of galaxies to be real angular neighbours, enabling us to disentangle chance superpositions in a probabilistic way. When objects for which no physical companion can be identified are excluded, all photometric redshift quality metrics improve significantly, confirming that their estimates were of lower quality. For our typical test configuration, the algorithm identifies a subset containing ~75% high-quality photometric redshifts, for which the dispersion is reduced by as much as 50% (from 0.08 to 0.04), while the fraction of outliers reduces from 3% to 0.8%. Moreover, we show that the spectroscopic redshift of the angular neighbour with the highest detection probability provides an excellent estimate of the redshift of the target galaxy, comparable to or even better than the corresponding template-fitting estimate.

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Euclid: Constraining ensemble photometric redshift distributions with stacked spectroscopy

The ESA Euclid mission will produce photometric galaxy samples over 15000 square degrees of the sky that will be rich for clustering and weak lensing statistics. The accuracy of the cosmological constraints derived from these measurements will depend on the knowledge of the underlying redshift distributions based on photometric redshift calibrations. A new approach is proposed to use the stacked spectra from Euclid slitless spectroscopy to augment broad-band photometric information to constrain the redshift distribution with spectral energy distribution fitting. The high spectral resolution available in the stacked spectra complements the photometry and helps to break the colour-redshift degeneracy and constrain the redshift distribution of galaxy samples. We modelled the stacked spectra as a linear mixture of spectral templates. The mixture may be inverted to infer the underlying redshift distribution using constrained regression algorithms. We demonstrate the method on simulated Vera C. Rubin Observatory and Euclid mock survey data sets based on the Euclid Flagship mock galaxy catalogue. We assess the accuracy of the reconstruction by considering the inference of the baryon acoustic scale from angular two-point correlation function measurements. We selected mock photometric galaxy samples at redshift z>1 using the self-organising map algorithm. Considering the idealised case without dust attenuation, we find that the redshift distributions of these samples can be recovered with 0.5% accuracy on the baryon acoustic scale. The estimates are not significantly degraded by the spectroscopic measurement noise due to the large sample size. However, the error degrades to 2% when the dust attenuation model is left free. We find that the colour degeneracies introduced by attenuation limit the accuracy considering the wavelength coverage of Euclid near-infrared spectroscopy.

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