Beecroft Institute for Particle Astrophysics and Cosmology

Abstract:

We present an emulator that accurately predicts the power spectrum of galaxies in redshift space as a function of cosmological parameters. Our emulator is based on a second-order Lagrangian bias expansion that is displaced to Eulerian space using cosmological N-body simulations. Redshift space distortions are then imprinted using the non-linear velocity field of simulated particles and haloes. We build the emulator using a forward neural network trained with the simulations of the BACCO project, which covers an eight-dimensional parameter space including massive neutrinos and dynamical dark energy. We show that our emulator provides unbiased cosmological constraints from the monopole, quadrupole, and hexadecapole of a mock galaxy catalogue that mimics the BOSS-CMASS sample down to non-linear scales (k ∼ 0.6hMpc⁻¹). This work opens up the possibility of robustly extracting cosmological information from small scales using observations of the large-scale structure of the universe.

Abstract:

Current and future imaging surveys require photometric redshifts (photo- z s) to be estimated for millions of galaxies. Improving the photo- z quality is a major challenge but is needed to advance our understanding of cosmology. In this paper we explore how the synergies between narrow-band photometric data and large imaging surveys can be exploited to improve broadband photometric redshifts. We used a multi-task learning (MTL) network to improve broadband photo- z estimates by simultaneously predicting the broadband photo- z and the narrow-band photometry from the broadband photometry. The narrow-band photometry is only required in the training field, which also enables better photo- z predictions for the galaxies without narrow-band photometry in the wide field. This technique was tested with data from the Physics of the Accelerating Universe Survey (PAUS) in the COSMOS field. We find that the method predicts photo- z s that are 13% more precise down to magnitude i AB < 23; the outlier rate is also 40% lower when compared to the baseline network. Furthermore, MTL reduces the photo- z bias for high-redshift galaxies, improving the redshift distributions for tomographic bins with z > 1. Applying this technique to deeper samples is crucial for future surveys such as Euclid or LSST. For simulated data, training on a sample with i AB < 23, the method reduces the photo- z scatter by 16% for all galaxies with i AB < 25. We also studied the effects of extending the training sample with photometric galaxies using PAUS high-precision photo- z s, which reduces the photo- z scatter by 20% in the COSMOS field.

Abstract:

The observation of the polarised emission from the Cosmic Microwave Background (CMB) from future ground-based and satellite-borne experiments holds the promise of indirectly detecting the elusive signal from primordial tensor fluctuations in the form of large-scale B-mode polarisation. Doing so, however, requires an accurate and robust separation of the signal from polarised Galactic foregrounds. We present a component separation method for multi-frequency CMB observations that combines some of the advantages of map-based and power-spectrum-based techniques, and which is direcly applicable to data in the presence of realistic foregrounds and instrumental noise. We demonstrate that the method is able to reduce the contamination from Galactic foregrounds below an equivalent tensor-to-scalar ratio r_FG ≲ 5 × 10^-4, as required for next-generation observatories, for a wide range of foreground models with varying degrees of complexity. This bias reduction is associated with a mild ∼20–30% increase in the final statistical uncertainties, and holds for large sky areas, and for experiments targeting both the reionisation and recombination bumps in theB-mode power spectrum.