Browsing by Author "Aghanim, N."

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Euclid preparation : L. Calibration of the halo linear bias in Λ(v)CDM cosmologies
(2024-11-01) Castro, T.; Fumagalli, A.; Angulo, R. E.; Bocquet, S.; Borgani, S.; Costanzi, M.; Dakin, J.; Dolag, K.; Monaco, P.; Saro, A.; Sefusatti, E.; Aghanim, N.; Amendola, L.; Andreon, S.; Baccigalupi, C.; Baldi, M.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Caillat, A.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castellano, M.; Castignani, G.; Cavuoti, S.; Cimatti, A.; Colodro-Conde, C.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Costille, A.; Courbin, F.; Courtois, H. M.; Da Silva, A.; Degaudenzi, H.; De Lucia, G.; Di Giorgio, A. M.; Douspis, M.; Niemi, S. M.; Sánchez, A. G.; Wang, Y.; Calabrese, M.; Gozaliasl, G.; Hall, A.; Hjorth, J.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Euclid mission, designed to map the geometry of the dark Universe, presents an unprecedented opportunity for advancing our understanding of the cosmos through its photometric galaxy cluster survey. Central to this endeavor is the accurate calibration of the mass- and redshift-dependent halo bias (HB), which is the focus of this paper. Our aim is to enhance the precision of HB predictions, which is crucial for deriving cosmological constraints from the clustering of galaxy clusters. Our study is based on the peak-background split (PBS) model linked to the halo mass function (HMF), and it extends it with a parametric correction to precisely align with results from an extended set of N-body simulations carried out with the OpenGADGET3 code. Employing simulations with fixed and paired initial conditions, we meticulously analyzed the matter-halo cross-spectrum and modeled its covariance using a large number of mock catalogs generated with Lagrangian perturbation theory simulations with the PINOCCHIO code. This ensures a comprehensive understanding of the uncertainties in our HB calibration. Our findings indicate that the calibrated HB model is remarkably resilient against changes in cosmological parameters, including those involving massive neutrinos. The robustness and adaptability of our calibrated HB model provide an important contribution to the cosmological exploitation of the cluster surveys to be provided by the Euclid mission. This study highlights the necessity of continuously refining the calibration of cosmological tools such as the HB to match the advancing quality of observational data. As we project the impact of our calibrated model on cosmological constraints, we find that given the sensitivity of the Euclid survey, a miscalibration of the HB could introduce biases in cluster cosmology analysis. Our work fills this critical gap, ensuring the HB calibration matches the expected precision of the Euclid survey.
Euclid preparation : XLIX. Selecting active galactic nuclei using observed colours
(2024-11-01) Bisigello, L.; Massimo, M.; Tortora, C.; Fotopoulou, S.; Allevato, V.; Bolzonella, M.; Gruppioni, C.; Pozzetti, L.; Rodighiero, G.; Serjeant, S.; Cunha, P. A.C.; Gabarra, L.; Feltre, A.; Humphrey, A.; La Franca, F.; Landt, H.; Mannucci, F.; Prandoni, I.; Radovich, M.; Ricci, F.; Salvato, M.; Shankar, F.; Stern, D.; Spinoglio, L.; Vergani, D.; Vignali, C.; Zamorani, G.; Yung, L. Y.A.; Charlot, S.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Battaglia, P.; Bender, R.; Bonino, D.; Branchini, E.; Brau-Nogue, S.; Brescia, M.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Euclid space mission will cover over 14 000 deg2 with two optical and near-infrared spectro-photometric instruments, and is expected to detect around ten million active galactic nuclei (AGN). This unique data set will make a considerable impact on our understanding of galaxy evolution in general, and AGN in particular. For this work we identified the best colour selection criteria for AGN, based only on Euclid photometry or including ancillary photometric observations, such as the data that will be available with the Rubin Legacy Survey of Space and Time (LSST) and observations already available from Spitzer/IRAC. The analysis was performed for unobscured AGN, obscured AGN, and composite (AGN and star-forming) objects. We made use of the spectro-photometric realisations of infrared-selected targets at all-z (SPRITZ) to create mock catalogues mimicking both the Euclid Wide Survey (EWS) and the Euclid Deep Survey (EDS). Using these mock catalogues, we estimated the best colour selection, maximising the harmonic mean (F1) of: (a) completeness, that is, the fraction of AGN correctly selected with respect to the total AGN sample; and (b) purity, that is, the fraction of AGN inside the selection with respect to the selected sample. The selection of unobscured AGN in both Euclid surveys (Wide and Deep) is possible with Euclid photometry alone with F1 = 0.22-0.23 (Wide and Deep), which can increase to F1 = 0.43-0.38 (Wide and Deep) if we limit out study to objects at z > 0.7. Such a selection is improved once the Rubin/LSST filters, that is, a combination of the u, g, r, or z filters, are considered, reaching an F1 score of 0.84 and 0.86 for the EDS and EWS, respectively. The combination of a Euclid colour with the [3.6] - [4.5] colour, which is possible only in the EDS, results in an F1 score of 0.59, improving the results using only Euclid filters, but worse than the selection combining Euclid and LSST colours. The selection of composite (fAGN = 0.05-0.65 at 8-40 μm) and obscured AGN is challenging, with F1 ≤ 0.3 even when including Rubin/LSST or IRAC filters. This is unsurprising since it is driven by the similarities between the broad-band spectral energy distribution of these AGN and star-forming galaxies in the wavelength range 0.3-5 μm.
Euclid preparation : XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events
(2024-01-01) Leuzzi, L.; Meneghetti, M.; Angora, G.; Metcalf, R. B.; Moscardini, L.; Rosati, P.; Bergamini, P.; Calura, F.; Clément, B.; Gavazzi, R.; Gentile, F.; Lochner, M.; Grillo, C.; Vernardos, G.; Aghanim, N.; Amara, A.; Amendola, L.; Auricchio, N.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Dinis, J.; Dubath, F.; Dupac, X.; Dusini, S.; Farrens, S.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Forthcoming imaging surveys will increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of billions of galaxies will have to be inspected to identify potential candidates. In this context, deep-learning techniques are particularly suitable for finding patterns in large data sets, and convolutional neural networks (CNNs) in particular can efficiently process large volumes of images. We assess and compare the performance of three network architectures in the classification of strong-lensing systems on the basis of their morphological characteristics. In particular, we implemented a classical CNN architecture, an inception network, and a residual network. We trained and tested our networks on different subsamples of a data set of 40 000 mock images whose characteristics were similar to those expected in the wide survey planned with the ESA mission Euclid, gradually including larger fractions of faint lenses. We also evaluated the importance of adding information about the color difference between the lens and source galaxies by repeating the same training on single- and multiband images. Our models find samples of clear lenses with 90% precision and completeness. Nevertheless, when lenses with fainter arcs are included in the training set, the performance of the three models deteriorates with accuracy values of ~0.87 to ~0.75, depending on the model. Specifically, the classical CNN and the inception network perform similarly in most of our tests, while the residual network generally produces worse results. Our analysis focuses on the application of CNNs to high-resolution space-like images, such as those that the Euclid telescope will deliver. Moreover, we investigated the optimal training strategy for this specific survey to fully exploit the scientific potential of the upcoming observations. We suggest that training the networks separately on lenses with different morphology might be needed to identify the faint arcs. We also tested the relevance of the color information for the detection of these systems, and we find that it does not yield a significant improvement. The accuracy ranges from ~0.89 to ~0.78 for the different models. The reason might be that the resolution of the Euclid telescope in the infrared bands is lower than that of the images in the visual band.
Euclid preparation LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
(2024-11-01) Enia, A.; Bolzonella, M.; Pozzetti, L.; Humphrey, A.; Cunha, P. A.C.; Hartley, W. G.; Dubath, F.; Paltani, S.; Lopez Lopez, X.; Quai, S.; Bardelli, S.; Bisigello, L.; Cavuoti, S.; De Lucia, G.; Ginolfi, M.; Grazian, A.; Siudek, M.; Tortora, C.; Zamorani, G.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Castignani, G.; Cimatti, A.; Colodro-Conde, C.; Congedo, G.; Niemi, S. M.; Schneider, P.; Wang, Y.; Calabrese, M.; Gozaliasl, G.; Hall, A.; Hjorth, J.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Euclid will collect an enormous amount of data during the mission’s lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning (ML) algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information entering the model (the features), to a level where the recovery of some well-established physical relationships between parameters might not be guaranteed – for example, the star-forming main sequence (SFMS). To forecast the reliability of Euclid photo-zs and PPs calculations, we produced two mock catalogs simulating the photometry with the UNIONS ugriz and Euclid filters. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF), alongside two auxiliary fields. We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-zs, PPs (stellar masses and star formation rates), and the SFMS on the simulated Euclid fields. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels and tested on the simulated ground truth. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior to the input features, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-z, PPs, and the SFMS.
Euclid preparation LI. Forecasting the recovery of galaxy physical properties and their relations with template-fitting and machine-learning methods
(2024-11-01) Enia, A.; Bolzonella, M.; Pozzetti, L.; Humphrey, A.; Cunha, P. A.C.; Hartley, W. G.; Dubath, F.; Paltani, S.; Lopez Lopez, X.; Quai, S.; Bardelli, S.; Bisigello, L.; Cavuoti, S.; De Lucia, G.; Ginolfi, M.; Grazian, A.; Siudek, M.; Tortora, C.; Zamorani, G.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Castignani, G.; Cimatti, A.; Colodro-Conde, C.; Congedo, G.; Niemi, S. M.; Schneider, P.; Wang, Y.; Calabrese, M.; Gozaliasl, G.; Hall, A.; Hjorth, J.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Euclid will collect an enormous amount of data during the mission’s lifetime, observing billions of galaxies in the extragalactic sky. Along with traditional template-fitting methods, numerous machine learning (ML) algorithms have been presented for computing their photometric redshifts and physical parameters (PPs), requiring significantly less computing effort while producing equivalent performance measures. However, their performance is limited by the quality and amount of input information entering the model (the features), to a level where the recovery of some well-established physical relationships between parameters might not be guaranteed – for example, the star-forming main sequence (SFMS). To forecast the reliability of Euclid photo-zs and PPs calculations, we produced two mock catalogs simulating the photometry with the UNIONS ugriz and Euclid filters. We simulated the Euclid Wide Survey (EWS) and Euclid Deep Fields (EDF), alongside two auxiliary fields. We tested the performance of a template-fitting algorithm (Phosphoros) and four ML methods in recovering photo-zs, PPs (stellar masses and star formation rates), and the SFMS on the simulated Euclid fields. To mimic the Euclid processing as closely as possible, the models were trained with Phosphoros-recovered labels and tested on the simulated ground truth. For the EWS, we found that the best results are achieved with a mixed labels approach, training the models with wide survey features and labels from the Phosphoros results on deeper photometry, that is, with the best possible set of labels for a given photometry. This imposes a prior to the input features, helping the models to better discern cases in degenerate regions of feature space, that is, when galaxies have similar magnitudes and colors but different redshifts and PPs, with performance metrics even better than those found with Phosphoros. We found no more than 3% performance degradation using a COSMOS-like reference sample or removing u band data, which will not be available until after data release DR1. The best results are obtained for the EDF, with appropriate recovery of photo-z, PPs, and the SFMS.
Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
(2024-09-01) Aussel, B.; Kruk, S.; Walmsley, M.; Huertas-Company, M.; Castellano, M.; Conselice, C. J.; Delli Veneri, M.; Domínguez Sánchez, H.; Duc, P. A.; Knapen, J. H.; Kuchner, U.; La Marca, A.; Margalef-Bentabol, B.; Marleau, F. R.; Stevens, G.; Toba, Y.; Tortora, C.; Wang, L.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H. M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Hall, A.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.
Euclid preparation XLIII. Measuring detailed galaxy morphologies for Euclid with machine learning
(2024-09-01) Aussel, B.; Kruk, S.; Walmsley, M.; Huertas-Company, M.; Castellano, M.; Conselice, C. J.; Delli Veneri, M.; Domínguez Sánchez, H.; Duc, P. A.; Knapen, J. H.; Kuchner, U.; La Marca, A.; Margalef-Bentabol, B.; Marleau, F. R.; Stevens, G.; Toba, Y.; Tortora, C.; Wang, L.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H. M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Hall, A.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Euclid mission is expected to image millions of galaxies at high resolution, providing an extensive dataset with which to study galaxy evolution. Because galaxy morphology is both a fundamental parameter and one that is hard to determine for large samples, we investigate the application of deep learning in predicting the detailed morphologies of galaxies in Euclid using Zoobot, a convolutional neural network pretrained with 450 000 galaxies from the Galaxy Zoo project. We adapted Zoobot for use with emulated Euclid images generated based on Hubble Space Telescope COSMOS images and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We experimented with different numbers of galaxies and various magnitude cuts during the training process. We demonstrate that the trained Zoobot model successfully measures detailed galaxy morphology in emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features, such as spiral arms, clumps, bars, discs, and central bulges. When compared to volunteer classifications, Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy of above 91% for the confident volunteer classifications across most morphology types. However, the performance varies depending on the specific morphological class. For the global classes, such as disc or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. On the other hand, for more detailed structures and complex tasks, such as detecting and counting spiral arms or clumps, the deviations are slightly higher, of namely around 12% with 60 000 galaxies used for training. In order to enhance the performance on complex morphologies, we anticipate that a larger pool of labelled galaxies is needed, which could be obtained using crowd sourcing. We estimate that, with our model, the detailed morphology of approximately 800 million galaxies of the Euclid Wide Survey could be reliably measured and that approximately 230 million of these galaxies would display features. Finally, our findings imply that the model can be effectively adapted to new morphological labels. We demonstrate this adaptability by applying Zoobot to peculiar galaxies. In summary, our trained Zoobot CNN can readily predict morphological catalogues for Euclid images.
Euclid preparation XXXIV. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear
(2024-03-01) Tanidis, K.; Cardone, V. F.; Martinelli, M.; Tutusaus, I.; Camera, S.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Branchini, E.; Brescia, M.; Brinchmann, J.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Da Silvay, A.; Degaudenzi, H.; Dinis, J.; Dubath, F.; Dupac, X.; Dusini, S.; Farina, M.; Farrens, S.; Ferriol, S.; Fosalba, P.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Garilli, B.; Gillard, W.; Gillis, B.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Context. The cosmological surveys that are planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Universe. This will allow us to test the concordance cosmological model and its extensions. However, precision pushes us to high levels of accuracy in the theoretical modelling of the LSS observables, so that no biases are introduced into the estimation of the cosmological parameters. In particular, effects such as redshift-space distortions (RSD) can become relevant in the computation of harmonic-space power spectra even for the clustering of the photometrically selected galaxies, as has previously been shown in literature. Aims. In this work, we investigate the contribution of linear RSD, as formulated in the Limber approximation by a previous work, in forecast cosmological analyses with the photometric galaxy sample of the Euclid survey. We aim to assess their impact and to quantify the bias on the measurement of cosmological parameters that would be caused if this effect were neglected. Methods. We performed this task by producing mock power spectra for photometric galaxy clustering and weak lensing, as is expected to be obtained from the Euclid survey. We then used a Markov chain Monte Carlo approach to obtain the posterior distributions of cosmological parameters from these simulated observations. Results. When the linear RSD is neglected, significant biases are caused when galaxy correlations are used alone and when they are combined with cosmic shear in the so-called 3 × 2 pt approach. These biases can be equivalent to as much as 5σ when an underlying ΛCDM cosmology is assumed. When the cosmological model is extended to include the equation-of-state parameters of dark energy, the extension parameters can be shifted by more than 1σ.
Euclid preparation XXXIV. The effect of linear redshift-space distortions in photometric galaxy clustering and its cross-correlation with cosmic shear
(2024-03-01) Tanidis, K.; Cardone, V. F.; Martinelli, M.; Tutusaus, I.; Camera, S.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Branchini, E.; Brescia, M.; Brinchmann, J.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Congedo, G.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Da Silvay, A.; Degaudenzi, H.; Dinis, J.; Dubath, F.; Dupac, X.; Dusini, S.; Farina, M.; Farrens, S.; Ferriol, S.; Fosalba, P.; Frailis, M.; Franceschi, E.; Fumana, M.; Galeotta, S.; Garilli, B.; Gillard, W.; Gillis, B.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Context. The cosmological surveys that are planned for the current decade will provide us with unparalleled observations of the distribution of galaxies on cosmic scales, by means of which we can probe the underlying large-scale structure (LSS) of the Universe. This will allow us to test the concordance cosmological model and its extensions. However, precision pushes us to high levels of accuracy in the theoretical modelling of the LSS observables, so that no biases are introduced into the estimation of the cosmological parameters. In particular, effects such as redshift-space distortions (RSD) can become relevant in the computation of harmonic-space power spectra even for the clustering of the photometrically selected galaxies, as has previously been shown in literature. Aims. In this work, we investigate the contribution of linear RSD, as formulated in the Limber approximation by a previous work, in forecast cosmological analyses with the photometric galaxy sample of the Euclid survey. We aim to assess their impact and to quantify the bias on the measurement of cosmological parameters that would be caused if this effect were neglected. Methods. We performed this task by producing mock power spectra for photometric galaxy clustering and weak lensing, as is expected to be obtained from the Euclid survey. We then used a Markov chain Monte Carlo approach to obtain the posterior distributions of cosmological parameters from these simulated observations. Results. When the linear RSD is neglected, significant biases are caused when galaxy correlations are used alone and when they are combined with cosmic shear in the so-called 3 × 2 pt approach. These biases can be equivalent to as much as 5σ when an underlying ΛCDM cosmology is assumed. When the cosmological model is extended to include the equation-of-state parameters of dark energy, the extension parameters can be shifted by more than 1σ.
Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
(2024-04-01) Lesci, G. F.; Sereno, M.; Radovich, M.; Castignani, G.; Bisigello, L.; Marulli, F.; Moscardini, L.; Baumont, L.; Covone, G.; Farrens, S.; Giocoli, C.; Ingoglia, L.; Miranda La Hera, S.; Vannier, M.; Biviano, A.; Maurogordato, S.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for galaxy clusters. Methods. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. Such colour selections are expressed as a function of the lens redshift. Results. We calibrated galaxy selections using simulated ground-based griz and Euclid YEJEHE photometry. Both selections produce a purity higher than 97%. The griz selection completeness ranges from 30% to 84% in the lens redshift range zl ∈ [0.2, 0.8]. With the full grizYEJEHE selection, the completeness improves by up to 25 percentage points, and the zl range extends up to zl = 1.5. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on griz bands provides excellent results on real external datasets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The griz selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percentage points higher completeness. The combination of colour and photo-z selections applied to simulated Euclid data yields up to 95% completeness, while the purity decreases down to 92% at high zl. We show that the calibrated colour selections provide robust results even when observations from a single band are missing from the ground-based data. Finally, we show that colour selections do not disrupt the shear calibration for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on the shear calibration.
Euclid preparation XXXVII. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
(2024-04-01) Lesci, G. F.; Sereno, M.; Radovich, M.; Castignani, G.; Bisigello, L.; Marulli, F.; Moscardini, L.; Baumont, L.; Covone, G.; Farrens, S.; Giocoli, C.; Ingoglia, L.; Miranda La Hera, S.; Vannier, M.; Biviano, A.; Maurogordato, S.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Aims. We derived galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections have been implemented in the Euclid data analysis pipelines for galaxy clusters. Methods. Given any set of photometric bands, we developed a method for the calibration of optimal galaxy colour selections that maximises the selection completeness, given a threshold on purity. Such colour selections are expressed as a function of the lens redshift. Results. We calibrated galaxy selections using simulated ground-based griz and Euclid YEJEHE photometry. Both selections produce a purity higher than 97%. The griz selection completeness ranges from 30% to 84% in the lens redshift range zl ∈ [0.2, 0.8]. With the full grizYEJEHE selection, the completeness improves by up to 25 percentage points, and the zl range extends up to zl = 1.5. The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on griz bands provides excellent results on real external datasets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The griz selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. We find excellent agreement in terms of purity and completeness between the analysis of an independent, simulated Euclid galaxy catalogue and our calibration sample, except for galaxies at high redshifts, for which we obtain up to 50 percentage points higher completeness. The combination of colour and photo-z selections applied to simulated Euclid data yields up to 95% completeness, while the purity decreases down to 92% at high zl. We show that the calibrated colour selections provide robust results even when observations from a single band are missing from the ground-based data. Finally, we show that colour selections do not disrupt the shear calibration for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on the shear calibration.
Euclid preparation. LII. Forecast impact of super-sample covariance on 3A- 2pt analysis with Euclid
(2024-11-01) Sciotti, D.; Gouyou Beauchamps, S.; Cardone, V. F.; Camera, S.; Tutusaus, I.; Lacasa, F.; Barreira, A.; Bonici, M.; Gorce, A.; Aubert, M.; Baratta, P.; Upham, R. E.; Carbone, C.; Casas, S.; Iliac, S.; Martinelli, M.; Sakr, Z.; Schneider, A.; Maoli, R.; Scaramella, R.; Escoffier, S.; Gillard, W.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bardelli, S.; Bernardeau, F.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Capobianco, V.; Carretero, J.; Castander, F. J.; Castellano, M.; Castignani, G.; Cavuoti, S.; Cimatti, A.; Cledassou, R.; Colodro-Conde, C.; Congedo, G.; Niemi, S. M.; Sánchez, A. G.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Context. Deviations from Gaussianity in the distribution of the fields probed by large-scale structure surveys generate additional terms in the data covariance matrix, increasing the uncertainties in the measurement of the cosmological parameters. Super-sample covariance (SSC) is among the largest of these non-Gaussian contributions, with the potential to significantly degrade constraints on some of the parameters of the cosmological model under study - especially for weak-lensing cosmic shear. Aims. We compute and validate the impact of SSC on the forecast uncertainties on the cosmological parameters for the Euclid photo-metric survey, and investigate how its impact depends on the specific details of the forecast. Methods. We followed the recipes outlined by the Euclid Collaboration (EC) to produce 1 constraints through a Fisher matrix analysis, considering the Gaussian covariance alone and adding the SSC term, which is computed through the public code PySSC. The constraints are produced both by using Euclid's photometric probes in isolation and by combining them in the '3A- 2pt'analysis. Results. We meet EC requirements on the forecasts validation, with an agreement at the 10% level between the mean results of the two pipelines considered, and find the SSC impact to be non-negligible - halving the figure of merit (FoM) of the dark energy parameters (w0, wa) in the 3A- 2pt case and substantially increasing the uncertainties on Ωm,0,w0, w0, and 8 for the weak-lensing probe. We find photometric galaxy clustering to be less affected as a consequence of the lower probe response. The relative impact of SSC, while highly dependent on the number and type of nuisance parameters varied in the analysis, does not show significant changes under variations of the redshift binning scheme. Finally, we explore how the use of prior information on the shear and galaxy bias changes the impact of SSC. We find that improving shear bias priors has no significant influence, while galaxy bias must be calibrated to a subpercent level in order to increase the FoM by the large amount needed to achieve the value when SSC is not included.
Euclid preparation. LIII. LensMC, weak lensing cosmic shear measurement with forward modelling and Markov Chain Monte Carlo sampling
(2024-11-01) Congedo, G.; Miller, L.; Taylor, A. N.; Cross, N.; Duncan, C. A.J.; Kitching, T.; Martinet, N.; Matthew, S.; Schrabback, T.; Tewes, M.; Welikala, N.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Cardone, V. F.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Conselice, C. J.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H. M.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dubath, F.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Hall, A.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
LENSMC is a weak lensing shear measurement method developed for Euclid and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies, sampling the posterior distribution of galaxy parameters via Markov chain Monte Carlo, and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by Euclid. We quantified the scientific performance through high-fidelity images based on the Euclid Flagship simulations and emulation of the Euclid VIS images, realistic clustering with a mean surface number density of 250 arcmin2 (IE < 29.5) for galaxies, and 6 arcmin2 (IE < 26) for stars, and a diffraction-limited chromatic PSF with a full width at half maximum of 02.22 and spatial variation across the field of view. LENSMC measured objects with a density of 90 arcmin2 (IE < 26.5) in 4500 deg2. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed in future work). We found measurement multiplicative and additive biases of m1 = (3.6 ± 0.2) A- 103, m2 = (4.3 ± 0.2) A- 103, c1 = (1.78 ± 0.03) A- 104, and c2 = (0.09 ± 0.03) A- 104; a large detection bias with a multiplicative component of 1.2 A- 102 and an additive component of 3 A- 104; and a measurement PSF leakage of α1 = (9 ± 3) A- 104 and α2 = (2 ± 3) A- 104. When model bias is suppressed, the obtained measurement biases are close to Euclid requirement and largely dominated by undetected faint galaxies (5 A- 103). Although significant, model bias will be straightforward to calibrate given its weak sensitivity on galaxy morphology parameters. LENSMC is publicly available at gitlab.com/gcongedo/LensMC.
Euclid preparation. LIII. LensMC, weak lensing cosmic shear measurement with forward modelling and Markov Chain Monte Carlo sampling
(2024-11-01) Congedo, G.; Miller, L.; Taylor, A. N.; Cross, N.; Duncan, C. A.J.; Kitching, T.; Martinet, N.; Matthew, S.; Schrabback, T.; Tewes, M.; Welikala, N.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Cardone, V. F.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Conselice, C. J.; Conversi, L.; Copin, Y.; Courbin, F.; Courtois, H. M.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dubath, F.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Hall, A.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
LENSMC is a weak lensing shear measurement method developed for Euclid and Stage-IV surveys. It is based on forward modelling in order to deal with convolution by a point spread function (PSF) with comparable size to many galaxies, sampling the posterior distribution of galaxy parameters via Markov chain Monte Carlo, and marginalisation over nuisance parameters for each of the 1.5 billion galaxies observed by Euclid. We quantified the scientific performance through high-fidelity images based on the Euclid Flagship simulations and emulation of the Euclid VIS images, realistic clustering with a mean surface number density of 250 arcmin2 (IE < 29.5) for galaxies, and 6 arcmin2 (IE < 26) for stars, and a diffraction-limited chromatic PSF with a full width at half maximum of 02.22 and spatial variation across the field of view. LENSMC measured objects with a density of 90 arcmin2 (IE < 26.5) in 4500 deg2. The total shear bias was broken down into measurement (our main focus here) and selection effects (which will be addressed in future work). We found measurement multiplicative and additive biases of m1 = (3.6 ± 0.2) A- 103, m2 = (4.3 ± 0.2) A- 103, c1 = (1.78 ± 0.03) A- 104, and c2 = (0.09 ± 0.03) A- 104; a large detection bias with a multiplicative component of 1.2 A- 102 and an additive component of 3 A- 104; and a measurement PSF leakage of α1 = (9 ± 3) A- 104 and α2 = (2 ± 3) A- 104. When model bias is suppressed, the obtained measurement biases are close to Euclid requirement and largely dominated by undetected faint galaxies (5 A- 103). Although significant, model bias will be straightforward to calibrate given its weak sensitivity on galaxy morphology parameters. LENSMC is publicly available at gitlab.com/gcongedo/LensMC.
Euclid preparation. LV. Exploring the properties of proto-clusters in the Simulated Euclid Wide Survey
(2025-01) Böhringer, H.; Chon, G.; Cucciati, O.; Dannerbauer, H.; Bolzonella, M.; De Lucia, G.; Cappi, A.; Moscardini, L.; Giocoli, C.; Castignani, G.; Hatch, N. A.; Andreon, S.; Bañados, E.; Ettori, S.; Fontanot, F.; Gully, H.; Hirschmann, M.; Maturi, M.; Mei, S.; Pozzetti, L.; Schlenker, T.; Spinelli, M.; Aghanim, N.; Altieri, B.; Auricchio, N.; Baccigalupi, C.; Baldi, M.; Bardelli, S.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castander, F. J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Niemi, S. M.; Sánchez, A. G.; Schneider, P.; Wang, Y.; Calabrese, M.; Gozaliasl, G.; Hall, A.; Hjorth, J.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Galaxy proto-clusters are receiving increased interest since most of the processes shaping the structure of clusters of galaxies and their galaxy population happen at the early stages of their formation. The Euclid Survey will provide a unique opportunity to discover a large number of proto-clusters over a large fraction of the sky (14 500 deg2). In this paper, we explore the expected observational properties of proto-clusters in the Euclid Wide Survey by means of theoretical models and simulations. We provide an overview of the predicted proto-cluster extent, galaxy density profiles, mass-richness relations, abundance, and sky-filling as a function of redshift. Useful analytical approximations for the functions of these properties are provided. The focus is on the redshift range z = 1.5-4. In particular we discuss the density contrast with which proto-clusters can be observed against the background in the galaxy distribution if photometric galaxy redshifts are used as supplied by the ESA Euclid mission together with the ground-based photometric surveys. We show that the obtainable detection significance is sufficient to find large numbers of interesting proto-cluster candidates. For quantitative studies, additional spectroscopic follow-up is required to confirm the proto-clusters and establish their richness.
Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-ΛCDM models
(2024-09-01) Bose, B.; Carrilho, P.; Marinucci, M.; Moretti, C.; Pietroni, M.; Carella, E.; Piga, L.; Wright, B. S.; Vernizzi, F.; Carbone, C.; Casas, S.; D'Amico, G.; Frusciante, N.; Koyama, K.; Pace, F.; Pourtsidou, A.; Baldi, M.; De La Bella, L. F.; Fiorini, B.; Giocoli, C.; Lombriser, L.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Bardelli, S.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Cardone, V. F.; Carretero, J.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Costille, A.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Hall, A.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Context. The Euclid space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the ?CDM model. Aims. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali-Gabadadze-Porrati model, the Hu-Sawicki f(R) model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein-de Sitter universe; we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos; we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya-Nishimishi-Saito prescription and the effective field theory of large-scale structure. This work provides a first validation of the various codes being considered by Euclid for the spectroscopic clustering probe in beyond-?CDM scenarios. Methods. We calculated and compared the χ2 statistic to assess the different modelling choices. This was done by fitting the spectroscopic clustering predictions to measurements from numerical simulations and perturbation theory-based mock data. We compared the behaviour of this statistic in the beyond-?CDM cases, as a function of the maximum scale included in the fit, to the baseline ?CDM case. Results. We find that the Einstein-de Sitter approximation without screening is surprisingly accurate for the modified gravity cases when comparing to the halo clustering monopole and quadrupole obtained from simulations and mock data. Further, we find the same goodness-of-fit for both cases - the one including and the one omitting non-standard physics in the predictions. Our results suggest that the inclusion of multiple redshift bins, higher-order multipoles, higher-order clustering statistics (such as the bispectrum), and photometric probes such as weak lensing, will be essential to extract information on massive neutrinos, modified gravity and dark energy. Additionally, we show that the three codes used in our analysis, namely, PBJ, Pybird and MG-Copter, exhibit sub-percent agreement for k ≤ 0.5 h Mpc-1 across all the models. This consistency underscores their value as reliable tools.
Euclid preparation. XXXIX. The effect of baryons on the halo mass function
(2024-05-01) Castro, T.; Borgani, S.; Costanzi, M.; Dakin, J.; Dolag, K.; Fumagalli, A.; Ragagnin, A.; Saro, A.; Le Brun, A. M.C.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dubath, F.; Duncan, C. A.J.; Dupac, X.; Farina, M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being subdominant to dark matter and dark energy, the baryonic component of our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model that can be used to precisely quantify the impact of baryons on the virial halo masses of galaxy clusters using the baryon fraction within a cluster as a proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters, which are calibrated using non-radiative cosmological hydrodynamical simulations, and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable 1% relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against variation of the numerical implementation of the subresolution physical processes included in the simulations. Our work substantiates previous claims regarding the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model arising from baryonic corrections to cluster masses are subdominant when compared to the precision with which mass-observable (i.e. richness) relations will be calibrated using Euclid and to our current understanding of the baryon fraction within galaxy clusters.
Euclid preparation. XXXIX. The effect of baryons on the halo mass function
(2024-05-01) Castro, T.; Borgani, S.; Costanzi, M.; Dakin, J.; Dolag, K.; Fumagalli, A.; Ragagnin, A.; Saro, A.; Le Brun, A. M.C.; Aghanim, N.; Amara, A.; Andreon, S.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Camera, S.; Capobianco, V.; Carbone, C.; Carretero, J.; Casas, S.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Cropper, M.; Da Silva, A.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dubath, F.; Duncan, C. A.J.; Dupac, X.; Farina, M.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The Euclid photometric survey of galaxy clusters stands as a powerful cosmological tool, with the capacity to significantly propel our understanding of the Universe. Despite being subdominant to dark matter and dark energy, the baryonic component of our Universe holds substantial influence over the structure and mass of galaxy clusters. This paper presents a novel model that can be used to precisely quantify the impact of baryons on the virial halo masses of galaxy clusters using the baryon fraction within a cluster as a proxy for their effect. Constructed on the premise of quasi-adiabaticity, the model includes two parameters, which are calibrated using non-radiative cosmological hydrodynamical simulations, and a single large-scale simulation from the Magneticum set, which includes the physical processes driving galaxy formation. As a main result of our analysis, we demonstrate that this model delivers a remarkable 1% relative accuracy in determining the virial dark matter-only equivalent mass of galaxy clusters starting from the corresponding total cluster mass and baryon fraction measured in hydrodynamical simulations. Furthermore, we demonstrate that this result is robust against changes in cosmological parameters and against variation of the numerical implementation of the subresolution physical processes included in the simulations. Our work substantiates previous claims regarding the impact of baryons on cluster cosmology studies. In particular, we show how neglecting these effects would lead to biased cosmological constraints for a Euclid-like cluster abundance analysis. Importantly, we demonstrate that uncertainties associated with our model arising from baryonic corrections to cluster masses are subdominant when compared to the precision with which mass-observable (i.e. richness) relations will be calibrated using Euclid and to our current understanding of the baryon fraction within galaxy clusters.
Euclid preparation. XXXVIII. Spectroscopy of active galactic nuclei with NISP
(2024-05-01) Lusso, E.; Fotopoulou, S.; Selwood, M.; Allevato, V.; Calderone, G.; Mancini, C.; Mignoli, M.; Scodeggio, M.; Bisigello, L.; Feltre, A.; Ricci, F.; La Franca, F.; Vergani, D.; Gabarra, L.; Le Brun, V.; Maiorano, E.; Palazzi, E.; Moresco, M.; Zamorani, G.; Cresci, G.; Jahnke, K.; Humphrey, A.; Landt, H.; Mannucci, F.; Marconi, A.; Pozzetti, L.; Salucci, P.; Salvato, M.; Shankar, F.; Spinoglio, L.; Stern, D.; Serjeant, S.; Aghanim, N.; Altieri, B.; Amara, A.; Andreon, S.; Auphan, T.; Auricchio, N.; Baldi, M.; Bardelli, S.; Bender, R.; Bonino, D.; Branchini, E.; Brescia, M.; Niemi, S. M.; Schneider, P.; Wang, Y.; Gozaliasl, G.; Hall, A.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
The statistical distribution and evolution of key properties of active galactic nuclei (AGN), such as their accretion rate, mass, and spin, remains a subject of open debate in astrophysics. The ESA Euclid space mission, launched on July 1 2023, promises a breakthrough in this field. We create detailed mock catalogues of AGN spectra from the rest-frame near-infrared down to the ultraviolet -including emission lines -to simulate what Euclid will observe for both obscured (type 2) and unobscured (type 1) AGN. We concentrate on the red grisms of the NISP instrument, which will be used for the wide-field survey, opening a new window for spectroscopic AGN studies in the near-infrared. We quantify the efficiency in the redshift determination as well as in retrieving the emission line flux of the Hα+[N II] complex, as Euclid is mainly focused on this emission line, given that it is expected to be the brightest one in the probed redshift range. Spectroscopic redshifts are measured for 83% of the simulated AGN in the interval where the Hα is visible (i.e. 0.89 < z < 1.83 at a line flux of > 2 × 10-16 erg s-1 cm-2, encompassing the peak of AGN activity at z ≃ 1 - 1.5) within the spectral coverage of the red grism. Outside this redshift range, the measurement efficiency decreases significantly. Overall, a spectroscopic redshift iscorrectly determined for about 90% of type 2 AGN down to an emission line flux of roughly 3 × 10-16 erg s-1 cm-2, and for type 1 AGN down to 8.5 × 10-16 erg s-1 cm-2. Recovered values for black hole mass show a small offset with respect to the input values by about 10%, but the agreement is good overall. With such a high spectroscopic coverage at z < 2, we will be able to measure AGN demography, scaling relations, and clustering from the epoch of the peak of AGN activity down to the present-day Universe for hundreds of thousands of AGN with homogeneous spectroscopic information.
Euclid preparation: XL. Impact of magnification on spectroscopic galaxy clustering
(2024-05-01) Jelic-Cizmek, G.; Sorrenti, F.; Lepori, F.; Bonvin, C.; Camera, S.; Castander, F. J.; Durrer, R.; Fosalba, P.; Kunz, M.; Lombriser, L.; Tutusaus, I.; Viglione, C.; Sakr, Z.; Aghanim, N.; Amara, A.; Andreon, S.; Baldi, M.; Bardelli, S.; Bodendorf, C.; Bonino, D.; Branchini, E.; Brescia, M.; Brinchmann, J.; Capobianco, V.; Carbone, C.; Cardone, V. F.; Carretero, J.; Casas, S.; Castellano, M.; Cavuoti, S.; Cimatti, A.; Congedo, G.; Conselice, C. J.; Conversi, L.; Copin, Y.; Corcione, L.; Courbin, F.; Courtois, H. M.; Cropper, M.; Degaudenzi, H.; Di Giorgio, A. M.; Dinis, J.; Dubath, F.; Dupac, X.; Niemi, S. M.; Schneider, P.; Starck, J. L.; Wang, Y.; Gozaliasl, G.; Sánchez, A. G.; , Euclid Collaboration
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
In this paper we investigate the impact of lensing magnification on the analysis of Euclid's spectroscopic survey using the multipoles of the two-point correlation function for galaxy clustering. We determine the impact of lensing magnification on cosmological constraints as well as the expected shift in the best-fit parameters if magnification is ignored. We considered two cosmological analyses: (i) a full-shape analysis based on the δ cold dark matter (CDM) model and its extension w0waCDM and (ii) a model-independent analysis that measures the growth rate of structure in each redshift bin. We adopted two complementary approaches in our forecast: the Fisher matrix formalism and the Markov chain Monte Carlo method. The fiducial values of the local count slope (or magnification bias), which regulates the amplitude of the lensing magnification, have been estimated from the Euclid Flagship simulations. We used linear perturbation theory and modelled the two-point correlation function with the public code coffe. For a δ CDM model, we find that the estimation of cosmological parameters is biased at the level of 0.4- 0.7 standard deviations, while for a w0waCDM dynamical dark energy model, lensing magnification has a somewhat smaller impact, with shifts below 0.5 standard deviations. For a model-independent analysis aimed at measuring the growth rate of structure, we find that the estimation of the growth rate is biased by up to 1.2 standard deviations in the highest redshift bin. As a result, lensing magnification cannot be neglected in the spectroscopic survey, especially if we want to determine the growth factor, one of the most promising ways to test general relativity with Euclid. We also find that, by including lensing magnification with a simple template, this shift can be almost entirely eliminated with minimal computational overhead.