Search

Cryogenic calorimeters, also known as bolometers, are among the leading technologies for searching for rare events. The CUPID experiment is exploiting this technology to deploy a tonne-scale detector to search for neutrinoless double-beta decay of $^{100}$Mo. The CUPID collaboration proposed an innovative approach to assembling bolometers in a stacked configuration, held in position solely by gravity. This gravity-based assembly method is unprecedented in the field of bolometers and offers several advantages, including relaxed mechanical tolerances and simplified construction. To assess and optimize its performance, we constructed a medium-scale prototype hosting 28 Li$_2$MoO$_4$ crystals and 30 Ge light detectors, both operated as cryogenic calorimeters at the Laboratori Nazionali del Gran Sasso (Italy). Despite an unexpected excess of noise in the light detectors, the results of this test proved (i) a thermal stability better than $\pm$0.5 mK at 10 mK, (ii) a good energy resolution of Li$_2$MoO$_4$ bolometers, (6.6 $\pm$ 2.2) keV FWHM at 2615 keV, and (iii) a Li$_2$MoO$_4$ light yield measured by the closest light detector of 0.36 keV/MeV, sufficient to guarantee the particle identification requested by CUPID.

CUPID, the CUORE Upgrade with Particle Identification, is a next-generation experiment to search for neutrinoless double beta decay ($0\nu\beta\beta$) and other rare events using enriched Li$_2$$^{100}$MoO$_4$ scintillating bolometers. It will be hosted by the CUORE cryostat located at the Laboratori Nazionali del Gran Sasso in Italy. The main physics goal of CUPID is to search for $0\nu\beta\beta$\ of $^{100}$Mo with a discovery sensitivity covering the full neutrino mass regime in the inverted ordering scenario, as well as the portion of the normal ordering regime with lightest neutrino mass larger than 10 meV. With a conservative background index of 10$^{-4}$ cnts/(keV$\cdot$kg$\cdot$yr), 240 kg isotope mass, 5 keV FWHM energy resolution and 10 live-years of data taking, CUPID will have a 90\% C.L. half-life exclusion sensitivity of 1.8 $\cdot$ 10$^{27}$ yr, corresponding to an effective Majorana neutrino mass ($m_{\beta\beta}$) sensitivity of 9--15 meV, and a $3\sigma$ discovery sensitivity of 1 $\cdot$ 10$^{27}$ yr, corresponding to an $m_{\beta\beta}$ range of 12--21 meV., Comment: Prepared for submission to EPJC; 24 pages, 6 figures, and 2 tables

Different studies have suggested the emergence of the so-called golden mass, corresponding to a virial mass of $\sim 10^{12} \, M_{\rm \odot}$ and a stellar mass of $\sim 5 \times 10^{10} \, M_{\rm \odot}$. This mass scale marks a maximum in star formation efficiency, where galaxies are minimally affected by processes like SN and AGN feedback. We use \textsc{camels} cosmological simulations, based on the IllustrisTNG subgrid, to study the origin of this mass scale and whether it persists when varying feedback from SN and AGN. We focus on the correlation between the total-to-stellar mass within the half-mass radius and stellar mass, which follows an inverted bell-shaped trend, with a minimum at the golden mass. SN feedback processes impact the emergence of the golden mass, which shifts to lower mass for high values of wind velocity and energy. We find that most AGN feedback parameters influence the emergence of the golden mass, altering the correlation slope at high mass: the black hole radiative efficiency is the most impactful, followed by the black hole feedback factor and quasar threshold. ETGs preserve the inverted bell-shaped trend, while LTGs have monotonically decreasing DM fractions with mass, with mild indication of an inversion only at low redshift, confirming results from observations. When connecting with global quantities, we see that star formation efficiency show a bell-shaped trend peaking at the golden mass, with behaviours that mirror the central quantities. In ETGs a peak at lower mass is seen, while LTGs mirror the behaviour in the central quantity, with mild indication of a maximum in the stellar fraction only at low redshift. Overall, we find that the emergence of the golden mass is driven by the SN- and AGN-feedback and appears earlier in cosmic time for stronger-feedback simulations, which faster quench star formation in the most massive galaxies. (abridged), Comment: Submitted to A&A, 11 pages, 6 figures, 4 tables

The intrinsic alignment of galaxies is a major astrophysical contaminant to weak gravitational lensing measurements, and the study of its dependence on galaxy properties helps provide meaningful physical priors that aid cosmological analyses. This work studies for the first time the dependence of intrinsic alignments on galaxy structural parameters. We measure the intrinsic alignments of bright galaxies, selected on $r$-band magnitude $r<20$, in the Kilo-Degree Survey. Machine-learning-based photometric redshift estimates are available for this galaxy sample that help obtain a clean measurement of its intrinsic alignment signal. We supplement this sample with a catalogue of structural parameters from S\'ersic profile fits to the surface brightness profiles of the galaxies. We split the sample in galaxy intrinsic colour, luminosity and S\'ersic index, and we fit the non-linear linear alignment model to galaxy position - shape projected correlation function measurements at large scales. We observe a power-law luminosity dependence of the large-scale intrinsic alignment amplitude, $A_\mathrm{IA}$, for both the red and high S\'ersic index ($n_s>2.5$) samples, and find no significant difference between the two samples. We measure a $\sim1.5\sigma$ lower $A_\mathrm{IA}$ for red galaxies that also have a S\'ersic index $n_s<4$, compared to the expected amplitude predicted using sample's luminosity. We also probe the intrinsic alignment of red galaxies as a function of galaxy scale by varying the radial weight employed in the shape measurement. We find no significant difference on large scales but on small scales, alignments increase with galaxy scale. For intrinsically blue galaxies, we find $A_\mathrm{IA}=-0.67\pm1.00$, consistent with previous works. We also find alignments to be consistent with zero for the low S\'ersic index ($n_s<2.5$) sample., Comment: 14 pages, 7 figures, comments welcome!

In the context of upcoming large-scale surveys like Euclid, the necessity for the automation of strong lens detection is essential. While existing machine learning pipelines heavily rely on the classification probability (P), this study intends to address the importance of integrating additional metrics, such as Information Content (IC) and the number of pixels above the segmentation threshold, to alleviate the false positive rate in unbalanced data-sets. In this work, we introduce a segmentation algorithm (U-Net) as a supplementary step in the established strong gravitational lens identification pipeline (Denselens), which primarily utilizes P$_{\rm mean}$ and IC$_{\rm mean}$ parameters for the detection and ranking. The results demonstrate that the inclusion of segmentation enables significant reduction of false positives by approximately 25 per cent in the final sample extracted from DenseLens, without compromising the identification of strong lenses. The main objective of this study is to automate the strong lens detection process by integrating these three metrics. To achieve this, a decision tree-based selection process is introduced, applied to the Kilo Degree Survey (KiDS) data. This process involves rank-ordering based on classification scores, filtering based on Information Content, and segmentation score. Additionally, the study presents 14 newly discovered strong lensing candidates identified by the U-Denselens network using the KiDS DR4 data.

Compact, star-forming galaxies with high star formation rate surface densities ($\Sigma_{\text{SFR}}$) are often efficient Lyman continuum (LyC) emitters at $z\leq 4.5$, likely as intense stellar feedback creates low-density channels that allow photons to escape. Irregular or disturbed morphologies, such as those resulting from mergers, can also facilitate LyC escape by creating anisotropic gas distributions. We investigate the influence of galaxy morphology on LyC production and escape at redshifts $5 \leq z \leq 7$ using observations from various \textit{James Webb Space Telescope} (JWST) surveys. Our sample consists of 436 sources, which are predominantly low-mass ($\sim 10^{8.15} M_\odot$), star-forming galaxies with ionizing photon efficiency ($\xi_{\rm ion}$) values consistent with canonical expectations. Since direct measurements of $f_{\rm esc}$ are not possible during the Epoch of Reionization (EoR), we predict $f_{\rm esc}$ for high-redshift galaxies by applying survival analysis to a subsample of LyC emitters from the Low-Redshift Lyman Continuum Survey (LzLCS), selected to be direct analogs of reionization-era galaxies. We find that these galaxies exhibit on average modest predicted escape fractions ($\sim 0.04$). Additionally, we assess the correlation between morphological features and LyC emission. Our findings indicate that neither $\xi_{\rm ion}$ nor the predicted $f_{\rm esc}$ values show a significant correlation with the presence of merger signatures. This suggests that in low-mass galaxies at $z \geq 5$, strong morphological disturbances are not the primary mechanism driving LyC emission and leakage. Instead, compactness and star formation activity likely play a more pivotal role in regulating LyC escape., Comment: Submitted to A&A

Early-type galaxies (ETGs) are reference systems to understand galaxy formation and evolution processes. The physics of their collapse and internal dynamics are codified in well-known scaling relations. Cosmological hydrodynamical simulations play an important role, providing insights into the 3D distribution of matter and galaxy formation mechanisms, as well as validating methods to infer the properties of real objects. In this work, we present the closest-to-reality sample of ETGs from the IllustrisTNG100-1 simulation, dubbed "virtual-ETGs," based on an observational-like algorithm that combines standard projected and three-dimensional galaxy structural parameters. We extract 2D photometric information by projecting the galaxies' light into three planes and modeling them via S\'ersic profiles. Aperture velocity dispersions, corrected for softened central dynamics, are calculated along the line-of-sight orthogonal to the photometric projection plane. Central mass density profiles assume a power-law model, while 3D masses remain unmodified from the IllustrisTNG catalogue. The final catalogue includes $10121$ galaxies at redshifts $z \leq 0.1$. By comparing the virtual properties with observations, we find that the virtual-ETG scaling relations (e.g., size-mass, size-central surface brightness, and Faber-Jackson), central density slopes, and scaling relations among total density slopes and galaxy structural parameters are generally consistent with observations. We make the virtual-ETG publicly available for galaxy formation studies and plan to use this sample as a training set for machine learning tools to infer galaxy properties in future imaging and spectroscopic surveys.

We present the Cosmic Evolution Early Release Science (CEERS) Survey, a 77.2 hour Director's Discretionary Early Release Science Program. CEERS demonstrates, tests, and validates efficient extragalactic surveys using coordinated, overlapping parallel observations with the JWST instrument suite, including NIRCam and MIRI imaging, NIRSpec low (R~100) and medium (R~1000) resolution spectroscopy, and NIRCam slitless grism (R~1500) spectroscopy. CEERS targets the Hubble Space Telescope-observed region of the Extended Groth Strip (EGS) field, supported by a rich set of multiwavelength data. CEERS facilitated immediate community science in both of the extragalactic core JWST science drivers ``First Light" and ``Galaxy Assembly," including: 1) The discovery and characterization of large samples of galaxies at z >~ 10 from ~90 arcmin^2 of NIRCam imaging, constraining their abundance and physical nature; 2) Deep spectra of >1000 galaxies, including dozens of galaxies at 63; and 4) Characterizing galaxy mid-IR emission with MIRI to study dust-obscured star-formation and supermassive black hole growth at z~1-3. As a legacy product for the community, the CEERS team has provided several data releases, accompanied by detailed notes on the data reduction procedures and notebooks to aid in reproducibility. In addition to an overview of the survey and quality of the data, we provide science highlights from the first two years with CEERS data., Comment: 38 pages, 13 figures, 6 tables

This Letter reports the precise measurement of reactor antineutrino spectrum and flux based on the full data set of 4.7 million inverse-beta-decay (IBD) candidates collected at Daya Bay near detectors. Expressed in terms of the IBD yield per fission, the antineutrino spectra from all reactor fissile isotopes and the specific $\mathrm{^{235}U}$ and $\mathrm{^{239}Pu}$ isotopes are measured with 1.3$\%$, 3$\%$ and 8$\%$ uncertainties respectively near the 3 MeV spectrum peak in reconstructed energy, reaching the best precision in the world. The total antineutrino flux and isotopic $\mathrm{^{235}U}$ and $\mathrm{^{239}Pu}$ fluxes are precisely measured to be $5.84\pm0.07$, $6.16\pm0.12$ and $4.16\pm0.21$ in units of $10^{-43} \mathrm{cm^2/fission}$. These measurements are compared with the Huber-Mueller (HM) model, the reevaluated conversion model based on the Kurchatov Institute (KI) measurement and the latest Summation Model (SM2023). The Daya Bay flux shows good consistency with KI and SM2023 models, but disagrees with HM model. The Daya Bay spectrum, however, disagrees with all model predictions.

Classification of spectra (1) and anomaly detection (2) are fundamental steps to guarantee the highest accuracy in redshift measurements (3) in modern all-sky spectroscopic surveys. We introduce a new Galaxy Spectra Neural Network (GaSNet-III) model that takes advantage of generative neural networks to perform these three tasks at once with very high efficiency. We use two different generative networks, an autoencoder-like network and U-Net, to reconstruct the rest-frame spectrum (after redshifting). The autoencoder-like network operates similarly to the classical PCA, learning templates (eigenspectra) from the training set and returning modeling parameters. The U-Net, in contrast, functions as an end-to-end model and shows an advantage in noise reduction. By reconstructing spectra, we can achieve classification, redshift estimation, and anomaly detection in the same framework. Each rest-frame reconstructed spectrum is extended to the UV and a small part of the infrared (covering the blueshift of stars). Owing to the high computational efficiency of deep learning, we scan the chi-squared value for the entire type and redshift space and find the best-fitting point. Our results show that generative networks can achieve accuracy comparable to the classical PCA methods in spectral modeling with higher efficiency, especially achieving an average of $>98\%$ classification across all classes ($>99.9\%$ for star), and $>99\%$ (stars), $>98\%$ (galaxies) and $>93\%$ (quasars) redshift accuracy under cosmology research requirements. By comparing different peaks of chi-squared curves, we define the ``robustness'' in the scanned space, offering a method to identify potential ``anomalous'' spectra. Our approach provides an accurate and high-efficiency spectrum modeling tool for handling the vast data volumes from future spectroscopic sky surveys., Comment: 17 pages and 16 figures. Submitted to MNRAS

The high-precision kaonic neon X-ray transitions measurement performed by the SIDDHARTA-2 collaboration at the DA$\Phi$NE collider is reported. Both the X-ray energies and yields for high-n transitions were measured, demonstrating the feasibility of sub-eV Xray spectroscopy for kaonic atoms using low-Z gaseous targets. The measurement provides valuable insights into the de-excitation processes in kaonic atoms, providing new input data for the refinement of the corresponding theoretical models, and a framework for testing Quantum Electrodynamics in strange exotic atoms., Comment: 9 pages, 3 figures, 1 table

In this paper, we study how absorption-line systems affect the spectra and redshifts of quasars (QSOs), using catalogs of Mg II absorbers from the early data release (EDR) and first data release (DR1) of the Dark Energy Spectroscopic Instrument (DESI). We determine the reddening effect of an absorption system by fitting an un-reddened template spectrum to a sample of 50,674 QSO spectra that contain Mg II absorbers. We find that reddening caused by intervening absorbers (voff > 3500 km/s) has an average color excess of E(B-V) = 0.04 magnitudes. We find that the E(B-V) tends to be greater for absorbers at low redshifts, or those having Mg II absorption lines with higher equivalent widths, but shows no clear trend with voff for intervening systems. However, the E(B-V) of associated absorbers, those at voff < 3500 km/s, shows a strong trend with voff , increasing rapidly with decreasing voff and peaking (approximately 0.15 magnitudes) around voff = 0 km/s. We demonstrate that Mg II absorbers impact redshift estimation for QSOs by investigating the distributions of voff for associated absorbers. We find that at z > 1.5 these distributions broaden and bifurcate in a nonphysical manner. In an effort to mitigate this effect, we mask pixels associated with the Mg II absorption lines and recalculate the QSO redshifts. We find that we can recover voff populations in better agreement with those for z < 1.5 absorbers and in doing so typically shift background QSO redshifts by delta_z approximately equal to plus or minus 0.005., Comment: 13 pages, 9 figures, submitted to AAS

Efficient and effective service delivery in Public Administration (PA) relies on the development and utilization of key performance indicators (KPIs) for evaluating and measuring performance. This paper presents an innovative framework for KPI construction within performance evaluation systems, leveraging Random Forest algorithms and variable importance analysis. The proposed approach identifies key variables that significantly influence PA performance, offering valuable insights into the critical factors driving organizational success. By integrating variable importance analysis with expert consultation, relevant KPIs can be systematically developed, ensuring that improvement strategies address performance-critical areas. The framework incorporates continuous monitoring mechanisms and adaptive phases to refine KPIs in response to evolving administrative needs. This study aims to enhance PA performance through the application of machine learning techniques, fostering a more agile and results-driven approach to public administration.

We measure the spectral properties of a sample of 20 galaxies at z~0.35 selected for having surprisingly red JWST/NIRCAM F200W-F444W colors. 19 galaxies were observed with JWST/NIRSpec in the PRISM configuration, while one galaxy was observed with the high resolution gratings. 17/20 galaxies in our sample exhibit strong 3.3 $\mu m$ polycyclic aromatic hydrocarbon (PAH$_{3.3}$) emission (equivalent width EW(PAH$_{3.3}$)$>0.03\mu m$). In these galaxies, the strength of the color excess does not depend on environment and it correlates with EW(PAH$_{3.3}$). Nonetheless, the presence of the PAH$_{3.3}$ alone can not fully explain the color excess, as an equivalent width of ~0.1$\mu m$ is able to increase the color of galaxies by only 0.13 mag. A contribution from a hot dust component is required to explain the excess. Both the EW(PAH$_{3.3}$) and flux correlate with the H$\alpha$ equivalent width and flux, suggesting that they are produced by the same mechanism. 5/20 galaxies showing PAH would be classified as passive based on broad band rest frame colors ((B-V) and/or UVJ diagrams) and are hence "faux-passive". Of these, 3 galaxies have a significantly lower EW(PAH$_{3.3}$) given their color and also have low EW(H$\alpha$) and we tentatively conclude this behaviour is due to the presence of an AGN. The three galaxies with no PAH$_{3.3}$ in emission have passive spectra, as do the 8 galaxies in our sample with normal F200W-F444W colors. We therefore conclude that the PAH$_{3.3}$ feature is linked to dust-enshrouded star formation. The dust corrected SFR from PAH$_{3.3}$ is a factor of 3.5 higher than the SFR obtained from H$\alpha$, suggesting that these galaxies are characterized by significant amounts of dust., Comment: A&A in press

We present an analysis of the UV continuum slope, beta, using a sample of 733 galaxies selected from a mixture of JWST ERS/GTO/GO observational programs and with z > 4. We consider spectroscopic data obtained with the low resolution PRISM/CLEAR NIRSpec configuration. Studying the correlation of beta with M_UV we find a decreasing trend of beta = (-0.056 +- 0.017) M_UV - (3.01 +- 0.34), consistent with brighter galaxies having redder beta as found in previous works. However, analysing the trend in separate redshift bins, we find that at high redshift the relation becomes much flatter, consistent with a flat slope. Furthermore, we find that beta decreases with redshift with an evolution as beta = (-0.075 +- 0.010) z - (1.496 +- 0.056), consistent with most previous results that show a steepening of the spectra going at higher z. We then select a sample of galaxies with extremely blue slopes (beta < -2.6): such slopes are steeper than what is predicted by stellar evolution models, even for dust free, young, metal poor populations, when the contribution of nebular emission is included. We select 51 extremely blue galaxies (XBGs) and we investigate the possible physical origin of their steep slopes, comparing them to a sub-sample of redder galaxies (matched in redshift and M_UV). We find that XBGs have younger stellar populations, stronger ionization fields, lower dust attenuation, and lower but not pristine metallicity (~ 10% solar) compared to red galaxies. However, these properties alone cannot explain the extreme beta values. By using indirect inference of Lyman continuum escape, using the most recent models, we estimate escape fractions f_esc > 10% in at least 25% of XBGs, while all the red sources have smaller f_esc. A reduced nebular continuum contribution as due to either a high escape fraction or to a bursty star-formation history is likely the origin of the extremely blue slopes., Comment: Submitted to A&A (12 pages, 6 figures, and 2 tables)

Investigating the ionizing emission of star-forming galaxies is critical to understanding their contribution to reionization and their impact on the surrounding environment. The number of ionizing photons available to reionize the intergalactic medium (IGM) depends not only on the abundance of galaxies but also on their efficiency in producing ionizing photons ($\xi_{ion}$). We aim to estimate the $\xi_{ion}$ using Balmer lines in a sample of 731 galaxies at $4\leq z \leq 10$ selected from different JWST surveys. We used the available HST and JWST photometry to perform a SED fitting in the sample to determine their physical properties. We used the BAGPIPES code and assumed a delayed exponential model for the star formation history. We used the NIRSpec spectra from prism or grating configurations to estimate Balmer luminosities and then constrained $\xi_{ion}$ values after dust correction. We find a mean value of 10$^{25.23}$Hz erg$^{-1}$ for $\xi_{ion}$ in the sample with an observed scatter of 0.42dex. We find an increase in the median values of $\xi_{ion}$ which confirms the redshift evolution of $\xi_{ion}$ found in other works. Regarding the relation with physical properties, we find a decrease of $\xi_{ion}$ with increasing stellar mass, indicating that low-mass galaxies are efficient producers of ionizing photons. We also find an increase of $\xi_{ion}$ with increasing specific star formation rate (sSFR) and increasing UV absolute magnitude, which indicates that faint galaxies and with high sSFR are also efficient producers. We also investigated the relation of $\xi_{ion}$ with the EW([OIII]$\lambda$5007) and find that galaxies with the higher EW([OIII]) are the more efficient producers of ionizing photons. Similarly, we find that galaxies with higher O32 ratios and lower gas-phase metallicities (based on the R23 calibration) show higher $\xi_{ion}$ values., Comment: Submitted to A&A. Comments are welcome

Physical processes impact galaxy formation and evolution in diverse ways, requiring validation of their implementation in cosmological simulations through comparisons with real data across various galaxy types and properties. In this second paper of the CASCO series, we compare the structural properties and dark matter (DM) content of early-type galaxies from the CAMELS IllustrisTNG simulations to three observational datasets (SPIDER, $\textrm{ATLAS}^{\textrm{3D}}$, and MaNGA DynPop), to constrain cosmological and astrophysical feedback parameters, contrasting these results with those obtained for late-type galaxies. We analyze the size-, internal DM fraction-, and DM mass-stellar mass relations, identifying the best-fit simulation for each dataset. For SPIDER, we find cosmological parameter values consistent with literature and results obtained from the comparison between simulations and late-type galaxies, with supernova feedback parameters differing from results derived for late-type galaxies. For $\textrm{ATLAS}^{\textrm{3D}}$, cosmological parameter results align with SPIDER, while supernova feedback parameters are more consistent with late-type galaxies results. MaNGA DynPop yields extreme cosmological parameter values but similar supernova feedback results to $\textrm{ATLAS}^{\textrm{3D}}$. However, no single simulation matches the full range of observational trends, especially when combining early- and late-type galaxies from MaNGA DynPop. These findings highlight the limitations of simulations in reproducing diverse galaxy properties, underscoring the challenge of capturing the complexity of galaxy formation across all types., Comment: 11 figures, 7 tables, 20 pages, accepted for publication on A&A

We present cosmological results from the measurement of clustering of galaxy, quasar and Lyman-$\alpha$ forest tracers from the first year of observations with the Dark Energy Spectroscopic Instrument (DESI Data Release 1). We adopt the full-shape (FS) modeling of the power spectrum, including the effects of redshift-space distortions, in an analysis which has been validated in a series of supporting papers. In the flat $\Lambda$CDM cosmological model, DESI (FS+BAO), combined with a baryon density prior from Big Bang Nucleosynthesis and a weak prior on the scalar spectral index, determines matter density to $\Omega_\mathrm{m}=0.2962\pm 0.0095$, and the amplitude of mass fluctuations to $\sigma_8=0.842\pm 0.034$. The addition of the cosmic microwave background (CMB) data tightens these constraints to $\Omega_\mathrm{m}=0.3056\pm 0.0049$ and $\sigma_8=0.8121\pm 0.0053$, while further addition of the the joint clustering and lensing analysis from the Dark Energy Survey Year-3 (DESY3) data leads to a 0.4% determination of the Hubble constant, $H_0 = (68.40\pm 0.27)\,{\rm km\,s^{-1}\,Mpc^{-1}}$. In models with a time-varying dark energy equation of state, combinations of DESI (FS+BAO) with CMB and type Ia supernovae continue to show the preference, previously found in the DESI DR1 BAO analysis, for $w_0>-1$ and $w_a<0$ with similar levels of significance. DESI data, in combination with the CMB, impose the upper limits on the sum of the neutrino masses of $\sum m_\nu < 0.071\,{\rm eV}$ at 95% confidence. DESI data alone measure the modified-gravity parameter that controls the clustering of massive particles, $\mu_0=0.11^{+0.45}_{-0.54}$, while the combination of DESI with the CMB and the clustering and lensing analysis from DESY3 constrains both modified-gravity parameters, giving $\mu_0 = 0.04\pm 0.22$ and $\Sigma_0 = 0.044\pm 0.047$, in agreement with general relativity. [Abridged.], Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/). 55 pages, 10 figures

We present the samples of galaxies and quasars used for DESI 2024 cosmological analyses, drawn from the DESI Data Release 1 (DR1). We describe the construction of large-scale structure (LSS) catalogs from these samples, which include matched sets of synthetic reference `randoms' and weights that account for variations in the observed density of the samples due to experimental design and varying instrument performance. We detail how we correct for variations in observational completeness, the input `target' densities due to imaging systematics, and the ability to confidently measure redshifts from DESI spectra. We then summarize how remaining uncertainties in the corrections can be translated to systematic uncertainties for particular analyses. We describe the weights added to maximize the signal-to-noise of DESI DR1 2-point clustering measurements. We detail measurement pipelines applied to the LSS catalogs that obtain 2-point clustering measurements in configuration and Fourier space. The resulting 2-point measurements depend on window functions and normalization constraints particular to each sample, and we present the corrections required to match models to the data. We compare the configuration- and Fourier-space 2-point clustering of the data samples to that recovered from simulations of DESI DR1 and find they are, generally, in statistical agreement to within 2\% in the inferred real-space over-density field. The LSS catalogs, 2-point measurements, and their covariance matrices will be released publicly with DESI DR1., Comment: This DESI Collaboration Key Publication is part of the 2024 publication series using the first year of observations (see https://data.desi.lbl.gov/doc/papers/)

We present the measurements and cosmological implications of the galaxy two-point clustering using over 4.7 million unique galaxy and quasar redshifts in the range $0.1

The VLT Survey Telescope Survey of Mass Assembly and Structural Hierarchy (VST-SMASH) aims to detect tidal features and remnants around very nearby galaxies, a unique and essential diagnostic of the hierarchical nature of galaxy formation. Leveraging optimal sky conditions at ESO's Paranal Observatory, combined with the VST's multi-band optical filters, VST-SMASH aims to be the definitive survey of stellar streams and tidal remnants in the Local Volume, targeting a low surface-brightness limit of $\mu \sim$ 30 mag arcsec$^{-2}$ in the g and r bands, and $\mu \sim$ 28 mag arcsec$^{-2}$ in the i band, in a volume-limited sample of local galaxies within 11 Mpc and the Euclid footprint., Comment: 4 pages, 2 figures, 1 table, published in the ESO Messenger 193

We present a decentralized reinforcement learning (RL) approach to address the multi-agent shepherding control problem, departing from the conventional assumption of cohesive target groups. Our two-layer control architecture consists of a low-level controller that guides each herder to contain a specific target within a goal region, while a high-level layer dynamically selects from multiple targets the one an herder should aim at corralling and containing. Cooperation emerges naturally, as herders autonomously choose distinct targets to expedite task completion. We further extend this approach to large-scale systems, where each herder applies a shared policy, trained with few agents, while managing a fixed subset of agents.

We present ALMA observations on the high-redshift galaxy GHZ2 and report a successful detection of the rest-frame 88um atomic transition from doubly-ionized Oxygen at z=12.3327+/-0.0005. Based on these observations, combined with additional constraints on the [OIII] 52um line luminosity and previous JWST data, we argue that GHZ2 is likely powered by compact and young star formation, and show that it follows well-established relationships found for giant HII regions and metal-poor star-forming dwarf galaxies that are known to host bright super star clusters. Additionally, these observations provide new constraints on the Oxygen electron density (100 < n_e[cm^-3] < 4,000) and dynamical mass (M_dyn=3-8x10^8M_sun). The existence of these massive starburst systems 13.3Gyr ago might explain the origin of today's globular clusters, a long-standing question in astronomy. To test this, we present observational probes to investigate whether sources like GHZ2 are linked to the formation of today's globular clusters or other more massive compact stellar systems., Comment: Accepted for publication in The Astrophysical Journal Letters

This paper proposes the RePAIR dataset that represents a challenging benchmark to test modern computational and data driven methods for puzzle-solving and reassembly tasks. Our dataset has unique properties that are uncommon to current benchmarks for 2D and 3D puzzle solving. The fragments and fractures are realistic, caused by a collapse of a fresco during a World War II bombing at the Pompeii archaeological park. The fragments are also eroded and have missing pieces with irregular shapes and different dimensions, challenging further the reassembly algorithms. The dataset is multi-modal providing high resolution images with characteristic pictorial elements, detailed 3D scans of the fragments and meta-data annotated by the archaeologists. Ground truth has been generated through several years of unceasing fieldwork, including the excavation and cleaning of each fragment, followed by manual puzzle solving by archaeologists of a subset of approx. 1000 pieces among the 16000 available. After digitizing all the fragments in 3D, a benchmark was prepared to challenge current reassembly and puzzle-solving methods that often solve more simplistic synthetic scenarios. The tested baselines show that there clearly exists a gap to fill in solving this computationally complex problem., Comment: NeurIPS 2024, Track Datasets and Benchmarks, 10 pages

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an $r$-band $5\sigma$ magnitude limit of $24.8$ and survey footprint of $1347$ deg$^2$, it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the $27\sigma$ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests., Comment: 29 pages, 27 figures, 4 tables; Accepted for publication on Astronomy & Astrophysics; The code used for this work is published on https://github.com/yanzastro/tiaogeng

Obtaining well-calibrated photometric redshift probability densities for galaxies without a spectroscopic measurement remains a challenge. Deep learning discriminative models, typically fed with multi-band galaxy images, can produce outputs that mimic probability densities and achieve state-of-the-art accuracy. However, such models may be affected by miscalibration that would result in discrepancies between the model outputs and the actual distributions of true redshifts. Our work develops a novel method called the Contrastive Learning and Adaptive KNN for Photometric Redshift (CLAP) that resolves this issue. It leverages supervised contrastive learning (SCL) and k-nearest neighbours (KNN) to construct and calibrate raw probability density estimates, and implements a refitting procedure to resume end-to-end discriminative models ready to produce final estimates for large-scale imaging data. The harmonic mean is adopted to combine an ensemble of estimates from multiple realisations for improving accuracy. Our experiments demonstrate that CLAP takes advantage of both deep learning and KNN, outperforming benchmark methods on the calibration of probability density estimates and retaining high accuracy and computational efficiency. With reference to CLAP, we point out that miscalibration is particularly sensitive to the method-induced excessive correlations among data instances in addition to the unaccounted-for epistemic uncertainties. Reducing the uncertainties may not guarantee the removal of miscalibration due to the presence of such excessive correlations, yet this is a problem for conventional deep learning methods rather than CLAP. These discussions underscore the robustness of CLAP for obtaining photometric redshift probability densities required by astrophysical and cosmological applications. This is the first paper in our series on CLAP., Comment: 22 + 6 pages, 9 + 5 figures

We present JWST/NIRSpec PRISM spectroscopic characterization of GHZ9 at z= 10.145 $\pm$ 0.010, currently the most distant source detected by the Chandra X-ray Observatory. The spectrum reveals several UV high-ionization lines, including CII, SiIV, [NIV], CIV, HeII, OIII], NIII], and CIII]. The prominent rest-frame equivalent widths (EW(CIV)$\simeq$65A, EW(HeII)$\simeq$18A, EW(CIII])$\simeq$48A) show the presence of a hard radiation field, while the analysis of line ratio diagnostics suggest this galaxy hosts both AGN and star-formation activity. GHZ9 is nitrogen-enriched (6--9.5 times solar), carbon-poor (0.2--0.65 times solar), metal-poor (Z = 0.01--0.1 Z$_{\odot}$), and compact ($<$ 106 pc), similarly to GNz11, GHZ2, and recently discovered N-enhanced high redshift objects. We exploited the newly available JWST/NIRSpec and NIRCam dataset to perform an independent analysis of the Chandra data confirming that GHZ9 is the most likely JWST source associated to X-ray emission at 0.5-7 keV. Assuming a spectral index $\Gamma$ = 2.3 (1.8), we estimate a black hole (BH) mass of 1.60 $\pm$ 0.31 (0.48 $\pm$ 0.09) $\times$ 10$^8$M$_{\odot}$, which is consistent either with Eddington-accretion onto heavy ($\geq$ 10$^6$ M$_{\odot}$) BH seeds formed at z=18, or super-Eddington accretion onto a light seed of $\sim$ 10$^2-10^4$ M$_{\odot}$ at z = 25. The corresponding BH-to-stellar mass ratio M$_{BH}$/M$_{star}$= 0.33$\pm$0.22 (0.10$\pm$0.07), with a stringent limit $>$0.02, implies an accelerated growth of the BH mass with respect to the stellar mass. GHZ9 is the ideal target to constrain the early phases of AGN-galaxy coevolution with future multi-frequency observations., Comment: Submitted to ApJ

The SIDDHARTA-2 collaboration aims to measure for the first time the shift and width induced on the $1s$ level of kaonic deuterium by the strong interaction. In the preliminary phase to the experiment, a test run using a Helium-4 target was performed to optimize the performance of the full experimental apparatus. This preliminary study highlighted the possibility to measure transition lines coming from intermediate mass kaonic atoms, such as kaonic carbon and kaonic aluminum. In order to measure transitions where strong interaction is manifesting at higher energies, out of the energy range of the SIDDHARTA-2 apparatus, the collaboration is testing a new detector system which exploits a novel compound semiconductor, the Cadmium Zinc Telluride. Tests are now running at DA$\Phi$NE to study the performance of this detector, exploring the possibility to build a dedicated setup., Comment: 10 pages, 3 figures, eQCD conference

The SIDDHARTA-2 collaboration built a new cadmium-zinc-telluride (CZT, CdZnTe)-based X-ray detection system, used for the first time in the DA$\Phi$NE electron-positron collider at INFN-LNF. The aim of this work is to show that these detectors present optimal long- and short-term linearity and stability to perform precise spectroscopic measurements in a collider environment. The spectra used as references for calibration are reported, and the results about the linearity and stability studies are presented. It is also discussed and showed what is the proper function to describe all the effects that alter the Gaussian shape in semiconductors, particularly evident in the CZT case. Good residuals and resolutions were obtained for all the calibrations. In a test run with the source and the collider beam on, it was demonstrated that the calibrations made with beam off are optimal also when the beam is on, and the actual systematics in a physics run were estimated. These promising results show the potentialities of this detector in the high rate environment of a particle collider, and pave the way for the use of CZT detectors in kaonic atoms researches and in accelerators, with applications for particle and nuclear physics., Comment: 15 pages, 9 figures, 3 tables

We present JWST/NIRSpec PRISM follow-up of candidate galaxies at z=9-11 selected from deep JWST/NIRCam photometry in GLASS-JWST Early Release Science data. We spectroscopically confirm six sources with secure redshifts at z = 9.52-10.43, each showing multiple emission lines. An additional object is likely at z = 10.66, based on its Lya-break and a single emission feature, while one source is a lower redshift interloper. The sample includes the first JWST-detected candidate at z=10, GHZ1/GLASS-z10, which we confirm at z = 9.875, and the X-ray detected AGN GHZ9 confirmed at z = 10.145. Three objects in our sample, including GHZ9, have EW(CIII])>20A and occupy a region compatible with AGN emission in the EW(CIII]) vs CIV/CIII] diagram. The spectroscopic sample confirms a high abundance of galaxies at z > 9. We measure a number density of z=10 galaxies in the GLASS-JWST ERS field that is a factor of >3 higher than other JWST-based estimates at demagnified rest-frame magnitudes of -21 < Muv < -19. We find that the positions of these galaxies in redshift and angular space are not consistent with all of them being part of a unique progenitor of present-day galaxy clusters. The high density of objects in the GLASS region can be explained either by clustering on large scales or by a superposition of different forming structures of which we observe only the brightest members. By considering all the spectroscopic z=10 sources in the Abell-2744 field, we identify two potential galaxy proto-clusters centered around GHZ9 and JD1, with relative separations between their members of 1-2 pMpc. The potential AGN nature of three of the sources in our sample lends support to a scenario in which the high abundance of bright sources determined by JWST surveys at cosmic dawn may be affected by AGN contribution to their UV luminosity., Comment: Submitted to A&A

Deep learning models have demonstrated remarkable success in various fields, including seismology. However, one major challenge in deep learning is the presence of mislabeled examples. Additionally, accurately estimating model uncertainty is another challenge in machine learning. In this study, we develop Convolutional Neural Networks (CNNs) to classify seismic waveforms based on first-motion polarity. We trained multiple CNN models with different settings. We also constructed ensembles of networks to estimate uncertainty. The results showed that each training setting achieved satisfactory performances, with the ensemble method outperforming individual networks in uncertainty estimation. We observe that the uncertainty estimation ability of the ensembles of networks can be enhanced using dropout layers. In addition, comparisons among different training settings revealed that the use of dropout improved the robustness of networks to mislabeled examples.

We present a novel deep learning method to separately extract the two-dimensional flux information of the foreground galaxy (deflector) and background system (source) of Galaxy-Galaxy Strong Lensing events using U-Net (GGSL-Unet for short). In particular, the segmentation of the source image is found to enhance the performance of the lens modeling, especially for ground-based images. By combining mock lens foreground+background components with real sky survey noise to train the GGSL-Unet, we show it can correctly model the input image noise and extract the lens signal. However, the most important result of this work is that the GGSL-UNet can accurately reconstruct real ground-based lensing systems from the Kilo Degree Survey (KiDS) in one second. We also test the GGSL-UNet on space-based (HST) lenses from BELLS GALLERY, and obtain comparable accuracy of standard lens modeling tools. Finally, we calculate the magnitudes from the reconstructed deflector and source images and use this to derive photometric redshifts (photo-z), with the photo-z of the deflector well consistent with spectroscopic ones. This first work, demonstrates the great potential of the generative network for lens finding, image denoising, source segmentation, and decomposing and modeling of strong lensing systems. For the upcoming ground- and space-based surveys, the GGSL-UNet can provide high-quality images as well as geometry and redshift information for precise lens modeling, in combination with classical MCMC modeling for best accuracy in the galaxy-galaxy strong lensing analysis., Comment: 27 pages, 19 figures. Commnets are welcome. Darft has been submitted to ApJS

The opening of the gravitational wave window has significantly enhanced our capacity to explore the universe's most extreme and dynamic sector. In the mHz frequency range, a diverse range of compact objects, from the most massive black holes at the farthest reaches of the Universe to the lightest white dwarfs in our cosmic backyard, generate a complex and dynamic symphony of gravitational wave signals. Once recorded by gravitational wave detectors, these unique fingerprints have the potential to decipher the birth and growth of cosmic structures over a wide range of scales, from stellar binaries and stellar clusters to galaxies and large-scale structures. The TianQin space-borne gravitational wave mission is scheduled for launch in the 2030s, with an operational lifespan of five years. It will facilitate pivotal insights into the history of our universe. This document presents a concise overview of the detectable sources of TianQin, outlining their characteristics, the challenges they present, and the expected impact of the TianQin observatory on our understanding of them., Comment: TianQin Gravitational Wave Whitepaper, 72 pages, 30 figures

Gravitationally strongly lensed quasars (SL-QSO) offer invaluable insights into cosmological and astrophysical phenomena. With the data from ongoing and next-generation surveys, thousands of SL-QSO systems can be discovered expectedly, leading to unprecedented opportunities. However, the challenge lies in identifying SL-QSO from enormous datasets with high recall and purity in an automated and efficient manner. Hence, we developed a program based on a Convolutional Neural Network (CNN) for finding SL-QSO from large-scale surveys and applied it to the Kilo-degree Survey Data Release 5 (KiDS DR5). Our approach involves three key stages: firstly, we pre-selected ten million bright objects (with $r$-band $\tt{MAG\_AUTO} < 22$), excluding stars from the dataset; secondly, we established realistic training and test sets to train and fine-tune the CNN, resulting in the identification of 4195 machine candidates, and the false positive rate (FPR) of $\sim$1/2000 and recall of 0.8125 evaluated by using the real test set containing 16 confirmed lensed quasars; thirdly, human inspections were performed for further selections, and then 272 SL-QSO candidates were eventually found in total, including 16 high-score, 118 median-score, and 138 lower-score candidates, separately. Removing the systems already confirmed or identified in other papers, we end up with 229 SL-QSO candidates, including 7 high-score, 95 median-score, and 127 lower-score candidates, and the corresponding catalog is publicly available online. We have also included an excellent quad candidate in the appendix, discovered serendipitously during the fine-tuning process of the CNN., Comment: 12 Figures, 4 Tables, accepted by ApJ. Comments Welcome!

Current regulations on powerful AI capabilities are narrowly focused on "foundation" or "frontier" models. However, these terms are vague and inconsistently defined, leading to an unstable foundation for governance efforts. Critically, policy debates often fail to consider the data used with these models, despite the clear link between data and model performance. Even (relatively) "small" models that fall outside the typical definitions of foundation and frontier models can achieve equivalent outcomes when exposed to sufficiently specific datasets. In this work, we illustrate the importance of considering dataset size and content as essential factors in assessing the risks posed by models both today and in the future. More broadly, we emphasize the risk posed by over-regulating reactively and provide a path towards careful, quantitative evaluation of capabilities that can lead to a simplified regulatory environment.

For the first time at the Beam Test Facility of the DA{\Phi}NE accelerator complex at the Laboratori Nazionali di Frascati of INFN, 450 MeV positrons have been deflected with high efficiency, using the Planar Channeling process in a bent silicon crystal. The deflection angle obtained is beyond 1 mrad. This interesting result finds several applications for manipulation of this kind of beams, in particular for slow extraction from leptons circular accelerators like DA{\Phi}NE. In this work the experimental apparatus, the measurement procedure and the experimental results are reported., Comment: Author list corrected. Title modified. Changed text of abstract and in sections 2 and 5 Added references. Typos corrected

Relic galaxies, the oldest ultra-compact massive galaxies (UCMGs), contain almost exclusively "pristine" stars formed during an intense star formation (SF) burst at high redshift. As such, they allow us to study in detail the early mechanism of galaxy assembly in the Universe. Using the largest catalogue of spectroscopically confirmed UCMGs for which a degree of relicness (DoR) had been estimated, the INSPIRE catalogue, we investigate whether or not relics prefer dense environments. The objective of this study is to determine if the DoR, which measures how extreme the SF history was, and the surrounding environment are correlated. In order to achieve this goal, we employ the AMICO galaxy cluster catalogue to compute the probability for a galaxy to be a member of a cluster, and measure the local density around each UCMG using machine learning-based photometric redshifts. We find that UCMGs can reside both in clusters and in the field, but objects with very low DoR (< 0.3, i.e., a relatively extended SF history) prefer under-dense environments. We additionally report a correlation between the DoR and the distance from the cluster centre: more extreme relics, when located in clusters, tend to occupy the more central regions of them. We finally outline potential evolution scenarios for UCMGs at different DoR to reconcile their presence in both clusters and field environments, Comment: Accepted for publication on MNRAS, 11 pages, 8 figure, 1 table

We present a sample of 50 H-alpha detected broad-line active galactic nuclei (BLAGN) at redshifts 3.50), independent of the contributions of emission lines to the broadband photometry. We construct the black hole (BH) mass function at 3.5

We present a set of photometric catalogs primarily aimed at providing the community with a comprehensive database for the study of galaxy populations in the high redshift Universe. The set gathers data from eight JWST NIRCam observational programs, targeting the Abell 2744 (GLASS-JWST, UNCOVER, DDT2756 and GO3990), EGS (CEERS), COSMOS and UDS (PRIMER), and GOODS North and South (JADES and NGDEEP) deep fields, for a total area of $\sim$0.2 sq. degrees. Photometric estimates are obtained by means of well-established techniques, including tailored improvements designed to enhance the performance on the specific dataset. We also include new measurements from HST archival data, thus collecting 16 bands spanning from 0.44 to 4.44 $\mu$m. A grand total of $\sim$530 thousand sources is detected on stacks of NIRCam 3.56 and 4.44 $\mu$m mosaics. We assess the photometric accuracy by comparing fluxes and colors against archival catalogs. We also provide photometric redshift estimates, statistically validated against a large set of robust spectroscopic data. The catalogs are publicly available on the Astrodeep website., Comment: 28 pages, 16 figures; accepted for publication on Astronomy & Astrophysics

We release fully reduced spectra obtained with NIRSpec onboard JWST as part of the GLASS-JWST Early Release Science Program and a follow-up Director's Discretionary Time program 2756. From these 263 spectra of 245 unique sources, acquired with low ($R =30-300$) and high dispersion ($R\sim2700$) gratings, we derive redshifts for 200 unique sources in the redshift range $z=0-10$. We describe the sample selection and characterize its high completeness as a function of redshift and apparent magnitude. Comparison with independent estimates based on different methods and instruments shows that the redshifts are accurate, with 80\% differing less than 0.005. We stack the GLASS-JWST spectra to produce the first high-resolution ($R \sim 2700$) JWST spectral template extending in the rest frame wavelength from 2000~\AA\ to 20, 000~\AA. Catalogs, reduced spectra, and template are made publicly available to the community., Comment: Accepted for publication in A&A

Ly$\alpha$ emission is key to understanding the process of cosmic reionisation. JWST is finally enabling us to measure Ly$\alpha$ emission deep into the epoch of reionisation for an increasing number of galaxies. However, discrepancies between measurements of Ly$\alpha$ equivalent widths (EW$_0$) of Ly$\alpha$ emitters (LAEs) have been noted between JWST and ground-based facilities. We employ SPICE, a suite of radiation-hydrodynamical simulations featuring different stellar feedback models, and investigate the impact of radiative transfer effects and observational systematics on the measured Ly$\alpha$ EW$_0$. We perform radiative transfer of Ly$\alpha$ and UV photons for SPICE galaxies to mimic slit spectroscopy for ground-based slits and JWST-MSA pseudo-slits. Spatial Ly$\alpha$-UV offsets exist independently of feedback model, and are common ($>70$% galaxies) with median values of $\approx0.07-0.11''$ and these predictions are consistent with the observed spatial offsets. Spatial Ly$\alpha$-UV offsets are a major cause of loss of flux for JWST-MSA observations, with median pseudo-slit losses of $\approx65$%, and $\approx30$% cases suffering from $>95$% pseudo-slit losses. Even in the absence of such spatial offsets, the presence of extended emission can cause median pseudo-slit losses of $40$%, with $4$% cases suffering from $>95$% pseudo-slit losses. Complex galaxy morphologies or misplaced JWST-MSA pseudo-slit can lead to under-estimated UV continuum, resulting in spuriously high estimates of EW$_0$ from JWST in $6-8$% of galaxies. We compare the predictions from {\tt SPICE} to 25 galaxies with Ly$\alpha$ emission observations from both the ground and JWST. The EW$_0^{\tt JWST}$ and the EW$_0^{\tt Ground}$ exhibit scatter in line with predictions, indicating that both physical and systematic effects are likely at play.

The Pauli Exclusion Principle (PEP) appears from fundamental symmetries in quantum field theories, but its physical origin is still to be understood. High-precision experimental searches for small PEP violations permit testing key assumptions of the Standard Model with high sensitivity. We report on a dedicated measurement with Gator, a low-background, high-purity germanium detector operated at the Laboratori Nazionali del Gran Sasso, aimed at testing PEP-violating atomic transitions in lead. The experimental technique, relying on forming a new symmetry state by introducing electrons into the pre-existing electron system through a direct current, satisfies the conditions of the Messiah-Greenberg superselection rule. No PEP violation has been observed, and an upper limit on the PEP violation probability of $\beta^2/2 < 4.8 \cdot 10^{-29}$ (90% CL) is set. This improves the previous constraint from a comparable measurement by more than one order of magnitude.

In this study, we investigated Rydberg atom-based microwave electrometry using polarization spectroscopy in a room-temperature vapor cell. By measuring Autler-Townes splitting in the electromagnetically induced transparency (EIT) spectrum, we determined that the minimum measurable microwave electric field is approximately five times lower than conventional EIT techniques. The results are well reproduced by a full optical Bloch equation model, which takes into account all the hyperfine levels involved. Subsequently, the EIT setup was used to characterize a custom microwave cylindrical lens, which increases the field at the focus by a factor of three, decreasing the minimum measurable microwave electric field by the same amount. Our results indicate that the combination of polarization spectroscopy and a microwave lens may enhance microwave electrometry.

Shapley Values are concepts established for eXplainable AI. They are used to explain black-box predictive models by quantifying the features' contributions to the model's outcomes. Since computing the exact Shapley Values is known to be computationally intractable on real-world datasets, neural estimators have emerged as alternative, more scalable approaches to get approximated Shapley Values estimates. However, experiments with neural estimators are currently hard to replicate as algorithm implementations, explainer evaluators, and results visualizations are neither standardized nor promptly usable. To bridge this gap, we present BONES, a new benchmark focused on neural estimation of Shapley Value. It provides researchers with a suite of state-of-the-art neural and traditional estimators, a set of commonly used benchmark datasets, ad hoc modules for training black-box models, as well as specific functions to easily compute the most popular evaluation metrics and visualize results. The purpose is to simplify XAI model usage, evaluation, and comparison. In this paper, we showcase BONES results and visualizations for XAI model benchmarking on both tabular and image data. The open-source library is available at the following link: https://github.com/DavideNapolitano/BONES., Comment: 6 pages