Distorted black holes radiate gravitational waves. In the so-called ringdown phase radiation is emitted in a discrete set of complex quasinormal frequencies, whose values depend only on the black hole's mass and angular momentum. Ringdown radiation could be detectable with large signal-to-noise ratio by the Laser Interferometer Space Antenna LISA. If more than one mode is detected, tests of the black hole nature of the source become possible. The detectability of different modes depends on their relative excitation, which in turn depends on the cause of the perturbation (i.e. on the initial data). A ``universal'', initial data-independent measure of the relative mode excitation is encoded in the poles of the Green's function that propagates small perturbations of the geometry (``excitation factors''). We compute for the first time the excitation factors for general-spin perturbations of Kerr black holes. We find that for corotating modes with $l=m$ the excitation factors tend to zero in the extremal limit, and that the contribution of the overtones should be more significant when the black hole is fast rotating. We also present the first analytical calculation of the large-damping asymptotics of the excitation factors for static black holes, including the Schwarzschild and Reissner-Nordstrom metrics. This is an important step to determine the convergence properties of the quasinormal mode expansion.

The distribution of close-in exoplanets is shaped by the interplay between atmospheric and dynamical processes. The Neptunian Desert, Ridge, and Savanna illustrate the sensitivity of these worlds to such processes, making them ideal to disentangle their roles. Determining how many Neptunes were brought close-in by early disk-driven migration (DDM; maintaining primordial spin-orbit alignment) or late high-eccentricity migration (HEM; generating large misalignments) is essential to understand how much atmosphere they lost. We propose a unified view of the Neptunian landscape to guide its exploration, speculating that the Ridge is a hot spot for evolutionary processes. Low-density Neptunes would mainly undergo DDM, getting fully eroded at shorter periods than the Ridge, while denser Neptunes would be brought to the Ridge and Desert by HEM. We embark on this exploration via ATREIDES, which relies on spectroscopy and photometry of 60 close-in Neptunes, their reduction with robust pipelines, and their interpretation through internal structure, atmospheric, and evolutionary models. We carried out a systematic RM census with VLT/ESPRESSO to measure the distribution of 3D spin-orbit angles, correlate its shape with system properties and thus relate the fraction of aligned-misaligned systems to DDM, HEM, and atmospheric erosion. Our first target, TOI-421c, lies in the Savanna with a neighboring sub-Neptune TOI-421b. We measured their 3D spin-orbit angles (Psib = 57+11-15 deg; Psic = 44.9+4.4-4.1 deg). Together with the eccentricity and possibly large mutual inclination of their orbits, this hints at a chaotic dynamical origin that could result from DDM followed by HEM. ATREIDES will provide the community with a wealth of constraints for formation and evolution models. We welcome collaborations that will contribute to pushing our understanding of the Neptunian landscape forward.

We investigate the effects of the sharpness of the phase transition between hadronic matter and quark matter on various properties of neutron stars. We construct hybrid equations of state by combining a hadronic model with a quark model using a Gaussian function. This approach introduces a smooth transition characterized by two parameters: one representing the overpressure relative to the first-order phase transition point, and the other related to the range over which the hybrid region extends in baryon chemical potential. We find that the sharpness of the phase transition significantly influences the equation of state, which can deviate by several tens of $\text{MeV fm}^{-3}$ from the one with a sharp first-order transition. The speed of sound exhibits diverse behaviors, including drastic drops, pronounced peaks, and oscillatory patterns, depending on the sharpness parameters. In terms of stellar structure, while the maximum neutron star mass remains largely unaffected by the sharpness of the phase transition, the stellar radii can vary significantly. Smoother transitions lead to a leftward shift (up to 1 km) of the mass-radius curve segment corresponding to hybrid stars. The tidal deformability decreases with smoother transitions, especially for higher-mass stars. Our results are quite general and do not qualitatively depend on the specific hadronic and quark matter models employed. In fact, the hybrid equation of state and stellar properties derived from microscopic models of quark-hadron pasta phases display the same behavior as described above.

NUCLEUS is a cryogenic detection experiment which aims to measure Coherent Elastic Neutrino-Nucleus Scattering (CE$\nu$NS) and to search for new physics at the Chooz nuclear power plant in France. This article reports on the prediction of particle-induced backgrounds, especially focusing on the sub-keV energy range, which is a poorly known region where most of the CE$\nu$NS signal from reactor antineutrinos is expected. Together with measurements of the environmental background radiations at the experimental site, extensive Monte Carlo simulations based on the Geant4 package were run both to optimize the experimental setup for background reduction and to estimate the residual rates arising from different contributions such as cosmic ray-induced radiations, environmental gammas and material radioactivity. The NUCLEUS experimental setup is predicted to achieve a total rejection power of more than two orders of magnitude, leaving a residual background component which is strongly dominated by cosmic ray-induced neutrons. In the CE$\nu$NS signal region of interest between 10 and 100 eV, a total particle background rate of $\sim$ 250 d$^{-1}$kg$^{-1}$keV$^{-1}$ is expected in the CaWO$_4$ target detectors. This corresponds to a signal-to-background ratio $\gtrsim$ 1, and therefore meets the required specifications in terms of particle background rejection for the detection of reactor antineutrinos through CE$\nu$NS.

First formulated by Sir Isaac Newton in his work "Philosophiae Naturalis Principia Mathematica", the concept of the Three-Body Problem was put forth as a study of the motion of the three celestial bodies within the Earth-Sun-Moon system. In a generalized definition, it seeks to predict the motion for an isolated system composed of three point masses freely interacting under Newton's law of universal attraction. This proves to be analogous to a multitude of interactions between celestial bodies, and thus, the problem finds applicability within the studies of celestial mechanics. Despite numerous attempts by renowned physicists to solve it throughout the last three centuries, no general closed-form solutions have been reached due to its inherently chaotic nature for most initial conditions. Current state-of-the-art solutions are based on two approaches, either numerical high-precision integration or machine learning-based. Notwithstanding the breakthroughs of neural networks, these present a significant limitation, which is their ignorance of any prior knowledge of the chaotic systems presented. Thus, in this work, we propose a novel method that utilizes Physics-Informed Neural Networks (PINNs). These deep neural networks are able to incorporate any prior system knowledge expressible as an Ordinary Differential Equation (ODE) into their learning processes as a regularizing agent. Our findings showcase that PINNs surpass current state-of-the-art machine learning methods with comparable prediction quality. Despite a better prediction quality, the usability of numerical integrators suffers due to their prohibitively high computational cost. These findings confirm that PINNs are both effective and time-efficient open-form solvers of the Three-Body Problem that capitalize on the extensive knowledge we hold of classical mechanics.

We demonstrate that Gaia's detection of stars on wide orbits around black holes opens a new observational window on dark matter structures -- such as scalar clouds and dark matter spikes -- predicted in a range of theoretical scenarios. Using precise radial velocity measurements of these systems, we derive state-of-the-art constraints on dark matter density profiles and particle masses in previously unexplored regions of parameter space. We also test the black hole hypothesis against the alternative of a boson star composed of light scalar fields.

This report describes the experimental strategy and technologies for XLZD, the next-generation xenon observatory sensitive to dark matter and neutrino physics. In the baseline design, the detector will have an active liquid xenon target of 60 tonnes, which could be increased to 80 tonnes if the market conditions for xenon are favorable. It is based on the mature liquid xenon time projection chamber technology used in current-generation experiments, LZ and XENONnT. The report discusses the baseline design and opportunities for further optimization of the individual detector components. The experiment envisaged here has the capability to explore parameter space for Weakly Interacting Massive Particle (WIMP) dark matter down to the neutrino fog, with a 3$\sigma$ evidence potential for WIMP-nucleon cross sections as low as $3\times10^{-49}\rm\,cm^2$ (at 40 GeV/c$^2$ WIMP mass). The observatory will also have leading sensitivity to a wide range of alternative dark matter models. It is projected to have a 3$\sigma$ observation potential of neutrinoless double beta decay of $^{136}$Xe at a half-life of up to $5.7\times 10^{27}$ years. Additionally, it is sensitive to astrophysical neutrinos from the sun and galactic supernovae.

The XLZD collaboration is developing a two-phase xenon time projection chamber with an active mass of 60 to 80 t capable of probing the remaining WIMP-nucleon interaction parameter space down to the so-called neutrino fog. In this work we show that, based on the performance of currently operating detectors using the same technology and a realistic reduction of radioactivity in detector materials, such an experiment will also be able to competitively search for neutrinoless double beta decay in $^{136}$Xe using a natural-abundance xenon target. XLZD can reach a 3$\sigma$ discovery potential half-life of 5.7$\times$10$^{27}$ yr (and a 90% CL exclusion of 1.3$\times$10$^{28}$ yr) with 10 years of data taking, corresponding to a Majorana mass range of 7.3-31.3 meV (4.8-20.5 meV). XLZD will thus exclude the inverted neutrino mass ordering parameter space and will start to probe the normal ordering region for most of the nuclear matrix elements commonly considered by the community.

The SNO+ Collaboration reports the first evidence of $^{8}\text{B}$ solar neutrinos interacting on $^{13}\text{C}$ nuclei. The charged current interaction proceeds through $^{13}\text{C} + \nu_e \rightarrow {}^{13}\text{N} + e^-$ which is followed, with a 10 minute half-life, by ${}^{13}\text{N} \rightarrow {}^{13}\text{C} + e^+ +\nu_e .$ The detection strategy is based on the delayed coincidence between the electron and the positron. Evidence for the charged current signal is presented with a significance of 4.2$\sigma$. Using the natural abundance of $^{13}\text{C}$ present in the scintillator, 5.7 tonnes of $^{13}\text{C}$ over 231 days of data were used in this analysis. The 5.6$^{+3.0}_{-2.3}$ detected events in the data set are consistent with the expectation of 4.7$^{+0.6}_{-1.3}$ events. This result is the second real-time measurement of CC interactions of $^{8}\text{B}$ neutrinos with nuclei and constitutes the lowest energy observation of neutrino interactions on $^{13}\text{C}$ generally. This enables the first direct measurement of the CC $\nu_e$ reaction to the ground state of ${}^{13}\text{N}$, yielding an average cross section of $(16.1 ^{+8.5}_{-6.7} (\text{stat.}) ^{+1.6}_{-2.7} (\text{syst.}) )\times 10^{-43}$ cm$^{2}$ over the relevant $^{8}\text{B}$ solar neutrino energies.

This paper introduces TuneNSearch, a hybrid transfer learning and local search approach for addressing diverse variants of the vehicle routing problem (VRP). Our method uses reinforcement learning to generate high-quality solutions, which are subsequently refined by an efficient local search procedure. To ensure broad adaptability across VRP variants, TuneNSearch begins with a pre-training phase on the multi-depot VRP (MDVRP), followed by a fine-tuning phase to adapt it to other problem formulations. The learning phase utilizes a Transformer-based architecture enhanced with edge-aware attention, which integrates edge distances directly into the attention mechanism to better capture spatial relationships inherent to routing problems. We show that the pre-trained model generalizes effectively to single-depot variants, achieving performance comparable to models trained specifically on single-depot instances. Simultaneously, it maintains strong performance on multi-depot variants, an ability that models pre-trained solely on single-depot problems lack. For example, on 100-node instances of multi-depot variants, TuneNSearch outperforms a model pre-trained on the CVRP by 44%. In contrast, on 100-node instances of single-depot variants, TuneNSearch performs similar to the CVRP model. To validate the effectiveness of our method, we conduct extensive computational experiments on public benchmark and randomly generated instances. Across multiple CVRPLIB datasets, TuneNSearch consistently achieves performance deviations of less than 3% from the best-known solutions in the literature, compared to 6-25% for other neural-based models, depending on problem complexity. Overall, our approach demonstrates strong generalization to different problem sizes, instance distributions, and VRP formulations, while maintaining polynomial runtime complexity despite the integration of the local search algorithm.

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to light dark matter-nucleus scattering in current direct detection dark matter searches. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three CRESST detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.

The critical curve of string tension as a function of the temperature is computed in SU(3) Lattice QCD. We present the results for the string tension utilizing a pair of Polyakov loop and antiloop, with two different techniques. We compare the colour averaged loop-antiloop which is gauge invariant but is only adequate to study the string tension, and the colour singlet loop-antiloop using the Landau gauge fixing which also enables to compute the coulomb part of the free energy.

We provide a comprehensive analysis of the phenomenology of axion-like particles (ALPs) produced in core-collapse supernovae (ccSNe) through interactions with electrons and muons, both of which have a non-negligible abundance in the SN plasma. We identify and calculate six significant ALP-production channels, two of which are loop-level processes involving photons. We then examine several observational constraints on the ALP-electron and ALP-muon parameter spaces. Those include the bounds on anomalous cooling, energy deposition, decay into photons, diffuse gamma rays, and the 511 keV line. Our results provide updated and robust constraints on ALP couplings to electrons and muons from an improved treatment of production and absorption processes. Furthermore, we quantify the uncertainties of the results by using three state-of-the-art supernova models based on two independent simulation codes, finding that constraints vary by factors of O(2-10).

The formation and evolution of ultra-short-period (USP) planets is poorly understood. However, it is widely thought that these planets migrated inwards through interactions with outer neighbours. We aim to confirm and characterise the USP Earth-sized planet K2-157 b ($P_{\rm orb}$ = 8.8 h). To do so, we measured 49 radial velocities (RVs) with the ESPRESSO spectrograph and derived the properties of the system through an RV and transit model. We detect two additional super-Neptune-mass planets within the warm Neptunian savanna, K2-157 c ($P_{\rm orb, c}$ = $25.942^{+0.045}_{-0.044}$ d, $M_{\rm p, c} \, \textrm{sin} \, i$ = $30.8 \pm 1.9$ $\rm M_{\oplus}$), and K2-157 d ($P_{\rm orb, d}$ = $66.50^{+0.71}_{-0.59}$ d, $M_{\rm p,d} \, \textrm{sin}\,i$ = $23.3 \pm 2.5$ $\rm M_{\oplus}$). The mass of K2-157 b, $M_{\rm p,b}$ = $1.14^{+0.41}_{-0.42}$ $\rm M_{\oplus}$ (< 2.4 $\rm M_{\oplus}$ at 3$\sigma$), together with its radius, $R_{\rm p}$ = 0.935 $\pm$ 0.090 $\rm R_{\oplus}$, make the planet compatible with a rocky composition. K2 data discard non-grazing transit configurations for K2-157 c ($i_{\rm c}$ < 88.4$^{\circ}$ at 3$\sigma$), and ESPRESSO data constrain the eccentricities of K2-157 c and K2-157 d to $e_{\rm c}$ < 0.2 and $e_{\rm d}$ < 0.5 at 3$\sigma$. At a population level, we find that the trend that the closest USP planets tend to orbit late-type stars does not hold when scaling the orbital separation to the Roche limit, which suggests that the orbital distribution of the closest planets across spectral types is determined by tidal disruption. The orbital architecture of K2-157 is unusual, with only one similar case reported to date: 55 Cnc. The USP planets of these systems, being accompanied by massive, long-period, relatively spaced, and possibly misaligned neighbours, could have migrated inwards through eccentricity-based mechanisms triggered by secular interactions.

With the rising adoption of dry dilution refrigerators across scientific and industrial domains, there has been a pressing demand for highly efficient vibration decoupling systems capable of operation at cryogenic temperatures in order to achieve the low vibration levels required for operation of sensitive equipment like cryogenic detectors or quantum devices. As part of the NUCLEUS experiment, a cryogenic spring pendulum has been engineered to effectively isolate pulse tube vibrations by establishing an autonomous frame of reference for the experimental volume, while sustaining temperatures below 10 mK. Attaining attenuation of up to two orders of magnitude within the region of interest of the NUCLEUS cryogenic detectors, we achieved displacement RMS values in the order of 1 nm in the axial direction and 100 pm radially, thereby reducing vibrations below typical environmental levels. Our successful detector operation across multiple cooldown cycles demonstrated negligible sensitivity to pulse tube induced vibrations, culminating in the achievement of an ultra-low $(6.22 \pm 0.07)$ eV baseline resolution on a gram-scale CaWO$_4$ cryogenic calorimeter during continuous pulse tube operation over the course of several weeks.

This paper proposes a novel method for human hands tracking using data from an event camera. The event camera detects changes in brightness, measuring motion, with low latency, no motion blur, low power consumption and high dynamic range. Captured frames are analysed using lightweight algorithms reporting 3D hand position data. The chosen pick-and-place scenario serves as an example input for collaborative human-robot interactions and in obstacle avoidance for human-robot safety applications. Events data are pre-processed into intensity frames. The regions of interest (ROI) are defined through object edge event activity, reducing noise. ROI features are extracted for use in-depth perception. Event-based tracking of human hand demonstrated feasible, in real time and at a low computational cost. The proposed ROI-finding method reduces noise from intensity images, achieving up to 89% of data reduction in relation to the original, while preserving the features. The depth estimation error in relation to ground truth (measured with wearables), measured using dynamic time warping and using a single event camera, is from 15 to 30 millimetres, depending on the plane it is measured. Tracking of human hands in 3D space using a single event camera data and lightweight algorithms to define ROI features (hands tracking in space).

Cryogenic phonon detectors with transition-edge sensors achieve the best sensitivity to light dark matter-nucleus scattering in current direct detection dark matter searches. In such devices, the temperature of the thermometer and the bias current in its readout circuit need careful optimization to achieve optimal detector performance. This task is not trivial and is typically done manually by an expert. In our work, we automated the procedure with reinforcement learning in two settings. First, we trained on a simulation of the response of three CRESST detectors used as a virtual reinforcement learning environment. Second, we trained live on the same detectors operated in the CRESST underground setup. In both cases, we were able to optimize a standard detector as fast and with comparable results as human experts. Our method enables the tuning of large-scale cryogenic detector setups with minimal manual interventions.

In this work, we conduct an extensive empirical study of several deep reinforcement learning algorithms on two challenging combinatorial optimization problems: the job-shop and flexible job-shop scheduling problems, both fundamental challenges with multiple industrial applications. Broadly, deep reinforcement learning algorithms fall into two categories: policy-gradient and value-based. While value-based algorithms have achieved notable success in domains such as the Arcade Learning Environment, the combinatorial optimization community has predominantly favored policy-gradient algorithms, often overlooking the potential of value-based alternatives. From our results, value-based algorithms demonstrated a lower variance and a more stable convergence profile compared to policy-gradient ones. Moreover, they achieved superior cross-size and cross-distribution generalization, that is, effectively solving instances that are substantially larger or structurally distinct from those seen during training. Finally, our analysis also suggests that the relative performance of each category of algorithms may be dependent on structural properties of the problem, such as problem flexibility and instance size. Overall, our findings challenge the prevailing assumption that policy-gradient algorithms are inherently superior for combinatorial optimization. We show instead that value-based algorithms can match or even surpass the performance of policy-gradient algorithms, suggesting that they deserve greater attention from the combinatorial optimization community. Our code is openly available at: this https URL

PLATO (PLAnetary Transits and Oscillations of stars) is ESA's M3 mission designed to detect and characterise extrasolar planets and perform asteroseismic monitoring of a large number of stars. PLATO will detect small planets (down to <2 R_(Earth)) around bright stars (<11 mag), including terrestrial planets in the habitable zone of solar-like stars. With the complement of radial velocity observations from the ground, planets will be characterised for their radius, mass, and age with high accuracy (5 %, 10 %, 10 % for an Earth-Sun combination respectively). PLATO will provide us with a large-scale catalogue of well-characterised small planets up to intermediate orbital periods, relevant for a meaningful comparison to planet formation theories and to better understand planet evolution. It will make possible comparative exoplanetology to place our Solar System planets in a broader context. In parallel, PLATO will study (host) stars using asteroseismology, allowing us to determine the stellar properties with high accuracy, substantially enhancing our knowledge of stellar structure and evolution. The payload instrument consists of 26 cameras with 12cm aperture each. For at least four years, the mission will perform high-precision photometric measurements. Here we review the science objectives, present PLATO's target samples and fields, provide an overview of expected core science performance as well as a description of the instrument and the mission profile at the beginning of the serial production of the flight cameras. PLATO is scheduled for a launch date end 2026. This overview therefore provides a summary of the mission to the community in preparation of the upcoming operational phases.