We look forward to welcoming you to the Euclid Spain Meeting 2024, at the Institut de Ciencies de L'Espai (ICE-CSIC) on 18th - 19st April 2024. The institute is located on the campus of the Universidad Autónoma de Barcelona (UAB), just one half an hour drive from Josep Tarradellas Barcelona-El Prat airport.
The meeting will take place entirely in the Alberto Lobo room, which will provide enough space for the participants. There is also a terrace at the institute where the two daily coffee breaks will take place. It is also intended to help early-stage students with mobility expenses.
To register for the meeting, please go to the 'Registration' tab on the left, follow the instructions and go to the link marked in red at the end of the page.
IMPORTANT: We kindly request speakers to upload your slides in this drive, before the day of your presentation.
A short overview of the work of the Euclid Consortium Diversity Committee
The presentation describes the main aspects of the electronic unit that controls the infrared instrument on board the Euclid space telescope, which the Polytechnic University of Cartagena is responsible for, in collaboration with the Institute of Astrophysics of the Canary Islands.
The talk describes the work of the “Barcelona Team” in the Filter Wheel Assembly of the NISP Instrument.
During this talk, I will briefly describe the characteristics and main contributions of the Spanish Science Data Center.
The Spanish teams at the University of Barcelona and Universidade da Coruña working in the Gaia Data Processing and Analysis Consortium are also contributing to the Euclid mission. The tasks they are contributing related to Euclid instrumentation include the evaluation of the NISP filters cut-on and cut-off limits, and obtaining the spectral energy distribution for some sources to be used to calibrate the chromaticity effect on the VIS PSF. The calibration for VIS PSF chromaticity uses sources extracted from the Gaia catalog at the bright end and WEAVE observations to be obtained for the faint end. With the expertise of this team in Milky Way studies with Gaia, they also belong to the Euclid's Milky Way and Resolved Stellar Populations working group. The tasks in this group include unsupervised classification using NISP spectroscopy and the study of the bridge of stars observed between the Magellanic Clouds.
Cosmological likelihood and theory pipeline overview
One of Euclid's key projects will be the so-called 3x2pt analysis, i.e., the combination of cosmic shear, photometric galaxy clustering, and galaxy-galaxy lensing (the cross-correlation of lens galaxy positions with the shapes of source galaxies). While Euclid has set quality requirements for the photometric redshift (photo-z) precision needed for the sources (used to measure cosmic shear), there are no requirements set for the photo-z precision of the lenses (used to measure photometric galaxy clustering). Moreover, previous work from the Dark Energy Survey indicates that the width of the lens redshift distributions (besides the mean) can have a significant impact on the resulting constraints. I will briefly present some preliminary tests on the impact of these uncertainties on the cosmological constraints from Euclid DR1 photometric galaxy clustering.
Understanding the nature of dark energy is one of the most important open questions in cosmology, and the large photometric survey of galaxies like Euclid will provide invaluable data on the Universe.
Accurate estimation of redshifts from photometric information is key to cosmological studies. This is often done using machine learning techniques. The selection of the spectroscopic training sample is crucial for the accurate estimation of the photometric redshift in machine learning approaches. Ideally, it should represent the entire target galaxy sample, covering the same colour-magnitude space. However, the spectroscopic studies used for training are often not as deep as the photometric data.
In this paper we present results obtained using the Directional Neighbourhood Fitting (DNF) algorithm to determine photometric redshifts in the Y3 DES Deep Fields catalogue. This field comprises four measured fields with eight bands (ugrizJHKs) covering approximately 5.88 deg², our analysis closely resembles the data we will have from the Euclid project. We examine the performance of the DNF algorithm, exploring its effectiveness in the context of Euclid-like data. In addition, we investigate the completeness of the training sample, selection strategies and confidence limits on photometric redshifts.
The precision of cosmological constraints derived from key observations in the Euclid imaging survey hinges on accurately measuring the first moments of the true redshift distributions of tomographic redshift bins, particularly their mean redshifts. A promising approach for achieving this is the clustering-redshifts technique, which relies on the angular cross-correlation between a target galaxy sample with unknown redshifts and a reference sample with spectroscopic redshifts. Such spectroscopic samples will be available from surveys such as DESI, 4MOST, or Euclid.
In our study, we generate photometric Euclid and spectroscopic mocks using the Flagship 2 simulation and optimise the clustering-redshifts algorithm for redshifts up to z = 1.8. We meticulously test each theoretical assumption, aiming to pinpoint primary systematics and sources of biases, and propose corrective measures. Our findings suggest that clustering redshifts prove highly effective for Euclid redshift calibration, provided that systematic biases can be identified, eliminated, or adequately addressed through realistic simulations.
Neural networks have achieved remarkable success in the estimation of photometric redshifts. However, their effectiveness is significantly
compromised by the presence of sample bias in the training datasets. These networks are predominantly trained on galaxies
with spectroscopically confirmed redshifts, using these observations as proxies for the actual redshift values. This approach introduces
a selection bias, as spectroscopic samples capture only a fraction of the diverse galaxy population observed in wide-field survey data.
In this study, we discuss the application of domain adaptation techniques to enhance the accuracy of photometric redshift predictions
for wide-field observations. Domain adaptation aims to uncover and align latent features common to both the source (spectroscopic)
and target (wide-field) domains, minimizing discrepancies in their feature distributions. Our investigation uses the Euclid-like wide survey
dataset from the photo-z challenge in Euclid Collaboration et al. 2020, to test the impact of domain adaptation. We observe a
reduction in the photo-z scatter by approximately 15 percent at the faint end (i > 24), where the spectroscopic data are notably scarce.
Domain adaptation also demonstrably enhances photo-z predictions in regions of the color space that are underrepresented by the
spectroscopic sample. Additionally, our methodology facilitates the prediction of the probability distribution of photometric redshifts
enabling the implementation of quality cuts for the final photo-z estimates.
We present a non-parametric methodology to reconstruct the primordial power spectrum $P_{\mathcal{R}}(k)$ from Large Scale Structure (LSS) data using Bayesian inference and nested sampling. We apply the method to two different classes of objects, one at low-$z$ (ELGs) and one at high-$z$ (QSOs), and two different photometric errors. The clustering of these objects is derived from different templates of the primordial power spectrum motivated by models of inflation: the Standard Model power law characterized by the two parameters $A_s$ and $n_s$; a local feature template; and a global oscillatory template. Our reconstruction method involves sampling $N$ knots in the log $\{k,P_{\mathcal{R}}(k)\}$ plane. We use two statistical tests to examine the reconstructions for signs of primordial features: a global test comparing the evidence ratios and a novel local test quantifying the power of the hypothesis test between the power law model and the marginalized probability over $N$-knots model. We discuss the performance of the method for a fixed and for a varying cosmology. The $S/N$ of the observed power spectrum of the studied catalogues and the inclusion of the cosmological parameters $H_0$, $\Omega_b$ and $\Omega_c$ to be sampled determines the sensitivity of the method to detect features, which can provide realistic estimations when applied to real data. The method is model independent, flexible, and suitable for its application to existing and future large surveys of the LSS, as Euclid.
Euclid will map the Universe with 3D positions of 30 million H-alpha Emission Line Galaxies to make unprecedented constrains in cosmology. The exquisite dataset will reduce extremely the statistical uncertainties, requiring a deeper look at different sources of systematic errors. One of them will come from the uncertainty of how ELGs relate to halos and its possible effect on galaxy clustering. Hence, a deeper understanding of this ELG-halo relation is needed. At the same time, Euclid must ensure that our constraints are robust to plausible variations of the ELG-halo connection.
In this talk, I will first review some of the lessons learned by studying the ELG-halo relation with eBOSS data (2007.09012). I will then present our recent findings (2312.13199) with a simulated Euclid-like ELG sample from a semi-analytical model of galaxy formation, SAGE. I will show that two proposed extensions of the classical HOD are required to recover the reference galaxy clustering. First, that ELG satellite occupation depends on whether or not the central galaxy is an ELG, a phenomenon known as conformity. Second, traditional profiles (Einasto, NFW) fail to reproduce the reference galaxy clustering, even when tuning their “free” parameters such as the concentration. We then propose an extension of the NFW curve that matches the observed ELG profile well and reproduces the reference galaxy clustering.
The Dark Energy Survey (DES) Y3 and Y6 analyses show that, given the current statistical power, it is crucial to know in depth the different sources of systematic errors and to correct for them appropriately. Galaxy clustering measurements are not an exception to this issue, where observational systematics are of particular relevance. In this regard, even if Euclid is not affected by the exact same observational systematics, some of them are similar or equivalent. Moreover, some of the mitigation techniques from DES can be translated to Euclid's framework. In this presentation we will show the the ongoing work on the correction of observational systematics within DES / LSST-DESC and how they could be incorporated to Euclid's galaxy clustering analysis and also how they could complement the techniques currently being developed within the Consortium.
I will briefly describe the Standard Project I am leading within Euclid on the exploitation of angular redshift fluctuations (ARF) with Euclid probes, namely with the weak lensing survey, and the photometric and spectroscopic galaxy surveys. I will first show how the addition of ARF computed in thin and wide redshift shells probed by Euclid will very significantly improve the measurements of redshift shell parameters like linear bias, lensing magnification bias, or photo-z error rms, while also shrinking the error ellipses in other key cosmological parameters like the \sigma_8, or the parameters of the dark energy equation of state (w_0, w_a). I will also briefly outline the potential of the ARF in Euclid x CMB cross-correlation studies, either in the context of dark energy characterization via the integrated Sachs-Wolfe effect, or when chasing the diffuse baryons giving rise to kinetic Sunyaev-Zeldovich anisotropies in the CMB at different cosmological epochs.
In this talk I will review the main simulations (Flagship) that have been produced to prepare the Euclid mission.
I will describe the BACCO framework that aims at providing accurately theoretical models to interpret EUCLID data.
In this work, we introduce a calibration pipeline for SciPIC specifically designed to constraining HOD parameters for accurately describing the observed clustering at low redshift. The pipeline consists of a simplified SciPIC version that generates a galaxy mock catalogue by assigning central and satellite galaxies within dark-matter halos with their corresponding positions, luminosities and colours. Then, it computes the 2-point correlation function in luminosity bins from the generated mock. The HOD parameters are optimized using MCMC sampling by comparing the obtained clustering with SDSS measurements at low redshift. To assess the performance of our implementation, we apply the pipeline to the halos identified on the Euclid Flagship N-body dark matter simulation, obtaining a good agreement between the fit HOD parameters and those adopted to generate FS2 galaxy mock catalogue. This pipeline will enable the proper calibration of future modifications and updates to be introduced into SciPIC. Additionally, it facilitates the production of mocks for various types of dark matter simulations used as input, which will be crucial for generating predictions for diverse cosmological scenarios.
Next generation galaxy surveys will measure the properties of our Universe with unprecedented precision. Thus, model testing and validation with numerical simulations is crucial to obtain accurate and unbiased estimates of the cosmological parameters. These mocks must faithfully reproduce the measurable properties of the galaxy population that will be observed.
In this talk, I will present HoDpipe, a new tool able to generate quick galaxy mocks replicating a number of properties given as inputs.
HoDpipe is flexible: it implements different Halo Occupation Distribution (HOD) models, halo profiles, can handle both assembly and velocity biases and can predict galaxy and clustering properties, redshift distributions and correlation functions.
HoDpipe can be used to generate galaxy mocks to test theoretical models, as a benchmark for analytical covariance calculations and as a validation tool for approximate methods.
Cluster number counts at visible and IR wavelengths will be a key cosmological probe in the next decade thanks to the Euclid satellite mission. For this purpose, the performance of cluster detection algorithms, which at these wavelengths are sensitive to the spatial distributions of the cluster galaxy members and their luminosity functions, need to be accurately characterized. Using The Three Hundred hydrodynamical and dark-matter-only simulations, we studied a complete sample of massive clusters beyond 7 (5) $\times$ 10$^{14}$ M$_{\odot}$ at redshift 0 (1) on a $(1.48 \ \mathrm{Gpc})^3$ volume. We find that the mass resolution of the current hydrodynamical simulations (1.5 $\times$ 10$^9$ M$_{\odot}$) is not enough to characterize the luminosity function of the sample in the perspective of Euclid data. Nevertheless, these simulations are still useful to characterize the spatial distribution of the cluster substructures assuming a common relative mass threshold for the different flavours and resolutions. By comparing with the dark-matter-only version of these simulations, we demonstrate that baryonic physics preserves significantly low-mass subhalos (galaxies), as has also been observed in previous studies with less statistics. Furthermore, by comparing the hydro simulations with higher resolution dark-matter-only simulations of the same objects and taking the same limit in subhalo mass, we find galaxy density profiles that are significantly more cuspy towards the centre of the clusters, where the low-mass substructures tend to concentrate. We conclude that using a dark-matter-only simulation may lead to some biases on the spatial distribution and density of galaxy cluster members.
Based on the preliminary analysis of few high-resolution hydro simulations we conclude that a mass resolution of 1.8 $\times$ 10$^8$ h$^{-1}$ M$_{\odot}$ will be needed for The Three Hundred simulations to approach the expected magnitude limits for the Euclid survey. These simulations are currently under way.
The J-PAS survey has started to observe wide areas of the Northern Sky with its unique set of 56 narrow band filters covering the entire optical wavelength range, providing, effectively, a low resolution spectra for every object detected. In this talk I will present the first J-PAS data and the first scientific results related to cosmology and galaxy evolution studies. I will focus in particular on the excellent photo-z quality that J-PAS can provide, and what that implies for the study of clusters as well as galaxies and quasars across a broad redshift range, stressing the potential of combining J-PAS photospectra with the exquisite Euclid imaging.
The observations of clusters of galaxies with Euclid is fundamental to achieve the major goals of this mission. I will give an overview of the major aims of the Science Working Group „Clusters of Galaxies“. Both I will present science cases directly related to the major goals of Euclid and projects/ideas related to legacy science.
Within the framework of the hierarchical growth of large-scale structures in the universe, mass is assembled inhomogeneously along walls and filaments which forms a "cosmic web", galaxy clusters are formed at the intersections of such filaments. Galaxy protoclusters (galaxy clusters in formation) are expected to contribute significantly to the star-formation rate density in the distant universe, thus understanding how clusters assembled their mass in the early universe is of critical importance. Both the the Euclid deep and wide surveys , together with the exquisite ground-based optical/imaging datasets, offer a unique opportunity to search for protoclusters in a systematic way and make a big leap forward in this research field. Several tools, calibrated on Euclid-like simulations, have been developed and tailored to find these structures in the Euclid datasets, . I will give an overview of the current status of this work package (WP11 within SWG Cluster of Galaxies) within Euclid and give an outline what is planned to be done with the Euclid Q1 and DR1 datasets.
Protoclusters of galaxies are non-virialized overdense structures in the distant universe which collapse into galaxy clusters with masses log(M/M$_*$) > 14 by redshift 0. They are the precursors of present-day massive galaxy clusters. The evolution of galaxies in these overdense structures depart from the one observed in the field. On the other hand, the assembly and gravitational collapse of protoclusters are of great interest in constraining cosmological models. The Euclid survey will cover 15000 square degrees on the sky, the resulting data will make possible to detect a large number of protolcusters spanning a wide-range of masses up to redshift 4 and possibly higher. In this work, we present a new algorithm to detect protoclusters of galaxies which is based in a multi-scaling approach. The algorithm was developed within the Euclid Consortium (EC) and applied to the simulations carried out in the consortium with great success. Other algorithms which find protoclusters of galaxies have also been developed within the EC and we are now comparing their performances. Here, we give a glimpse of these algorithms and some preliminary results. The construction of a catalog of protoclusters is essential to execute some Key Projects approved in the EC.
Deep observations of galaxy clusters reveal a diffuse light component, the intracluster light (ICL), which contains a record of all the processes that the system has undergone and provides information about the mechanisms that have shaped the population within the cluster.
In this talk I will present the most comprehensive study to date of the ICL of the Perseus cluster, taking advantage of the exquisite high-resolution multi-wavelength images from Euclid's ERO. Thanks to Euclid's sensitivity and high spatial resolution, we are able to measure the radial surface brightness profiles of the brightest cluster galaxy (BCG) and detect the intracluster globular clusters (ICGCs) out to 600 kpc. The BCG+ICL profile requires two Sérsic components: a compact component, which we associate with the BCG, and an extended component, which we associate with the ICL and which provides most (64% in H) of the light and stellar mass within 500 kpc.
We find that the contours of the ICL and ICGC are not centred on the BCG at the largest scales (>200 kpc), but are instead offset by 60 kpc to the west of the BCG core, which we interpret as a sign of recent merger activity.
The colour of the ICL and the luminosity function of the ICGCs suggest that both intracluster stellar components were tidally stripped from the outskirts of massive ellipticals with masses of a few 10^10 Msun.
Weak lensing effect is a sensitive probe for galaxy cluster detection, that needs of a large enough number of background galaxies observed to be profitable.
Euclid will conduct a 14,500 square degree weak lensing survey, measuring the shapes of billions of galaxies and opening a unique window for detecting galaxy clusters in this way.
The task of coordinating the science within Euclid related to galaxy clusters detected via weak lensing is carried out from the CG-WP10 (Clusters of Galaxies - Work Package 10).
On behalf of its members, I will describe our current activities, which right now are focused on testing and comparing different methods to detect clusters of galaxies on Euclid-like mocks from the DUSTGRAIN-pathfinder simulations. All of our work is aimed to the construction of a weak lensing selected cluster catalogue, which will be used for cluster counts, mass estimations, mass-observable relations, and other interesting properties of clusters.
In this talk we briefly review the strong lensing science to be done with Euclid
We present a lensing model for the ERO cluster A2390 combining strong and weak lensing data from Euclid. Euclid's large feld of view offers unique opportunities to constrain with precision the masses of the most massive clusters, which opens a door for cosmology.
The two current main methods to measure shear, model-fitting and moments-based, both suffer from noise bias for galaxies with lower SNR. As Euclid has very strict requirements for shear bias, there is motivation to remove noise from images before performing the shape measurement to increase the precision. We present two deep learning algorithms: one to denoise galaxy images and another to measure the shapes of galaxies, with results comparable to GalSim adaptive moments and 8.2 times greater performance speed. Currently, we are developing a joint training framework to simultaneously train the denoiser and shape measurement in order to optimise the denoising for the shape measurement.
This presentation synthesizes findings from our recent studies, that focus on the magnification bias observed in high-redshift submillimeter galaxies (SMGs). Our methodology employs SMGs as a background sample, emphasizing their distinctive cross-correlation signals and their role as an alternative, independent probe for cosmological studies related to mass distribution. Precise measurements of the cosmological parameters can only be obtained by using complementary techniques, putting in relevance the importance of studying alternative tools for measuring the geometry of the Universe. In this context, SMGs' Magnification Bias emerges as a powerful and sensitive cosmological probe, uniquely complementary and independent of shear. Moreover, SMGs' Magnification Bias, observable across various lens types (galaxies, QSOs, or galaxy clusters), avoids the omega_m-sigma8 degeneracy and furnishes additional direct constraints on related quantities, such as the Halo Mass Function and neutrino masses.
Building upon methodological improvements, our recent analyses refine the measurement of magnification bias signals within the ΛCDM model. By incorporating weak lensing signals within the halo model formalism and employing a Markov chain Monte Carlo algorithm, we achieve remarkable enhancements in constraining both halo occupation distribution and cosmological parameters (mean values of $\Omega_m=0.27^{+0.02}_{-0.04}$ and $\sigma_8=0.72^{+0.04}_{-0.04}$ and $h=0.79^{+0.13}_{-0.14}$). In a tomographic scenario, we can explore not only the ΛCDM model but also the evolving dark energy density in $w_0CDM$ and $w_0w_aCDM$ frameworks. In the $w_0w_aCDM$ model, the results are $−1.09^{+0.43}_{-0.63}$ for $w_0$ and $−0.19^{+1.29}_{-1.69}$ for $w_a$. Our latest works focuses on optimising computational efficiency and exploring strategies for analysing different redshift bins while ensuring measurement precision.
Finally, we highlight the potential impact of incorporating additional wide area fields observed by Herschel and updated foreground catalogues as the Euclid mission, for future enhancements of this alternative cosmological probe.
I will briefly review the different galaxy formation related science cases that Euclid will address, with a special focus on galaxy morphology
The Local Universe Science Working Group (LU-SWG) is the Euclid consortium's team that works in galaxy evolution in the nearby Universe (z < 0.3). Euclid will provide an unmatched view of these objects considering our combination of spatial resolution, sky coverage and photometric depth. Remarkably, we also have the power of transforming the survey removing nuisances such us different sky backgrounds and cirrus. All these features have enabled us to peer into the Low Surface Brightness regime of galaxies, a very much unexplored "terra incognita" in the lives of galaxies. We will provide a brief review of our activities, trying to find possible synergies with the rest of Euclid's group.
Ultradeep observations coming from HST and JWST are revealing abrupt drops in the external parts of galaxy mass profiles. These Low Surface Brightness features, dubbed as galaxy edges or truncations, deliver a physically-motivated size indicator. They represent the limit of the radial location of the gas density enabling efficient star formation, i.e. the outermost extension of the in-situ formed galaxy stellar component. We will present our results in Buitrago et al. (2024) for the identification of these edges in a sample of 1048 massive (M_stellar > 10^10 M_Sun) disc galaxies in the HST CANDELS fields. Our conclusion is that Milky Way-like galaxies decrease their size by a factor of 2 since z = 1, while at the same time their density at the truncation position increases by an order of magnitude, reflecting the progressively different star formation conditions. We will also show in Fernández-Iglesias et al. (2024) that we are able to obtain high-accuracy results by using Machine Learning U-Net networks. Comparing our outcomes with those from effective radii --that are biased by light concentration--, a more dramatic size evolution proportional to (1+z)^-1 is displayed, and we will outline our plans for extending such measurements for millions of objects utilizing Machine Learning Domain Adaptation techniques for Euclid.
Stellar disk truncations are a long-sought galactic size indicator based on the radial location of the gas density threshold for star formation, i.e., the edge/limit of the luminous matter in a galaxy. The study of galaxy sizes is crucial for understanding the physical processes that shape galaxy evolution across cosmic time. Current and future ultradeep and large-area imaging surveys, such as the ESA's Euclid mission, will allow us to explore the growth of galaxies and trace the limits of star formation in their outskirts.
The task of identifying the disk truncations in galaxy images is equivalent to what is called (informed) image segmentation in computer vision. Recently, the Meat AI research team published the Segment Anything Model (SAM, Kirillov et al. 2023). SAM is a foundational deep learning model that is capable of segmenting any type of data (including text, images, and audio) into smaller components or segments. The model is designed to be highly adaptable and versatile making it suitable for a wide range of applications.
In preparation for automatically identifying disk truncations in the galaxy images that will be soon released by Euclid, we run SAM over a dataset of more than 1000 disc galaxies within the HST CANDELS fields presented in Buitrago & Trujillo 2024. We 'euclidize' the HST galaxy images by making composite RGB images using the H, J and I+V HST filters, respectively. Using these images as input for the SAM, we retrieve various truncation masks for each galaxy image given different configurations of the input dataset (i.e. varying the stretch and normalization of the input images). Finally, we present a comparison of the truncations obtained with the SAM on the whole 'euclidized' dataset with the results presented in Buitrago & Trujillo 2024, in which truncations are evaluated using the radial positions of the edge in the light profiles of galaxies. Our findings indicate the exceptional capability of SAM to identify truncations in large samples of galaxies in a fully automated manner, eliminating the need for extensive data preprocessing or a labeled dataset.
A complete and satisfactory understanding of the processes that led to the formation and evolution of the variety of today’s galaxy types is still beyond our reach. To solve this problem, we need both large datasets reaching high redshifts and novel methodologies for dealing with them. The recent cutting-edge Dark Energy Spectroscopic Instrument (DESI) has already provided ~20 million spectroscopic redshifts, more than the combination of any other previous study, covering a large redshift range. By harnessing the power of unsupervised machine-learning algorithms, we automate and enhance the process of categorizing galaxies transcending the limitations of standardly used classifications complemented by the analysis of their physical properties. I will present how this knowledge can be transferred for Euclid, with a particular interest in unrevealing obscured and faint AGN in low-mass galaxies. I will also discuss the synergies of DESI and Euclid data to enhance understanding of galaxies & AGN evolution.
The first Euclid data have shown the enormous potential of this astronomical facility to address a large number of scientific cases beyond the science core of the project. In particular, the Early Release Observations, and their dedicated processing pipeline as an ESA led effort, have shown the enormous potential to explore the low surface brightness regimes, of great interest for the extragalactic community. The Local Universe Working Group has promoted a pilot study in the framework of the ESA Datalabs platform, to produce an alternative version of the Euclid data in VIS and NISP, with the aim of having a processing similar to that of the ERO fields, producing high efficiency in the photometry of diffuse regions, and therefore of great interest to the extragalactic community. In this talk I will summarize the work done in this pilot study, describing the general characteristics of the developed pipeline, and the potential of the data produced by it.
We as the Euclid ILS team, hereby present our preliminary results of one of the Euclid ERO programme, probing the young free-floating planets in the hotbed of the sigma Orionis cluster. We used known, spectroscopically-confirmed substellar benchmarks to proceed with high-purity selection for new members in the Euclid catalogue and we were able to probe down to objects with 4 Jovian masses. The low-mass-tail of the IMF has been estimated which follows a multi-power-law distribution. We would like to present our preparation of exploring and characterizing substellar objects in the canonical surveys and deep surveys, including ultracool dwarf template selection in the three deep fields and their reconnaissance spectroscopy with 10.4-m Gran Telescopio Canarias and 8.2-m Very Large Telescope.
The cross-correlation between the cosmic microwave background (CMB) anisotropies and matter tracers encodes important cosmological information. With Euclid, an unprecedented sensitivity and depth will be reached in the era of galaxy surveys. We forecast by a Fisher matrix approach the capabilities of the cross-correlation between the CMB and the main Euclid probes (galaxy clustering and weak lensing) for constraining extensions of the standard LCDM cosmological model. In particular, cross-correlating the CMB lensing with Euclid will be useful for measuring parameters with implications on inflation and fundamental physics such as the local primordial non-Gaussianity parameter fNL. Just by using two-point statistics in a 2D tomographic approach, with Euclid we will be able to measure fNL through the scale-dependent galaxy bias with an uncertainty ~5, of the same order of the current constraints by the Planck bispectrum.
In this talk I will discuss some of my involvements in several projects of research on, or using, supernovae and transients. I joined the EUCLID consortium and the SWG SNe and Transients in December 2023. I will comment on the ongoing work and plans in this working group.
Since the discovery of the accelerated expansion of the Universe more than two decades ago, Type Ia Supernovae (SNe Ia) have been extensively used as standardisable candles in the optical. However, SNe Ia have shown to be more homogeneous in the near-infrared (NIR), where the effect of dust extinction is also attenuated. Actually, one single epoch in J and/or H band imaging, plus good gr-band coverage, may be enough to get an accurate estimation of peak magnitudes in the J (Jmax) and H (Hmax) bands, and therefore precise distances. We are currently performing a SN Ia NIR imaging survey in J- and H-bands to build a sample of 10^3 SNe Ia and get systematics-limited (better than 3%) distances with minimal resources (SNFLOWS survey), with the main goal of expanding our view of Laniakea out to z=0.1. In this talk I will present SNFLOWS and the possible extension using serendipitious observations of SNIa in the Wide Surevy footprint of EUCLID.