We develop a framework for learning properties of quantum states beyond the assumption of independent and identically distributed (i.i.d.) input states. We prove that, given any learning problem (under reasonable assumptions), an algorithm designed for i.i.d. input states can be adapted to handle input states of any nature, albeit at the expense of a polynomial increase in training data size (aka sample complexity). Importantly, this polynomial increase in sample complexity can be substantially improved to polylogarithmic if the learning algorithm in question only requires non-adaptive, single-copy measurements. Among other applications, this allows us to generalize the classical shadow framework to the non-i.i.d. setting while only incurring a comparatively small loss in sample efficiency. We use rigorous quantum information theory to prove our main results. In particular, we leverage permutation invariance and randomized single-copy measurements to derive a new quantum de Finetti theorem that mainly addresses measurement outcome statistics and, in turn, scales much more favorably in Hilbert space dimension.

The CGM around unobscured AGN has received much attention in recent years. Comparatively, nebulae associated with obscured AGN are less studied. Here, we simulate the Ly$\alpha$, H$\alpha$, and HeII nebulae around the two types of AGN at $z=2-3$ with ten massive systems from the FIRE simulations based on the unified model to show their differences and to test if they can be used to constrain the AGN model. We post-process the data with the CLOUDY and the Ly$\alpha$ radiative transfer code, RASCAS. Overall, we find that the Ly$\alpha$ nebulae around the unobscured AGN (type-I nebulae) and obscured AGN (type-II nebulae) do not exhibit significant differences in the luminosity, area, and HeII/Ly$\alpha$ when the simulated cutout is set to the halo virial radius. Whereas, the type-II nebulae exhibit less symmetric morphologies, flatter surface brightness profiles, and larger emission line widths (at $R\geq 10$ kpc) than those of the type-I nebulae. These nebulae properties exhibit complicated correlations with the AGN, indicating that nebulae observations can be applied to constrain the AGN engine. However, independent observations on nebulae in the mentioned emissions are insufficient to test the unified model as a priori in observations is not possible to know the direction and opening angle of the ionization cone. We prompt that the joint observations of Ly$\alpha$ nebulae and radio jets can help to reveal the ionization cone to probe the unified model. Our calculations suggest that this method requires $\geq 75$ type-II Ly$\alpha$ nebulae with current instruments to reach a confidence level of $\geq 95\%$.

JWST has revealed an abundance of supermassive black holes (BHs) in the early Universe, and yet the lowest mass seed black holes that gave rise to these populations remain elusive. Here we present a systematic search for broad-line Active Galactic Nuclei (AGNs) in some of the faintest high-$z$ galaxies surveyed yet by combining ultra-deep JWST/NIRSpec G395M spectroscopy with the strong lensing aid in Abell S1063. By employing the profile of the [OIII]$\lambda 5007$ emission lines as a template for narrow-line components and carefully cross-validating with mock observations, we identify a sample of ten broad-line AGNs at $4.5

DeepInverse is an open-source PyTorch-based library for solving imaging inverse problems. The library covers all crucial steps in image reconstruction from the efficient implementation of forward operators (e.g., optics, MRI, tomography), to the definition and resolution of variational problems and the design and training of advanced neural network architectures. In this paper, we describe the main functionality of the library and discuss the main design choices.

Dwarf galaxies provide powerful laboratories for studying galaxy formation physics. Their early assembly, shallow gravitational potentials, and bursty, clustered star formation histories make them especially sensitive to the processes that regulate baryons through multi-phase outflows. Using high-resolution, cosmological zoom-in simulations of a dwarf galaxy from \textit{the Pandora suite}, we explore the impact of stellar radiation, magnetic fields, and cosmic ray feedback on star formation, outflows, and metal retention. We find that our purely hydrodynamical model without non-thermal physics - in which supernova feedback is boosted to reproduce realistic stellar mass assembly - drives violent, overly enriched outflows that suppress the metal content of the host galaxy. Including radiation reduces the clustering of star formation and weakens feedback. However, the additional incorporation of cosmic rays produces fast, mass-loaded, multi-phase outflows consisting of both ionized and neutral gas components, in better agreement with observations. These outflows, which entrain a denser, more temperate ISM, exhibit broad metallicity distributions while preserving metals within the galaxy. Furthermore, the star formation history becomes more bursty, in agreement with recent JWST findings. These results highlight the essential role of non-thermal physics in galaxy evolution and the need to incorporate it in future galaxy formation models.

Multivariate longitudinal data of mixed-type are increasingly collected in many science domains. However, algorithms to cluster this kind of data remain scarce, due to the challenge to simultaneously model the within- and between-time dependence structures for multivariate data of mixed kind. We introduce the Mixture of Mixed-Matrices (MMM) model: reorganizing the data in a three-way structure and assuming that the non-continuous variables are observations of underlying latent continuous variables, the model relies on a mixture of matrix-variate normal distributions to perform clustering in the latent dimension. The MMM model is thus able to handle continuous, ordinal, binary, nominal and count data and to concurrently model the heterogeneity, the association among the responses and the temporal dependence structure in a parsimonious way and without assuming conditional independence. The inference is carried out through an MCMC-EM algorithm, which is detailed. An evaluation of the model through synthetic data shows its inference abilities. A real-world application on financial data is presented.

We report on the observation and measurement of astrometry, photometry, morphology, and activity of the interstellar object 3I/ATLAS, also designated C/2025 N1 (ATLAS), with the NSF-DOE Vera C. Rubin Observatory. The third interstellar object, comet 3I/ATLAS, was first discovered on UT 2025 July 1. Serendipitously, the Rubin Observatory collected imaging in the area of the sky inhabited by the object during regular commissioning activities. We successfully recovered object detections from Rubin visits spanning UT 2025 June 21 (10 days before discovery) to UT 2025 July 7. Facilitated by Rubin's high resolution and large aperture, we report on the detection of cometary activity as early as June 21st, and observe it throughout. We measure the location and magnitude of the object on 37 Rubin images in r, i, and z bands, with typical precision of about 20 mas (100 mas, systematic) and about 10 mmag, respectively. We use these to derive improved orbit solutions, and to show there is no detectable photometric variability on hourly timescales. We derive a V-band absolute magnitude of H_V = (13.7 +/- 0.2) mag, and an equivalent effective nucleus radius of around (5.6 +/- 0.7) km. These data represent the earliest observations of this object by a large (8-meter class) telescope reported to date, and illustrate the type of measurements (and discoveries) Rubin's Legacy Survey of Space and Time (LSST) will begin to provide once operational later this year.

We present an overview of the JWST GLIMPSE program, highlighting its survey design, primary science goals, gravitational lensing models, and first results. GLIMPSE provides ultra-deep JWST/NIRCam imaging across seven broadband filters (F090W, F115W, F200W, F277W, F356W, F444W) and two medium-band filters (F410M, F480M), with exposure times ranging from 20 to 40 hours per filter. This yields a 5$\sigma$ limiting magnitude of 30.9 AB (measured in a 0.2 arcsec diameter aperture). The field is supported by extensive ancillary data, including deep HST imaging from the Hubble Frontier Fields program, VLT/MUSE spectroscopy, and deep JWST/NIRSpec medium-resolution multi-object spectroscopy. Exploiting the strong gravitational lensing of the galaxy cluster Abell S1063, GLIMPSE probes intrinsic depths beyond 33 AB magnitudes and covers an effective source-plane area of approximately 4.4 arcmin$^2$ at $z \sim 6$. The program's central aim is to constrain the abundance of the faintest galaxies from $z \sim 6$ up to the highest redshifts, providing crucial benchmarks for galaxy formation models, which have so far been tested primarily on relatively bright systems. We present an initial sample of $\sim 540$ galaxy candidates identified at 6 < z < 16, with intrinsic UV magnitudes spanning $M_{\mathrm UV}$ = $-$20 to $-$12. This enables unprecedented constraints on the extreme faint end of the UV luminosity function at these epochs. In addition, GLIMPSE opens new windows for spatially resolved studies of star clusters in early galaxies and the detection and characterization of faint high-$z$ active galactic nuclei. This paper accompanies the first public data release, which includes reduced JWST and HST mosaics, photometric catalogs, and gravitational lensing models.

The Fermi Large Area Telescope (LAT) has revealed a mysterious extended excess of GeV gamma-ray emission around the Galactic Center, which can potentially be explained by unresolved emission from a population of pulsars, particularly millisecond pulsars (MSPs), in the Galactic bulge. We used the distributed volunteer computing system Einstein@Home to search the Fermi-LAT data for gamma-ray pulsations from sources in the inner Galaxy, to try to identify the brightest members of this putative population. We discovered four new pulsars, including one new MSP and one young pulsar whose angular separation to the Galactic Center of 0.93° is the smallest of any known gamma-ray pulsar. We demonstrate a phase-resolved difference imaging technique that allows the flux from this pulsar to be disentangled from the diffuse Galactic Center emission. No radio pulsations were detected from the four new pulsars in archival radio observations or during the MPIfR-MeerKAT Galactic Plane Survey. While the distances to these pulsars remain uncertain, we find that it is more likely that they are all foreground sources from the Galactic disk, rather than pulsars originating from the predicted bulge population. Nevertheless, our results are not incompatible with an MSP explanation for the GC excess, as only one or two members of this population would have been detectable in our searches.

The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogenic liquid xenon (8.5 tonnes total mass in cryostat). The experiment is expected to extend the sensitivity to WIMP dark matter by more than an order of magnitude compared to XENON1T, thanks to the larger active mass and the significantly reduced background, improved by novel systems such as a radon removal plant and a neutron veto. This article describes the XENONnT experiment and its sub-systems in detail and reports on the detector performance during the first science run.

Enshrouded in several well-known controversies, dwarf galaxies have been extensively studied to learn about the underlying cosmology, notwithstanding that physical processes regulating their properties are poorly understood. To shed light on these processes, we introduce the Pandora suite of 17 high-resolution (3.5 parsec half-cell side) dwarf galaxy formation cosmological simulations. Commencing with thermo-turbulent star formation and mechanical supernova feedback, we gradually increase the complexity of physics incorporated leading to full-physics models combining magnetism, on-the-fly radiative transfer and the corresponding stellar photoheating, and SN-accelerated cosmic rays. We investigate combinations of these processes, comparing them with observations to constrain what are the main mechanisms determining dwarf galaxy properties. We find hydrodynamical `SN feedback-only' simulations struggle to produce realistic dwarf galaxies, leading either to overquenched or too centrally concentrated, dispersion dominated systems when compared to observed field dwarfs. Accounting for radiation with cosmic rays results in extended and rotationally-supported systems. Spatially `distributed' feedback leads to realistic stellar and HI masses as well as kinematics. Furthermore, resolved kinematic maps of our full-physics models predict kinematically distinct clumps and kinematic misalignments of stars, HI and HII after star formation events. Episodic star formation combined with its associated feedback induces more core-like dark matter central profiles, which our `SN feedback-only' models struggle to achieve. Our results demonstrate the complexity of physical processes required to capture realistic dwarf galaxy properties, making tangible predictions for integral field unit surveys, radio synchrotron emission, and for galaxy and multi-phase interstellar medium properties that JWST will probe.

This paper tackles two key challenges: detecting small, dense, and overlapping objects (a major hurdle in computer vision) and improving the quality of noisy images, especially those encountered in industrial environments. [1, 2]. Our focus is on evaluating methods built on supervised deep learning. We perform an analysis of these methods, using a newly de- veloped dataset comprising over 10k images and 120k in- stances. By evaluating their performance, accuracy, and com- putational efficiency, we identify the most reliable detection systems and highlight the specific challenges they address in industrial applications. This paper also examines the use of deep learning models to improve image quality in noisy industrial environments. We introduce a lightweight model based on a fully connected convolutional network. Addition- ally, we suggest potential future directions for further enhanc- ing the effectiveness of the model. The repository of the dataset and proposed model can be found at: this https URL, this https URL

Both the Rubin Observatory and the first telescopes of the CTAO will be collecting data by 2026, marking a new era in optical and gamma-ray astronomy. Compared to predecessors, their enhanced sensitivity will extend extragalactic observations to a redshift of at least 2.5. This advancement offers insights into non-thermal astrophysical sources, particularly blazars. The 3-night cadence monitoring with Rubin, in one of its six filters, will produce blazar light curves that, when combined with targeted in-depth observations from the CTAO, could help distinguish acceleration and radiative models. Existing data from the ZTF and Fermi-LAT, though less sensitive, offer insights into what Rubin and the CTAO may achieve. However, the real-time processing of the immense data stream coming from Rubin/LSST presents a major challenge. Addressing this challenge is the work of brokers such as Fink, which we develop for multi-messenger astrophysics. Fink processes data in real-time before sending relevant information to other observatories like the CTAO. In this contribution, we present how we characterize the optical variability of blazars that emit in the gamma-ray range using the ZTF, with timescales spanning from the intra-night to multi-years. We identify properties in the resulting parameter space that could not only enable the identification of blazar-like sources, but also the characterization of the continuum of states. We describe our fast identification of transitions from one state to another, enabling the trigger of observations in the gamma-ray band and follow-up spectroscopic observations. Finally, we review the communication channel we set from the ZTF to the CTAO via Fink for blazars and discuss its outlook in light of the Rubin Observatory. This method is also applicable to other astrophysical sources and helps lay the groundwork for a fruitful era for time-domain astronomy.

The status of several representative gauge theories on various quantum space-times, mainly focusing on Yang-Mills type extensions together with a few matrix model formulations is overviewed. The common building blocks are derivation based differential calculus possibly twisted and noncommutative analog of the Koszul connection. The star-products related to the quantum space-times are obtained from a combination of harmonic analysis of group algebras combined with Weyl quantization. The remaining problems inherent to gauge theories on Moyal spaces in their two different formulations are outlined. A family of gauge invariant matrix models on $\mathbb{R}^3_\lambda$, a deformation of $\mathbb{R}^3$ is presented among which a solvable model. The characterization of 11 new quantum Minkowski space-times through their $*$-algebras is given. A gauge theory of Yang-Mills type is constructed on one recently explored of these space-times and compared to its counterpart built on the popular $\kappa$-Minkowski.

We study the phenomenology of physics beyond the Standard Model in long-baseline neutrino oscillation experiments using the most general parametrisation of heavy new physics in the framework of Standard Model Effective Theory (SMEFT), as well as its counterpart below the electroweak scale, Weak Effective Field Theory (WEFT). We compute neutrino production, oscillation, and detection rates in these frameworks, consistently accounting for renormalisation group running as well as SMEFT/WEFT matching. We moreover use appropriately modified neutrino--nucleus cross sections, focusing specifically on the regime of quasi-elastic scattering. Compared to the traditional formalism of non-standard neutrino interactions (NSI), our approach is theoretically more consistent, and it allows for straightforward joint analyses of data taken at different energy scales and by different experiments including not only neutrino oscillation experiments, but also searches for charged lepton flavour violation, low-energy precision measurements, and the LHC. As a specific example, we carry out a sensitivity study for the DUNE experiment and compute projected limits on the WEFT and SMEFT Wilson coefficients. Together with this paper, we also release a public simulation package called ``GLoBES-EFT'' for consistently simulating long-baseline neutrino oscillation experiments in the presence of new physics parameterized either in WEFT or in SMEFT. GLoBES-EFT is available from \href{this https URL}{GitHub}.

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the particle-level spectra across complex observables. Given that these approaches are conceptually diverse, they offer an exciting toolkit for a new class of measurements that can probe the Standard Model with an unprecedented level of detail and may enable sensitivity to new phenomena.

We leverage JWST's superb resolution to derive strong lensing mass maps of 14 clusters, spanning a redshift range of $z\sim0.25 - 1.06$ and a mass range of $M_{500}\sim2-12 \times 10^{14}M_\odot$, from the Strong LensIng and Cluster Evolution (SLICE) JWST program. These clusters represent a small subsample of the first clusters observed in the SLICE program that are chosen based on the detection of new multiple image constraints in the SLICE-JWST NIRCam/F150W2 and F322W2 imaging. These constraints include new lensed dusty galaxies and new substructures in previously identified lensed background galaxies. Four clusters have never been modeled before. For the remaining 10 clusters, we present updated models based on JWST and HST imaging and, where available, ground-based spectroscopy. We model the global mass profile for each cluster and report the mass enclosed within 200 and 500 kpc. We report the number of new systems identified in the JWST imaging, which in one cluster is as high as 19 new systems. The addition of new lensing systems and constraints from substructure clumps in lensed galaxies improves the ability of strong lensing models to accurately reproduce the interior mass distribution of each cluster. We also report the discovery of a candidate transient in a lensed image of the galaxy cluster SPT-CL J0516-5755. All lens models and their associated products are available for download at the Strong Lensing Cluster Atlas Data Base, which is hosted at Laboratoire d'Astrophysique de Marseille.

Numerous high-$z$ galaxies have recently been observed with the James Webb Space Telescope (JWST), providing new insights into early galaxy evolution. Their physical properties are typically derived through spectral energy distribution (SED) fitting, but the reliability of this approach for such early systems remains uncertain. Applying {\sc Bagpipes} on simulated SEDs at $z=6$ from the {\sc Sphinx$^{20}$} cosmological simulation, we examine uncertainties in the recovery of stellar masses, star formation rates (SFR$_{10}$), and stellar metallicities from mock JWST/Near-Infrared Camera photometry. Even without dust or emission lines, fitting the intrinsic stellar continuum overestimates the stellar mass by about 60\% on average (and by up to a factor of five for low-mass galaxies with recent starbursts) and underestimates SFR$_{10}$ by a factor of two, owing to inaccurate star formation histories and age-metallicity degeneracies. The addition of dust and nebular emission further amplifies these biases, yielding offsets of approximately +0.3 and -0.4 dex in stellar mass and SFR$_{10}$, respectively, while leaving stellar metallicities largely unconstrained. Incorporating bands free of strong emission lines, such as F410M, helps mitigate stellar mass overestimation by disentangling line emission from older stellar populations. We also find that best-fit or likelihood-weighted estimates are generally more accurate than median posterior values. Although stellar mass functions are reproduced reasonably well, the slope of the star formation main sequence depends sensitively on the adopted fitting model. Overall, these results underscore the importance of careful modelling when interpreting high-$z$ photometry, particularly for galaxies with recent star formation burst and/or strong emission lines, to minimise systematic biases in derived physical properties.

We present a Sugawara-type construction for boundary charges in 4d BF theory and in a general family of related TQFTs. Starting from the underlying current Lie algebra of boundary symmetries, this gives rise to well-defined quadratic charges forming an algebra of vector fields. In the case of 3d BF theory (i.e. 3d gravity), it was shown in [PRD 106 (2022), arXiv:2012.05263 [hep-th]] that this construction leads to a two-dimensional family of diffeomorphism charges which satisfy a certain modular duality. Here we show that adapting this construction to 4d BF theory first requires to split the underlying gauge algebra. Surprisingly, the space of well-defined quadratic generators can then be shown to be once again two-dimensional. In the case of tangential vector fields, this canonically endows 4d BF theory with a $\mathrm{diff}(S^2)\times\mathrm{diff}(S^2)$ or $\mathrm{diff}(S^2)\ltimes\mathrm{vect}(S^2)_\mathrm{ab}$ algebra of boundary symmetries depending on the gauge algebra. The prospect is to then understand how this can be reduced to a gravitational symmetry algebra by imposing Plebański simplicity constraints.