Euclidpreparation

Astronomy & Astrophysics EDP Sciences 664 (2022) A196-A196

Authors:

R Saglia, S De Nicola, M Fabricius, V Guglielmo, J Snigula, R Zöller, R Bender, J Heidt, D Masters, D Stern, S Paltani, A Amara, N Auricchio, M Baldi, C Bodendorf, D Bonino, E Branchini, M Brescia, J Brinchmann, S Camera, V Capobianco, C Carbone, J Carretero, M Castellano, S Cavuoti, CAJ Duncan

Abstract:

The Complete Calibration of the Color-Redshift Relation survey (C3R2) is a spectroscopic programme designed to empirically calibrate the galaxy color-redshift relation to the Euclid depth (I_E=24.5), a key ingredient for the success of Stage IV dark energy projects based on weak lensing cosmology. A spectroscopic calibration sample as representative as possible of the galaxies in the Euclid weak lensing sample is being collected, selecting galaxies from a self-organizing map (SOM) representation of the galaxy color space. Here, we present the results of a near-infrared H- and K-bands spectroscopic campaign carried out using the LUCI instruments at the LBT. For a total of 251 galaxies, we present new highly-reliable redshifts in the 1.3<= z <=1.7 and 2<= z<=2.7 ranges. The newly-determined redshifts populate 49 SOM cells which previously contained no spectroscopic measurements and almost double the occupation numbers of an additional 153 SOM cells. A final optical ground-based observational effort is needed to calibrate the missing cells in particular in the redshift range 1.7<= z<=2.7 that lack spectroscopic calibration. In the end, Euclid itself will deliver telluric-free NIR spectra that can complete the calibration...

First measurement of projected phase correlations and large-scale structure constraints

(2022)

Authors:

Felipe Oliveira Franco, Boryana Hadzhiyska, David Alonso

The scatter in the galaxy-halo connection: a machine learning analysis

Monthly Notices of the Royal Astronomical Society Oxford University Press 514:3 (2022) 4026-4045

Authors:

Richard Stiskalek, Deaglan J Bartlett, Harry Desmond, Dhayaa Anbajagane

Abstract:

We apply machine learning (ML), a powerful method for uncovering complex correlations in high-dimensional data, to the galaxy-halo connection of cosmological hydrodynamical simulations. The mapping between galaxy and halo variables is stochastic in the absence of perfect information, but conventional ML models are deterministic and hence cannot capture its intrinsic scatter. To overcome this limitation, we design an ensemble of neural networks with a Gaussian loss function that predict probability distributions, allowing us to model statistical uncertainties in the galaxy-halo connection as well as its best-fitting trends. We extract a number of galaxy and halo variables from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent to which knowledge of some subset of one enables prediction of the other. This allows us to identify the key features of the galaxy-halo connection and investigate the origin of its scatter in various projections. We find that while halo properties beyond mass account for up to 50 per cent of the scatter in the halo-To-stellar mass relation, the prediction of stellar half-mass radius or total gas mass is not substantially improved by adding further halo properties. We also use these results to investigate semi-Analytic models for galaxy size in the two simulations, finding that assumptions relating galaxy size to halo size or spin are not successful.

A challenge to the standard cosmological model

Astrophysical Journal Letters (2022)

Authors:

Nathan Secrest, Sebastian VON HAUSEGGER, Mohamed Rameez, Roya Mohayaee, Subir Sarkar

Reionization Era Bright Emission Line Survey: Selection and Characterization of Luminous Interstellar Medium Reservoirs in the z > 6.5 Universe

The Astrophysical Journal American Astronomical Society 931:2 (2022) 160-160

Authors:

RJ Bouwens, R Smit, S Schouws, M Stefanon, R Bowler, R Endsley, V Gonzalez, H Inami, D Stark, P Oesch, J Hodge, M Aravena, E da Cunha, P Dayal, I de Looze, A Ferrara, Y Fudamoto, L Graziani, C Li, T Nanayakkara, A Pallottini, R Schneider, L Sommovigo, M Topping, P van der Werf

Abstract:

Abstract The Reionization Era Bright Emission Line Survey (REBELS) is a cycle-7 ALMA Large Program (LP) that is identifying and performing a first characterization of many of the most luminous star-forming galaxies known in the z > 6.5 universe. REBELS is providing this probe by systematically scanning 40 of the brightest UV-selected galaxies identified over a 7 deg 2 area for bright [C ii ] 158 μ m and [O iii ] 88 μ m lines and dust-continuum emission. Selection of the 40 REBELS targets was done by combining our own and other photometric selections, each of which is subject to extensive vetting using three completely independent sets of photometry and template-fitting codes. Building on the observational strategy deployed in two pilot programs, we are increasing the number of massive interstellar medium (ISM) reservoirs known at z > 6.5 by ∼4–5× to >30. In this manuscript, we motivate the observational strategy deployed in the REBELS program and present initial results. Based on the first-year observations, 18 highly significant ≥ 7 σ [C ii ] 158 μ m lines have already been discovered, the bulk of which (13/18) also show ≥3.3 σ dust-continuum emission. These newly discovered lines more than triple the number of bright ISM-cooling lines known in the z > 6.5 universe, such that the number of ALMA-derived redshifts at z > 6.5 rival Ly α discoveries. An analysis of the completeness of our search results versus star formation rate (SFR) suggests an ∼79% efficiency in scanning for [C ii ] 158 μ m when the SFR UV+IR is >28 M ⊙ yr −1 . These new LP results further demonstrate ALMA’s efficiency as a “redshift machine,” particularly in the Epoch of Reionization.