IAS-LAB PUBLICATIONS
Lithologies of Dimorphos revealed by boulder morphological classes
Authors: Tusberti F.; Pajola M.; Penasa L.; Lucchetti A.; Massironi M.; Munaretto G.; Beccarelli J.; Pozzobon R.; Rossi C.; Poggiali G.; Murdoch N.; Robin C. Q.; Duchene A.; Amoroso M.; Bertini I.; Brucato J. R.; Capannolo A.; Caporali S.; Ceresoli M.; Cremonese G.; Dall'Ora M.; Della Corte V.; Deshapriya J. D. P.; Dotto E.; Epifani E. Mazzotta; Gai I.; Gramigna E.; Hasselmann P. H. A.; Ieva S.; Impresario G.; Ivanovski S.; Manghi R. Lasagni; Lavagna M.; Lombardo M.; Modenini D.; Palumbo P.; Perna D.; Pirrotta S.; Rossi A.; Tortora P.; Zannoni M.; Zanotti G.; Zinzi A.; Buratti B.; Trigo-Rodrigez J. M.; Casajus L. Gomez; Robin C.Q.; Brucato J.R.; Corte V. Della; Deshapriya J.D.P.; Hasselmann P.H.A.; Trigo-Rodrigez J.M.
Journal: ICARUS
Published: 2025
DOI: 10.1016/j.icarus.2025.116744
The stony/Sq-type binary system (65803) Didymos consists of two rubble pile bodies, Didymos, the primary, and its moonlet Dimorphos. In 2022, the first planetary defense mission Double Asteroid Redirection Test (DART) reached Dimorphos and collected unprecedented high-resolution images of its surface. They revealed a variegated surface completely covered by stacked boulders and cobbles with different shapes and textures. Their morphological heterogeneity likely reflects the lithologies originally present within the Didymos and Dimorphos’ parent body, before its fragmentation. We present a lithologic study of Dimorphos’ surface, based on a morphological analysis of its boulders. Our approach considers each boulder’s 2D outline, perceived 3D shape, and surface texture characteristics. Based on these features, we identified two main morphological classes. A total of 178 boulders were classified as Angular morphology covering 38.5 % of the mapped area. This type is characterized by cohesive boulders with straight and angular outlines as well as sharp three-dimensional edges, which form well-defined sub-planar facets. Additionally, the texture of this kind of rocks show low-to-mid Roughness and the presence of lineations on some boulders. On the other hand, 210 boulders were categorized as Hummocky morphology, which covers 61.5 % of the area. Such morphology appears more friable and features boulders with rounded shaped and ragged and irregular 2D perimeter. Their 3D perceived shape appears as flat-to-curve, with rough and hummocky surface textures due to embedded clasts. We interpret the Hummocky morphology as breccia lithology. On the other hand, although Angular morphology can be associated with multiple potential lithologies, we interpret it as either an achondritic/igneous lithology or a highly metamorphosed chondritic lithology. The breccias are likely characteristic of the parent body’s outer layers, actively involved in impact and sedimentary processes. Conversely, the lithologies associated with the An morphology should represent deeper regions of the plantesimal, possibly exhumed by impacts. All these results will be further complemented by data from the Hera mission, which will arrive at (65803) Didymos system at the end of 2026.
Volume: 442
Keywords: Asteroids; Geological processes; Near-Earth objects; Satellites of asteroids; Surfaces;
Hepatitis C Eradication Improves Oncologic and Clinical Outcomes in Patients Treated With Atezolizumab Plus Bevacizumab
Authors: Stella Leonardo; Cabibbo Giuseppe; Celsa Ciro; Ciccia Roberta; Sparacino Alba; Piscaglia Fabio; Tovoli Francesco; Arleo Andrea; Stefanini Bernardo; Iavarone Massimo; D'Ambrosio Roberta; Cerrito Lucia; Pallozzi Maria; Santopaolo Francesco; Marra Fabio; Campani Claudia; Mazzarelli Chiara; Vigano Raffaella; Tortora Raffaella; Aghemo Alessio; Nicola Stella De; Pressiani Tiziana; Rimassa Lorenza; Bhoori Sherrie; Corallo Salvatore; Maiocchi Laura; Martini Andrea; Solda Caterina; Russo Francesco Paolo; Gasbarrini Antonio; Ponziani Francesca Romana; Viganò Raffaella; Soldà Caterina
Journal: LIVER INTERNATIONAL
Published: 2025
DOI: 10.1111/liv.70362
Background and Aims: Hepatitis C virus (HCV) is a key driver of hepatocellular carcinoma (HCC). However, the impact of HCV eradication on systemic therapy remains unclear. We aimed to assess the safety and efficacy of direct-acting antivirals (DAA) in patients treated with Atezolizumab plus Bevacizumab (AtezoBev). Methods: This retrospective multicentre study included patients with HCV-related unresectable/advanced HCC treated with AtezoBev between 2021 and 2024. Three groups of patients were compared: Group A (n = 22), concurrent DAA with AtezoBev; Group B (n = 95), antiviral therapy before AtezoBev; and Group C (n = 22), active infection. Results: Group A showed the longest median overall survival (42.8 months) compared to Group B (26.8 months; p = 0.03) and Group C (19.7 months; p = 0.01). Time to progression and progression-free survival were significantly prolonged in Group A versus Groups B and C. Moreover, Group A exhibited a higher disease control rate than the other groups. Post-DAA decompensation rates were significantly lower in Group A (4.5%) compared to Groups B (26.3%) and C (36.4%). Treatment-related adverse events of grade ≥ 3 were similar across groups. In the multivariate competing risk analysis with adjustment for time-dependent variables, achieving sustained virologic response during AtezoBev showed a protective effect against liver decompensation (sHR 0.02, p = 0.003) or tumour progression (sHR 0.14, p = 0.009), and was also associated with reduced mortality (HR 0.29, p = 0.005). Conclusions: Achieving a SVR during AtezoBev seems to improve oncologic outcomes and reduce liver decompensation in patients with unresectable/advanced HCC. An integrated therapeutic approach can optimise systemic treatment efficacy, particularly in patients eligible for conversion strategies. Trial Registration: Protocol ID: 5890.
Volume: 45
Keywords: cirrhosis; DAA; HCV; hepatocellular carcinoma; immunotherapy; liver decompensation; survival;
From simulations to observations: Methodology and data release of mock TNG50 galaxies at 0.3 < z < 0.7 for WEAVE-StePS
Authors: Ikhsanova A.; Costantin L.; Pizzella A.; Corsini E. M.; Morelli L.; Ditrani F. R.; Ferre-Mateu A.; Gabarra L.; Gullieuszik M.; Haines C. P.; Iovino A.; Longhetti M.; Mercurio A.; Ragusa R.; Sanchez-Blazquez P.; Tortora C.; Vulcani B.; Zhou S.; Gafton E.; Pistis F.
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202555132
Volume: 703
Euclid: Early Release Observations – Globular clusters in the Fornax galaxy cluster, from dwarf galaxies to the intracluster field
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; GEH-7593-2022; GDB-0545-2022; GDD-0521-2022; J-4088-2012; HEO-6319-2022; DWB-0787-2022; GCT-4656-2022; DVE-7652-2022; AAK-8191-2020; GBG-9412-2022; DTQ-3078-2022; DWO-2405-2022; EVW-7270-2022; GFK-2340-2022; OOP-8239-2025; DAV-8065-2022; MSI-3188-2025; LXV-7382-2024; B-9633-2012; KPF-2019-2024; GFH-0803-2022; LTE-2549-2024; GNE-1283-2022; ABB-9156-2021; GBC-8404-2022; PGU-9738-2026; O-9495-2015; B-4348-2013; HQE-3410-2023; AFS-1680-2022; QGM-4004-2026; JCO-0131-2023; EYY-4006-2022; DXA-1243-2022; LBZ-7918-2024; ISA-3008-2023; DXL-4304-2022; MDQ-9712-2025; I-6648-2019; DYF-3433-2022; H-4394-2019; EDW-5764-2022; Z-4828-2019; JHF-8266-2023; FQI-9285-2022; AAV-1857-2021; EJM-8740-2022; FZO-1254-2022; FYJ-9637-2022; DUY-3094-2022; C-4378-2014; EKA-7986-2022; IUT-7926-2023; LXX-3952-2024; GBF-1843-2022; DVB-2560-2022; L-8385-2017; OBC-0525-2025; LUN-9319-2024; HWT-5982-2023; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; IVA-4275-2023; B-4650-2017; IRQ-6937-2023; H-8587-2015; DVC-6323-2022; NHE-3385-2025; PGG-2427-2026; AGZ-3259-2022; FZL-7353-2022; A-2693-2010; PCA-2324-2025; HLX-2021-2023; HKB-2933-2023; EUO-2530-2022; EUK-3820-2022; J-3686-2012; COQ-7299-2022; CPC-6980-2022; AAR-6622-2021; CQF-5798-2022; I-5515-2016; DXA-1952-2022; HPT-5858-2023; JWF-2506-2024; GBB-1832-2022; DWB-6758-2022; GQH-6424-2022; CQR-5759-2022; DWK-1716-2022; JGR-4365-2023; GAX-2002-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; EYM-5386-2022; HRW-8595-2023; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; DWQ-9372-2022; AAT-5867-2020; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; FGD-1080-2022; DAV-9216-2022; CYT-5449-2022; AAF-6025-2021; DUU-4676-2022; B-8502-2016; FIF-2657-2022; LYD-9061-2024; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; DFQ-7859-2022; DFY-8508-2022; MWK-2416-2025; AAX-3485-2021; D-1300-2016; FNO-5530-2022; AAA-1489-2019; GNG-7078-2022; FNC-4379-2022; DFC-8070-2022; FLD-9518-2022; MWC-3186-2025; FNA-5485-2022; KJY-7272-2024; DWT-4779-2022; KSI-9422-2024; KNP-2716-2024; MNN-0179-2025; FNB-0821-2022; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; DMG-4306-2022; FVO-0175-2022; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; FSO-8783-2022; DNY-0415-2022; OWQ-1639-2025; PIF-6131-2026; K-4114-2015; OON-3882-2025; DNW-6364-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DXM-5348-2022; DPD-7597-2022; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; FZJ-5145-2022; NGE-0152-2025; EAA-4768-2022; L-8068-2014; MQB-6975-2025; DZM-7523-2022; GDK-6495-2022; T-7378-2018; AAB-2503-2019; KLN-4310-2024; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; FCD-8153-2022; AAC-2083-2020; NKT-4492-2025; MTT-8732-2025; OZU-7134-2025; EAZ-0566-2022; O-9396-2015; AAO-6325-2021; DTO-7937-2022; FVK-3262-2022; JCM-8241-2023; CEX-0810-2022; NBS-6714-2025; 57204700965; 57103783400; 6701673913; 10042256200; 57195321311; 7003267532; 7402297037; 35494536400; 57188565951; 8833942000; 6603186973; 7004293616; 7402054273; 55538241000; 56216916000; 7003825248; 56592660200; 6603351766; 7006440295; 57193617511; 6603939854; 18838264600; 57545939600; 57202215260; 6701409861; 56261663500; 6602409206; 7003910265; 7102219947; 56925206200; 7404584619; 57194407496; 36663730200; 57192921002; 59170382200; 55337098600; 57190443165; 9533992100; 57339831000; 57206423651; 55929371000; 57374950500; 7005317106; 6506425834; 7102775303; 57193523315; 6701390827; 14629998500; 56176939800; 8651648800; 24482926400; 6505819655; 57225389323; 58849865400; 7102960752; 6602293713; 6701447926; 7004168457; 7004279376; 35117442400; 56592859600; 8316050500; 57193414472; 57090221700; 24439181000; 54924573500; 56260193000; 37121732700; 6507398813; 55757270100; 8856476200; 59636105400; 7004529134; 26663174300; 6603213706; 7006071419; 57212263363; 6506323808; 6601991850; 6602678698; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202450784
We present an analysis of Euclid observations of a 0.6 deg2 field in the central region of the Fornax galaxy cluster that were acquired during the performance verification phase. With these data, we investigated the potential of Euclid to identify globular clusters (GCs) at 20 Mpc and validated the search methods using artificial GCs and known GCs within the field from the literature. Our analysis of artificial GCs injected into the data shows that Euclid’s data in the IE band is 80% complete at about IE ∼ 26.0 mag (MV ∼ -5.0 mag), and it resolves GCs as small as rh = 2.5 pc. In the IE band, we detected more than 95% of the known GCs from previous spectroscopic surveys and GC candidates of the ACS Fornax Cluster Survey, of which more than 80% are resolved. We identify more than 5000 new GC candidates within the field of view down to IE = 25.0 mag, about 1.5 mag fainter than the typical GC luminosity function turn-over magnitude, and we investigated their spatial distribution within the intracluster field. We then focused on the GC candidates around dwarf galaxies and investigated their numbers, stacked luminosity distribution, and stacked radial distribution. While the overall GC properties are consistent with those in the literature, we found an interesting over-representation of relatively bright candidates within a small number of relatively GC-rich dwarf galaxies. Our work confirms the capabilities of Euclid data in detecting GCs and separating them from foreground and background contaminants at a distance of 20 Mpc, particularly for low GC-count systems such as dwarf galaxies.
Volume: 697
Keywords: galaxies: clusters: individual: Fornax; galaxies: clusters: intracluster medium; galaxies: dwarf; galaxies: star clusters: general; methods: observational;
Euclid: Early Release Observations – Dwarf galaxies in the Perseus galaxy cluster
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; DVE-7652-2022; DWB-0787-2022; ABB-2950-2020; DTQ-3078-2022; DWO-2405-2022; JCO-0131-2023; GFK-2340-2022; DYO-3408-2022; FPW-4436-2022; MDH-6494-2025; KPF-2019-2024; GEH-7593-2022; LTE-2549-2024; I-6648-2019; GNE-1283-2022; LPM-0745-2024; O-9495-2015; GDD-0521-2022; DAV-8065-2022; MSI-3188-2025; GBG-9412-2022; DYF-3433-2022; HTB-3700-2023; ABE-1497-2020; FZK-6500-2022; DXA-1243-2022; AAH-3277-2019; GBG-8291-2022; H-4394-2019; AAK-8191-2020; DTZ-1231-2022; EJM-8740-2022; FZO-1254-2022; FYJ-9637-2022; DUY-3094-2022; CEY-5520-2022; C-4378-2014; EKA-7986-2022; IUT-7926-2023; IUQ-9509-2023; GBF-1843-2022; EKV-4052-2022; L-8385-2017; OBC-0525-2025; LUN-9319-2024; DVP-6438-2022; CJL-9982-2022; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; IVA-4275-2023; B-4650-2017; IRQ-6937-2023; H-8587-2015; B-4348-2013; DVC-6323-2022; HQF-6437-2023; PGG-2427-2026; AGZ-3259-2022; FZL-7353-2022; A-2693-2010; PCA-2324-2025; CNE-2384-2022; HLX-2021-2023; HKB-2933-2023; PWI-2374-2026; LZM-1403-2025; EUK-3820-2022; J-3686-2012; CNP-7538-2022; CPC-6980-2022; AAR-6622-2021; CQF-5798-2022; I-5515-2016; KLD-3528-2024; DXA-1952-2022; HPT-5858-2023; JWF-2506-2024; GBB-1832-2022; DWB-6758-2022; GWP-3456-2022; GQH-6424-2022; CQR-5759-2022; DWK-1716-2022; JGR-4365-2023; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FCZ-2066-2022; FBF-5584-2022; EYM-5386-2022; HRW-8595-2023; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; NRV-3859-2025; EYY-4006-2022; DWQ-9372-2022; AAT-5867-2020; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; FGD-1080-2022; DAV-9216-2022; CYT-5449-2022; AAF-6025-2021; OOP-8239-2025; DUU-4676-2022; B-8502-2016; HXO-8043-2023; LYD-9061-2024; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; DFQ-7859-2022; DFY-8508-2022; MWK-2416-2025; AAX-3485-2021; D-1300-2016; FNO-5530-2022; AAA-1489-2019; GNG-7078-2022; FNC-4379-2022; FLD-9518-2022; MWC-3186-2025; DVP-3997-2022; FNA-5485-2022; AAW-4410-2021; DWT-4779-2022; KSI-9422-2024; KNP-2716-2024; MNN-0179-2025; HXJ-7641-2023; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; DMG-4306-2022; FVO-0175-2022; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; OWQ-1639-2025; PIF-6131-2026; K-4114-2015; OON-3882-2025; DNW-6364-2022; DNY-1328-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; KCY-7756-2024; DPD-7597-2022; IZJ-2041-2023; GBV-4959-2022; DZW-2293-2022; GWX-9207-2022; FZJ-5145-2022; NGE-0152-2025; EAA-4768-2022; L-8068-2014; MQB-6975-2025; DZM-7523-2022; GDK-6495-2022; T-7378-2018; KLN-4310-2024; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; EFN-6430-2022; AAC-2083-2020; NRT-6530-2025; MTT-8732-2025; OZU-7134-2025; EAZ-0566-2022; O-9396-2015; AAO-6325-2021; DTO-7937-2022; FVK-3262-2022; DYK-4428-2022; JCM-8241-2023; FQI-9285-2022; JHF-8266-2023; NBS-6714-2025; 35494536400; 7003267532; 10042256200; 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36657273100; 8527480900; 57203599140; 9270789600; 14008117700; 9337191600; 16028244600; 6603519641; 10240830400; 7102513091; 7202555066; 7006022077; 16024707000; 6603380199; 59834471800; 36663730200; 55539553700; 35425530800; 55885669700; 35227493200; 37123976000; 8899520500; 6506922416; 55578049300; 7003478641; 56216916000; 7102120605; 6603602446; 7005760971; 6603623670; 6603205767; 6603770482; 56403356600; 36195926600; 14058603600; 10239419900; 14025617800; 6506385309; 36933808800; 55779479900; 13407562800; 7102174334; 56229506700; 8058520300; 14056466700; 7102846243; 55665939900; 7005525798; 14832846900; 7004208543; 35299820900; 7004629002; 55668172300; 8764087600; 7006833728; 14050522100; 58095754900; 57203234808; 55913343900; 57544565000; 7003604949; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 7004336793; 55337191500; 7402364894; 57225899623; 35350466600; 57190443165; 8915699600; 6603819159; 7004160690; 15129157800; 9533992100; 57218941481; 14063887300; 54797318800; 6701484532; 55845420026; 6602208520; 6602315420; 14823864100; 57220082325; 55414427600; 35314080800; 58937209900; 17436196900; 57194416363; 57191960842; 6701439004; 6602565951; 57191419742; 59019204600; 56286395400; 10244106400; 7101771030; 7102717554; 8042894900; 9333441800; 7004185737; 6603819488; 56512377200; 7003963996; 55435714500; 7102775303; 6506425834; 7404952697
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202450799
We make use of the unprecedented depth, spatial resolution, and field of view of the Euclid Early Release Observations (EROs) of the Perseus galaxy cluster to detect and characterise the dwarf galaxy population in this massive system. Using a dedicated annotation tool, the Euclid high-resolution VIS and combined VIS+Near Infrared Spectrometer and Photometer (NISP) colour images were visually inspected and dwarf galaxy candidates were identified. Their morphologies, the presence of nuclei, and their globular cluster (GC) richness were visually assessed richness were visually assessed, complementing an automatic detection of the GC candidates. Structural and photometric parameters, including Euclid filter colours, were extracted from two-dimensional fitting. Based on this analysis, a total of 1100 dwarf candidates were found across the image; 606 of these appear to be new identifications. The majority (96%) are classified as dwarf ellipticals, 53% are nucleated, 26% are GC-rich, and 6% show disturbed morphologies. A relatively high fraction of galaxies, 8%, are categorised as ultra diffuse galaxies. The majority of the dwarfs follow the expected scaling relations of galaxies. Globally, the GC specific frequency, SN, of the Perseus dwarf candidates is intermediate between those measured in the Virgo and Coma clusters. While the dwarf candidates with the largest GC counts are found throughout the Euclid field of view, the dwarfs located around the east–west strip, where most of the brightest cluster members are found, exhibit higher SN values on average. The spatial distribution of the dwarfs, GCs, and intracluster light show a main iso-density and isophotal centre displaced to the west of the bright galaxy light distribution. The ERO imaging of the Perseus cluster demonstrates the unique capability of Euclid to concurrently detect and characterise large samples of dwarf galaxies, their nuclei, and their GC systems, allowing us to construct a detailed picture of the formation and evolution of galaxies over a wide range of mass scales and environments.
Volume: 697
Keywords: galaxies: clusters: general; galaxies: clusters: individual: Abell 426; galaxies: dwarf; galaxies: fundamental parameters; galaxies: nuclei; galaxies: star clusters: general;
Euclid: Early Release Observations – Programme overview and pipeline for compact- and diffuse-emission photometry
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; DWB-0787-2022; AAU-1020-2020; O-9495-2015; H-2913-2012; FZO-2634-2022; CUZ-3547-2022; DXA-1243-2022; DVI-4148-2022; GDD-0521-2022; FGC-9351-2022; FKB-6410-2022; GEH-7593-2022; GBG-9412-2022; DYF-3433-2022; ABB-9156-2021; EJM-8740-2022; GBC-8404-2022; GAQ-2193-2022; FZP-9853-2022; HPI-3723-2023; PGU-9738-2026; GCA-0669-2022; C-1439-2017; DUU-1945-2022; MKJ-9958-2025; HTB-3700-2023; K-1263-2013; MCF-4404-2025; HIK-2775-2022; CJO-3675-2022; K-9464-2019; ABB-2950-2020; DTQ-3078-2022; FYQ-3698-2022; CLX-1205-2022; H-8587-2015; DWH-0490-2022; MOZ-1236-2025; U-6390-2019; I-2511-2015; AFS-1680-2022; HKB-2933-2023; IDQ-0489-2023; DWO-2405-2022; JLC-7108-2023; LNV-3966-2024; EVW-7270-2022; ABE-1497-2020; GMD-3106-2022; DWU-8294-2022; GWP-3456-2022; GQH-6424-2022; DTZ-1231-2022; FZX-7187-2022; QGM-4004-2026; JCO-0131-2023; PMS-5356-2026; FZK-6500-2022; DAV-9216-2022; FDX-5194-2022; MPG-4199-2025; GFK-2340-2022; DYO-3408-2022; B-1966-2015; NLL-4796-2025; Z-4828-2019; A-7919-2015; NKY-6883-2025; GCT-0363-2022; GCT-4656-2022; MSI-3188-2025; JHF-8266-2023; GBI-4899-2022; DVE-7652-2022; KBO-4868-2024; ISA-3008-2023; HVT-6155-2023; MWC-3186-2025; FMN-9310-2022; JGB-4169-2023; FMT-8159-2022; MDH-6494-2025; AAH-3277-2019; FQI-9285-2022; PCC-0635-2025; IRR-1266-2023; IFW-1485-2023; LXV-7382-2024; AFN-4775-2022; C-6230-2011; B-9633-2012; KPF-2019-2024; MQV-6996-2025; FSO-8783-2022; GFH-0803-2022; OON-3882-2025; DWT-7233-2022; AAV-1857-2021; IHL-1858-2023; DQP-5038-2022; MDQ-9712-2025; GFN-2673-2022; AAN-1908-2021; I-6648-2019; GBG-8291-2022; DZW-2642-2022; GDK-6495-2022; JEZ-2766-2023; H-4394-2019; GNE-1283-2022; AAK-8191-2020; GDB-0545-2022; DXW-9679-2022; NKW-7334-2025; DYZ-0102-2022; LPM-0745-2024; FYJ-9637-2022; DUY-3094-2022; CEY-5520-2022; C-4378-2014; EKA-7986-2022; IUT-7926-2023; LXX-3952-2024; GBF-1843-2022; EKV-4052-2022; L-8385-2017; HPR-3987-2023; OBC-0525-2025; LUN-9319-2024; HWT-5982-2023; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; E-8021-2017; IVA-4275-2023; B-4650-2017; GBS-0220-2022; B-4348-2013; DVC-6323-2022; NHE-3385-2025; PGG-2427-2026; AGZ-3259-2022; FZL-7353-2022; PCU-7129-2025; A-2693-2010; PCA-2324-2025; HLX-2021-2023; PWI-2374-2026; ETL-7525-2022; EUK-3820-2022; J-3686-2012; COQ-7299-2022; CPC-6980-2022; AAR-6622-2021; CQF-5798-2022; I-5515-2016; KLD-3528-2024; DXA-1952-2022; HPT-5858-2023; GBB-1832-2022; DWB-6758-2022; CQR-5759-2022; DWK-1716-2022; JGR-4365-2023; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; EYM-5386-2022; HRW-8595-2023; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; DWQ-9372-2022; AAT-5867-2020; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; FGD-1080-2022; CYT-5449-2022; AAF-6025-2021; OOP-8239-2025; DUU-4676-2022; B-8502-2016; HXO-8043-2023; FIO-1016-2022; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; DFQ-7859-2022; DFY-8508-2022; MWK-2416-2025; AAX-3485-2021; D-1300-2016; FNO-5530-2022; AAA-1489-2019; GNG-7078-2022; FLD-9518-2022; FNA-5485-2022; DYJ-3666-2022; KJY-7272-2024; DWT-4779-2022; KSI-9422-2024; KNP-2716-2024; MNN-0179-2025; FNB-0821-2022; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; GCU-3410-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; DMG-4306-2022; FVO-0175-2022; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; OWQ-1639-2025; PIF-6131-2026; K-4114-2015; DNW-6364-2022; DNY-1328-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; H-1761-2016; DPD-7597-2022; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; JCM-8241-2023; FZJ-5145-2022; NGE-0152-2025; NES-1075-2025; EAA-4768-2022; L-8068-2014; MQB-6975-2025; DZM-7523-2022; T-7378-2018; KLN-4310-2024; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; FCD-8153-2022; NLB-2929-2025; MTT-8732-2025; OZU-7134-2025; EAZ-0566-2022; O-9396-2015; DTO-7937-2022; LYP-7992-2024; FVK-3262-2022; NKX-2133-2025; 7003267532; 7005656061; 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36657273100; 8527480900; 57203599140; 9270789600; 14008117700; 9337191600; 6603519641; 10240830400; 7102513091; 7202555066; 7006022077; 16024707000; 6603380199; 36663730200; 55539553700; 35425530800; 55885669700; 35227493200; 37123976000; 6701865592; 8899520500; 55578049300; 7003478641; 56216916000; 7102120605; 6603602446; 7005760971; 6602251719; 6603205767; 6603770482; 56403356600; 36195926600; 14058603600; 10239419900; 14025617800; 6506385309; 36933808800; 55779479900; 13407562800; 7102174334; 56229506700; 8058520300; 14056466700; 55665939900; 7004208543; 7201701805; 35299820900; 7004629002; 55668172300; 8764087600; 7006833728; 14050522100; 58095754900; 57203234808; 55913343900; 55596380000; 57544565000; 7003604949; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 7004336793; 55337191500; 57225899623; 35350466600; 57190443165; 8915699600; 6603819159; 7004160690; 15129157800; 57218941481; 13407890400; 14063887300; 54797318800; 55845420026; 55435714500; 6602208520; 6602315420; 35100980600; 14823864100; 57220082325; 55414427600; 35314080800; 17436196900; 57194416363; 57191960842; 6701439004; 6602565951; 57191419742; 59019204600; 10244106400; 7101771030; 7102717554; 8042894900; 9333441800; 6603819488; 58848684700; 56512377200; 7404952697
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202450803
The Euclid Early Release Observations (ERO) showcase Euclid’s capabilities in advance of its main mission by targeting 17 astronomical objects, including galaxy clusters, nearby galaxies, globular clusters, and star-forming regions. A total of 24 hours of observing time was allocated in the early months of operation, and the scientific community was engaged through an early public data release. We describe the development of the ERO pipeline to create visually compelling images while simultaneously meeting the scientific demands within months of launch by leveraging a pragmatic data-driven development strategy. The pipeline’s key requirements are to preserve the image quality and to provide flux calibration and photometry for compact and extended sources. The pipeline’s five pillars are removal of instrumental signatures, astrometric calibration, photometric calibration, image stacking, and the production of science-ready catalogues for both the VIS and NISP instruments. We report a point spread function (PSF) with a full width at half maximum of 000 . 16 in the optical IE-band and 000 . 49 in the near-infrared (NIR) bands YE, JE, and HE. Our VIS mean absolute flux calibration is accurate to about 1%, and the accuracy is 10% for NISP due to a limited calibration set; both instruments have considerable colour terms for individual sources. The median depth is 25.3 and 23.2 AB mag with a signal-to-noise ratio (S/N) of ten for galaxies, while it is 27.1 and 24.5 AB mag at an S/N of five for point sources for VIS and NISP, respectively. Euclid’s ability to observe diffuse emission is exceptional due to its extended PSF nearly matching a pure diffraction halo, the best ever achieved by a wide-field high-resolution imaging telescope. Euclid offers unparalleled capabilities for exploring the low-surface brightness (LSB) Universe across all scales, providing high precision within a wide field of view (FoV), and opening a new observational window in the NIR. Median surface-brightness levels of 29.5 and 27.9, AB mag arcsec-2 are achieved for VIS and NISP, respectively, for detecting a 1000 × 1000 extended feature at the 1 σ level.
Volume: 697
Keywords: astrometry; catalogs; space vehicles: instruments; techniques: image processing; techniques: photometric;
Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; JNB-1152-2023; MYS-2354-2025; EFH-6710-2022; OMN-3792-2025; GAV-3687-2022; FYJ-4908-2022; FHL-5547-2022; MMS-5823-2025; JBR-8488-2023; JMS-1539-2023; MXB-2135-2025; CGD-2351-2022; JAX-2768-2023; CKU-5761-2022; H-4394-2019; DXZ-7810-2022; LEQ-1557-2024; DWU-8294-2022; D-1237-2017; EPI-1133-2022; AGZ-3259-2022; LHK-9354-2024; DVB-8405-2022; AFJ-2074-2022; MDG-9557-2025; GMD-3106-2022; ILM-3517-2023; DWQ-9372-2022; MYW-7907-2025; INU-5783-2023; DUZ-7625-2022; DFW-8877-2022; IFC-0531-2023; LXV-7382-2024; KJZ-3935-2024; FTV-5671-2022; DRO-1214-2022; FXH-0557-2022; IUA-7590-2023; DYG-8338-2022; HWB-5458-2023; MNN-3541-2025; EBD-7764-2022; MQB-8395-2025; HZW-5449-2023; DXA-1243-2022; GEH-7593-2022; GBG-9412-2022; DYF-3433-2022; HFV-0042-2022; FYH-4361-2022; FYH-7305-2022; MYL-2765-2025; DWY-3410-2022; DWN-4354-2022; CHZ-5646-2022; L-2472-2017; EQY-9285-2022; AAD-3011-2021; H-2913-2012; DAV-8065-2022; GBC-8404-2022; FZO-1254-2022; FYJ-9637-2022; CEY-5520-2022; C-4378-2014; EKA-7986-2022; IUT-7926-2023; IUQ-9509-2023; DVB-2560-2022; L-8385-2017; JWI-9457-2024; M-2616-2015; CJD-7824-2022; HWT-5982-2023; GBO-0318-2022; E-2727-2014; L-8237-2014; IVA-4275-2023; EOV-3838-2022; B-4650-2017; HZM-8546-2023; HTG-8587-2023; H-8587-2015; B-4348-2013; DVC-6323-2022; HQF-6437-2023; FZL-7353-2022; A-2693-2010; EVT-3533-2022; CNE-2384-2022; HLX-2021-2023; HKB-2933-2023; EUO-2530-2022; EUK-3820-2022; J-3686-2012; CPC-6980-2022; AAR-6622-2021; IBV-9243-2023; CQF-5798-2022; DXA-1952-2022; HPT-5858-2023; GBB-1832-2022; OVM-5938-2025; GQH-6424-2022; ABF-7029-2021; DWK-1716-2022; JGR-4365-2023; CTE-6775-2022; CSK-3817-2022; FBF-5584-2022; FBE-0351-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; EYY-4006-2022; FBM-0217-2022; GZL-0460-2022; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; CYT-5449-2022; FEG-4298-2022; DUU-4676-2022; B-8502-2016; LYD-9061-2024; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; MTO-5925-2025; DWZ-6747-2022; DFQ-7859-2022; AAH-9937-2020; V-6916-2017; MWK-2416-2025; AAX-3485-2021; D-1300-2016; GNG-7078-2022; FNC-4379-2022; DFC-8070-2022; FLD-9518-2022; DVP-3997-2022; KEK-6332-2024; KJY-7272-2024; DWT-4779-2022; KNP-2716-2024; DJO-8166-2022; MNN-0179-2025; KFB-7397-2024; ABB-2322-2020; DKF-4281-2022; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; MTQ-2344-2025; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; K-4114-2015; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; MXB-9468-2025; GCT-2940-2022; DXO-8435-2022; FXS-9180-2022; FXG-6905-2022; H-1761-2016; DXM-5348-2022; GFJ-2734-2022; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; OWM-0849-2025; KKE-9686-2024; NES-1075-2025; EAA-4768-2022; LGB-5701-2024; L-8068-2014; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; DYT-7473-2022; HNI-8187-2023; GCA-5567-2022; FCD-8153-2022; JCG-3503-2023; NBS-7222-2025; ABD-6783-2021; EAZ-0566-2022; O-9396-2015; DTO-7937-2022; LWL-2178-2024; FQI-9285-2022; IWG-9653-2023; FVK-3262-2022; DYK-4428-2022; MXA-3751-2025; 58666622600; 58419113700; 57535859700; 58544914400; 59137911400; 57190942170; 57205730219; 57958980000; 57207846185; 7003485288; 57191290632; 58918946600; 57210265918; 55731742600; 55929371000; 58911394800; 8856476200; 7801607411; 7101983827; 6602458029; 59636105400; 59317606600; 55862177400; 55668778200; 59317261300; 57322480400; 57473067700; 55539553700; 57188806951; 36126412600; 57193489486; 7005525798; 56949991000; 6603351766; 57199319204; 55932248600; 22836264500; 57211567711; 13204971700; 12786945200; 7005244190; 25951796800; 7401938216; 55822387500; 6602409206; 57192921002; 57204700965; 8833942000; 57206423651; 56153006200; 57221950386; 57213830231; 7004666481; 57193558463; 6701685211; 7006398476; 55976971800; 35241782000; 57218513471; 10738797800; 7003825248; 6701409861; 14629998500; 56176939800; 57220414927; 24482926400; 6505819655; 57225389323; 35421870300; 6602293713; 6701447926; 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14050522100; 56118600700; 58095754900; 57203234808; 55913343900; 57544565000; 7003604949; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 55337191500; 7402364894; 57225899623; 57190443165; 8915699600; 6603819159; 7004160690; 57219119015; 56881732300; 15129157800; 48663031800; 57218941481; 13407890400; 14063887300; 54797318800; 55845420026; 6602208520; 6602315420; 35100980600; 14823864100; 58502049600; 57220082325; 58937209900; 17436196900; 57203391123; 57191960842; 6701439004; 6602565951; 57191419742; 14832839700; 56286395400; 59730206000; 7101771030; 8042894900; 9333441800; 6603819488; 57198031424; 7102775303; 57220131178; 56512377200; 7003963996; 55541304900
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202453152
The Euclid Wide Survey (EWS) is predicted to find approximately 170 000 galaxy-galaxy strong lenses from its lifetime observation of 14 000 deg2 of the sky. Detecting this many lenses by visual inspection with professional astronomers and citizen scientists alone is infeasible. As a result, machine learning algorithms, particularly convolutional neural networks (CNNs), have been used as an automated method of detecting strong lenses, and have proven fruitful in finding galaxy-galaxy strong lens candidates, such that the usage of CNNs in lens identification has increased. We identify the major challenge to be the automatic detection of galaxy-galaxy strong lenses while simultaneously maintaining a low false positive rate, thus producing a pure and complete sample of strong lens candidates from Euclid with a limited need for visual inspection. One aim of this research is to have a quantified starting point on the achieved purity and completeness with our current version of CNN-based detection pipelines for the VIS images of EWS. This work is vital in preparing our CNN-based detection pipelines to be able to produce a pure sample of the >100 000 strong gravitational lensing systems widely predicted for Euclid. We select all sources with VIS IE < 23 mag from the Euclid Early Release Observation imaging of the Perseus field. We apply a range of CNN architectures to detect strong lenses in these cutouts. All our networks perform extremely well on simulated data sets and their respective validation sets. However, when applied to real Euclid imaging, the highest lens purity is just ∼11%. Among all our networks, the false positives are typically identifiable by human volunteers as, for example, spiral galaxies, multiple sources, and artifacts, implying that improvements are still possible, perhaps via a second, more interpretable lens selection filtering stage. There is currently no alternative to human classification of CNN-selected lens candidates. Given the expected ∼105 lensing systems in Euclid, this implies 106 objects for human classification, which while very large is not in principle intractable and not without precedent.
Volume: 696
Keywords: dark matter; large-scale structure of Universe; methods: data analysis; surveys;
Euclid: Early Release Observations – Interplay between dwarf galaxies and their globular clusters in the Perseus galaxy cluster☆
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; GEH-7593-2022; GDD-0521-2022; FZP-9871-2022; DWB-0787-2022; DXH-1132-2022; OWP-8743-2025; DWO-2405-2022; ABE-1497-2020; FZK-6500-2022; OFI-3304-2025; GFK-2340-2022; DVE-7652-2022; MYY-8327-2025; NWP-1675-2025; H-4394-2019; MFP-6153-2025; GDB-0545-2022; HZW-5449-2023; H-2913-2012; NYV-4209-2025; MWG-8499-2025; NAA-8765-2025; DWK-1716-2022; Z-4828-2019; DBY-6056-2022; MDH-6494-2025; KPF-2019-2024; EJM-8740-2022; OWJ-2849-2025; MFN-2097-2025; CDR-2303-2022; CEY-5520-2022; ENZ-8382-2022; NIH-4106-2025; EKA-7986-2022; MPK-2619-2025; LXX-3952-2024; IUQ-9509-2023; AAO-6325-2021; EMQ-6210-2022; OXU-5068-2025; EOE-6462-2022; ENY-9969-2022; HTU-7350-2023; OTR-9915-2025; PFB-7368-2025; GBO-0318-2022; JTJ-0113-2023; JGB-4169-2023; OYX-1138-2025; IVA-4275-2023; OWA-5096-2025; HZM-8546-2023; HOH-0341-2023; IRQ-6937-2023; HTG-8587-2023; H-8587-2015; B-4348-2013; MLE-5410-2025; OUB-9437-2025; OUQ-7327-2025; AGZ-3259-2022; NKT-5952-2025; PGO-8625-2026; PCA-2324-2025; CNE-2384-2022; IDQ-0489-2023; GBB-1963-2022; EUO-2530-2022; LZM-1403-2025; EUK-3820-2022; EVA-7948-2022; LQC-6518-2024; OXU-8438-2025; ETK-1170-2022; IBV-9243-2023; CQF-5798-2022; CQN-5681-2022; OWH-3656-2025; HPT-5858-2023; JWF-2506-2024; OWJ-3544-2025; PDR-5897-2025; CQR-5759-2022; HTH-2171-2023; CTE-6775-2022; CSK-3817-2022; FBF-5584-2022; EYM-5386-2022; ORP-2127-2025; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; DWQ-9372-2022; AAT-5867-2020; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; CYT-5449-2022; OOP-8239-2025; MKH-2584-2025; B-8502-2016; HXO-8043-2023; DAV-8065-2022; Q-5758-2017; LYD-9061-2024; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; HLM-5105-2023; OYW-4457-2025; DEG-2174-2022; U-7309-2018; MWK-2416-2025; AAX-3485-2021; JBX-4604-2023; FNO-5530-2022; GNG-7078-2022; FNC-4379-2022; FLD-9518-2022; OUB-8222-2025; DVP-3997-2022; FNA-5485-2022; FQI-9285-2022; KJY-7272-2024; DWT-4779-2022; KNP-2716-2024; DJO-8166-2022; FNB-0821-2022; OVR-3480-2025; HTJ-4919-2023; MAJ-2831-2025; DLB-6897-2022; HTM-1531-2023; OWI-2937-2025; DMG-4306-2022; IVG-7504-2023; FSY-2184-2022; DMX-5934-2022; DNQ-7220-2022; DNY-0415-2022; OWQ-1639-2025; IHD-3727-2023; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; MXB-9468-2025; DXO-8435-2022; JVJ-6571-2024; FXG-6905-2022; HFW-5845-2022; MWY-9362-2025; DRA-2090-2022; NKY-6871-2025; OWD-1829-2025; NIO-4355-2025; DZW-2293-2022; HPZ-9398-2023; FZJ-5145-2022; KKE-9686-2024; EAA-4768-2022; LGB-5701-2024; L-8068-2014; MQB-6975-2025; GDK-6495-2022; JEZ-2766-2023; KJV-8369-2024; AAB-2503-2019; KLN-4310-2024; GCB-5227-2022; DYT-7473-2022; OAE-4195-2025; A-9058-2016; MRJ-8187-2025; NDK-0367-2025; JCG-3503-2023; PGR-2221-2026; EEM-5070-2022; EAZ-0566-2022; EFP-5424-2022; NYS-2102-2025; DTO-7937-2022; JHF-8266-2023; FVK-3262-2022
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202554667
Volume: 703
For what algebraic systems does a useful privacy homomorphism exist?
Authors: Grazian Valentina; Tortora Antonio; Tota Maria
Journal: AIMS MATHEMATICS
Published: 2025
DOI: 10.3934/math.2025440
Homomorphic encryption plays a crucial role in the challenging problem of privacy preservation. In this survey, we describe a number of homomorphic schemes providing the relevant definitions to make the topic accessible to both cryptographers and mathematicians. We classify the schemes according to the timeline of appearance and, for some of them, we verify that they are correct with respect to decryption and evaluation, providing proofs or references. Recent research directions are also briefly discussed in this context.
Volume: 10 Pages: 9539-9562
Keywords: cloud computing; fully homomorphic encryption; privacy preservation;
Euclid preparation LXXIII. Spatially resolved stellar populations of local galaxies with Euclid: A proof of concept using synthetic images with the TNG50 simulation
Authors: AREA MIN. 02 - Scienze fisiche; Non assegn; AREA MIN. 14 - Scienze politiche e sociali; AREA MIN. 01 - Scienze matematiche e informatiche; AREA MIN. 09 - Ingegneria industriale e dell'informazione; AREA MIN. 06 - Scienze mediche; ASTRONOMY & ASTROPHYSICS###0004-6361; 57202215260; 55929371000; 56261663500; 57189593362; 58817124800; 6602409206; 6701673913; 56426999100; 55941325100; 7005183546; 6603186973; 7003910265; 57200793436; 7402054273; 55178467000; 55538241000; 56033190100; 7005317106; 56234721400; 35494536400; 7006440295; 57222380960; 6603939854; 15754453800; 57193523315; 57204700965; 7004408758; 60098455000; 35957375500; 57203047758; 57103783400; 6701409861; 56176939800; 57220414927; 24482926400; 57225389323; 7004185737; 55600275300; 6602293713; 6701447926; 7004168457; 7004279376; 55543112300; 35117442400; 57219376526; 56592859600; 8316050500; 57193414472; 57090221700; 24439181000; 55948641800; 54924573500; 7007018277; 56260193000; 55543336500; 37121732700; 6507398813; 55757270100; 8856476200; 59636105400; 7004529134; 26663174300; 6603213706; 6602521535; 7006071419; 57212263363; 6701458135; 6506323808; 6601991850; 6602678698; 6602348000; 56181792800; 57202592808; 57200514857; 24587025200; 9639653200; 56239931500; 36627225700; 6506341877; 56592156500; 24173378000; 14630273900; 36657273100; 8527480900; 57203599140; 12809267200; 9270789600; 14008117700; 7202555066; 16024707000; 6603380199; 36663730200; 55539553700; 35425530800; 55885669700; 35227493200; 37123976000; 6701865592; 55578049300; 56216916000; 7102120605; 6603602446; 56149076900; 6603205767; 6603770482; 56403356600; 36195926600; 35070066100; 14058603600; 10239419900; 14025617800; 6506385309; 36933808800; 56463558800; 55779479900; 13407562800; 7102174334; 14056466700; 7102846243; 36542679900; 55665939900; 7005525798; 14832846900; 7004208543; 7102775303; 35299820900; 7004629002; 14050522100; 56118600700; 58095754900; 57203234808; 55913343900; 57544565000; 57203250534; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 55337191500; 7402364894; 57225899623; 57190443165; 8915699600; 6603819159; 7004160690; 57219119015; 15129157800; 48663031800; 8833942000; 57218941481; 13407890400; 14063887300; 6506381727; 54797318800; 55845420026; 55435714500; 6602208520; 6602315420; 14823864100; 58502049600; 57220082325; 55414427600; 58937209900; 7004109829; 17436196900; 57203391123; 57191960842; 6602565951; 6506892358; 57191419742; 6603196124; 56286395400; 10244106400; 7101771030; 8042894900; 9333441800; 36905906400; 6603819488; 57198031424; 9244606800; 58696967900; 57845873200; 26326923900; 57220131178; 57203270249; 56512377200; 35194662000; 7003963996; 57203063840; 35387346400; 46461103400; 42260895600; 36730729100; 6701547091; 56818885600; 23027139300; 14820320500; 59421337500; 7006764136; 57205381665; 57214989073; 6602458029; 57193874792; 9337037600; 57211860571; 7103030457; 56653598400; 58621464800; 9335763100; 35112881300; 34569356300; 57199061795; 55976971800; 24074399500; 58112082700; 57218304683; 7003900144; 55420010100; 25723173900; 7004144883; 7003645652; 57193849410; 55944081300; 56242244500; 7005222927; 15131601400; 14630220200; 58030558500; 7202545187; 36674792500; 55505778800; 8833942900; 55741929700; 23050749700; 57224183772; 36966126400; 59774401700; 57218097629; 56153170500; 26642611400; 57193558463; 27169527700; 57213569252; 57892676500; 57213763435; 57219756241; 6701851021; 57218549969; 57210924350; 6508080858; 6701309093; 6701685211; 6603292899; 6506425834; 22951241500; 56383649900; 7101983827; 55246080700; 7006221760; 57218766355; 35422761600; 57189089616; 35463408300; 57192212259; 7003762062; 56285291900; 7101903552; 16031797900; 57222902516; 55672552800; 10642144300; 57207198854; 57201003368; 58696967800; 55965473600; 7005050491; 57211858405; 7102146471; 6603851717; 6701718244; 56448179900; 57132747000; 58483607200; 6504758580; 14632583100; 57215412075; 55195649400
Journal: 26750
Published: 2025
DOI: 10.1051/0004-6361/202554516
The European Space Agency’s Euclid mission will observe approximately 14 000 deg2 of the extragalactic sky and deliver high-quality imaging of a large number of galaxies. The depth and high spatial resolution of the data will enable a detailed analysis of the stellar population properties of local galaxies through spatially resolved spectral energy distribution (SED) fitting. In this study, we test our pipeline for spatially resolved SED fitting using synthetic images of Euclid, LSST, and GALEX generated from the TNG50 simulation using the SKIRT 3D radiative transfer code. Our pipeline uses functionalities in piXedfit for processing the simulated data cubes and carrying out SED fitting. We apply our pipeline to 25 simulated galaxies at z ∼ 0 to recover their resolved stellar population properties. For each galaxy, we produce three types of data cubes: GALEX + LSST + Euclid, LSST + Euclid, and Euclid-only. We performed the SED fitting tests with two stellar population synthesis (SPS) models in a Bayesian framework. Because the age, metallicity (Z), and dust attenuation estimates are biased when applying only classical formulations of flat priors (even with the combined GALEX + LSST + Euclid data), we examined the effects of additional physically motivated priors in the forms of mass-age and mass-metallicity relations, constructed using a combination of empirical and simulated data. Stellar-mass surface densities can be recovered well using any of the three data cubes, regardless of the SPS model and prior variations. The new priors then significantly improve the measurements of mass-weighted age and Z compared to results obtained without priors, but they may play an excessive role compared to the data in determining the outcome when no ultraviolet (UV) data is available. Compared to varying the spectral extent of the data cube or including and discarding the additional priors, replacing one SPS model family with the other has little effect on the results. The spatially resolved SED fitting method is powerful for mapping the stellar population properties of many galaxies with the current abundance of high-quality imaging data. Our study re-emphasizes the gain added by including multi-wavelength data from ancillary surveys and the roles of priors in Bayesian SED fitting. With the Euclid data alone, we will be able to generate complete and deep stellar mass maps of galaxies in the local Universe (z . 0.1), exploiting the telescope’s wide field, near-infrared sensitivity, and high spatial resolution.
Volume: 702
Keywords: galaxies: evolution; galaxies: formation; galaxies: fundamental parameters; galaxies: stellar content; galaxies: structure;