Cosmology

Abstract:

Context. Computing the matter power spectrum, P(k), as a function of cosmological parameters can be prohibitively slow in cosmological analyses, hence emulating this calculation is desirable. Previous analytic approximations are insufficiently accurate for modern applications, so black-box, uninterpretable emulators are often used.

Aims. We aim to construct an efficient, differentiable, interpretable, symbolic emulator for the redshift zero linear matter power spectrum which achieves sub-percent level accuracy. We also wish to obtain a simple analytic expression to convert A_s to σ₈ given the other cosmological parameters.

Methods. We utilise an efficient genetic programming based symbolic regression framework to explore the space of potential mathematical expressions which can approximate the power spectrum and σ₈. We learn the ratio between an existing low-accuracy fitting function for P(k) and that obtained by solving the Boltzmann equations and thus still incorporate the physics which motivated this earlier approximation.

Results. We obtain an analytic approximation to the linear power spectrum with a root mean squared fractional error of 0.2% between k = 9 × 10⁻³ − 9 h Mpc⁻¹ and across a wide range of cosmological parameters, and we provide physical interpretations for various terms in the expression. Our analytic approximation is 950 times faster to evaluate than CAMB and 36 times faster than the neural network based matter power spectrum emulator BACCO. We also provide a simple analytic approximation for σ₈ with a similar accuracy, with a root mean squared fractional error of just 0.1% when evaluated across the same range of cosmologies. This function is easily invertible to obtain A_s as a function of σ₈ and the other cosmological parameters, if preferred.

Conclusions. It is possible to obtain symbolic approximations to a seemingly complex function at a precision required for current and future cosmological analyses without resorting to deep-learning techniques, thus avoiding their black-box nature and large number of parameters. Our emulator will be usable long after the codes on which numerical approximations are built become outdated.

Abstract:

Using deep JWST imaging from JADES, JEMS, and SMILES, we characterize optically faint and extremely red galaxies at z > 3 that were previously missing from galaxy census estimates. The data indicate the existence of abundant, dusty, and poststarburst-like galaxies down to 108 M ⊙, below the sensitivity limit of Spitzer and the Atacama Large Millimeter/submillimeter Array (ALMA). Modeling the NIRCam and Hubble Space Telescope (HST) photometry of these red sources can result in extremely high values for both stellar mass and star formation rate (SFR); however, including seven MIRI filters out to 21 μm results in decreased masses (median 0.6 dex for log10(M∗/M⊙) > 10) and SFRs (median 10× for SFR > 100 M ⊙ yr−1). At z > 6, our sample includes a high fraction of “little red dots” (LRDs; NIRCam-selected dust-reddened active galactic nucleus (AGN) candidates). We significantly measure older stellar populations in the LRDs out to rest-frame 3 μm (the stellar bump) and rule out a dominant contribution from hot dust emission, a signature of AGN contamination to stellar population measurements. This allows us to measure their contribution to the cosmic census at z > 3, below the typical detection limits of ALMA (L IR < 1012 L ⊙). We find that these sources, which are overwhelmingly missed by HST and ALMA, could effectively double the obscured fraction of the star formation rate density at 4 < z < 6 compared to some estimates, showing that prior to JWST, the obscured contribution from fainter sources could be underestimated. Finally, we identify five sources with evidence for Balmer breaks and high stellar masses at 5.5 < z < 7.7. While spectroscopy is required to determine their nature, we discuss possible measurement systematics to explore with future data.