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In Sunyaev-Zeldovich (SZ) cluster cosmology, two tools are needed to be able to exploit data from large scale surveys in the millimeter-wave domain. An accurate description of the IntraCluster Medium (ICM) pressure profile is needed along with the scaling relation connecting the SZ brightness to the mass. With its high angular resolution and large field of view, The NIKA2 camera, operating at 150 and 260 GHz, is perfectly suited for precise cluster SZ mapping. The SZ Large Program (LPSZ) of the NIKA2 collaboration is dedicated to the observation of a sample of 38 SZ-selected clusters at intermediate to high redshift and observed both in SZ and X-ray. The current status is that all LPSZ clusters have been observed and the analysis toward the final results is ongoing. We present in detail how NIKA2-LPSZ will obtain a robust estimation of the SZ-Mass scaling relation and how it will be used to obtain cosmological constraints., Comment: 4 pages, 3 figures, contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

The Earth Observation Satellite Planning (EOSP) is a difficult optimization problem with considerable practical interest. A set of requested observations must be scheduled on an agile Earth observation satellite while respecting constraints on their visibility window, as well as maneuver constraints that impose varying delays between successive observations. In addition, the problem is largely oversubscribed: there are much more candidate observations than what can possibly be achieved. Therefore, one must select the set of observations that will be performed while maximizing their weighted cumulative benefit, and propose a feasible schedule for these observations. As previous work mostly focused on heuristic and iterative search algorithms, this paper presents a new technique for selecting and scheduling observations based on Graph Neural Networks (GNNs) and Deep Reinforcement Learning (DRL). GNNs are used to extract relevant information from the graphs representing instances of the EOSP, and DRL drives the search for optimal schedules. Our simulations show that it is able to learn on small problem instances and generalize to larger real-world instances, with very competitive performance compared to traditional approaches., Comment: Accepted at 17th European Workshop on Reinforcement Learning (EWRL 2024)

Discontinuous visibility changes remain a major bottleneck when optimizing surfaces within a physically-based inverse renderer. Many previous works have proposed sophisticated algorithms and data structures to sample visibility silhouettes more efficiently. Our work presents another solution: instead of differentiating a tentative surface locally, we differentiate a volumetric perturbation of a surface. We refer this as a many-worlds representation because it models a non-interacting superposition of conflicting explanations (worlds) of the input dataset. Each world is optically isolated from others, leading to a new transport law that distinguishes our method from prior work based on exponential random media. The resulting Monte Carlo algorithm is simpler and more efficient than prior methods. We demonstrate that our method promotes rapid convergence, both in terms of the total iteration count and the cost per iteration.

In this article, we study Poincar\'e maps of harmonic fields in toroidal domains using a shape variational approach. Given a bounded domain of $\mathbb{R}^3$, we define its harmonic fields as the set of magnetic fields which are curl free and tangent to the boundary. For toroidal domains, this space is one dimensional, and one may thus single out a harmonic field by specifying a degree of freedom, such as the circulation along a toroidal loop. We are then interested in the Poincar\'e maps of such fields restricted to the boundary, which produce diffeomorphisms of the circle. We begin by proving a general shape differentiability result of such Poincar\'e maps in the smooth category, and obtain a general formula for the shape derivative. We then investigate two specific examples of interest; axisymmetric domains, and domains for which the harmonic field has a diophantine rotation number on the boundary. We prove that, in the first case, the shape derivative of the Poincar\'e map is always identically zero, whereas in the second case, assuming an additional condition on the geometry of the domain, the shape derivative of the Poincar\'e map may be any smooth function of the circle by choosing an appropriate perturbation of the domain.

We provide a mathematical analysis of the Dynamical Mean-Field Theory, a celebrated representative of a class of approximations in quantum mechanics known as embedding methods. We start by a pedagogical and self-contained mathematical formulation of the Dynamical Mean-Field Theory equations for the finite Hubbard model. After recalling the definition and properties of one-body time-ordered Green's functions and self-energies, and the mathematical structure of the Hubbard and Anderson impurity models, we describe a specific impurity solver, namely the Iterated Perturbation Theory solver, which can be conveniently formulated using Matsubara's Green's functions. Within this framework, we prove under certain assumptions that the Dynamical Mean-Field Theory equations admit a solution for any set of physical parameters. Moreover, we establish some properties of the solution(s)., Comment: 40 pages, 5 figures

Despite recent progress, the question of what regulates the star formation efficiency in galaxies remains one of the most debated problems in astrophysics. According to the dominant picture, star formation (SF) is regulated by turbulence and feedback, and the SFE is 1-2% per local free-fall time. In an alternate scenario, the SF rate in galactic disks is linearly proportional to the mass of dense gas above a critical density threshold. We aim to discriminate between these two pictures thanks to high-resolution observations tracing dense gas and young stellar objects (YSOs) for a comprehensive sample of 49 nearby massive SF complexes out to d < 3 kpc in the Galactic disk. We use data from CAFFEINE, a 350/450 $\mu$m survey with APEX/ArT\'eMiS of the densest portions of all southern molecular clouds, in combination with Herschel data to produce column density maps at 8" resolution. Our maps are free of saturation and resolve the structure of dense gas and the typical 0.1 pc width of molecular filaments at 3 kpc, which is impossible with Herschel data alone. Coupled with SFR estimates derived from Spitzer observations of the YSO content of the same clouds, this allows us to study the dependence of the SFE with density in the CAFFEINE clouds. We also combine our findings with existing SFE measurements in nearby clouds to extend our analysis down to lower column densities. Our results suggest that the SFE does not increase with density above the critical threshold and support a scenario in which the SFE in dense gas is approximately constant. However, the SFE measurements traced by Class I YSOs in nearby clouds are more inconclusive, since they are consistent with both the presence of a density threshold and a dependence on density above the threshold. Overall, we suggest that the SFE in dense gas is primarily governed by the physics of filament fragmentation into protostellar cores., Comment: In press; accepted: 18/05/2024

In this study, we investigate the potential of electronic interferometers for probing the quantum state of electromagnetic radiation on a chip at sub-nanosecond time scales. We propose to use single electron excitations propagating within an electronic Mach-Zehnder interferometer in the Aharonov-Bohm dominated regime. We discuss how information about the quantum state of the electromagnetic radiation is encoded into the interference contribution to the average outgoing electrical current. By investigating squeezed radiation and single edge magnetoplasmons probed by Leviton pulses in a realistic setup, we show that single electron interferometers have the potential to probe quantum radiation in the time domain with sub-nanosecond to pico-second time resolution. Our research could have significant implications for probing the fundamental properties of light in the microwave to tera-Hertz domains at extremely short time scales., Comment: 31 pages, 15 figures

Neural Ordinary Differential Equations typically struggle to generalize to new dynamical behaviors created by parameter changes in the underlying system, even when the dynamics are close to previously seen behaviors. The issue gets worse when the changing parameters are unobserved, i.e., their value or influence is not directly measurable when collecting data. We introduce Neural Context Flow (NCF), a framework that encodes said unobserved parameters in a latent context vector as input to a vector field. NCFs leverage differentiability of the vector field with respect to the parameters, along with first-order Taylor expansion to allow any context vector to influence trajectories from other parameters. We validate our method and compare it to established Multi-Task and Meta-Learning alternatives, showing competitive performance in mean squared error for in-domain and out-of-distribution evaluation on the Lotka-Volterra, Glycolytic Oscillator, and Gray-Scott problems. This study holds practical implications for foundational models in science and related areas that benefit from conditional neural ODEs. Our code is openly available at https://github.com/ddrous/ncflow., Comment: 14 pages, 5 figures

Existence of quasi-static negative capacitance (QSNC) was proposed from an interpretation of the widely accepted Landau model of ferroelectrics. However, many works showed not to support the QSNC theory, making it controversial. In this letter we show the Landau model when used together with transition-state-theory, can connect various models including first-principles, Landau, Preisach and nucleation limited switching while it does not predict the existence of QSNC.

Next-generation accelerator concepts which hinge on the precise shaping of beam distributions, demand equally precise diagnostic methods capable of reconstructing beam distributions within 6-dimensional position-momentum spaces. However, the characterization of intricate features within 6-dimensional beam distributions using conventional diagnostic techniques necessitates hundreds of measurements, using many hours of valuable beam time. Novel phase space reconstruction techniques are needed to substantially reduce the number of measurements required to reconstruct detailed, high-dimensional beam features in order to resolve complex beam phenomena, and as feedback in precision beam shaping applications. In this study, we present a novel approach to reconstructing detailed 6-dimensional phase space distributions from experimental measurements using generative machine learning and differentiable beam dynamics simulations. We demonstrate that for a collection of synthetic beam distribution test cases that this approach can be used to resolve 6-dimensional phase space distributions using basic beam manipulations and as few as 20 2-dimensional measurements of the beam profile, without the need for prior data collection or model training. We also demonstrate an application of the reconstruction method in an experimental setting at the Argonne Wakefield Accelerator, where it is able to reconstruct the beam distribution and accurately predict previously unseen measurements 75x faster than previous methods.

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

The microbunching instability has been a long-standing issue for high-brightness free-electron lasers (FELs), and is a significant show-stopper to achieving full longitudinal coherence in the x-ray regime. This paper reports the first experimental demonstration of microbunching instability mitigation through transverse Landau damping, based on linear optics control in a dispersive region. Analytical predictions for the microbunching content are supported by numerical calculations of the instability gain and confirmed through the experimental characterization of the spectral brightness of the FERMI FEL under different transverse optics configurations of the transfer line between the linear accelerator and the FEL.

Job-Shop Scheduling Problem (JSSP) is a combinatorial optimization problem where tasks need to be scheduled on machines in order to minimize criteria such as makespan or delay. To address more realistic scenarios, we associate a probability distribution with the duration of each task. Our objective is to generate a robust schedule, i.e. that minimizes the average makespan. This paper introduces a new approach that leverages Deep Reinforcement Learning (DRL) techniques to search for robust solutions, emphasizing JSSPs with uncertain durations. Key contributions of this research include: (1) advancements in DRL applications to JSSPs, enhancing generalization and scalability, (2) a novel method for addressing JSSPs with uncertain durations. The Wheatley approach, which integrates Graph Neural Networks (GNNs) and DRL, is made publicly available for further research and applications., Comment: To be published at CPAIOR 2024

We develop a new framework to constrain the source redshift. The method jointly accounts for the detection/non-detection of spectral lines and the prior information from the photometric redshift and total infrared luminosity from spectral energy distribution analysis. The method uses the estimated total infrared luminosity to predict the line fluxes at given redshifts and generates model spectra. The redshift-dependent spectral models are then compared with the observed spectra to find the redshift. Results. We apply the aforementioned joint redshift analysis method to four high-z dusty star-forming galaxy candidates selected from the NIKA2 observations of the HLSJ091828.6+514223 (HLS) field, and further observed by NOEMA with blind spectral scans. These sources only have SPIRE/Herschel photometry as ancillary data. They were selected because of very faint or no SPIRE counterparts, as to bias the sample towards the highest redshift candidates. The method finds the spectroscopic redshift of 4 in the 5 NOEMA-counterpart detected sources, with z>3. Based on these measurements, we derive the CO/[CI] lines and millimeter continuum fluxes from the NOEMA data and study their ISM and star-formation properties. We find cold dust temperatures in some of the HLS sources compared to the general population of sub-millimeter galaxies, which might be related to the bias introduced by the SPIRE-dropout selection. Our sources, but one, have short gas depletion time of a few hundred Myrs, which is typical among high-z sub-millimeter galaxies. The only exception shows a longer gas depletion time, up to a few Gyrs, comparable to that of main-sequence galaxies at the same redshift. Furthermore, we identify a possible over-density of dusty star-forming galaxies at z=5.2, traced by two sources in our sample, as well as the lensed galaxy HLSJ091828.6+514223. (abridged), Comment: A&A in press

Tuning particle accelerators is a challenging and time-consuming task, but can be automated and carried out efficiently through the use of suitable optimization algorithms. With successful applications at various facilities, Bayesian optimization using Gaussian process modeling has proven to be a particularly powerful tool to address these challenges in practice. One of its major benefits is that it allows incorporating prior information, such as knowledge about the shape of the objective function or predictions based on archived data, simulations or surrogate models, into the model. In this work, we propose the use of a neural network model as an efficient way to include prior knowledge about the objective function into the Bayesian optimization process to speed up convergence. We report results obtained in simulations and experiments using neural network priors to perform optimization of electron and heavy-ion accelerator facilities, specifically the Linac Coherent Light Source and the Argonne Tandem Linear Accelerator System. Finally, we evaluate how the accuracy of the prior mean predictions affect optimization performance.

Beams with cross-plane coupling or extreme asymmetries between the two transverse phase spaces are often encountered in particle accelerators. Flat beams with large transverse-emittance ratios are critical for future linear colliders. Similarly, magnetized beams with significant cross-plane coupling are expected to enhance the performance of electron cooling in hadron beams. Preparing these beams requires precise control and characterization of the four-dimensional transverse phase space. In this study, we employ generative phase space reconstruction (GPSR) techniques to rapidly characterize magnetized and flat-beam phase-space distributions using a conventional quadrupole-scan method. The reconstruction technique is experimentally demonstrated on an electron beam produced at the Argonne Wakefield Accelerator and successfully benchmarked against conventional diagnostics techniques. Specifically, we show that predicted beam parameters from the reconstructed phase-space distributions (e.g. as magnetization and flat beam emittances) are in excellent agreement with those measured from the conventional diagnostic methods.

The rapid development of brain-inspired computing requires new artificial components and architectures for its hardware implementation. In this regard, memristive devices emerged as potential candidates for artificial synapses because of their ability to emulate the plasticity of the biological synapses. In this work, the synaptic behavior of the TiN/La2NiO4+{\delta}/Pt memristive devices based on thermally annealed La2NiO4+{\delta} films is thoroughly investigated. Using electron energy loss spectroscopy, we show that annealing using reducing (Ar) or oxidizing (O2) atmospheres affects the interstitial oxygen content ({\delta}) in the La2NiO4+{\delta} films. Electrical characterization shows that both devices exhibit long-term potentiation/depression and spike-timing-dependent plasticity, which makes them suitable for neuromorphic applications. At the same time, the Ar annealed TiN/La2NiO4+{\delta}/Pt device demonstrates non-volatile properties with low energy consumption during the learning process. On the other hand, in the O2 annealed TiN/La2NiO4+{\delta}/Pt device the resistive switching behavior is more volatile and requires more energy for synaptic learning. Finally, the simulation tools show that spiking neural network architectures with unsupervised learning rules based on the experimental data achieve high inference accuracy in the digit recognition task, which proves the potential of TiN/La2NiO4+{\delta}/Pt devices for artificial synapse applications.

In this paper, we introduce a new shape functional defined for toroidal domains that we call harmonic helicity, and study its shape optimization. Given a toroidal domain, we consider its associated harmonic field. The latter is the magnetic field obtained uniquely up to normalization when imposing zero normal trace and zero electrical current inside the domain. We then study the helicity of this field, which is a quantity of interest in magneto-hydrodynamics corresponding to the L2 product of the field with its image by the Biot--Savart operator. To do so, we begin by discussing the appropriate functional framework and an equivalent PDE characterization. We then focus on shape optimization, and we identify the shape gradient of the harmonic helicity. Finally, we study and implement an efficient numerical scheme to compute harmonic helicity and its shape gradient using finite elements exterior calculus.

This paper introduces Qrlew, an open source library that can parse SQL queries into Relations -- an intermediate representation -- that keeps track of rich data types, value ranges, and row ownership; so that they can easily be rewritten into differentially-private equivalent and turned back into SQL queries for execution in a variety of standard data stores. With Qrlew, a data practitioner can express their data queries in standard SQL; the data owner can run the rewritten query without any technical integration and with strong privacy guarantees on the output; and the query rewriting can be operated by a privacy-expert who must be trusted by the owner, but may belong to a separate organization.

Recent experiments have shown that the edge states of a quantum Hall sample can be coupled to a superconductor, so that incoming electrons in the edge states can be Andreev converted on the superconductor as coherent superpositions of an electron and a hole. In addition to this, single-electron emitters that operate in the gigahertz regime have been realized. Motivated by this remarkable experimental progress, we here analyze the Andreev reflections of incoming charge pulses on the interface with a superconductor. To this end, we employ a Wigner function representation that allows us to visualize the response function of the interface both in the time and in the frequency domain. We analyze the response of an interface between a normal-metal and a singlet or triplet superconductor with and without an insulating barrier in between them. As a special case, we analyze the Andreev reflections of clean single-particle excitations that are generated by the application of lorentzian-shaped voltage pulses to the contacts of the inputs. Our predictions may be tested in future experiments with edge states coupled to superconductors., Comment: 14 pages, 11 figures

Electron quantum optics explores the coherent propagation and interference of single-electron charge pulses in electronic nanoscale circuits that are similar to table-top setups with photons. So far, experiments with dynamic single-electron emitters have focused on normal-state conductors, however, the inclusion of superconductors would pave the way for a wide range of applications that exploit the electron-hole degree of freedom, for example, for quantum information processing or quantum sensing. Here, we propose and analyze a tunable mechanism for the on-demand conversion of single-electron pulses into holes through Andreev processes on a superconductor. To this end, we develop a Floquet-Nambu scattering formalism that allows us to describe the dynamic conversion of charge pulses on a superconductor, and we show that it is possible to generate arbitrary superpositions of electrons and holes with the degree of mixing controlled by the magnetic flux in an interferometric setup. We provide a detailed discussion of the optimal operating conditions in realistic situations and demonstrate that our proposal is feasible based on current technology., Comment: 7 pages, 3 figures

Accelerator physics relies on numerical algorithms to solve optimization problems in online accelerator control and tasks such as experimental design and model calibration in simulations. The effectiveness of optimization algorithms in discovering ideal solutions for complex challenges with limited resources often determines the problem complexity these methods can address. The accelerator physics community has recognized the advantages of Bayesian optimization algorithms, which leverage statistical surrogate models of objective functions to effectively address complex optimization challenges, especially in the presence of noise during accelerator operation and in resource-intensive physics simulations. In this review article, we offer a conceptual overview of applying Bayesian optimization techniques towards solving optimization problems in accelerator physics. We begin by providing a straightforward explanation of the essential components that make up Bayesian optimization techniques. We then give an overview of current and previous work applying and modifying these techniques to solve accelerator physics challenges. Finally, we explore practical implementation strategies for Bayesian optimization algorithms to maximize their performance, enabling users to effectively address complex optimization challenges in real-time beam control and accelerator design.

A portable imaging system for the on-site detection of shoulder injury is necessary to identify its extent and avoid its development to severe condition. Here, firstly a microwave tomography system is introduced using state-of-the-art numerical modeling and parallel computing for imaging different tissues in the shoulder. The results show that the proposed method is capable of accurately detecting and localizing rotator cuff tears of different size. In the next step, an efficient design in terms of computing time and complexity is proposed to detect the variations in the injured model with respect to the healthy model. The method is based on finite element discretization and uses parallel preconditioners from the domain decomposition method to accelerate computations. It is implemented using the open source FreeFEM software.

To understand the evolution of dust properties in molecular clouds in the course of the star formation process, we constrain the changes in the dust emissivity index from star-forming filaments to prestellar and protostellar cores to T Tauri stars. Using the NIKA2 continuum camera on the IRAM 30~m telescope, we observed the Taurus B211/B213 filament at 1.2\,mm and 2\,mm with unprecedented sensitivity and used the resulting maps to derive the dust emissivity index $\beta$. Our sample of 105 objects detected in the $\beta$ map of the B211/B213 filament indicates that, overall, $\beta$ decreases from filament and prestellar cores ($\beta \sim 2\pm0.5$) to protostellar cores ($\beta \sim 1.2 \pm 0.2$) to T-Tauri protoplanetary disk ($\beta < 1$). The averaged dust emissivity index $\beta$ across the B211/B213 filament exhibits a flat ($\beta \sim 2\pm0.3$) profile. This may imply that dust grain sizes are rather homogeneous in the filament, start to grow significantly in size only after the onset of the gravitational contraction/collapse of prestellar cores to protostars, reaching big sizes in T Tauri protoplanetary disks. This evolution from the parent filament to T-Tauri disks happens on a timescale of about 1-2~Myr., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

High-resolution mapping of the hot gas in galaxy clusters is a key tool for cluster-based cosmological analyses. Taking advantage of the NIKA2 millimeter camera operated at the IRAM 30-m telescope, the NIKA2 SZ Large Program seeks to get a high-resolution follow-up of 38 galaxy clusters covering a wide mass range at intermediate to high redshift. The measured SZ fluxes will be essential to calibrate the SZ scaling relation and the galaxy clusters mean pressure profile, needed for the cosmological exploitation of SZ surveys. We present in this study a method to infer a mean pressure profile from cluster observations. We have designed a pipeline encompassing the map-making and the thermodynamical properties estimates from maps. We then combine all the individual fits, propagating the uncertainties on integrated quantities, such as $R_{500}$ or $P_{500}$, and the intrinsic scatter coming from the deviation to the standard self-similar model. We validate the proposed method on realistic LPSZ-like cluster simulations., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

We present the first preliminary results of the project \textit{ICED}, focusing on the face-on galaxy NGC4254. We use the millimetre maps observed with NIKA2 at IRAM-30m, as part of the IMEGIN Guaranteed Time Large Program, and of a wide collection of ancillary data (multi-wavelength photometry and gas phase spectral lines) that are publicly available. We derive the global and local properties of interstellar dust grains through infrared-to-radio spectral energy distribution fitting, using the hierarchical Bayesian code HerBIE, which includes the grain properties of the state-of-the-art dust model, THEMIS. Our method allows us to get the following dust parameters: dust mass, average interstellar radiation field, and fraction of small grains. Also, it is effective in retrieving the intrinsic correlations between dust parameters and interstellar medium properties. We find an evident anti-correlation between the interstellar radiation field and the fraction of small grains in the centre of NGC4254, meaning that, at strong radiation field intensities, very small amorphous carbon grains are efficiently destroyed by the ultra-violet photons coming from newly formed stars, through photo-desorption and sublimation. We observe a flattening of the anti-correlation at larger radial distances, which may be driven by the steep metallicity gradient measured in NGC4254., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

Three galaxy clusters selected from the XXL X-ray survey at high redshift and low mass ($z\sim1$ and $M_{500} \sim 1-2 \times 10^{14}$ M$_{\odot}$) were observed with NIKA2 to image their Sunyaev-Zel'dovich effect (SZ) signal. They all present an SZ morphology, together with the comparison with X-ray and optical data, that indicates dynamical activity related to merging events. Despite their disturbed intracluster medium, their high redshifts, and their low masses, the three clusters follow remarkably well the pressure profile and the SZ flux-mass relation expected from standard evolution. This suggests that the physics that drives cluster formation is already in place at $z \sim 1$ down to $M_{500} \sim 10^{14}$ M$_{\odot}$., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

The thermodynamical properties of the intracluster medium (ICM) are driven by scale-free gravitational collapse, but they also reflect the rich astrophysical processes at play in galaxy clusters. At low masses ($\sim 10^{14}$ M$_{\odot}$) and high redshift ($z \gtrsim 1$), these properties remain poorly constrained observationally, due to the difficulty in obtaining resolved and sensitive data. This paper aims at investigating the inner structure of the ICM as seen through the Sunyaev-Zel'dovich (SZ) effect in this regime of mass and redshift. Focus is set on the thermal pressure profile and the scaling relation between SZ flux and mass, namely the $Y_{\rm SZ} - M$ scaling relation. The three galaxy clusters XLSSC~072 ($z=1.002$), XLSSC~100 ($z=0.915$), and XLSSC~102 ($z=0.969$), with $M_{500} \sim 2 \times 10^{14}$ M$_{\odot}$, were selected from the XXL X-ray survey and observed with the NIKA2 millimeter camera to image their SZ signal. XMM-Newton X-ray data were used in complement to the NIKA2 data to derive masses based on the $Y_X - M$ relation and the hydrostatic equilibrium. The SZ images of the three clusters, along with the X-ray and optical data, indicate dynamical activity related to merging events. The pressure profile is consistent with that expected for morphologically disturbed systems, with a relatively flat core and a shallow outer slope. Despite significant disturbances in the ICM, the three high-redshift low-mass clusters follow remarkably well the $Y_{\rm SZ}-M$ relation expected from standard evolution. These results indicate that the dominant physics that drives cluster evolution is already in place by $z \sim 1$, at least for systems with masses above $M_{500} \sim 10^{14}$ M$_{\odot}$., Comment: 24 pages, published in A&A. Note that the title number had to be changed. This version matches the one from A&A

The NIKA2 camera operating at the IRAM 30 m telescope excels in high-angular resolution mapping of the thermal Sunyaev-Zeldovich effect towards galaxy clusters at intermediate and high-redshift. As part of the NIKA2 guaranteed time, the SZ Large Program (LPSZ) aims at tSZ-mapping a representative sample of SZ-selected galaxy clusters in the catalogues of the Planck satellite and of the Atacama Cosmology Telescope, and also observed in X-ray with XMM Newton or Chandra. Having completed observations in January 2023, we present tSZ maps of 38 clusters spanning the targeted mass ($3 < M_{500}/10^{14} M_{\odot} < 10$) and redshift ($0.5 < z < 0.9$) ranges. The first in depth studies of individual clusters highlight the potential of combining tSZ and X-ray observations at similar angular resolution for accurate mass measurements. These were milestones for the development of a standard data analysis pipeline to go from NIKA2 raw data to the thermodynamic properties of galaxy clusters for the upcoming LPSZ data release. Final products will include unprecedented measurements of the mean pressure profile and mass observable scaling relation using a distinctive SZ-selected sample, which will be key for ultimately improving the accuracy of cluster based cosmology., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

In the framework of the IMEGIN Large Program, we used the NIKA2 camera on the IRAM 30-m telescope to observe the edge-on galaxy NGC 891 at 1.15 mm and 2 mm and at a FWHM of 11.1" and 17.6", respectively. Multiwavelength data enriched with the new NIKA2 observations fitted by the HerBIE SED code (coupled with the THEMIS dust model) were used to constrain the physical properties of the ISM. Emission originating from the diffuse dust disk is detected at all wavelengths from mid-IR to mm, while mid-IR observations reveal warm dust emission from compact HII regions. Indications of mm excess emission have also been found in the outer parts of the galactic disk. Furthermore, our SED fitting analysis constrained the mass fraction of the small (< 15 Angstrom) dust grains. We found that small grains constitute 9.5% of the total dust mass in the galactic plane, but this fraction increases up to ~ 20% at large distances (|z| > 3 kpc) from the galactic plane., Comment: To appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

This study presents the first millimeter continuum mapping observations of two nearby galaxies, the starburst spiral galaxy NGC2146 and the dwarf galaxy NGC2976, at 1.15 mm and 2 mm using the NIKA2 camera on the IRAM 30m telescope, as part of the Guaranteed Time Large Project IMEGIN. These observations provide robust resolved information about the physical properties of dust in nearby galaxies by constraining their FIR-radio SED in the millimeter domain. After subtracting the contribution from the CO line emission, the SEDs are modeled spatially using a Bayesian approach. Maps of dust mass surface density, temperature, emissivity index, and thermal radio component of the galaxies are presented, allowing for a study of the relations between the dust properties and star formation activity (using observations at 24$\mu$m as a tracer). We report that dust temperature is correlated with star formation rate in both galaxies. The effect of star formation activity on dust temperature is stronger in NGC2976, an indication of the thinner interstellar medium of dwarf galaxies. Moreover, an anti-correlation trend is reported between the dust emissivity index and temperature in both galaxies., Comment: To appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

The field of Optimal Control under Partial Differential Equations (PDE) constraints is rapidly changing under the influence of Deep Learning and the accompanying automatic differentiation libraries. Novel techniques like Physics-Informed Neural Networks (PINNs) and Differentiable Programming (DP) are to be contrasted with established numerical schemes like Direct-Adjoint Looping (DAL). We present a comprehensive comparison of DAL, PINN, and DP using a general-purpose mesh-free differentiable PDE solver based on Radial Basis Functions. Under Laplace and Navier-Stokes equations, we found DP to be extremely effective as it produces the most accurate gradients; thriving even when DAL fails and PINNs struggle. Additionally, we provide a detailed benchmark highlighting the limited conditions under which any of those methods can be efficiently used. Our work provides a guide to Optimal Control practitioners and connects them further to the Deep Learning community., Comment: 8 pages, 4 figures, 3 tables

In cluster cosmology, cluster masses are the main parameter of interest. They are needed to constrain cosmological parameters through the cluster number count. As the mass is not an observable, a scaling relation is needed to link cluster masses to the integrated Compton parameters Y, i.e. the Sunyaev-Zeldovich observable (SZ). Planck cosmological results obtained with cluster number counts are based on a scaling relation measured with clusters at low redshift ($z$<0.5) observed in SZ and X-ray. In the SZ Large Program (LPSZ) of the NIKA2 collaboration, the scaling relation will be obtained with a sample of 38 clusters at intermediate to high redshift ($0.5

Dusty starless cores play an important role in regulating the initial phases of the formation of stars and planets. In their interiors, dust grains coagulate and ice mantles form, thereby changing the millimeter emissivities and hence the ability to cool. We mapped four regions with more than a dozen cores in the nearby Galactic filaments of Taurus and Perseus using the NIKA2 camera at the IRAM 30-meter telescope. Combining the 1mm to 2mm flux ratio maps with dust temperature maps from Herschel allowed to create maps of the dust emissivity index $\beta_{1,2}$ at resolutions of 2430 and 5600 a.u. in Taurus and Perseus, respectively. Here, we study the variation with total column densities and environment. $\beta_{1,2}$ values at the core centers ($A_V=12-19$mag) vary significantly between $\sim1.1$ and $2.3$. Several cores show a strong rise of $\beta_{1,2}$ from the outskirts at $\sim4$mag to the peaks of optical extinctions, consistent with the predictions of grain models and the gradual build-up of ice mantles on coagulated grains in the dense interiors of starless cores., Comment: to appear in Proc. of the mm Universe 2023 conference, Grenoble (France), June 2023, published by F. Mayet et al. (Eds), EPJ Web of conferences, EDP Sciences

In this article we examine recent developments in the research area concerning the creation of end-to-end models for the complete optimization of measuring instruments. The models we consider rely on differentiable programming methods and on the specification of a software pipeline including all factors impacting performance -- from the data-generating processes to their reconstruction and the extraction of inference on the parameters of interest of a measuring instrument -- along with the careful specification of a utility function well aligned with the end goals of the experiment. Building on previous studies originated within the MODE Collaboration, we focus specifically on applications involving instruments for particle physics experimentation, as well as industrial and medical applications that share the detection of radiation as their data-generating mechanism., Comment: 70 pages, 17 figures. To be submitted to journal