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Euclid preparation XXXIII. Characterization of convolutional neural networks for the identification of galaxy-galaxy strong-lensing events

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Publications associées (24)

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Euclid: Searches for strong gravitational lenses using convolutional neural nets in Early Release Observations of the Perseus field

Georges Meylan, Benjamin Yvan Alexandre Clement, Olivia Mueller

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 scientist ...

2025

Euclid V. The Flagship galaxy mock catalogue: A comprehensive simulation for the Euclid mission

Frédéric Courbin, Georges Meylan

We present the Flagship galaxy mock, a simulated catalogue of billions of galaxies designed to support the scientific exploitation of the Euclid mission. Euclid is a medium-class mission of the European Space Agency optimised to determine the properties of ...

2025

Comparing Compressed and Full-Modeling analyses with FOLPS: implications for DESI 2024 and beyond

Antoine Philippe Jacques Rocher

The Dark Energy Spectroscopic Instrument (DESI) will provide unprecedented information about the large-scale structure of our Universe. In this work, we study the robustness of the theoretical modelling of the power spectrum of FOLPS, a novel effective fie ...

IOP Publishing Ltd2025

Here There Be (dusty) Monsters: High-redshift Active Galactic Nuclei Are Dustier Than Their Hosts

Michaela Hirschmann

JWST spectroscopy has discovered a population of z greater than or similar to 3.5 galaxies with broad Balmer emission lines and narrow forbidden lines that are consistent with hosting active galactic nuclei (AGN). Many of these systems, now known as "littl ...

IOP Publishing Ltd2025

Function-Space Optimality of Neural Architectures with Multivariate Nonlinearities

Michaël Unser, Rahul Parhi

We investigate the function-space optimality (specifically, the Banach-space optimality) of a large class of shallow neural architectures with multivariate nonlinearities/activation functions. To that end, we construct a new family of Banach spaces defined ...

SIAM PUBLICATIONS2025

Virgo Filaments. V. Disrupting the Baryon Cycle in the NGC 5364 Galaxy Group

Pascale Jablonka, Yannick Michael Bahé, Damien Louis Alexandre Spérone-Longin, Gautam Ramakrishnan Nagaraj

The Virgo Filament Survey (VFS) is a comprehensive study of galaxies that reside in the extended filamentary structures surrounding the Virgo Cluster, out to 12 virial radii. The primary goal is to characterize all of the dominant baryonic components withi ...

2025

Masked Diffusion Strategy for Privacy-Preserving Distributed Learning

To protect both local gradients and estimated parameters in distributed learning, this paper introduces a masked diffusion (MD) strategy, leading to two algorithms: the MD stochastic gradient (MD-SG) and the MD primal-dual stochastic gradient (MPD-SG). The ...

2025

Surface distributions and vertical profiles of trace gases (CO, O3, NO, NO2) in the Arctic wintertime boundary layer using low-cost sensors during ALPACA-2022

Julia Schmale, Andrea Baccarini, Roman Pohorsky

Electrochemical gas sensors (EGSs) have been used to measure the surface distributions and vertical profiles of trace gases in the wintertime Arctic boundary layer during the Alaskan Layered Pollution and Chemical Analysis (ALPACA) field experiment in Fair ...

COPERNICUS GESELLSCHAFT MBH2025

Characterising the z ≈ 7.66 Type-II AGN candidate SMACS S06355 using BEAGLE-AGN and JWST NIRSpec/NIRCam

Michaela Hirschmann, Adèle Marie Françoise Plat

The presence of Active Galactic Nuclei (AGN) in low mass (M⋆ ≲ 109 M⊙) galaxies at high redshift has been established, and it is important to characterise these objects and the impact of their feedback on the host galaxies. In this paper we apply the Spect ...

Oxford University Press (OUP)2025

Combinatorial Aspects of Continuous Optimization: Graph Polynomials and Neural Networks

Amire Bendjeddou

In this thesis, we study how combinatorial structures and symmetries emerge from analytic properties of graph polynomials and neural networks. Graph polynomials encode combinatorial aspects of the underlying graph in their coefficients and are remarkably u ...

EPFL2025

HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data

Background Estimating the time since HIV infection (TSI) at population level is essential for tracking changes in the global HIV epidemic. Most methods for determining TSI give a binary classification of infections as recent or non-recent within a window o ...

Springer Science and Business Media LLC2025

Non-equilibrium control of biomolecular condensates

Alexandra Shelest

Biomolecular condensates are cellular organelles without a membrane, which have come into the scientific focus about 15 years ago. They are made from a mix of proteins and RNAs. It is hypothesized that they maintain their shape and function through liquid- ...

EPFL2025

Optimization frameworks for bespoke sensory encoding in neuroprosthetics

Silvestro Micera, Solaiman Shokur, Franklin Leong

Restoring natural sensation via neuroprosthetics relies on the possibility of encoding complex and nuanced information. For example, an ideal brain–machine interface with sensory feedback would provide the user with sensation about movement, pressure, curv ...

AIP Publishing2025

Euclid preparation: XLII. A unified catalogue-level reanalysis of weak lensing by galaxy clusters in five imaging surveys

Frédéric Courbin, Georges Meylan, Austin Chandler Peel

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the Euclid era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys wit ...

2024

How Two-Layer Neural Networks Learn, One (Giant) Step at a Time

Florent Gérard Krzakala, Bruno Loureiro, Ludovic Théo Stephan, Yatin Dandi, Luca Pesce

For high-dimensional Gaussian data, we investigate theoretically how the features of a two-layer neural network adapt to the structure of the target function through a few large batch gradient descent steps, leading to an improvement in the approximation c ...

2024

BEAGLE-AGN I: simultaneous constraints on the properties of gas in star-forming and AGN narrow-line regions in galaxies

Michaela Hirschmann

We present the addition of nebular emission from the narrow-line regions (NLR) surrounding active galactic nuclei (AGNs) to BEAGLE (BayEsian Analysis of GaLaxy sEds). Using a set of idealized spectra, we fit to a set of observables (emission-line ratios an ...

Oxford Univ Press2024

Observation of χcJ → p(p)over-barKS0K- π+ + c.c

By analyzing (27.12 +/- 0.14) x 10(8) psi(3686) events collected with the BESIII detector operating at the BEPCII collider, the decays chi(cJ) -> p (p) over barK(S)(0)K(-)pi(+) + c.c. (J = 0, 1, 2) are observed for the first time with statistical significa ...

American Physical Society2024

A luminous and young galaxy at z = 12.33 revealed by a JWST/MIRI detection of Hα and [O iii]

Michaela Hirschmann

The James Webb Space Telescope has discovered a surprising population of bright galaxies in the very early Universe (≲500 Myr after the Big Bang) that is hard to explain with conventional galaxy-formation models and whose physical properties are not fully ...

2024

Rehabilitation exoskeleton system with bidirectional virtual reality feedback training strategy

Virtual reality (VR) technology revitalises rehabilitation training by creating rich, interactive virtual rehabilitation scenes and tasks that deeply engage patients. Robotics with immersive VR environments have the potential to significantly enhance the s ...

2024

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

Jean-Philippe Thiran, Tobias Kober, Bénédicte Marie Maréchal

With the rise of open data, identifiability of individuals based on 3D renderings obtained from routine structural magnetic resonance imaging (MRI) scans of the head has become a growing privacy concern. To protect subject privacy, several algorithms have ...

Wiley2024