IAS-LAB PUBLICATIONS
AREA MIN. 13 – Scienze economiche e statistiche – John Wiley and Sons Inc
Authors: AREA MIN. 13 - Scienze economiche e statistiche
DOI: 10.1002/clt2.70147
Immune-mediated necrotizing myopathies: clinico-serological features and treatment outcomes of a large Italian cohort of patients
Authors: Bonanno Silvia; Salvi Erika; Cheli Marta; Salerno Franco; Lucchini Matteo; Cicia Alessandra; Carlomagno Vincenzo; Lauletta Antonio; Tufano Laura; Girolamo Francesco; De Gaetano Lucia Nicolini; Fornaro Marco; Pancheri Elia; Zaottini Federico; Faedo Elena; Querini Patrizia Rovere; De Lorenzo Rebecca; Scarlato Marina; Bottazzi Francesca; Riguzzi Pietro; Gibertini Sara; Risi Barbara; Pugliese Alessia; Zoppi Dario; Ruggiero Lucia; Ricci Giulia; Manganotti Paolo; Siciliano Gabriele; Rodolico Carmelo; Filosto Massimiliano; Bello Luca; Pegoraro Elena; Cavagna Lorenzo; Previtali Stefano Carlo; Fiorillo Chiara; Grandis Marina; Vattemi Gaetano; Tonin Paola; Iannone Florenzo; Antonini Giovanni; Garibaldi Matteo; Mirabella Massimiliano; Maggi Lorenzo
Journal: 16751
Published: 2026
DOI: 10.1007/s00415-026-14089-1
Background: Immune-mediated necrotizing myopathy (IMNM) is a distinct entity with limited large-cohort data. We aimed to characterize clinico-serological features and treatment outcomes of anti-SRP, anti-HMGCR, and seronegative IMNM in a large Italian cohort. Methods: Retrospective multicenter study of adults diagnosed with IMNM (224th ENMC criteria) across 14 Italian neuromuscular centers between 2019 and 2022 was performed. Results: We included 159 subjects (57% female, median age 62 years): 40 SRP+, 71 HMGCR+, 41 seronegative, and 7 untested. SRP+ more frequently presented with proximal upper limb weakness and axial involvement versus HMGCR+ (OR = 3.89, padj = 0.007, OR = 3.47, padj = 0.04) and seronegative (OR = 4.00, padj = 0.014, OR = 15.2, padj = 0.005). Lung involvement was higher in SRP+ (7.5%), vs none in HMGCR+ and 2.4% in seronegative (p=0.028). Extramuscular features were more frequent in HMGCR+ (OR = 3.78, p = 0.019, padj = 0.057). SRP+ had higher odds of loss of motor independence (OR=4.22, padj =0.011) and relapse (OR = 14.4, padj = 0.010) than HMGCR+. Seronegative had better functional outcomes than SRP+ (p=0.047). Cancer associated myositis predicted worse disability (OR = 6.93, p = 0.0044). Overall, 71.7% improved with corticosteroids (OR=3.83, padj =0.01) and immunosuppressants (OR=2.296, padj =0.037), without serotype differences. CK decreased in 81.8% and normalized in 40.3% of patients; ΔCK was associated with improvement (p=0.017), clinical remission (p=0.002) and correlated with post-treatment functional scores (R=0.19, p= 0.044). Conclusion: Subtype-specific differences and prognostic factors emerge. Exploratory functional assessment and ΔCK may capture treatment response in real-world settings.
Volume: 273
Keywords: Anti-HMGCR antibodies; Anti-SRP antibodies; Creatine kinase; Immune-mediated necrotizing myopathy; Seronegative myopathy;
AREA MIN. 02 – Scienze fisiche||AREA MIN. 02 – Scienze fisiche||AREA MIN. 02 – Scienze fisiche – Elsevier B.V.
Authors: AREA MIN. 02 - Scienze fisiche
Non assegn||AREA MIN. 09 – Ingegneria industriale e dell’informazione – Advanced Visual Interfaces for Cultural Heritage
Authors: Non assegn; AREA MIN. 09 - Ingegneria industriale e dell'informazione
1778196017914
Published: 08/05/2026 01:20:17
1778195668214
Published: 08/05/2026 01:14:28
Euclid Quick Data Release (Q1): VIII. Exploring galaxy morphology across cosmic time through Sérsic fits
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; 58303888900; 36720530600; 6602294488; 56153006200; 36237905600; 55178467000; 23050749700; 56216916000; 57558773800; 49362192900; 56261663500; 36238621200; 55868798300; 57217929177; 57222380960; 35957375500; 55929371000; 6701390827; 6701409861; 14629998500; 56176939800; 8651648800; 35241679100; 57220414927; 24482926400; 6505819655; 57225389323; 35421870300; 7102960752; 7004185737; 55600275300; 6602293713; 6701447926; 7004168457; 7004279376; 35117442400; 57219376526; 56592859600; 8316050500; 57193414472; 57090221700; 7004614794; 24439181000; 55948641800; 54924573500; 7007018277; 56260193000; 55543336500; 37121732700; 7003910265; 6507398813; 55757270100; 26645887600; 8856476200; 59636105400; 7004529134; 26663174300; 6603213706; 6602521535; 7006071419; 38961266500; 6701458135; 55978579700; 6601991850; 6602678698; 6602348000; 24461026200; 56181792800; 57202592808; 24587025200; 9639653200; 6701688696; 36627225700; 6506341877; 56592156500; 14630273900; 57206536839; 36657273100; 8527480900; 12809267200; 22979454100; 9270789600; 14008117700; 6603519641; 7102513091; 7202555066; 7006022077; 16024707000; 6603380199; 36663730200; 55539553700; 35425530800; 23485209600; 55885669700; 35227493200; 37123976000; 55578049300; 7102120605; 6603602446; 58754493800; 56149076900; 6603623670; 6602251719; 6603205767; 6603770482; 56403356600; 36195926600; 35070066100; 14058603600; 10239419900; 14025617800; 57815020000; 6506385309; 56463558800; 55779479900; 13407562800; 7102174334; 56229506700; 8058520300; 14056466700; 7102846243; 36542679900; 55665939900; 7005525798; 14832846900; 7004208543; 7102775303; 35299820900; 7004629002; 8764087600; 35216145800; 14050522100; 56118600700; 58095754900; 57203234808; 55913343900; 57544565000; 7003604949; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 55337191500; 7402364894; 57225899623; 57190443165; 8915699600; 7004160690; 57219119015; 56881732300; 15129157800; 48663031800; 36844794400; 57218941481; 60098455000; 14063887300; 54797318800; 6701484532; 55845420026; 55435714500; 6602208520; 6602315420; 14823864100; 58502049600; 57220082325; 55414427600; 35314080800; 58937209900; 56768046600; 7004109829; 17436196900; 57203391123; 57191960842; 6602565951; 6506892358; 57191419742; 60212929500; 56286395400; 59730206000; 7101771030; 56273947300; 8042894900; 7005350024; 55158076000; 9333441800; 46461103400; 56818885600; 6602409206; 36905906400; 6603819488; 6602458029; 9337037600; 9244606800; 58696967900; 58073729200; 24279354600; 6506425834; 57845873200; 26326923900; 7101983827; 59146418100; 57220131178; 57203270249; 57222902516; 56512377200; 7005050491; 35194662000; 7003963996; 57203063840; 35387346400; 57190942170; 42260895600; 36730729100; 6701547091; 7102844957; 7102798954; 23027139300; 35789534500; 56426999100; 14820320500; 59421337500; 7006764136; 7405386498; 57214989073; 36195346900; 57193874792; 57226191281; 57211860571; 7103030457; 56653598400; 57216741288; 58621464800; 9335763100; 35112881300; 34569356300; 35072386500; 7003369464; 57199061795; 55976971800; 7401603740; 24074399500; 7006314412; 7004293616; 57200793436; 57193553705; 58112082700; 57189231035; 7003900144; 57322480400; 7004144883; 7003645652; 57193849410; 8703100100; 7801607411; 55944081300; 56242244500; 15131601400; 58030558500; 7202545187; 36674792500; 6603265387; 8833942900; 55741929700; 35239938700; 36966126400; 59774401700; 57218097629; 56153170500; 56403277600; 26642611400; 57193558463; 57213763435; 57219756241; 57193556700; 59464420900; 57219691072; 6701851021; 55886521200; 57218549969; 57210924350; 6508080858; 35490928600; 6701309093; 7004197698; 17346045900; 6701685211; 6603292899; 22951241500; 56383649900; 55246080700; 7006221760; 57218766355; 35422761600; 57189089616; 57220897961; 55885031100; 58127526800; 57192212259; 7003762062; 56285291900; 16031797900; 56993656500; 56187440700; 55672552800; 58182594100; 10642144300; 35401143000; 57207198854; 22836264500; 6701410946; 57201003368; 58696967800; 55965473600; 55888485900; 55370653600; 35417736300; 57211858405; 6603851717; 59735517100; 56448179900; 57132747000; 58483607200; 6504758580; 57188565951; 14632583100; 57215412075; 55195649400; 35463408300; 55365150900; 23983560400; 7404952697
Journal: 26750
Published: 2026
DOI: 10.1051/0004-6361/202554585
We present the results of the single-component Sérsic profile fitting for the magnitude-limited sample of IE < 23 galaxies within the 63.1 deg2 area of the Euclid Quick Data Release (Q1). The associated morphological catalogue includes two sets of structural parameters fitted using SourceXtractor++: one for VIS IE images and one for a combination of three NISP images in YE, JE, and HE bands. We compared the resulting Sérsic parameters to other morphological measurements provided in the Q1 data release and to the equivalent parameters based on higher-resolution Hubble Space Telescope imaging. These comparisons confirmed the consistency and the reliability of the fits to Q1 data. Our analysis of colour gradients shows that NISP profiles systematically have smaller effective radii (Re) and larger Sérsic indices (n) than in VIS. In addition, we highlight trends in NISP-to-VIS parameter ratios with both magnitude and nVIS. From the 2D bimodality of the (u − r) colour-log(n) plane, we defined a (u − r)lim(n) that separates early- and late-type galaxies (ETGs and LTGs). We used the two sub-populations to examine the variations of n across well-known scaling relations at z < 1. The ETGs display a steeper size–stellar mass relation than the LTGs, indicating a difference in the main drivers of their mass assembly. Similarly, LTGs and ETGs occupy different parts of the stellar mass–star-formation rate plane, with ETGs at higher masses than LTGs and further below the main sequence of star-forming galaxies. This clear separation highlights the link known between the shutdown of star formation and morphological transformations in the Euclid imaging data set. In conclusion, our analysis demonstrates both the robustness of the Sérsic fits available in the Q1 morphological catalogue and the wealth of information they provide for studies of galaxy evolution with Euclid.
Volume: 711
Keywords: galaxies: evolution; galaxies: statistics; galaxies: structure;
Euclid Quick Data Release (Q1): XXXI. LEMON – LEns MOdelling with Neural networks. Automated and fast modelling of Euclid gravitational lenses with singular isothermal ellipsoid mass profile
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; 58544914400; 55929371000; 7101983827; 56118600700; 7005525798; 57851709700; 7801607411; 57477518400; 57201285159; 55731742600; 6507131754; 6602116553; 8856476200; 57191290632; 23100209800; 57199319204; 7404952697; 56149076900; 55886521200; 6701390827; 6701409861; 14629998500; 56176939800; 35241679100; 57220414927; 24482926400; 57225389323; 35421870300; 7004185737; 6701447926; 7004168457; 7004279376; 35117442400; 57219376526; 56592859600; 8316050500; 7004719199; 57193414472; 57090221700; 24439181000; 55948641800; 54924573500; 7007018277; 56260193000; 55543336500; 37121732700; 7003910265; 6507398813; 55757270100; 59636105400; 7004529134; 26663174300; 6603213706; 22940235700; 6602521535; 7006071419; 57212263363; 6701458135; 6506323808; 6601991850; 6602678698; 6602348000; 56181792800; 57202592808; 24587025200; 9639653200; 56239931500; 36627225700; 6506341877; 56592156500; 14630273900; 57206536839; 8724245000; 36657273100; 8527480900; 12809267200; 22979454100; 9270789600; 14008117700; 6603519641; 7202555066; 7006022077; 16024707000; 6603380199; 36663730200; 55539553700; 35425530800; 23485209600; 55885669700; 35227493200; 37123976000; 55578049300; 56216916000; 7102120605; 6603602446; 58754493800; 6603205767; 6603770482; 56403356600; 36195926600; 35070066100; 14058603600; 10239419900; 14025617800; 6506385309; 36933808800; 56463558800; 55779479900; 13407562800; 7102174334; 56229506700; 14056466700; 7102846243; 55665939900; 14832846900; 7004208543; 7102775303; 35299820900; 7004629002; 8764087600; 35216145800; 14050522100; 58095754900; 57203234808; 55913343900; 57544565000; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 55337191500; 7402364894; 57225899623; 57190443165; 8915699600; 7004160690; 57219119015; 56881732300; 15129157800; 48663031800; 36844794400; 8833942000; 57218941481; 13407890400; 14063887300; 6506381727; 54797318800; 6701484532; 55845420026; 55435714500; 6602208520; 6602315420; 8789254100; 14823864100; 58502049600; 57220082325; 55414427600; 58937209900; 7004109829; 17436196900; 57203391123; 57191960842; 6602565951; 6506892358; 57191419742; 56286395400; 59730206000; 7101771030; 8042894900; 9333441800; 46461103400; 56818885600; 6602409206; 36905906400; 6603819488; 6602458029; 57198031424; 9244606800; 58696967900; 58073729200; 23050749700; 57845873200; 26326923900; 6603351766; 59146418100; 57220131178; 57203270249; 56512377200; 35194662000; 7003963996; 57203063840; 35387346400; 57719423900; 57190942170; 42260895600; 36730729100; 6701547091; 23027139300; 35789534500; 14820320500; 59421337500; 7006764136; 7405386498; 57214989073; 57193874792; 57226191281; 9337037600; 57211860571; 7103030457; 56653598400; 58621464800; 56614074300; 57216129429; 35112881300; 34569356300; 7003369464; 57199061795; 55976971800; 24074399500; 7006314412; 56925206200; 57200793436; 57193553705; 58112082700; 57189231035; 7003900144; 57322480400; 7004144883; 7003645652; 57193849410; 8703100100; 55944081300; 56242244500; 7005222927; 15131601400; 58030558500; 7202545187; 36674792500; 24279354600; 55505778800; 6603265387; 8833942900; 55741929700; 36966126400; 59774401700; 57218097629; 56153170500; 56403277600; 26642611400; 57193558463; 7005254067; 57892676500; 57213763435; 57219756241; 57193556700; 59464420900; 6701851021; 57218549969; 36602942600; 57210924350; 6508080858; 35490928600; 6701309093; 6701685211; 6603292899; 6506425834; 22951241500; 56383649900; 55246080700; 7006221760; 57218766355; 35422761600; 42262265600; 57189089616; 9272122900; 57220897961; 55885031100; 58127526800; 57192212259; 7003762062; 56285291900; 16031797900; 56993656500; 56187440700; 57222902516; 55672552800; 58182594100; 10642144300; 57207198854; 57201003368; 56186567400; 58696967800; 55965473600; 7005050491; 55370653600; 7004680980; 57211858405; 7102146471; 6603851717; 59735517100; 56448179900; 57132747000; 58483607200; 6504758580; 57188565951; 14632583100; 55822387500; 57215412075; 55195649400; 35463408300
Journal: 26750
Published: 2026
DOI: 10.1051/0004-6361/202554538
The Euclid mission aims to survey around 14 000 deg2 of extragalactic sky, providing around 105 gravitational lens images. Modelling of gravitational lenses is fundamental to estimate the total mass of the lens galaxy, along with its dark matter content. Traditional modelling of gravitational lenses is computationally intensive and requires manual input. In this paper, we use a Bayesian neural network, LEns MOdelling with Neural networks (LEMON), to model Euclid gravitational lenses with a singular isothermal ellipsoid mass profile. Our method estimates key lens mass profile parameters, such as the Einstein radius, while also predicting the light parameters of foreground galaxies and their uncertainties. We validate LEMON’s performance on both mock Euclid datasets, real lenses observed with Hubble Space Telescope (HST) that have been degraded to match observations with the same depth of the Euclid Wide Survey, and real Euclid lenses, demonstrating the ability of LEMON to predict parameters of both simulated and real lenses. Results show promising accuracy and reliability in predicting the Einstein radius, mass and light ellipticities, effective radius, Sérsic index, lens magnitude, and unlensed source position for simulated lens galaxies. The application to real data, including the latest Quick Release 1 strong lens candidates, provides encouraging results in the recovery of the parameters for real lenses. We also verified that LEMON has the potential to accelerate traditional modelling methods, by giving to the classical optimiser the LEMON predictions as starting points, resulting in a speed-up of up to 26 times the original time needed to model a sample of gravitational lenses, a result that would be impossible with randomly initialised guesses. Moreover, LEMON can be used to cross-validate results from the traditional modelling methods, and thus has the potential to reduce the failure rate of the Euclid modelling pipeline. This work represents a significant step towards efficient, automated gravitational lens modelling, which is crucial for handling the large data volumes expected from Euclid.
Volume: 711
Keywords: cD; galaxies: elliptical and lenticular; gravitational lensing: strong; methods: data analysis;
Euclid Quick Data Release (Q1): XXXIII. The first catalogue of strong-lensing galaxy clusters
Authors: AREA MIN. 02 - Scienze fisiche; ASTRONOMY & ASTROPHYSICS###0004-6361; 57194590572; 7005525798; 57188639623; 55731742600; 6602409206; 23469392100; 7101924117; 56228966200; 58090912100; 57200294162; 57221950386; 6602458029; 58419113700; 35112881300; 55668778200; 59773938400; 7006314412; 60121655900; 57218283726; 7801607411; 57226400265; 57213830231; 55539553700; 55886521200; 58521236800; 56274915100; 57188806951; 57205730219; 7101983827; 55798121500; 60121993500; 58250422500; 14627922300; 55932248600; 59317261300; 22836264500; 8833942000; 57211567711; 13204971700; 7406740762; 55929371000; 55822387500; 8270263200; 57535859700; 58283672000; 55443707500; 6701390827; 6701409861; 14629998500; 57194514014; 56176939800; 8651648800; 35241679100; 57220414927; 24482926400; 6505819655; 57225389323; 58849865400; 35421870300; 7102960752; 7004185737; 55600275300; 6602293713; 6701447926; 7004168457; 7004279376; 55543112300; 35117442400; 57219376526; 56592859600; 8316050500; 57193414472; 57090221700; 7004614794; 24439181000; 55948641800; 54924573500; 7007018277; 56260193000; 55543336500; 37121732700; 7003910265; 6507398813; 55757270100; 8856476200; 59636105400; 7004529134; 26663174300; 6603213706; 6602521535; 7006071419; 38961266500; 6701458135; 6506323808; 6601991850; 6602678698; 57198386027; 6602348000; 24461026200; 56181792800; 57202592808; 24587025200; 9639653200; 56239931500; 6701688696; 36627225700; 6506341877; 56592156500; 24173378000; 14630273900; 57206536839; 36657273100; 8527480900; 57203599140; 12809267200; 22979454100; 9270789600; 14008117700; 9337191600; 6603519641; 7102513091; 7202555066; 16024707000; 6603380199; 36663730200; 35425530800; 23485209600; 55885669700; 35227493200; 37123976000; 6701865592; 6506922416; 55578049300; 7003478641; 56216916000; 7102120605; 6603602446; 7005760971; 7003825248; 60719245100; 56149076900; 6603623670; 6602251719; 6603205767; 6603770482; 56403356600; 36195926600; 35070066100; 14058603600; 10239419900; 14025617800; 57815020000; 6506385309; 56463558800; 55779479900; 13407562800; 7102174334; 56229506700; 14056466700; 7102846243; 36542679900; 55665939900; 14832846900; 7004208543; 7102775303; 35299820900; 7004629002; 60121993600; 8764087600; 35216145800; 7006833728; 14050522100; 56118600700; 58095754900; 57203234808; 55913343900; 57544565000; 7003604949; 57203250534; 15770290900; 6602930238; 57190439701; 7006538931; 8842216700; 6506955003; 36490343100; 55337191500; 7402364894; 57225899623; 57190443165; 8915699600; 6603044987; 7004160690; 57219119015; 15129157800; 48663031800; 36844794400; 57218941481; 14063887300; 54797318800; 6701484532; 55845420026; 55435714500; 6602208520; 6602315420; 57194728774; 14823864100; 58502049600; 57220082325; 55414427600; 35314080800; 58937209900; 56768046600; 7004109829; 17436196900; 57203391123; 57194416363; 57191960842; 6701439004; 6602565951; 6506892358; 57191419742; 60212929500; 56286395400; 59730206000; 7101771030; 56273947300; 8042894900; 7005350024; 9333441800; 46461103400; 56818885600; 36905906400; 6603819488; 9337037600; 58848684700; 9244606800; 58696967900; 58073729200; 6506425834; 57845873200; 23393165800; 26326923900; 6603351766; 59146418100; 57220131178; 57203270249; 57222902516; 56512377200; 7005050491; 35194662000; 7003963996; 57203063840; 35387346400; 57190942170; 42260895600; 36730729100; 6701547091; 7102844957; 7102798954; 23027139300; 35789534500; 14820320500; 59421337500; 7005840588; 7006764136; 7405386498; 57214989073; 57193874792; 57226191281; 57211860571; 7103030457; 56653598400; 58621464800; 34569356300; 35072386500; 7003369464; 57199061795; 55976971800; 7401603740; 24074399500; 7004293616; 57200793436; 57193553705; 58112082700; 57189231035; 7003900144; 55420010100; 57322480400; 7004144883; 7003645652; 57193849410; 8703100100; 55944081300; 56242244500; 7005222927; 7404584619; 15131601400; 58030558500; 7202545187; 36674792500; 24279354600; 6603265387; 8833942900; 55741929700; 35239938700; 35748664400; 36966126400; 59774401700; 57218097629; 56153170500; 56403277600; 26642611400; 57193558463; 57892676500; 57213763435; 57219756241; 57193556700; 59464420900; 57219691072; 6701851021; 57218549969; 36602942600; 57210924350; 6508080858; 35490928600; 6701309093; 6701685211; 6603292899; 22951241500; 56383649900; 35092838400; 55246080700; 7006221760; 57218766355; 35422761600; 57219752207; 42262265600; 57189089616; 57220897961; 55885031100; 58127526800; 57192212259; 7003762062; 56285291900; 16031797900; 56993656500; 56187440700; 36495525900; 55672552800; 7003352706; 58182594100; 10642144300; 35401143000; 57207198854; 57201003368; 56186567400; 58696967800; 55965473600; 55888485900; 55370653600; 35417736300; 57211858405; 7102146471; 6603851717; 59735517100; 56448179900; 57132747000; 58483607200; 6504758580; 57188565951; 14632583100; 57215412075; 55195649400; 35463408300; 23983560400; 7404952697
Journal: 26750
Published: 2026
DOI: 10.1051/0004-6361/202554577
We present the first catalogue of strong-lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering 63.1‚ÄÜdeg2). This catalogue is the result of the visual inspection of 1260 Euclid image cutouts of previously identified galaxy clusters. Each galaxy cluster was assigned a lensing score, ùí´lens, derived from the aggregated votes of multiple visual inspectors, the majority of whom possess extensive expertise in strong gravitational lensing. Votes were assigned based on the assessed credibility and plausibility of the identified strong-lensing features. Specifically, we identified 83 gravitational lenses with ùí´lens‚ÄÑ>‚ÄÑ0.5, of which 14 have ùí´lens‚ÄÑ=‚ÄÑ1 and clearly exhibit secure strong-lensing features, such as giant tangential and radial arcs, and multiple images. Considering the measured number density of lensing galaxy clusters, approximately 0.3‚ÄÜdeg‚àí2 for ùí´lens‚ÄÑ>‚ÄÑ0.9, we predict that Euclid will likely observe more than 4500 strong-lensing clusters over the course of the mission. Notably, only three of the identified cluster-scale lenses had previously been observed from space. Thus, Euclid has provided the first high-resolution imaging for the remaining 80 strong-lensing galaxy cluster candidates, including those with the highest scores. The identified strong-lensing features will be used to train deep-learning models to automatically identify gravitational arcs and multiple images in the Euclid observations. This study confirms the great potential of Euclid for finding new strong-lensing clusters, enabling exciting new discoveries on the nature of dark matter and dark energy and the study of the high-redshift Universe.
Volume: 711
Keywords: cosmology: observations; dark matter; galaxies: clusters: general; gravitational lensing: strong;