24 research outputs found
Organizational Factors Related to Safety in a Psychiatric Hospital: Employee Perceptions
Systemic inflammation in xenograft recipients (SIXR): A new paradigm in pig-to-primate xenotransplantation?
Euclid preparation. Optical emission-line predictions of intermediate-z galaxy populations in GAEA for the Euclid Deep and Wide Surveys
International audienceIn anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model to self-consistently model nebular emission from HII regions, narrow-line regions of active galactic nuclei (AGN), and evolved stellar populations. Our analysis focuses on seven optical emission lines: H, H, [SII], [NII], [OI], [OIII], and [OII]. We find that Euclid will predominantly observe massive, star-forming, and metal-rich line-emitters. Interstellar dust, modelled using a Calzetti law with mass-dependent scaling, may decrease observable percentages by a further 20-30% with respect to our underlying emission-line populations from GAEA. We predict Euclid to observe around 30-70% of H-, [NII]-, [SII]-, and [OIII]-emitting galaxies at redshift below 1 and under 10% at higher redshift. Observability of H-, [OII]-, and [OI]- emission is limited to below 5%. For the Euclid-observable sample, we find that BPT diagrams can effectively distinguish between different galaxy types up to around redshift 1.8, attributed to the bias toward metal-rich systems. Moreover, we show that the relationships of H and [OIII]+H to the star-formation rate, and the [OIII]-AGN luminosity relation, exhibit minimal changes with increasing redshift. Based on line ratios [NII]/H, [NII]/[OII], and [NII]/[SII], we further propose novel z-invariant tracers for the black hole accretion rate-to-star formation rate ratio. Lastly, we find that commonly used metallicity estimators display gradual shifts in normalisations with increasing redshift, while maintaining the overall shape of local calibrations. This is in tentative agreement with recent JWST data
Euclid preparation. Optical emission-line predictions of intermediate-z galaxy populations in GAEA for the Euclid Deep and Wide Surveys
International audienceIn anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model to self-consistently model nebular emission from HII regions, narrow-line regions of active galactic nuclei (AGN), and evolved stellar populations. Our analysis focuses on seven optical emission lines: H, H, [SII], [NII], [OI], [OIII], and [OII]. We find that Euclid will predominantly observe massive, star-forming, and metal-rich line-emitters. Interstellar dust, modelled using a Calzetti law with mass-dependent scaling, may decrease observable percentages by a further 20-30% with respect to our underlying emission-line populations from GAEA. We predict Euclid to observe around 30-70% of H-, [NII]-, [SII]-, and [OIII]-emitting galaxies at redshift below 1 and under 10% at higher redshift. Observability of H-, [OII]-, and [OI]- emission is limited to below 5%. For the Euclid-observable sample, we find that BPT diagrams can effectively distinguish between different galaxy types up to around redshift 1.8, attributed to the bias toward metal-rich systems. Moreover, we show that the relationships of H and [OIII]+H to the star-formation rate, and the [OIII]-AGN luminosity relation, exhibit minimal changes with increasing redshift. Based on line ratios [NII]/H, [NII]/[OII], and [NII]/[SII], we further propose novel z-invariant tracers for the black hole accretion rate-to-star formation rate ratio. Lastly, we find that commonly used metallicity estimators display gradual shifts in normalisations with increasing redshift, while maintaining the overall shape of local calibrations. This is in tentative agreement with recent JWST data
Euclid preparation. Optical emission-line predictions of intermediate-z galaxy populations in GAEA for the Euclid Deep and Wide Surveys
International audienceIn anticipation of the Euclid Wide and Deep Surveys, we present optical emission-line predictions at intermediate redshifts from 0.4 to 2.5. Our approach combines a mock light cone from the GAEA semi-analytic model to self-consistently model nebular emission from HII regions, narrow-line regions of active galactic nuclei (AGN), and evolved stellar populations. Our analysis focuses on seven optical emission lines: H, H, [SII], [NII], [OI], [OIII], and [OII]. We find that Euclid will predominantly observe massive, star-forming, and metal-rich line-emitters. Interstellar dust, modelled using a Calzetti law with mass-dependent scaling, may decrease observable percentages by a further 20-30% with respect to our underlying emission-line populations from GAEA. We predict Euclid to observe around 30-70% of H-, [NII]-, [SII]-, and [OIII]-emitting galaxies at redshift below 1 and under 10% at higher redshift. Observability of H-, [OII]-, and [OI]- emission is limited to below 5%. For the Euclid-observable sample, we find that BPT diagrams can effectively distinguish between different galaxy types up to around redshift 1.8, attributed to the bias toward metal-rich systems. Moreover, we show that the relationships of H and [OIII]+H to the star-formation rate, and the [OIII]-AGN luminosity relation, exhibit minimal changes with increasing redshift. Based on line ratios [NII]/H, [NII]/[OII], and [NII]/[SII], we further propose novel z-invariant tracers for the black hole accretion rate-to-star formation rate ratio. Lastly, we find that commonly used metallicity estimators display gradual shifts in normalisations with increasing redshift, while maintaining the overall shape of local calibrations. This is in tentative agreement with recent JWST data
Euclid Preparation. TBD. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
International audienceWe derive galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections are implemented in the Euclid data analysis pipelines for galaxy clusters. Given any set of photometric bands, we develop 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. We calibrate galaxy selections using ground-based and Euclid bands. Both selections produce a purity higher than 97%. The selection completeness ranges from 30% to 84% in the lens redshift range . With the full selection, the completeness improves by up to percentage points, and the range extends up to . The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on bands provides excellent results on real and simulated external data sets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. 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 imply variations within the 1 uncertainty in the mean multiplicative shear bias, , for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on
Euclid Preparation. TBD. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
International audienceWe derive galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections are implemented in the Euclid data analysis pipelines for galaxy clusters. Given any set of photometric bands, we develop 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. We calibrate galaxy selections using ground-based and Euclid bands. Both selections produce a purity higher than 97%. The selection completeness ranges from 30% to 84% in the lens redshift range . With the full selection, the completeness improves by up to percentage points, and the range extends up to . The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on bands provides excellent results on real and simulated external data sets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. 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 imply variations within the 1 uncertainty in the mean multiplicative shear bias, , for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on
Euclid Preparation. TBD. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
International audienceWe derive galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections are implemented in the Euclid data analysis pipelines for galaxy clusters. Given any set of photometric bands, we develop 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. We calibrate galaxy selections using ground-based and Euclid bands. Both selections produce a purity higher than 97%. The selection completeness ranges from 30% to 84% in the lens redshift range . With the full selection, the completeness improves by up to percentage points, and the range extends up to . The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on bands provides excellent results on real and simulated external data sets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. 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 imply variations within the 1 uncertainty in the mean multiplicative shear bias, , for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on
Euclid Preparation. TBD. Galaxy colour selections with Euclid and ground photometry for cluster weak-lensing analyses
International audienceWe derive galaxy colour selections from Euclid and ground-based photometry, aiming to accurately define background galaxy samples in cluster weak-lensing analyses. These selections are implemented in the Euclid data analysis pipelines for galaxy clusters. Given any set of photometric bands, we develop 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. We calibrate galaxy selections using ground-based and Euclid bands. Both selections produce a purity higher than 97%. The selection completeness ranges from 30% to 84% in the lens redshift range . With the full selection, the completeness improves by up to percentage points, and the range extends up to . The calibrated colour selections are stable to changes in the sample limiting magnitudes and redshift, and the selection based on bands provides excellent results on real and simulated external data sets. Furthermore, the calibrated selections provide stable results using alternative photometric aperture definitions obtained from different ground-based telescopes. The selection is also purer at high redshift and more complete at low redshift compared to colour selections found in the literature. 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 imply variations within the 1 uncertainty in the mean multiplicative shear bias, , for stage III surveys. The first Euclid data releases will provide further insights into the impact of background selections on
Euclid preparation. Measuring detailed galaxy morphologies for Euclid with Machine Learning
International audienceThe Euclid mission is expected to image millions of galaxies with high resolution, providing an extensive dataset to study galaxy evolution. We investigate the application of deep learning to predict the detailed morphologies of galaxies in Euclid using Zoobot a convolutional neural network pretrained with 450000 galaxies from the Galaxy Zoo project. We adapted Zoobot for emulated Euclid images, generated based on Hubble Space Telescope COSMOS images, and with labels provided by volunteers in the Galaxy Zoo: Hubble project. We demonstrate that the trained Zoobot model successfully measures detailed morphology for emulated Euclid images. It effectively predicts whether a galaxy has features and identifies and characterises various features such as spiral arms, clumps, bars, disks, and central bulges. When compared to volunteer classifications Zoobot achieves mean vote fraction deviations of less than 12% and an accuracy 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 disk or smooth galaxies, the mean deviations are less than 10%, with only 1000 training galaxies necessary to reach this performance. For more detailed structures and complex tasks like detecting and counting spiral arms or clumps, the deviations are slightly higher, around 12% with 60000 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 crowdsourcing. 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