IAS-LAB PUBLICATIONS
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;
Euclid I. Overview of the Euclid mission
Authors: AREA MIN. 02 - Scienze fisiche; Non assegn; ASTRONOMY & ASTROPHYSICS###0004-6361; FLD-9518-2022; ABB-9156-2021; CGD-2351-2022; EJF-3121-2022; HQD-3085-2023; EJJ-2811-2022; EJI-6443-2022; EJM-8740-2022; JTL-4413-2023; JMF-0642-2023; CEY-5704-2022; EJD-4812-2022; CBW-9165-2022; GMB-3707-2022; NRR-3240-2025; EJV-2058-2022; KLG-9557-2024; CCW-8236-2022; JHB-9875-2023; JZK-7057-2024; EKN-8524-2022; M-4834-2013; HPI-3910-2023; FZO-1254-2022; CBM-5148-2022; ELN-8298-2022; FYJ-4908-2022; JNB-8974-2023; EKO-8371-2022; CDD-9382-2022; HSM-2226-2023; GXI-6108-2022; HTG-7108-2023; MNS-3973-2025; NLD-1441-2025; MBW-8974-2025; MWW-2922-2025; ELE-7094-2022; EMH-3888-2022; LUN-9784-2024; NRE-4878-2025; MJK-3407-2025; MKU-1930-2025; HRF-0089-2023; MYB-1698-2025; EJP-5690-2022; HSL-3945-2023; CDR-2303-2022; ENM-6579-2022; KKX-1153-2024; H-1374-2012; CCI-8559-2022; GPF-5137-2022; ICC-1978-2023; DVO-2403-2022; CEY-5520-2022; LXB-7798-2024; GBU-9674-2022; JYY-9592-2024; GAF-9076-2022; N-7472-2018; HPI-5715-2023; GMC-5713-2022; CET-2621-2022; CCQ-1808-2022; HZQ-9553-2023; CEU-2310-2022; EKA-7986-2022; IYE-9818-2023; EKS-5104-2022; CEX-0810-2022; CDP-1330-2022; ENB-4426-2022; IUT-7926-2023; NRF-8920-2025; CFX-9814-2022; MNO-9855-2025; ELT-8576-2022; GWF-4497-2022; LRW-4798-2024; NPR-6255-2025; LXX-3952-2024; IUQ-9509-2023; CEM-2577-2022; AAG-2324-2020; CGF-9764-2022; MNR-3415-2025; FZV-5603-2022; CDP-6815-2022; CEB-5555-2022; EML-3726-2022; EKL-5400-2022; HJF-3296-2022; HQQ-1196-2023; CGJ-4278-2022; GNJ-2760-2022; EKW-9241-2022; IBL-1815-2023; IBE-5307-2023; MJX-3374-2025; CFL-2667-2022; EQG-0473-2022; MOE-9658-2025; CGH-8811-2022; EOA-9903-2022; JHC-4470-2023; F-3305-2011; NRQ-9110-2025; JAL-4401-2023; MGX-9029-2025; ADL-2084-2022; HNI-9120-2023; MXO-2726-2025; CGQ-7607-2022; HIV-4758-2022; H-2445-2014; CHK-6722-2022; EKV-4052-2022; CFS-4854-2022; HQD-7277-2023; ENE-3351-2022; HZW-5449-2023; EMQ-6210-2022; DXP-4308-2022; CHO-3061-2022; CGE-2377-2022; DVF-6208-2022; EOE-6462-2022; 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FBI-2817-2022; MQG-2678-2025; EZH-1360-2022; FCZ-2066-2022; NRQ-4915-2025; NRR-9928-2025; IUR-5258-2023; GBK-2999-2022; HST-6093-2023; FBI-9285-2022; CTE-6491-2022; NIY-2007-2025; CSS-0465-2022; FZK-6500-2022; FBF-5584-2022; JMX-5918-2023; GTG-7507-2022; HQJ-2322-2023; HTH-8470-2023; GFC-5625-2022; JOL-7661-2023; KDQ-3614-2024; CTA-9883-2022; EXX-4126-2022; CTK-3057-2022; CTP-7970-2022; KLP-3493-2024; KGG-1931-2024; JDI-7888-2023; KNK-3731-2024; EYM-5386-2022; HRW-8595-2023; CWB-4290-2022; EXF-2880-2022; FBE-0351-2022; NBT-5940-2025; EZW-5630-2022; CTZ-4163-2022; GBH-2365-2022; NRR-9298-2025; NRK-6634-2025; EYY-4006-2022; ILN-9494-2023; KRH-1914-2024; FBH-9855-2022; JQE-0542-2023; MHS-6760-2025; JEG-8812-2023; LYQ-5466-2024; MNW-1319-2025; GRD-7772-2022; CVM-9293-2022; AAK-4578-2020; HST-5130-2023; NMQ-1781-2025; LYS-9921-2024; CUW-7080-2022; FBX-9828-2022; MTL-9359-2025; MDJ-5344-2025; CWO-3287-2022; ILS-2135-2023; HSK-3386-2023; HPW-2820-2023; CWY-0165-2022; MPD-8237-2025; JML-9573-2023; JKX-6494-2023; B-1966-2015; JYM-2246-2024; FBM-0217-2022; D-5933-2011; NIQ-3499-2025; JWD-0263-2024; FCV-2900-2022; MGT-4091-2025; JZW-8042-2024; HRR-2616-2023; NRS-7976-2025; NRN-3271-2025; JCW-4739-2023; JXC-9786-2024; CWS-7520-2022; CWV-4019-2022; CYQ-0685-2022; FDG-6756-2022; NDB-3420-2025; DYK-3119-2022; MNZ-8396-2025; CZF-1112-2022; FFG-2233-2022; LSJ-9812-2024; FCS-1018-2022; NRR-0017-2025; NRF-9045-2025; HRX-7202-2023; FGD-1080-2022; CYQ-3539-2022; DAZ-7704-2022; Z-4828-2019; HRM-1931-2023; A-7919-2015; DAV-9216-2022; MYL-2765-2025; HGN-2939-2022; CZQ-4310-2022; MXU-8300-2025; IUI-6764-2023; KJS-6941-2024; CZN-8033-2022; NQK-8653-2025; MTY-4531-2025; GMM-4439-2022; NKL-3434-2025; CYT-5449-2022; KNJ-5611-2024; AAF-6025-2021; GWA-7849-2022; KAK-4177-2024; JAI-5774-2023; DCG-5571-2022; FYU-3294-2022; G-9631-2012; FFY-6602-2022; FIF-2657-2022; JPY-8767-2023; NIN-1609-2025; FIK-3831-2022; J-8907-2012; MDB-8943-2025; JGX-3888-2023; DBQ-1340-2022; FIF-1822-2022; FGP-4216-2022; JGY-3165-2023; HSU-3428-2023; HPY-6920-2023; NEZ-3286-2025; HTZ-8133-2023; DCR-4002-2022; MEV-5100-2025; HWZ-9076-2023; FIK-7572-2022; N-2429-2017; JNC-9013-2023; FIV-8514-2022; Q-5758-2017; FFN-8027-2022; DVM-3899-2022; NRJ-9083-2025; GYG-7175-2022; HYU-3434-2023; DCT-5227-2022; FJF-4165-2022; MDM-2414-2025; LYD-9061-2024; NRF-6534-2025; NPX-1842-2025; DBO-1835-2022; FJT-8530-2022; AAV-9023-2021; DBI-3005-2022; MMS-5823-2025; NAU-8484-2025; GAV-5026-2022; LYK-3518-2024; DDB-6234-2022; HYH-6107-2023; A-2699-2012; NRG-6873-2025; KEL-4830-2024; FNN-4043-2022; MDN-2641-2025; LWS-5330-2024; NRS-6675-2025; NLE-8119-2025; FIV-3763-2022; A-2664-2019; FIQ-7669-2022; MHM-6337-2025; DEC-2913-2022; LBZ-7918-2024; DCP-5865-2022; HQS-3374-2023; FII-8063-2022; KCR-4488-2024; DDY-1012-2022; DDX-0583-2022; HFG-2597-2022; IMW-2405-2023; LFX-5249-2024; GQU-8893-2022; KND-8351-2024; DFA-0403-2022; DVI-9193-2022; MOV-6335-2025; IMM-0353-2023; MPQ-9451-2025; FLK-4707-2022; HLM-5105-2023; MTO-5925-2025; FJK-3463-2022; Q-4575-2017; JCQ-0002-2023; IES-5642-2023; MDZ-3436-2025; FHL-5547-2022; DWN-4354-2022; DWZ-6747-2022; NRP-0502-2025; KEZ-0532-2024; GDK-6541-2022; HUJ-7899-2023; GBI-4899-2022; MXA-3751-2025; DFQ-7859-2022; IVG-8235-2023; ITP-6249-2023; DFY-8508-2022; DVE-7652-2022; FMC-3868-2022; FJJ-3655-2022; JHF-8266-2023; GNV-2271-2022; JHX-6515-2023; MVK-6229-2025; MWK-2416-2025; NRF-5521-2025; MVA-1492-2025; HFG-7438-2022; KNZ-0005-2024; D-1300-2016; IUI-2505-2023; HPN-9567-2023; HXW-4678-2023; DGC-7489-2022; GBF-5775-2022; FJX-8996-2022; MKJ-0611-2025; DFZ-7309-2022; NRU-0154-2025; HVT-6155-2023; AAA-1489-2019; NBX-2664-2025; DGJ-6322-2022; MDD-7519-2025; GNG-7078-2022; KJS-1644-2024; FNC-4379-2022; DFC-8070-2022; DII-6557-2022; JMA-2509-2023; MXQ-8599-2025; HRS-2811-2023; MWC-3186-2025; HMK-3035-2023; DHS-8142-2022; D-1237-2017; KEK-6332-2024; MRN-9860-2025; IWF-0425-2023; NRQ-9955-2025; FMN-9310-2022; KNO-5341-2024; D-2437-2012; DXV-8969-2022; FMT-8159-2022; DHH-6443-2022; DYJ-3666-2022; HLL-5972-2023; HLL-6309-2023; GXE-4405-2022; FQI-9285-2022; DVX-6694-2022; KJY-7272-2024; JHX-6642-2023; GCF-0434-2022; FNU-9642-2022; A-4353-2011; DIC-9742-2022; DIQ-9971-2022; FOW-5617-2022; GFU-8637-2022; DJD-5051-2022; F-3052-2014; IRO-7983-2023; NRC-8224-2025; KSI-9422-2024; HSQ-1225-2023; NLM-4855-2025; IMH-2807-2023; FSF-3897-2022; ITX-8160-2023; MYS-2354-2025; HSY-8397-2023; LUL-9658-2024; DKR-3047-2022; HVM-5324-2023; DKO-3585-2022; MDA-3054-2025; NRG-1953-2025; DJO-8166-2022; FZX-4882-2022; JWR-5706-2024; NLF-5244-2025; HQV-1445-2023; MNN-0179-2025; HRN-5646-2023; NQK-3445-2025; FTW-0640-2022; DJN-3869-2022; KZF-8490-2024; FNW-0431-2022; MBT-8435-2025; FOS-7937-2022; LXV-7382-2024; JAO-7873-2023; ABA-3428-2020; DLM-9200-2022; MYA-0386-2025; IGV-6651-2023; AFN-4775-2022; FTY-8452-2022; GZD-9043-2022; IFK-3302-2023; DJX-2897-2022; HQS-3256-2023; GDL-7739-2022; DKG-0898-2022; HRH-6112-2023; GCM-7974-2022; DKF-4281-2022; DLU-8112-2022; HKO-2302-2023; DLP-9078-2022; IWG-2396-2023; HTJ-4919-2023; DLB-8458-2022; MQI-8881-2025; JHP-1576-2023; GSC-5225-2022; JVJ-0877-2024; IFM-0779-2023; IXM-0724-2023; HMZ-6395-2023; DKH-9248-2022; IHG-7220-2023; LZJ-8709-2025; DKA-5086-2022; HRS-4550-2023; IXU-1248-2023; MKP-3087-2025; DNA-3793-2022; GCU-3410-2022; IOF-6591-2023; DLB-6897-2022; DNL-3219-2022; FPR-3817-2022; AAA-6117-2020; HZS-7341-2023; AGE-0251-2022; HTM-1531-2023; EAA-9698-2022; DZO-2834-2022; KBT-5668-2024; KRL-0334-2024; MQC-5441-2025; DNN-3533-2022; DKX-5481-2022; DNY-7864-2022; IOY-7230-2023; HOK-2124-2023; DMR-6962-2022; MTQ-2344-2025; IFV-8400-2023; DXB-7503-2022; KNM-1316-2024; KSG-5747-2024; IVG-7504-2023; FTV-6637-2022; FSY-2184-2022; GGM-6223-2022; DNI-8823-2022; DMX-5934-2022; MVC-4382-2025; MRE-1110-2025; JCV-3612-2023; MST-2965-2025; MRW-3611-2025; KPF-2019-2024; NDK-0885-2025; HPO-8234-2023; IAH-4272-2023; FSG-6281-2022; DMI-3454-2022; FTR-6470-2022; LFC-7290-2024; DMD-3023-2022; MQV-6996-2025; FSO-8783-2022; KCW-5804-2024; MKU-3938-2025; IAF-7465-2023; MDF-9976-2025; LSP-5837-2024; DOA-5683-2022; DNY-0415-2022; DMI-7584-2022; IRP-1078-2023; FUH-9925-2022; NRP-1035-2025; HTE-6970-2023; KQW-8294-2024; GEF-7978-2022; CDP-4089-2022; DQB-9835-2022; JMR-9144-2023; GCQ-0851-2022; HQU-3434-2023; HRX-2781-2023; NRU-8946-2025; DMK-3227-2022; FSF-9689-2022; GCU-4080-2022; DNX-4243-2022; NRR-2948-2025; HEL-3409-2022; FSR-7582-2022; DNY-1328-2022; DWR-3357-2022; DNV-0835-2022; DWT-7233-2022; DPR-5415-2022; GZX-3937-2022; LJL-6604-2024; GCB-0930-2022; MNC-6953-2025; AAV-1857-2021; MVR-8365-2025; INQ-1213-2023; MXB-9468-2025; HPR-0960-2023; FVF-5606-2022; HTK-0092-2023; MDM-6969-2025; MZD-7939-2025; LYL-7929-2024; FVI-7225-2022; R-9864-2019; DPV-0687-2022; HVK-7100-2023; HUZ-7198-2023; IHL-1858-2023; CJV-1729-2022; NRH-6805-2025; IGN-7320-2023; MWU-5876-2025; GEH-7593-2022; JVJ-6571-2024; KKM-4015-2024; HWH-2286-2023; HIF-4249-2022; MUA-9482-2025; FSR-8046-2022; FXG-6905-2022; NVH-1944-2025; FVQ-4930-2022; HFW-5845-2022; MWU-0013-2025; HQL-1128-2023; DYT-3191-2022; NAO-9053-2025; CDE-5677-2022; FYD-1647-2022; LQG-0636-2024; FWJ-9813-2022; DOX-4367-2022; MDQ-9712-2025; DRO-1214-2022; IRI-1547-2023; AAY-7554-2020; DRA-2090-2022; GYJ-2164-2022; NDD-4439-2025; FSV-8899-2022; KLA-7316-2024; MSO-9043-2025; NRQ-6647-2025; NBF-7788-2025; NRJ-0532-2025; DPD-7597-2022; GEQ-9811-2022; JWG-7083-2024; HYZ-8827-2023; JLJ-3892-2023; JUW-8998-2023; NRU-6306-2025; KKU-1164-2024; NGW-4569-2025; NRN-5390-2025; FVK-3262-2022; IZJ-2041-2023; IZM-5556-2023; GBY-3944-2022; DZW-2293-2022; FZC-0117-2022; FVQ-3296-2022; DQK-6454-2022; INY-7970-2023; JMS-1539-2023; IHY-7449-2023; DQG-9931-2022; MUY-9401-2025; N-7309-2013; IOB-3689-2023; NKZ-4607-2025; KKA-8210-2024; FXL-6886-2022; MVM-9062-2025; GAI-0880-2022; HZG-0455-2023; HRO-4465-2023; KKE-9686-2024; IPT-3669-2023; DSO-0650-2022; IUS-5192-2023; FXZ-9990-2022; FXE-2183-2022; MLB-4828-2025; IQA-1300-2023; GBU-9294-2022; FYQ-2273-2022; GBG-8291-2022; MYI-9415-2025; HQO-2360-2023; LRA-7407-2024; JAN-6167-2023; HSW-9999-2023; DXK-9781-2022; EAA-4768-2022; EAO-6360-2022; IZV-0842-2023; EAH-7958-2022; LGB-5701-2024; HRT-8813-2023; DYF-3433-2022; GDI-9617-2022; DXB-1015-2022; LWK-9386-2024; KFZ-7504-2024; GDI-2600-2022; DYG-8338-2022; GFA-8250-2022; GEW-1581-2022; HSQ-8325-2023; AAZ-6431-2020; IUL-9103-2023; L-8068-2014; MLN-0990-2025; GJD-6077-2022; JBT-5511-2023; CFK-7257-2022; MQB-6975-2025; MFQ-3826-2025; EAF-6483-2022; GAO-4645-2022; NHN-5701-2025; LYZ-9663-2024; DZM-7523-2022; GDK-6495-2022; GCP-4138-2022; GMU-6430-2022; GDF-8239-2022; CCA-9555-2022; MDF-0079-2025; MYA-6012-2025; EBZ-1499-2022; CCD-8687-2022; GGL-1794-2022; NRJ-1728-2025; KJV-8369-2024; CDW-8542-2022; GIH-9607-2022; NRL-6707-2025; MVR-7884-2025; H-4394-2019; NCZ-5702-2025; MRP-4108-2025; EBM-0782-2022; KZQ-2739-2024; EBJ-4660-2022; CGP-8495-2022; IIK-6853-2023; KLN-4310-2024; JSZ-6163-2023; ECA-8225-2022; LGO-6654-2024; MXH-3619-2025; GNE-1283-2022; JMS-3616-2023; EBM-4748-2022; HFJ-5559-2022; KZP-0311-2024; DYT-7473-2022; HNI-8187-2023; ECF-2024-2022; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202450810
The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. Euclid is a medium-class mission in the Cosmic Vision 2015–2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14 000 deg2 of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.
Volume: 697
Keywords: cosmology: observations; instrumentation: detectors; instrumentation: spectrographs; space vehicles: instruments; surveys; telescopes;
ECMR 2025 Cover Page
Authors: Gasteratos Antonios; Bellotto Nicola; Tortora Stefano
Journal: 21101337815
Published: 2025
Ecofriendly Synthesis of Titanium Dioxide for Glyphosate Adsorption
Authors: Marconi Eleonora; Lopez Alberto; Bertela Federica; Meneghini Carlo; Battocchio Chiara; Gattia Daniele Mirabile; Vanga Giuseppina; Centomo Paolo; Zecca Marco; Rizzotto Valentina; Pettenuzzo Silvia; Roverso Marco; Bogialli Sara; Simon Ulrich; Chen Peirong; Tortora Luca; Bertelà Federica; Mirabile Gattia Daniele
Journal: EUROPEAN JOURNAL OF INORGANIC CHEMISTRY
Published: 2025
The widespread use of glyphosate for weed control in agricultural, silvicultural, and urban areas worldwide is causing concerns about its potential toxicity and mobility in environment. Consequently, there is a pressing need to develop treatment processes to mitigate the effects of glyphosate, its metabolites, and/or coadjutants on the environment. Titanium dioxide nanoparticles (TNPs), among the most widely used engineered NPs, have recently attracted significant attention for glyphosate adsorption. In this study, TNPs were obtained through two synthesis procedures working at low temperatures and with a reduced amount of organic solvent. The first involves a polycondensation reaction of alkoxide precursors at 70 °C in a water-in-oil (W/O) microemulsion with a 1 : 1 volume ratio, employing cetylpyridinium bromide (CPB) as a cationic surfactant to obtain crystalline TNPs. In the second method, two resins characterized by different morphologies were used as a template, to grow amorphous TNPs. Glyphosate adsorption tests performed at different pH values, i.e. 4.0 and 8.5, revealed that the analyte is more effectively retained by the TNPs at pH 4.0, at which TiO2 surfaces are positively charged and better interact with anionic glyphosate species. TNPs obtained via microemulsion using titanium alkoxide as the precursor and calcined at 450 °C showed excellent performance, removing from the solution nearly 99% of glyphosate, even at pH 8.5. Similarly, resin composites work efficiently in eliminating a high percentage of glyphosate, taking advantage of a better dispersion of amorphous TiO2 and a higher surface-to-volume ratio. This study demonstrates the effective capture of glyphosate by titanium dioxide nanoparticles, highlighting some factors contributing to the adsorption ability, and their potential as a unique tool for adsorbing glyphosate in a one-pot process.
Volume: 28
Keywords: adsorption; glyphosate; nanoparticle; polymerization; titanium dioxide;
Euclid: Early Release Observations – The surface brightness and colour profiles of the far outskirts of galaxies in the Perseus cluster
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; MDH-6494-2025; CFE-6365-2022; DWB-0787-2022; FYQ-3698-2022; NAA-8765-2025; HZW-5449-2023; GBG-8291-2022; DXA-1243-2022; FZK-6500-2022; DVE-7652-2022; GBG-9412-2022; H-2913-2012; K-9464-2019; H-4394-2019; MQV-6996-2025; OVM-5938-2025; JYY-9592-2024; OAA-4277-2025; HRO-8503-2023; Z-4828-2019; EJM-8740-2022; FZO-1254-2022; FYJ-9637-2022; CEY-5520-2022; EKA-7986-2022; IUT-7926-2023; IUQ-9509-2023; AAO-6325-2021; L-8385-2017; JWI-9457-2024; LUN-9319-2024; HFL-6092-2022; E-2727-2014; IVA-4275-2023; EOV-3838-2022; B-4650-2017; IRQ-6937-2023; H-8587-2015; B-4348-2013; DVC-6323-2022; NHE-3385-2025; LEQ-1557-2024; AGZ-3259-2022; NKT-5952-2025; CNE-2384-2022; EUK-3820-2022; HPK-1894-2023; CPC-6980-2022; IBV-9243-2023; CQF-5798-2022; KLD-3528-2024; CQR-5759-2022; DWK-1716-2022; CSK-3817-2022; FBF-5584-2022; FBE-0351-2022; CTZ-4163-2022; GBH-2365-2022; DWQ-9372-2022; FBM-0217-2022; DWS-1040-2022; DXH-0671-2022; GEK-4486-2022; CYT-5449-2022; GWA-7849-2022; B-8502-2016; Q-5758-2017; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; HUJ-7899-2023; DFQ-7859-2022; V-6916-2017; GNG-7078-2022; FLD-9518-2022; DVP-3997-2022; KEK-6332-2024; DWT-4779-2022; HXJ-7641-2023; DKF-4281-2022; DWD-4131-2022; DLB-6897-2022; HTM-1531-2023; GBD-7573-2022; MTQ-2344-2025; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; K-4114-2015; DNW-6364-2022; DXL-4304-2022; DXO-8435-2022; NKY-6871-2025; IZJ-2041-2023; GWX-9207-2022; OWM-0849-2025; LGB-5701-2024; GDK-6495-2022; T-7378-2018; AAB-2503-2019; GCB-5227-2022; OAE-4195-2025; GCA-5567-2022; NZU-7226-2025; NHV-9369-2025; DZE-1995-2022; O-9396-2015; DTO-7937-2022; P-2194-2018; 59165454500; 6603023234; 7003267532; 55505051100; 57206423651; 6602409206; 55365150900; 57192921002; 8970939400; 35494536400; 8833942000; 10738797800; 36195346900; 55929371000; 58303888900; 57206536839; 56261663500; 57204700965; 58114843000; 7005317106; 6701390827; 14629998500; 56176939800; 57220414927; 6505819655; 57225389323; 35421870300; 7004185737; 6701447926; 7004168457; 7004279376; 56592859600; 8316050500; 24439181000; 55948641800; 54924573500; 56260193000; 37121732700; 7003910265; 6507398813; 55757270100; 8856476200; 59636105400; 7004529134; 6602521535; 6601991850; 24461026200; 56181792800; 24587025200; 9639653200; 36627225700; 36657273100; 8527480900; 14008117700; 6603519641; 7202555066; 16024707000; 6603380199; 55539553700; 35425530800; 55885669700; 35227493200; 6701865592; 55578049300; 56216916000; 6603602446; 56149076900; 6603205767; 6603770482; 56403356600; 36195926600; 14058603600; 10239419900; 14025617800; 57815020000; 6506385309; 56463558800; 14056466700; 55665939900; 14832846900; 7004208543; 7004629002; 14050522100; 58095754900; 55913343900; 57544565000; 7003604949; 57203250534; 15770290900; 57190439701; 7006538931; 8842216700; 55337191500; 57225899623; 57190443165; 7004160690; 57218941481; 14063887300; 55845420026; 6602208520; 58502049600; 58937209900; 17436196900; 57203391123; 57191960842; 6602565951; 57191419742; 59730206000; 7101771030; 7005350024; 9333441800; 6603819488; 56512377200
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202554838
The Perseus field captured by Euclid as part of its Early Release Observations provides a unique opportunity to study cluster environment ranging from outskirts to dense regions. Leveraging unprecedented optical and near-infrared depths, we investigate the stellar structure of massive disc galaxies in this field. This study focuses on outer disc profiles, including simple exponential (Type I), down-bending break (Type II) and up-bending break (Type III) profiles, and their associated colour gradients, to trace late assembly processes across various environments. Type II profiles, though relatively rare in high dense environments, appear stabilised by internal mechanisms like bars and resonances, even within dense cluster cores. Simulations suggest that in dense environments, Type II profiles tend to evolve into Type I profiles over time. Type III profiles often exhibit small colour gradients beyond the break, hinting at older stellar populations, potentially due to radial migration or accretion events. We analyse correlations between galaxy mass, morphology, and profile types. Mass distributions show weak trends of decreasing mass from the centre to the outskirts of the Perseus cluster. Type III profiles become more prevalent, while Type I profiles decrease in lower-mass galaxies with cluster centric distance. Type I profiles dominate in spiral galaxies, while Type III profiles are more common in S0 galaxies. Type II profiles are consistently observed across all morphological types. While the limited sample size restricts statistical power, our findings shed light on the mechanisms shaping galaxy profiles in cluster environments. Future work should extend observations to the cluster outskirts to enhance statistical significance and explore looser environments. Additionally, 3D velocity maps are needed to achieve a non-projected view of galaxy positions, offering deeper insights into spatial distribution and dynamics.
Volume: 699
Keywords: galaxies: clusters: individual: Perseus; galaxies: evolution; galaxies: interactions; Galaxy: disk;
The Hera Space Mission in the Context of Small Near-Earth Asteroid Missions in the Past, Present and Future
Authors: Michel Patrick; Küppers Michael; Fitzsimmons Alan; Green Simon; Lazzarin Monica; Ulamec Stephan; Abell Paul; Sugita Seiji; Campo Bagatin Adriano; Carry Benoit; Charnoz Sébastien; de León Julia; Ferrari Fabio; Hérique Alain; Jutzi Martin; Karatekin Özgür; Kohout Tomas; Murdoch Naomi; Okada Tatsuaki; Palomba Ernesto; Pravec Petr; Raducan Sabina; Snodgrass Colin; Tortora Paolo; Vincent Jean-Baptiste; Wünnemann Kai
Journal: 28261
Published: 2025
DOI: 10.1007/s11214-025-01195-1
The Hera mission of the European Space Agency was launched successfully on October 7, 2024 and will perform the first rendezvous with a binary asteroid in fall 2026. It will measure in great detail the characteristics of the binary asteroid (65803) Didymos. This will include for the first time the interior of an asteroid, as well as the outcome of the impact of the NASA DART mission on the small moon, called Dimorphos; of the binary system. The first asteroid deflection test will thus be fully validated, enabling impact model extrapolations to other cases. Hera uses a unique architecture that includes for the first time a main spacecraft and two cubesats for deep space asteroid exploration. It takes place in the context of the golden age of asteroid exploration, with no less than 8 missions in development or already flying to asteroids and great successes of past missions, in particular the two recent asteroid sample return missions OSIRIS-REx by NASA and Hayabusa2 by JAXA. Up to now, all new asteroids visited by a spacecraft have generated great surprises, especially regarding their often counter-intuitive response to external actions, showing that we are still far from fully understanding the properties of these bodies in their low-gravity environment. By investigating and interacting with small asteroids, we should eventually be able to better understand and predict their properties as a function of common characteristics identified by ground-based observations. We are not there yet. In this paper, we present how Hera will contribute to this endeavor.
Volume: 221
Keywords: Asteroids; Binary asteroids; Planetary defense; Space mission;
Long-term effectiveness, safety, and liver stiffness dynamics of PBC treatment with obeticholic acid in real-world
Authors: AREA MIN. 06 - Scienze mediche; ITA; Goal 3: Good health and well-being###25122; EAE-4108-2022; AAB-7965-2019; GXH-0637-2022; ABA-7458-2022; AAM-5199-2020; GYG-3745-2022; DQA-3167-2022; EEZ-3131-2022; DWH-6622-2022; DJL-4541-2022; GDO-6870-2022; EPF-4015-2022; HXI-6162-2023; CER-4322-2022; EPL-8933-2022; CLX-8747-2022; R-6598-2016; CUO-7026-2022; CDG-6373-2022; EAA-0713-2022; CMD-8697-2022; HMC-6765-2023; DWO-5433-2022; EUY-7780-2022; OWZ-9517-2025; GJN-4882-2022; EPQ-0611-2022; CST-5700-2022; DGV-4842-2022; NOF-1514-2025; AAC-1949-2019; AAE-3161-2022; DVV-9362-2022; CFM-6817-2022; ELB-9576-2022; AAA-5759-2019; LFT-8668-2024; GNF-9719-2022; J-8600-2018; CEH-2506-2022; GDY-8839-2022; CGI-5783-2022; J-8463-2018; E-1120-2012; DBM-5248-2022; JHF-5645-2023; NCV-8425-2025; DTY-5484-2022; DXS-4726-2022; OUU-0112-2025; CJX-2082-2022; JCS-8414-2023; EPL-1215-2022; FSR-1611-2022; GVU-0673-2022; DWF-4268-2022; FIH-4311-2022; MUO-3257-2025; DPA-2383-2022; EQL-9934-2022; DQK-8599-2022; GBJ-7157-2022; GDZ-6667-2022; ORW-9434-2025; EQJ-1234-2022; FQB-6797-2022; MQU-5709-2025; K-4255-2019; FLO-9066-2022; ESJ-6426-2022; DKA-8928-2022; J-4394-2019; DZH-2825-2022; EIZ-6768-2022; DGZ-4325-2022; FUU-1830-2022; EMO-8688-2022; GPG-1122-2022; IAZ-8837-2023; AFM-2519-2022; PHG-8971-2026; KJQ-3800-2024; PFZ-5711-2026; GDC-3815-2022; JVS-9756-2024; DPL-3467-2022; CGZ-1469-2022; DTZ-5739-2022; LWM-3292-2024; DPL-0586-2022; GBB-8947-2022; GDJ-0380-2022; CDY-1662-2022; K-7706-2016; JCS-0572-2023; CEV-0715-2022; E-2565-2011; MYM-7264-2025; FQT-1261-2022; CWY-4285-2022; CFF-4678-2022; FZV-1726-2022; MNU-4399-2025; BBE-1333-2022
Journal: JHEP REPORTS
Published: 2025
DOI: 10.1016/j.jhepr.2025.101448
Volume: 7
The Economic Burden, Epidemiological Insights, and Treatment Patterns of Wilson’s Disease: A Real-World Study in Italy
Authors: Sciattella Paolo; Scortichini Matteo; Cazzagon Nora; Loudianos Georgios; Zuin Massimo; Battezzati Pier Maria; Maggiore Giuseppe; Grieco Antonio; Baglione Eugenio; Senzolo Marco; Mazza Silvia; Della Corte Claudia; Tortora Annalisa; Di Dato Fabiola; Matarazzo Margherita; Iorio Raffaele
Journal: DRUGS-REAL WORLD OUTCOMES
Published: 2025
DOI: 10.1007/s40801-025-00506-w
Volume: 12 Pages: 391-398
Euclid preparation: LXVIII. Extracting physical parameters from galaxies with machine learning
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; KJS-6941-2024; PGU-9738-2026; C-9225-2017; KKS-3168-2024; JQO-3317-2023; ABB-9156-2021; HNI-9120-2023; O-9495-2015; H-4394-2019; MXW-4784-2025; B-4650-2017; B-4348-2013; AHB-3798-2022; GFK-2340-2022; FDK-8162-2022; AAY-1704-2020; Z-4828-2019; DVE-7652-2022; PCC-0635-2025; B-9633-2012; AAV-1857-2021; PHP-8037-2026; FXZ-5996-2022; GEH-7593-2022; OIQ-4966-2025; O-8727-2015; ETW-6961-2022; FZO-1254-2022; FYJ-9637-2022; CDR-2303-2022; CEY-5520-2022; C-4378-2014; IUT-7926-2023; IUQ-9509-2023; GBF-1843-2022; EKV-4052-2022; DVB-2560-2022; EOE-6462-2022; L-8385-2017; OBC-0525-2025; IAE-2305-2023; HWT-5982-2023; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; E-8021-2017; IVA-4275-2023; OZD-6988-2025; IRQ-6937-2023; HTG-8587-2023; H-8587-2015; DVC-6323-2022; NHE-3385-2025; PGG-2427-2026; AGZ-3259-2022; A-2693-2010; PCA-2324-2025; ERD-3189-2022; HLX-2021-2023; HKB-2933-2023; PWI-2374-2026; EUO-2530-2022; EUK-3820-2022; J-3686-2012; CNP-7538-2022; CPC-6980-2022; AAR-6622-2021; IBV-9243-2023; CQF-5798-2022; I-5515-2016; DXA-1952-2022; HPT-5858-2023; GBB-1832-2022; CQR-5759-2022; DWK-1716-2022; CTE-6775-2022; CSK-3817-2022; CUA-0149-2022; FBF-5584-2022; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; DWQ-9372-2022; AAT-5867-2020; GZL-0460-2022; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; GEK-4486-2022; CYT-5449-2022; AAF-6025-2021; OOP-8239-2025; DUU-4676-2022; B-8502-2016; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLK-4707-2022; MTO-5925-2025; DWZ-6747-2022; HUJ-7899-2023; DFQ-7859-2022; DFY-8508-2022; U-7309-2018; MWK-2416-2025; AAX-3485-2021; D-1300-2016; GNG-7078-2022; FNC-4379-2022; DFC-8070-2022; FLD-9518-2022; MWC-3186-2025; DVP-3997-2022; FNA-5485-2022; KJY-7272-2024; DWT-4779-2022; FNB-0821-2022; ABB-2322-2020; C-3218-2017; HTJ-4919-2023; DWD-4131-2022; DLB-6897-2022; GBD-7573-2022; DMG-4306-2022; FVO-0175-2022; FSY-2184-2022; DMX-5934-2022; ABC-8644-2021; DNY-0415-2022; OYX-8116-2025; K-4114-2015; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; JVJ-6571-2024; FXG-6905-2022; H-1761-2016; DXM-5348-2022; GBG-9412-2022; DPD-7597-2022; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; FZJ-5145-2022; NGE-0152-2025; EAA-4768-2022; LGB-5701-2024; L-8068-2014; PYK-2395-2026; MQB-6975-2025; DZM-7523-2022; GDK-6495-2022; T-7378-2018; AAB-2503-2019; GCB-5227-2022; HNI-8187-2023; GCA-5567-2022; FCD-8153-2022; JCG-3503-2023; NNO-6919-2025; MTT-8732-2025; DZU-8266-2022; EAZ-0566-2022; O-9396-2015; AAO-6325-2021; MKL-0317-2025; DTO-7937-2022; LWL-2178-2024; CMV-6954-2022; CDE-1189-2022; DUJ-9002-2022; GBY-6621-2022; LRV-2049-2024; FJX-8996-2022; GGM-6223-2022; FVK-3262-2022; P-2194-2018; DYK-4428-2022; ECX-7840-2022; AAH-3743-2019; DTU-2081-2022; HPI-3910-2023; FXV-4290-2022; ELC-7230-2022; A-7379-2017; IYE-9818-2023; DXH-1132-2022; L-6160-2017; KTI-3074-2024; CGZ-3153-2022; EPI-1133-2022; JNZ-6253-2023; EQF-3895-2022; GBB-5111-2022; O-9391-2015; QDC-1960-2026; KDL-3231-2024; S-1204-2016; HOH-0341-2023; AAQ-1509-2021; ISJ-4889-2023; EQT-2114-2022; CNT-5485-2022; L-2472-2017; ETR-0407-2022; L-6378-2014; IDQ-0489-2023; AAW-1061-2020; EZB-5943-2022; ETN-0093-2022; KSN-3481-2024; GBD-4336-2022; DTP-1685-2022; GBY-7028-2022; EVC-7104-2022; CRZ-8120-2022; PBH-2493-2025; EXD-3015-2022; CQL-4862-2022; MWU-0619-2025; DWN-8747-2022; FYO-9802-2022; ABB-8257-2020; KLF-9653-2024; NNI-3312-2025; GCU-3708-2022; KGG-1931-2024; KNK-3731-2024; NIQ-3499-2025; HRR-2616-2023; ITW-2356-2023; GEC-5455-2022; HRX-7202-2023; Q-5758-2017; GYG-7175-2022; DBI-3005-2022; GAV-5026-2022; IZP-8032-2023; FZY-7746-2022; GQU-8893-2022; DWL-3001-2022; DWN-4354-2022; ABA-3922-2020; JHF-8266-2023; MVA-1492-2025; GFP-2203-2022; D-1237-2017; FMN-9310-2022; Z-3406-2019; GXE-4405-2022; FQI-9285-2022; DXO-2849-2022; AAI-1245-2021; DNL-3219-2022; DYG-8551-2022; HXX-2997-2023; GCY-0967-2022; LQA-8898-2024; DWT-7233-2022; C-6308-2008; GEP-1274-2022; DRO-1214-2022; R-3469-2017; GCB-1754-2022; ABC-3828-2020; JCM-8241-2023; JAN-6167-2023; DXU-7894-2022; EAO-6360-2022; MLN-0990-2025; MLR-9932-2025; GDF-8239-2022; EBV-8310-2022; JEZ-2766-2023; IRI-6836-2023; CDU-7975-2022; JSZ-6163-2023; ECF-2024-2022; A-9058-2016; KCV-5780-2024; IOX-4199-2023; CEW-0728-2022; 58817124800; 56261663500; 57189593362; 58817722400; 9334587700; 57202215260; 56426999100; 6602409206; 55929371000; 57203047758; 54924573500; 7003910265; 57200793436; 55538241000; 57325670900; 56033190100; 7005317106; 35494536400; 56949991000; 7006440295; 57193523315; 57222380960; 7004408758; 57204700965; 57022106200; 35957375500; 57414680000; 14629998500; 56176939800; 8651648800; 57220414927; 24482926400; 57225389323; 35421870300; 7102960752; 6506892241; 6602293713; 36614022800; 6701447926; 7004168457; 7004279376; 35117442400; 56592859600; 8316050500; 57193414472; 57090221700; 7004614794; 24439181000; 55948641800; 56260193000; 55543336500; 37121732700; 6507398813; 55757270100; 8856476200; 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57225899623; 57190443165; 8915699600; 6603819159; 7004160690; 57219119015; 56881732300; 15129157800; 48663031800; 8833942000; 57218941481; 13407890400; 14063887300; 54797318800; 55845420026; 6602208520; 6602315420; 14823864100; 58502049600; 57220082325; 54790157700; 55414427600; 35314080800; 58937209900; 17436196900; 57203391123; 57191960842; 6602565951; 57191419742; 59019204600; 56286395400; 10244106400; 7101771030; 56273947300; 8042894900; 9333441800; 7004185737; 36905906400; 6603819488; 57198031424; 9244606800; 58696967900; 56239931500; 12809267200; 57845873200; 26326923900; 57220131178; 56512377200; 35194662000; 7003963996; 57203063840; 35387346400; 46461103400; 57719423900; 42260895600; 36730729100; 6701547091; 56818885600; 35789534500; 59421337500; 7006764136; 57214989073; 6602458029; 57193874792; 57226191281; 57219376526; 9337037600; 57211860571; 7103030457; 56653598400; 7007018277; 9335763100; 35112881300; 34569356300; 57199061795; 55976971800; 7401603740; 24074399500; 6701458135; 6602348000; 58112082700; 57189231035; 57218304683; 7003900144; 55420010100; 25723173900; 58073729200; 7004144883; 7003645652; 57193849410; 8703100100; 55944081300; 56242244500; 7005222927; 15131601400; 7202545187; 36674792500; 55505778800; 8833942900; 55741929700; 57224183772; 36966126400; 57218097629; 56153170500; 26642611400; 56149076900; 57892676500; 6701851021; 57218549969; 57210924350; 6508080858; 35490928600; 6701309093; 6701685211; 6603292899; 6506425834; 22951241500; 56383649900; 7101983827; 55246080700; 7006221760; 58848224900; 7102775303; 57218766355; 35422761600; 35463408300; 7003762062; 7101903552; 16031797900; 57222902516; 55672552800; 10642144300; 57207198854; 22836264500; 57201003368; 7005050491; 55888485900; 55435714500; 57194728774; 7004680980; 35417736300; 57211858405; 7102146471; 6603851717; 6701718244; 7004109829; 59800191100; 57132747000; 58483607200; 6504758580; 6506892358; 14632583100; 57215412075; 55195649400
Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202453111
The Euclid mission is generating a vast amount of imaging data in four broadband filters at a high angular resolution. This data will allow for the detailed study of mass, metallicity, and stellar populations across galaxies that will constrain their formation and evolutionary pathways. Transforming the Euclid imaging for large samples of galaxies into maps of physical parameters in an efficient and reliable manner is an outstanding challenge. Here, we investigate the power and reliability of machine learning techniques to extract the distribution of physical parameters within well-resolved galaxies. We focus on estimating stellar mass surface density, mass-averaged stellar metallicity, and age. We generated noise-free synthetic high-resolution (100 pc × 100 pc) imaging data in the Euclid photometric bands for a set of 1154 galaxies from the TNG50 cosmological simulation. The images were generated with the SKIRT radiative transfer code, taking into account the complex 3D distribution of stellar populations and interstellar dust attenuation. We used a machine learning framework to map the idealised mock observational data to the physical parameters on a pixel-by-pixel basis. We find that stellar mass surface density can be accurately recovered with a ≤0.130 dex scatter. Conversely, stellar metallicity and age estimates are, as expected, less robust, but they still contain significant information that originates from underlying correlations at a sub-kiloparsec scales between stellar mass surface density and stellar population properties. As a corollary, we show that TNG50 follows a spatially resolved mass-metallicity relation that is consistent with observations. Due to its relatively low computational and time requirements, which has a time-frame of minutes without dedicated high performance computing infrastructure once it has been trained, our method allows for fast and robust estimates of the stellar mass surface density distributions of nearby galaxies from four-filter Euclid imaging data. Equivalent estimates of stellar population properties (stellar metallicity and age) are less robust but still hold value as first-order approximations across large samples.
Volume: 695
Keywords: Galaxies: general; Galaxies: photometry; Methods: statistical;
Euclid: The Early Release Observations Lens Search Experiment
Authors: Non assegn; AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; CGD-2351-2022; JAX-2768-2023; CKU-5761-2022; H-4394-2019; DXZ-7810-2022; PGG-2427-2026; DWU-8294-2022; D-1237-2017; OMN-3792-2025; FYJ-4908-2022; EPI-1133-2022; AGZ-3259-2022; LHK-9354-2024; DVB-8405-2022; AFJ-2074-2022; MDG-9557-2025; GMD-3106-2022; ILM-3517-2023; GBU-8717-2022; DWQ-9372-2022; MYW-7907-2025; AAC-7835-2021; JBR-8488-2023; DUZ-7625-2022; MWC-3186-2025; F-3052-2014; PCC-0635-2025; LXV-7382-2024; HFV-0042-2022; GEI-1893-2022; FTV-5671-2022; DRO-1214-2022; FXH-0557-2022; AAN-1908-2021; IUS-5192-2023; DYG-8338-2022; GFN-8936-2022; NLB-5121-2025; GCJ-5104-2022; GCV-8309-2022; H-2913-2012; GCS-2631-2022; AAD-3011-2021; O-9495-2015; DWB-0787-2022; DXA-1243-2022; GEH-7593-2022; GBG-9412-2022; DYF-3433-2022; OTJ-2099-2025; FYH-4361-2022; L-2472-2017; FYH-7305-2022; MYL-2765-2025; NKL-3434-2025; MMS-5823-2025; FHL-5547-2022; DWN-4354-2022; MYS-2354-2025; JNB-1152-2023; IRI-1547-2023; EFH-6710-2022; GBC-8404-2022; FZO-1254-2022; FYJ-9637-2022; DUY-3094-2022; CEY-5520-2022; C-4378-2014; EKA-7986-2022; IUT-7926-2023; LXX-3952-2024; IUQ-9509-2023; GBF-1843-2022; DVB-2560-2022; L-8385-2017; OBC-0525-2025; LUN-9319-2024; CJD-7824-2022; HWT-5982-2023; CJL-9982-2022; GBO-0318-2022; E-2727-2014; L-8237-2014; FZR-9687-2022; IVA-4275-2023; OZD-6988-2025; B-4650-2017; IRQ-6937-2023; HTG-8587-2023; H-8587-2015; B-4348-2013; DVC-6323-2022; NHE-3385-2025; GBN-8818-2022; NKT-5952-2025; A-2693-2010; PCA-2324-2025; CNE-2384-2022; HKB-2933-2023; EUO-2530-2022; EUK-3820-2022; J-3686-2012; CPC-6980-2022; AAR-6622-2021; CQF-5798-2022; DXA-1952-2022; HPT-5858-2023; 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; FBF-5584-2022; HRW-8595-2023; FBV-0790-2022; S-8590-2017; CTZ-4163-2022; GBH-2365-2022; AAT-5867-2020; GZL-0460-2022; DWS-1040-2022; DXH-0671-2022; FFG-2233-2022; CYT-5449-2022; DUU-4676-2022; B-8502-2016; LYD-9061-2024; GFM-0308-2022; A-2699-2012; GAU-7672-2022; FIV-3763-2022; FLM-0394-2022; MTO-5925-2025; DWZ-6747-2022; HUJ-7899-2023; DFQ-7859-2022; U-7309-2018; MWK-2416-2025; AAX-3485-2021; D-1300-2016; GNG-7078-2022; DFC-8070-2022; FLD-9518-2022; DVP-3997-2022; FNA-5485-2022; KJY-7272-2024; DWT-4779-2022; KSI-9422-2024; KNP-2716-2024; DJO-8166-2022; MNN-0179-2025; FNB-0821-2022; ABB-2322-2020; 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; OYX-8116-2025; K-4114-2015; OON-3882-2025; DNW-6364-2022; DXL-4304-2022; GCA-5113-2022; GCT-2940-2022; DXO-8435-2022; JVJ-6571-2024; FXG-6905-2022; H-1761-2016; NKY-6871-2025; FZX-9985-2022; IZJ-2041-2023; GBV-4959-2022; GWX-9207-2022; FZJ-5145-2022; NGE-0152-2025; NES-1075-2025; EAA-4768-2022; LGB-5701-2024; L-8068-2014; PYK-2395-2026; MQB-6975-2025; Q-2220-2015; T-7378-2018; AAB-2503-2019; GCB-5227-2022; 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Journal: ASTRONOMY & ASTROPHYSICS
Published: 2025
DOI: 10.1051/0004-6361/202451868
We investigated the ability of the Euclid telescope to detect galaxy-scale gravitational lenses. To do so, we performed a systematic visual inspection of the 0.7 deg2 Euclid Early Release Observations data towards the Perseus cluster using both the high-resolution IE band and the lower-resolution YE, JE, and HE bands. Each extended source brighter than magnitude 23 in IE was inspected by 41 expert human classifiers. This amounts to 12 086 stamps of 1000 × 1000. We found 3 grade A and 13 grade B candidates. We assessed the validity of these 16 candidates by modelling them and checking that they are consistent with a single source lensed by a plausible mass distribution. Five of the candidates pass this check, five others are rejected by the modelling, and six are inconclusive. Extrapolating from the five successfully modelled candidates, we infer that the full 14 000 deg2 of the Euclid Wide Survey should contain 100 000+-7030000000 galaxy-galaxy lenses that are both discoverable through visual inspection and have valid lens models. This is consistent with theoretical forecasts of 170 000 discoverable galaxy-galaxy lenses in Euclid. Our five modelled lenses have Einstein radii in the range 000 . 68 < θE < 100 . 24, but their Einstein radius distribution is on the higher side when compared to theoretical forecasts. This suggests that our methodology is likely missing small-Einstein-radius systems. Whilst it is implausible to visually inspect the full Euclid dataset, our results corroborate the promise that Euclid will ultimately deliver a sample of around 105 galaxy-scale lenses.
Volume: 697
Keywords: galaxies: clusters: individual: Perseus; gravitational lensing: strong; methods: data analysis; methods: observational;