Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
A. Iovino
B. M. Poggianti
A. Mercurio
M. Longhetti
M. Bolzonella
G. Busarello
M. Gullieuszik
F. LaBarbera
P. Merluzzi
L. Morelli
C. Tortora
D. Vergani
S. Zibetti
C. P. Haines
L. Costantin
F. R. Ditrani
L. Pozzetti
J. Angthopo
M. Balcells
S. Bardelli
Abstract： The upcoming new generation of optical spectrographs on four-meter-class telescopes will provide valuable opportunities for forthcoming galaxy surveys through their huge multiplexing capabilities, excellent spectral resolution, and unprecedented wavelength coverage. WEAVE is a new wide-field spectroscopic facility mounted on the 4.2 m William Herschel Telescope in La Palma. WEAVE-StePS is one of the five extragalactic surveys that will use WEAVE during its first five years of operations. It will observe galaxies using WEAVE MOS (~950 fibres across a field of view of ~3 deg2 on the sky) in low-resolution mode (R~5000, spanning the wavelength range 3660-9590 AA). WEAVE-StePS will obtain high-quality spectra (S/N ~ 10 per AA at R~5000) for a magnitude-limited (I_AB = 20.5) sample of ~25,000 galaxies, the majority selected at z>=0.3. The survey goal is to provide precise spectral measurements in the crucial interval that bridges the gap between LEGA-C and SDSS data. The wide area coverage of ~25 deg2 will enable us to observe galaxies in a variety of environments. The ancillary data available in each observed field (including X-ray coverage, multi-narrow-band photometry and spectroscopic redshift information) will provide an environmental characterisation for each observed galaxy. This paper presents the science case of WEAVE-StePS, the fields to be observed, the parent catalogues used to define the target sample, and the observing strategy chosen after a forecast of the expected performance of the instrument for our typical targets. WEAVE-StePS will go back further in cosmic time than SDSS, extending its reach to encompass more than ~6 Gyr, nearly half of the age of the Universe. The spectral and redshift range covered by WEAVE-StePS will open a new observational window by continuously tracing the evolutionary path of galaxies in the largely unexplored intermediate-redshift range.
Peer Review Status:
Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Euclid Collaboration
S. E. van Mierlo
K. I. Caputi
M. Ashby
H. Atek
M. Bolzonella
R. A. A. Bowler
G. Brammer
C. J. Conselice
J. Cuby
P. Dayal
A. Díaz-Sánchez
S. L. Finkelstein
H. Hoekstra
A. Humphrey
O. Ilbert
H. J. McCracken
B. Milvang-Jensen
P. A. Oesch
R. Pello
Abstract： (Abridged) The Euclid mission is expected to discover thousands of z>6 galaxies in three Deep Fields, which together will cover a ~40 deg2 area. However, the limited number of Euclid bands and availability of ancillary data could make the identification of z>6 galaxies challenging. In this work, we assess the degree of contamination by intermediate-redshift galaxies (z=1-5.8) expected for z>6 galaxies within the Euclid Deep Survey. This study is based on ~176,000 real galaxies at z=1-8 in a ~0.7 deg2 area selected from the UltraVISTA ultra-deep survey, and ~96,000 mock galaxies with 25.3$\leq$H<27.0, which altogether cover the range of magnitudes to be probed in the Euclid Deep Survey. We simulate Euclid and ancillary photometry from the fiducial, 28-band photometry, and fit spectral energy distributions (SEDs) to various combinations of these simulated data. Our study demonstrates that identifying z>6 with Euclid data alone will be very effective, with a z>6 recovery of 91(88)% for bright (faint) galaxies. For the UltraVISTA-like bright sample, the percentage of z=1-5.8 contaminants amongst apparent z>6 galaxies as observed with Euclid alone is 18%, which is reduced to 4(13)% by including ultra-deep Rubin (Spitzer) photometry. Conversely, for the faint mock sample, the contamination fraction with Euclid alone is considerably higher at 39%, and minimized to 7% when including ultra-deep Rubin data. For UltraVISTA-like bright galaxies, we find that Euclid (I-Y)>2.8 and (Y-J)<1.4 colour criteria can separate contaminants from true z>6 galaxies, although these are applicable to only 54% of the contaminants, as many have unconstrained (I-Y) colours. In the most optimistic scenario, these cuts reduce the contamination fraction to 1% whilst preserving 81% of the fiducial z>6 sample. For the faint mock sample, colour cuts are infeasible.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Euclid Collaboration
A. Humphrey
L. Bisigello
P. A. C. Cunha
M. Bolzonella
S. Fotopoulou
K. Caputi
C. Tortora
G. Zamorani
P. Papaderos
D. Vergani
J. Brinchmann
M. Moresco
A. Amara
N. Auricchio
M. Baldi
R. Bender
D. Bonino
E. Branchini
M. Brescia
Abstract： The Euclid Space Telescope will provide deep imaging at optical and near-infrared wavelengths, along with slitless near-infrared spectroscopy, across ~15,000 sq deg of the sky. Euclid is expected to detect ~12 billion astronomical sources, facilitating new insights into cosmology, galaxy evolution, and various other topics. To optimally exploit the expected very large data set, there is the need to develop appropriate methods and software. Here we present a novel machine-learning based methodology for selection of quiescent galaxies using broad-band Euclid I_E, Y_E, J_E, H_E photometry, in combination with multiwavelength photometry from other surveys. The ARIADNE pipeline uses meta-learning to fuse decision-tree ensembles, nearest-neighbours, and deep-learning methods into a single classifier that yields significantly higher accuracy than any of the individual learning methods separately. The pipeline has `sparsity-awareness', so that missing photometry values are still informative for the classification. Our pipeline derives photometric redshifts for galaxies selected as quiescent, aided by the `pseudo-labelling' semi-supervised method. After application of the outlier filter, our pipeline achieves a normalized mean absolute deviation of ~< 0.03 and a fraction of catastrophic outliers of ~< 0.02 when measured against the COSMOS2015 photometric redshifts. We apply our classification pipeline to mock galaxy photometry catalogues corresponding to three main scenarios: (i) Euclid Deep Survey with ancillary ugriz, WISE, and radio data; (ii) Euclid Wide Survey with ancillary ugriz, WISE, and radio data; (iii) Euclid Wide Survey only. Our classification pipeline outperforms UVJ selection, in addition to the Euclid I_E-Y_E, J_E-H_E and u-I_E,I_E-J_E colour-colour methods, with improvements in completeness and the F1-score of up to a factor of 2. (Abridged)
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
D. Camarena
V. Marra
Z. Sakr
S. Nesseris
A. Da Silva
J. Garcia-Bellido
P. Fleury
L. Lombriser
M. Martinelli
C. J. A. P. Martins
J. Mimoso
D. Sapone
C. Clarkson
S. Camera
C. Carbone
S. Casas
S. Ilić
V. Pettorino
I. Tutusaus
N. Aghanim
Abstract： The Copernican principle, the notion that we are not at a special location in the Universe, is one of the cornerstones of modern cosmology and its violation would invalidate the Friedmann-Lema\^{\i}tre-Robertson-Walker (FLRW) metric, causing a major change in our understanding of the Universe. Thus, it is of fundamental importance to perform observational tests of this principle. We determine the precision with which future surveys will be able to test the Copernican principle and their ability to detect any possible violations. We forecast constraints on the inhomogeneous Lema\^{\i}tre-Tolman-Bondi model with a cosmological constant $\Lambda$ ($\Lambda$LTB), basically a cosmological constant $\Lambda$ and cold dark matter ($\Lambda$CDM) model, but endowed with a spherical inhomogeneity. We consider combinations of currently available data and simulated Euclid data, together with external data products, based on both $\Lambda$CDM and $\Lambda$LTB fiducial models. These constraints are compared to the expectations from the Copernican principle. When considering the $\Lambda$CDM fiducial model, we find that Euclid data, in combination with other current and forthcoming surveys, will improve the constraints on the Copernican principle by about $30\%$, with $\pm10\%$ variations depending on the observables and scales considered. On the other hand, when considering a $\Lambda$LTB fiducial model, we find that future Euclid data, combined with other current and forthcoming data sets, will be able to detect Gpc-scale inhomogeneities of contrast $-0.1$. Next-generation surveys, such as Euclid, will thoroughly test homogeneity at large scales, tightening the constraints on possible violations of the Copernican principle.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Euclid Collaboration
H. Bretonnière
U. Kuchner
M. Huertas-Company
E. Merlin
M. Castellano
D. Tuccillo
F. Buitrago
C. J. Conselice
A. Boucaud
B. Häußler
M. Kümmel
W. G. Hartley
A. Alvarez Ayllon
E. Bertin
F. Ferrari
L. Ferreira
R. Gavazzi
D. Hernández-Lang
G. Lucatelli
Abstract： The various Euclid imaging surveys will become a reference for studies of galaxy morphology by delivering imaging over an unprecedented area of 15 000 square degrees with high spatial resolution. In order to understand the capabilities of measuring morphologies from Euclid-detected galaxies and to help implement measurements in the pipeline, we have conducted the Euclid Morphology Challenge, which we present in two papers. While the companion paper by Merlin et al. focuses on the analysis of photometry, this paper assesses the accuracy of the parametric galaxy morphology measurements in imaging predicted from within the Euclid Wide Survey. We evaluate the performance of five state-of-the-art surface-brightness-fitting codes DeepLeGATo, Galapagos-2, Morfometryka, Profit and SourceXtractor++ on a sample of about 1.5 million simulated galaxies resembling reduced observations with the Euclid VIS and NIR instruments. The simulations include analytic S\'ersic profiles with one and two components, as well as more realistic galaxies generated with neural networks. We find that, despite some code-specific differences, all methods tend to achieve reliable structural measurements (10% scatter on ideal S\'ersic simulations) down to an apparent magnitude of about 23 in one component and 21 in two components, which correspond to a signal-to-noise ratio of approximately 1 and 5 respectively. We also show that when tested on non-analytic profiles, the results are typically degraded by a factor of 3, driven by systematics. We conclude that the Euclid official Data Releases will deliver robust structural parameters for at least 400 million galaxies in the Euclid Wide Survey by the end of the mission. We find that a key factor for explaining the different behaviour of the codes at the faint end is the set of adopted priors for the various structural parameters.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
S. Contarini
G. Verza
A. Pisani
N. Hamaus
M. Sahlén
C. Carbone
S. Dusini
F. Marulli
L. Moscardini
A. Renzi
C. Sirignano
L. Stanco
M. Aubert
M. Bonici
G. Castignani
H. M. Courtois
S. Escoffier
D. Guinet
A. Kovacs
G. Lavaux
Abstract： The Euclid mission $-$ with its spectroscopic galaxy survey covering a sky
area over $15\,000 \ \mathrm{deg}^2$ in the redshift range $0.9
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
L. Cabayol
M. Eriksen
J. Carretero
R. Casas
F. J. Castander
E. Fernández
J. Garcia-Bellido
E. Gaztanaga
H. Hildebrandt
H. Hoekstra
B. Joachimi
R. Miquel
C. Padilla
A. Pocino
E. Sanchez
S. Serrano
I. Sevilla
M. Siudek
P. Tallada-Crespí
N. Aghanim
Abstract： Current and future imaging surveys require photometric redshifts (photo-zs) 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-zs 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-zs, which reduces the photo-z scatter by 20% in the COSMOS field.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
K. Naidoo
H. Johnston
B. Joachimi
J. L. van den Busch
H. Hildebrandt
O. Ilbert
O. Lahav
N. Aghanim
B. Altieri
A. Amara
M. Baldi
R. Bender
C. Bodendorf
E. Branchini
M. Brescia
J. Brinchmann
S. Camera
V. Capobianco
C. Carbone
J. Carretero
Abstract： Cosmological constraints from key probes of the Euclid imaging survey rely
critically on the accurate determination of the true redshift distributions,
$n(z)$, of tomographic redshift bins. We determine whether the mean redshift,
$
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
S. Radinović
S. Nadathur
H. -A. Winther
W. J. Percival
A. Woodfinden
E. Massara
E. Paillas
S. Contarini
N. Hamaus
A. Kovacs
A. Pisani
G. Verza
M. Aubert
A. Amara
N. Auricchio
M. Baldi
D. Bonino
E. Branchini
M. Brescia
S. Camera
Abstract： We investigate the cosmological constraints that can be expected from measurement of the cross-correlation of galaxies with cosmic voids identified in the Euclid spectroscopic survey, which will include spectroscopic information for tens of millions of galaxies over $15\,000$ deg$^2$ of the sky in the redshift range $0.9\leq z<1.8$. We do this using simulated measurements obtained from the Flagship mock catalogue, the official Euclid mock that closely matches the expected properties of the spectroscopic data set. To mitigate anisotropic selection-bias effects, we use a velocity field reconstruction method to remove large-scale redshift-space distortions from the galaxy field before void-finding. This allows us to accurately model contributions to the observed anisotropy of the cross-correlation function arising from galaxy velocities around voids as well as from the Alcock-Paczynski effect, and we study the dependence of constraints on the efficiency of reconstruction. We find that Euclid voids will be able to constrain the ratio of the transverse comoving distance $D_{\rm M}$ and Hubble distance $D_{\rm H}$ to a relative precision of about $0.3\%$, and the growth rate $f\sigma_8$ to a precision of between $5\%$ and $8\%$ in each of four redshift bins covering the full redshift range. In the standard cosmological model, this translates to a statistical uncertainty $\Delta\Omega_\mathrm{m}=\pm0.0028$ on the matter density parameter from voids, better than can be achieved from either Euclid galaxy clustering and weak lensing individually. We also find that voids alone can measure the dark energy equation of state to $6\%$ precision.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Euclid Collaboration
L. Gabarra
C. Mancini
L. Rodriguez Munoz
G. Rodighiero
C. Sirignano
M. Scodeggio
M. Talia
S. Dusini
W. Gillard
B. R. Granett
E. Maiorano
M. Moresco
L. Paganin
E. Palazzi
L. Pozzetti
A. Renzi
E. Rossetti
D. Vergani
V. Allevato
Abstract： This work focuses on the pilot run of a simulation campaign aimed at investigating the spectroscopic capabilities of the Euclid Near-Infrared Spectrometer and Photometer (NISP), in terms of continuum and emission line detection in the context of galaxy evolutionary studies. To this purpose we constructed, emulated, and analysed the spectra of 4992 star-forming galaxies at $0.3 \leq z \leq 2.5$ using the NISP pixel-level simulator. We built the spectral library starting from public multi-wavelength galaxy catalogues, with value-added information on spectral energy distribution (SED) fitting results, and from Bruzual and Charlot (2003) stellar population templates. Rest-frame optical and near-IR nebular emission lines were included using empirical and theoretical relations. We inferred the 3.5$\sigma$ NISP red grism spectroscopic detection limit of the continuum measured in the $H$ band for star-forming galaxies with a median disk half-light radius of \ang{;;0.4} at magnitude $H= 19.5\pm0.2\,$AB$\,$mag for the Euclid Wide Survey and at $H = 20.8\pm0.6\,$AB$\,$mag for the Euclid Deep Survey. We found a very good agreement with the red grism emission line detection limit requirement for the Wide and Deep surveys. We characterised the effect of the galaxy shape on the detection capability of the red grism and highlighted the degradation of the quality of the extracted spectra as the disk size increases. In particular, we found that the extracted emission line signal to noise ratio (SNR) drops by $\sim\,$45$\%$ when the disk size ranges from \ang{;;0.25} to \ang{;;1}. These trends lead to a correlation between the emission line SNR and the stellar mass of the galaxy and we demonstrate the effect in a stacking analysis unveiling emission lines otherwise too faint to detect.
Peer Review Status:Awaiting Review
Subjects: Astronomy >> Astrophysical processes submitted time 2023-02-19
Euclid Collaboration
L. Bisigello
C. J. Conselice
M. Baes
M. Bolzonella
M. Brescia
S. Cavuoti
O. Cucciati
A. Humphrey
L. K. Hunt
C. Maraston
L. Pozzetti
C. Tortora
S. E. van Mierlo
N. Aghanim
N. Auricchio
M. Baldi
R. Bender
C. Bodendorf
D. Bonino
Abstract： Next generation telescopes, like Euclid, Rubin/LSST, and Roman, will open new windows on the Universe, allowing us to infer physical properties for tens of millions of galaxies. Machine learning methods are increasingly becoming the most efficient tools to handle this enormous amount of data, because they are often faster and more accurate than traditional methods. We investigate how well redshifts, stellar masses, and star-formation rates (SFR) can be measured with deep learning algorithms for observed galaxies within data mimicking the Euclid and Rubin/LSST surveys. We find that Deep Learning Neural Networks and Convolutional Neutral Networks (CNN), which are dependent on the parameter space of the training sample, perform well in measuring the properties of these galaxies and have a better accuracy than methods based on spectral energy distribution fitting. CNNs allow the processing of multi-band magnitudes together with $H_{\scriptscriptstyle\rm E}$-band images. We find that the estimates of stellar masses improve with the use of an image, but those of redshift and SFR do not. Our best results are deriving i) the redshift within a normalised error of less than 0.15 for 99.9$\%$ of the galaxies with S/N>3 in the $H_{\scriptscriptstyle\rm E}$-band; ii) the stellar mass within a factor of two ($\sim0.3 \rm dex$) for 99.5$\%$ of the considered galaxies; iii) the SFR within a factor of two ($\sim0.3 \rm dex$) for $\sim$70$\%$ of the sample. We discuss the implications of our work for application to surveys as well as how measurements of these galaxy parameters can be improved with deep learning.
Peer Review Status:Awaiting Review
Hits
Hits
Downloads
Comment