We review how the local particle number cutoff introduced in [11] is used to build trial states for the dilute Bose gas that capture the substantial correlation structure of the ground state in the thermodynamic limit. In particular, we provide a simplified derivation of the Lee-Huang-Yang correction as an upper bound for the ground state energy.

This paper studies the propagation of finite-sample uncertainty under nonlinear transformations commonly used in statistical decision systems. In particular, we consider process capability indices, which are widely used in manufacturing practice but are estimated from finite samples, rendering the resulting approval decisions inherently uncertain. We show that such uncertainty cannot be fully explained by estimator variability alone, but is substantially influenced by a nonlinear amplification mechanism through which capability uncertainty is transformed into defect-risk metrics. While capability estimators vary approximately linearly with process dispersion, defect probabilities depend on tail curvature, causing small estimation errors to be disproportionately amplified in measures such as defect probability and parts-per-million (PPM) rates. Consequently, capability assessments that appear stable in index space may exhibit substantial variability in defect-risk space, particularly near decision thresholds. This insight provides a unified explanation of finite-sample decision instability, motivates reliability-aware decision formulations, and links sample-size requirements directly to decision reliability. Monte Carlo simulations and industrial data analyses validate the proposed mechanism and demonstrate its practical implications, including the impact of distributional assumptions on defect-risk estimation.

We present the results of our investigations of spectrally and spatially resolved (sub)millimeter HCN-to-HCO$^{+}$ flux ratios at J=2-1, J=3-2, and/or J=4-3 in 18 nearby ($z <$ 0.15) ultraluminous infrared galaxies (ULIRGs), using ALMA $\lesssim$0.2" ($\lesssim$500 pc) resolution data. The geometry of elevated HCN-to-HCO$^{+}$ flux ratios (with $>$3$σ$ detections for both molecular lines) in position-position-velocity (PPV) space is visually classified into (i) spherical shell (spectrally and spatially distinct), (ii) spectrally distinct and spatially compact, and (iii) filled (spectrally filled and spatially compact). These can naturally be explained by the elevation of the flux ratio due to (i) a spatially resolved outflow, (ii) an AGN and/or a spatially unresolved outflow with blueshifted and redshifted emission components, and (iii) an AGN and/or a spatially confined outflow with not clearly separated blueshifted and redshifted velocity components, respectively. Signatures of elevated HCN-to-HCO$^{+}$ flux ratios originated from (a) spatially resolved outflow and (b) AGN and/or spatially unresolved outflow are seen in seven and nine ULIRGs, respectively. In the former spatially resolved outflow-origin case, modest-velocity components relative to the maximum outflow velocity tend to be probed by spaxels with elevated HCN-to-HCO$^{+}$ flux ratios. The spectrally and spatially resolved HCN-to-HCO$^{+}$ flux ratios can provide additional information on the physical origin of the elevated flux ratios in nearby ULIRG nuclei, compared to previously conducted spatially integrated and/or velocity-integrated analyses.

Asteroid reflectance spectra provide key constraints on surface composition. Gaia DR3 enables the study of 60,518 asteroids through NUV to visible reflectance spectra. We aim to classify asteroids using Gaia DR3 spectra and provide a homogeneous framework. Owing to systematics affecting Gaia DR3 data, direct comparison with previous taxonomies has to be taken with caution; thus, we developed a classification scheme tailored to Gaia and linked the resulting taxa to established classes. We selected the highest-quality spectra using Gaia DR3 quality flags and applied uncertainty thresholds to mitigate spectral artifacts, retaining over one-third of the original sample at the least noisy wavelength. To improve compositional discrimination, we included albedo, reducing the final sample to about one-fourth of its initial size. We then iteratively applied dimensionality reduction and clustering to identify the spectral taxa. We classified 14,042 asteroids into 13 taxonomic classes: A, B, C, D, E, F, G, K, L, M, P, S, and V, representing an increase of three compared to the number of objects classified in previous spectral classifications. The largest relative increase is found for the K class. The inclusion of NUV wavelengths allows the separation of B and F types within the C-complex and facilitates the identification of G types. The dynamical distribution follows expected trends, with Stypes dominating the inner and middle Main Belt, C-complex asteroids prevalent in the outer Main Belt, and D types beyond. We present a taxonomical classification of 14,042 asteroids based on Gaia DR3 reflectance spectra. NUV coverage is critical for disentangling primitive classes within the C-complex. Although artifacts in Gaia DR3 require caution when comparing median spectra with other datasets, this classification provides a robust reference for future Gaia releases, with larger observed samples.

In this paper, the strong and doubly metric dimensions of Johnson and Kneser graphs are considered. The exact value of the strong metric dimension of Johnson graph $J_{n,k}$ is obtained using the well-known results from the literature. The strong metric dimension of Kneser graph $K_{n,k}$ has been obtained for $n \ge 3k-1$. Finally, it has been shown that the doubly metric dimensions of Johnson graph $J_{n,2}$ and Kneser graph $K_{n,2}$ are both $\lceil \frac{2n}{3} \rceil$.

The Hubble Advanced Spectral Products (HASP) program is designed to robustly coadd Cosmic Origins Spectrograph (COS) and Space Telescope Imaging Spectrograph (STIS) spectra within the Mikulski Archive for Space Telescopes (MAST) in an automated fashion such that coadds are available for new data or archival data with updated calibrations. For each target within a visit or program, HASP employs a meticulous multi-stage filtering process to ensure data quality and creates coadded products for all central wavelengths (CENWAVEs) within specific gratings, as well as combined products using different gratings and instruments. The project also emphasizes making the code accessible to the user community for custom coaddition. As calibrations improve and new data are added to the archive, HASP products are re-created automatically so that they represent the best reduction of a given visit or program. Automated coadditions like those achieved by HASP can significantly enhance the combination of different CENWAVES, increase signal-to-noise ratios, and increase wavelength coverage. These properties make HASP a vital resource for astronomers using archival spectroscopic data from HST.

Majorana zero modes are central to the pursuit of fault-tolerant topological quantum computation. While traditionally sought in one-dimensional hybrid nanowires, a robust alternative platform involves heterostructures combining superconductors with noncollinear magnets. This work focuses on a particularly promising system: a chiral ferromagnet hosting a magnetic skyrmion coupled to a superconducting film containing a superconducting vortex. Such skyrmion-vortex pairs have recently been realized experimentally and are theorized to harbor localized Majorana states, offering a potential pathway for braiding operations. We present a comprehensive theoretical analysis of the low-energy quasiparticle bound states in these heterostructures. Extending previous studies, we develop an analytical framework for the Majorana wavefunctions as well as the wavefunctions and spectrum of other lowlying states within a Bogoliubov-de Gennes approach. Our analytical results explicitly demonstrate the critical role of spin-orbit coupling for the stabilization of Majorana modes and provides approximate analytical expressions for low-lying states localized at the vortex, both with and without an accompanying skyrmion. The derived analytical results show excellent agreement with numerical simulations. We further elucidate the role of realistic effects, including vector potentials and texture perturbations from stray magnetic fields, to assess their impact.

Burnout affects software developers' mental and physical well-being and contributes to turnover, generating strong concerns in the software industry. Prior research has shown that lack of belonging is associated with higher levels of burnout among software developers, while a sense of belonging is linked to resilience, job satisfaction, engagement, and well-being. In this paper, we revisit recent studies on belongingness in software development teams, including proprietary software organizations and open-source software communities, to offer evidence-based guidelines for cultivating belongingness and reducing developer burnout. We summarize characteristics of belongingness, such as trust, acceptance, value recognition, friendship, membership, mutual support, and being known by others, as well as factors associated with belongingness, including recognition, psychological safety, intrinsic motivation, English confidence, tenure, gender, and cultural power distance. Based on these findings, we propose practical guidelines for leaders and communities, including timely and consistent recognition, transparent promotion rules, inclusive benefits and initiatives, intentional connections through collaborative tools, blameless postmortems, optional in-person opportunities, informal newcomer gatherings, and continuous monitoring of belongingness and burnout. These guidelines can help software organizations and open-source communities foster healthier, more inclusive environments that support developer well-being.

We study the spectral properties of sample covariance matrices constructed from pooled sequence representations, where token embeddings are drawn from a fixed two-class Gaussian mixture table and pooled via (fixed) attention weights. Working in the high-dimensional regime $d,V,N\to\infty$ with $d/V\toδ$ and $d/N\toγ$, we derive exact characterizations of the limiting eigenvalue distribution, outlier eigenvalues, and eigenvector alignment with the hidden signal. The bulk spectrum follows a non-Marchenko--Pastur law given by the free multiplicative convolution $κ(MP_δ\boxtimes MP_γ)$, reflecting the finite vocabulary structure. Signal recovery undergoes two successive BBP-type phase transitions characterized by the scalars: $δ,γ,α=w^{\top} R w$ and $κ=\|w\|^2$, where $w$ denotes the attention pooling weights and $R$ the positional correlation matrix. An aftermath of our analysis demonstrates that the optimal attention weights maximizing the signal-to-noise ratio $α/κ$ are given by the (normalized) top eigenvector of $R$, and we show (as a particular case of our analysis) that parameter-free causal self-attention with $τ/d$ score scaling yields deterministic harmonic weights that improve signal recovery over mean pooling whenever early tokens carry more signal. Extensive simulations confirm sharp agreement between theory and finite-dimensional experiments.

We give a constructive proof that every weakly negative definite plumbing tree can be transformed into a negative definite one by a finite sequence of Neumann moves. The argument combines Neumann's plumbing calculus with the diagonalization algorithm of Duchon, Eisenbud, and Neumann, which extracts the eigenvalues of the framing matrix directly from the combinatorics of the tree. We show that any positive eigenvalues are supported on linear branches and can be eliminated systematically via controlled applications of Neumann moves. This provides an explicit algorithm reducing weakly negative definite plumbing trees to negative definite ones.

Fluid antenna (FA) technology has recently emerged as an effective means of exploiting spatial diversity through position-domain reconfigurability. This paper investigates the integration of FA into over-the-air federated learning (OTA-FL) systems with the aim of improving aggregation reliability and user participation under realistic channel conditions. By dynamically selecting antenna positions, FA-equipped users can exploit additional spatial degrees of freedom to realize more favorable channel conditions, thereby increasing the probability of successful contribution to the OTA aggregation process in each communication round. We consider an uplink OTA-FL framework consisting of a single fixed-antenna access point and multiple FA-enabled users operating over spatially correlated fading channels. Unlike existing studies that primarily rely on optimization-based designs or numerical evaluations, we develop a tractable analytical framework that enables a rigorous performance characterization of FA-assisted OTA-FL. In particular, closed-form expressions are derived for the aggregation error outage probability and the expected number of participating users per round. Spatial channel correlation across FA ports is modeled using a copula-based approach, where the Clayton copula is adopted to capture lower-tail dependence relevant to worst-case fading conditions. Numerical results validate the analytical findings and demonstrate that FA-assisted OTA-FL significantly outperforms conventional fixed-antenna schemes in terms of aggregation reliability and participation efficiency, while providing insights under practical system considerations.

Estimating time-varying correlation matrices is challenging because existing methods may adapt slowly to structural changes, impose insufficient regularization, or produce diffuse posterior uncertainty. In moderate dimensions, an additional difficulty is summarizing the estimated evolving dependence structure for downstream decision-making tasks. We propose a Bayesian approach based on a low-rank factor representation, with latent states evolving under a dynamic shrinkage prior and observation errors following a multivariate factor stochastic volatility model. This specification allows locally adaptive regularization of the estimated correlation structure over time and informative uncertainty quantification. We establish, to our knowledge, a first-of-its-kind posterior contraction result for dynamically regularized Bayesian models, showing contraction around the true model parameters at an explicit rate under averaged Hellinger distance. To summarize the estimated correlation matrices, we build on the information-theoretic concept of total correlation to obtain a scalar measure of cross-sectional dependence. Simulation studies show improved accuracy and responsiveness relative to competing methods in a range of challenging scenarios. We then apply our method to monitoring the correlation evolution of equity portfolios during periods of financial market stress, providing an ex post framework for assessing the changing benefits of diversification in backtesting analyses.

Database Management Systems (DBMSs) are widely used to store, retrieve, and manage the data handled by modern applications. Although prior work has studied the co-evolution of DBMSs and application source code, less is known about DBMS adoption, co-use, and replacement in real systems. This paper presents a historical study of DBMS usage in 362 popular open-source Java projects hosted on GitHub. We investigated the adoption of the top DBMSs ranked by DB-Engines, covering relational and non-relational systems. Using source-code heuristics, we analyzed DBMS popularity, stability, migration patterns, co-occurrence, and the role of Object-Relational Mappers (ORMs). Our findings show that MySQL and PostgreSQL are the most popular DBMSs in our corpus. Among non-relational DBMSs, Redis and MongoDB are the most frequently used and tend to remain stable after adoption. In contrast, systems such as HyperSQL are more often replaced as projects evolve. We also observed frequent co-use of multiple DBMSs, suggesting patterns of polyglot persistence in which projects combine systems to handle different data needs. Finally, we found that ORM frameworks are commonly used to mediate interactions between applications and DBMSs. Overall, our study provides empirical evidence on how DBMSs are adopted, combined, and replaced over time, offering guidance for developers, architects, educators, and DBMS vendors.

Societies are quintessential open systems, shaped by internal dynamics as well as external influences. The question is how these external influences alter the collective behavior and network dynamics. To answer this, we investigate coevolutionary balance dynamics in a system of independent and open networks. Here, the system consists of two interacting networks with directed (asymmetric) coupling: an independent network evolving autonomously and an open (dependent) network whose dynamics are influenced by the former. Using a mean-field framework, we demonstrate a transition temperature: below the transition temperature, the independent network reaches a state of structural balance, while the open network is destabilized by persistent imbalance states and enters a sustained imbalance phase. This coupling also induces a measurable upward shift in the transition temperature. Direct numerical simulations robustly confirm these analytical predictions.

The event horizon of a black hole is arguably the most dramatic manifestation of the fact that in General Relativity, causal structure is dynamical and spacetimes can be separated into distinct regions by causal boundaries. Causal set quantum gravity is an approach to quantum gravity in which causal relations between spacetime points constitute the basic structure on which the theory is based. This raises the question how a discrete horizon can be identified in a causal set. In our paper, we first construct a local diagnostic to approximate a global concept, namely the event horizon, based on discrete timelike curves. We then turn to the concept of an apparent horizon, which is based on local properties of geodesics, rather than global properties of the entire spacetime. We undertake first steps towards detecting apparent horizons in causal sets, using so-called ladders as tracers of null geodesics. We find that a discrete counterpart of the expansion changes sign across the black-hole horizon, as it should. Finally, we introduce the notion of a fuzzy ladder, which enables us to track null geodesics for larger intervals of the affine parameter. Thereby, we construct a portion of a discrete horizon in a toy-model for a black-hole spacetime in 1+1 dimensions.

We introduce a formalism for conformal field theory in four dimensions: a symplectic bi-Grassmannian representation of CFT$_4$ Wightman correlators. Working in Klein space with off-shell spinor-helicity variables, we show that correlators of $Δ= 2$ scalars and symmetric-traceless conserved currents are encoded by integrals over a pair of $n$-planes in a $2n$-dimensional symplectic vector space. These planes are constrained to be mutually symplectically orthogonal and aligned with the external kinematics. Conformal invariance, momentum conservation, and little-group covariance all follow geometrically from this structure. We derive all two- and three-point functions involving scalars, fermions, conserved currents, and stress tensors. As a non-trivial test, we show that the construction reproduces the full set of independent conformally invariant structures of $\langle JJJ\rangle$ and $\langle TTT\rangle$ in CFT$_4$. The resulting expressions are considerably more compact than their momentum-space counterparts. They also make manifest the double copy between Yang--Mills $\langle JJJ \rangle$ and Einstein-gravity $\langle TTT \rangle$. We further present a helicity-basis reformulation that makes the GL(1,R) and SL(2,R) weights of individual helicity components explicit. This basis also provides a natural starting point for a twistor-space formulation of the correlators.

We present the design, fabrication, and measurement of a monolithically integrated optical receiver analog front end, where low power operation is a primary consideration with a goal of supporting 56 Gbaud intensity modulated direct detect transceivers. The need for low-power consumption and low-noise operation motivates a monolithic, layout driven design approach which begins with circuit topology selection and analysis. Various transistor unit cell layout configurations are explored, minimizing parasitics, enabling wide analog bandwidth and reduced input referred noise. The post-layout analog front end achieves a 28.9 GHz bandwidth with a low-frequency gain of 61.7 dBΩ. This circuit was designed within the GlobalFoundries FotonixTM monolithic silicon photonics platform. The fabricated device is characterized by its DC operation, noise characteristics, and time domain behavior. The final design was validated by on-off keyed and PAM-4 electrical eye diagram measurements to 64 GBaud, consuming 9.22 mW of power from a 1.2 V supply with less than 737 nA RMS integrated input referred noise current and 0.08 pJ/bit.

The common envelope (CE) phase plays a key role in the formation of binary compact object systems. Its final outcome strongly depends on the envelope binding energy, but this quantity is often estimated using fitting formulas that are not fully consistent with the underlying stellar evolution models adopted in population-synthesis codes. Here, we investigate envelope binding energies across the most extensive stellar grid considered to date. Our stellar tracks, evolved with PARSEC v2.0, include hydrogen (H) -rich stars with metallicities ranging from $Z = 10^{-11}$ (Population III stars) to $Z = 0.03$, and initial masses between 2 and 2000 M$_\odot$, as well as pure-helium stars with masses from 0.36 to 350 M$_\odot$. We examine the sensitivity of the envelope binding energies to the selected core-envelope boundary definition and to different internal energy source contributions. For H-rich stars, we find that internal energy sources can alter the envelope binding energy by more than an order of magnitude, whereas the core boundary criteria play a secondary role. In contrast, for pure helium stars, the core-boundary criterion becomes the dominant factor. The envelope binding energies derived from different stellar tracks can show deviations of several orders of magnitude, with larger differences for more massive stars and higher metallicities.Finally, by implementing our new envelope binding energy prescriptions into the binary population synthesis code SEVN, we show that the predicted merger rate densities of compact binaries can differ by more than an order of magnitude compared to previous models. Our results highlight the importance of using envelope binding energies that are consistent with the underlying stellar evolution models and caution against extrapolating empirical fits beyond the considered parameter space.

Over the past decade, the AI industry has come to exert an unprecedented economic, political and societal power and influence. It is therefore critical that we comprehend the extent and depth of pervasive and multifaceted capture of AI regulation by corporate actors in order to contend and challenge it. In this paper, we first develop a taxonomy of mechanisms enabling capture to provide a comprehensive understanding of the problem. Grounded in design science research (DSR) methodologies and extensive scoping review of existing literature and media reports, our taxonomy of capture consists of 27 mechanisms across five categories. We then develop an annotation template incorporating our taxonomy, and manually annotate and analyse 100 news articles. The purpose behind this analysis is twofold: validate our taxonomy and provide a novel quantification of capture mechanisms and dominant narratives. Our analysis identifies 249 instances of capture mechanisms, often co-occurring with narratives that rationalise such capture. We find that the most recurring categories of mechanisms are Discourse & Epistemic Influence, concerning narrative framing, and Elusion of law, related to violations and contentious interpretations of antitrust, privacy, copyright and labour laws. We further find that Regulation stifles innovation, Red tape and National Interest are the most frequently invoked narratives used to rationalise capture. We emphasize the extent and breadth of regulatory capture by coalescing forces -- Big AI and governments -- as something policy makers and the public ought to treat as an emergency. Finally, we put forward key lessons learned from other industries along with transferable tactics for uncovering, resisting and challenging Big AI capture as well as in envisioning counter narratives.

We present VLA C-band (5~cm) continuum, K-band (1.3~cm) continuum, and water maser (22.235 GHz) monitoring of the protostar HOPS-373. We additionally present the contemporaneous monitoring for 95 sources within the 5~cm field of view for over two years during the peak of the HOPS-373 outburst and an additional epoch in 2026. HOPS-373 is a binary Class 0 protostar located in the Orion star forming region that was found to have a $\sim$4$\times$ luminosity burst from the JCMT Transient Survey and NEOWISE monitoring. We do not find evidence for a change in the free-free emission traced by VLA 5~cm continuum during the peak of its outburst or during the decline. Moreover, the 1.3~cm continuum does not show significant variability between the NE and SW components of the HOPS-373 binary. The water maser emission is highly variable toward HOPS-373, multiple velocity components are detected at different (or the same) times and the maser spots are located close to the 1.3~cm continuum source of HOPS-373-SW. There is tentative evidence for the water maser spots to be propagating away from the source, but there is not a robust connection between the outburst and the observed maser activity. The lack of correlation between outburst and free-free emission from HOPS-373 indicates that the free-free emission may not directly respond to increases in the accretion rate and subsequently the outflow rate. The lack of a link could be due to the outflow mostly being neutral, or there may be offsets in the timescale for the free-free response.

Many modern solvers and program analyzers rely on non-monotone reasoning (e.g. negation-as-failure, speculative updates, backtracking) for which classical monotone fixed-point methods do not apply. The general problem of finding the fixed points of these processes is a difficult one. For this reason, there have been theoretical efforts in existing Approximation Fixpoint Theory (AFT) from the domain of logic programming to approximate fixed points of non-monotone operators. Tight approximations of these fixed points are highly useful for accelerating non-monotonic computations by restricting the search space. In practice, however, even the best approximations obtained through AFT can be coarse and computationally expensive. We aim to address both issues to make AFT approximation methods practical for use in programming languages (PL) settings. To mitigate inefficiency, we prove the soundness of an abstract interpretation for approximating operators. To improve upon coarse approximations, we carefully introduce controlled unsoundness to design an effective yet practical algorithm for partitioning and tightening AFT's best approximations. This algorithm is sound, anytime, and guarantees termination on finite-height lattices. We further present a modification that ensures polynomial-time complexity. We instantiate these methods in two settings: (1) answer set programming, where it serves as a convergence-accelerating pre-processor, and (2) speculative program analysis, where it reduces rollback while preserving soundness. In both settings, we focus on implementation-level details to demonstrate the practical applicability of our methods.

We investigate the claims by Park and Haldane [Phys. Rev. B 90, 045123 (2014)] of an intrinsic protected value of the electric dipole moment at the physical edge of fractional quantum Hall (FQH) systems. Contrary to prevailing expectations, we find that the edge dipole takes the expected intrinsic value only in certain very special cases. We identify key limitations in earlier numerical studies and employ density matrix renormalization group (DMRG) methods to accurately compute the ground-state dipole. We focus on three representative systems: the $ν=1/3$-vacuum edge, the $ν=2/3$-vacuum edge, and the interface between Pfaffian and anti-Pfaffian phases. We find that the expected intrinsic dipole value occurs only at $ν=1/3$, whereas the other systems do not exhibit the claimed intrinsic value. We give arguments based on composite fermions as to why hierarchy states should generally not have protected intrinsic dipoles. These results have important implications for the energetics and edge structure of FQH states.

We derive the three-dimensional $\mathcal{N}=1$ effective theories obtained by compactifying all five ten-dimensional string theories on generic seven-dimensional manifolds with $G_2$ structure. The resulting flux compactifications are worked out explicitly, including the full moduli dependence of the scalar potential, kinetic terms, axionic sectors, gauge fields, Stückelberg couplings, and the allowed geometric and form-flux data. Our results extend previous analyses by incorporating fields and fluxes that are generically present in $G_2$ reductions, and provide a unified framework for comparing type IIA, type IIB, type I and heterotic compactifications to three dimensions. In particular, the effective theories organize naturally in terms of the real superpotential formulation of three-dimensional $\mathcal{N}=1$ supergravity, making the relation between fluxes, torsion, Chern--Simons data, and moduli potentials manifest.

Rutile RuO$_2$ has attracted significant interest due to its putative unconventional electronic and magnetic properties and its proximity to superconductivity. However, the measurement and interpretation of its electronic structure has been complicated by a strongly three-dimensional crystal structure. Here, we demonstrate how the preparation of targeted $(110)$ and $(100)$ surfaces via focused ion beam (FIB)-engineered cleaving allows the acquisition of high-quality measurements of the electronic structure using angle-resolved photoemission spectroscopy. Our results demonstrate that ARPES spectra of RuO$_2$ are, in fact, largely dominated by signatures of distinct surface electronic states. From comparison with density-functional theory, we resolve a surface termination-dependent variation of these, and disentangle them from highly-three-dimensional bulk states and surface resonances. Moreover, we find a marked role of the substantial spin-orbit coupling of the Ru 4$d$ orbitals in the surface region, where a breaking of spatial inversion symmetry leads to significant Rashba-type spin splittings of the surface bands.

We explore an uncharted corner of dark matter phenomenology: non-supersymmetric spin-$3/2$ dark matter with baryon number violating interactions. In an effective field theory description, we identify the leading baryonic portal between the spin-$3/2$ state and Standard Model quarks and show that it can account for the observed dark matter abundance through UV freeze-in and Boltzmann-suppressed freeze-in, while the freeze-out region is completely excluded. The resulting phenomenology is distinctive, with relic production controlled by the competition between baryon-violating single-particle processes and baryon-conserving pair production. We map the viable parameter space against indirect detection, direct detection, and LHC monojet bounds, finding strong complementarity between these probes and especially stringent limits when production and decay are tied to the same operator. We also present a dark QCD-like ultraviolet completion in which the spin-$3/2$ particle arises as a composite baryon, naturally generating the effective interactions and mitigating the main theoretical obstacles of elementary higher-spin states. This framework opens a novel and testable connection between baryonic portals, confining dark sectors, and higher-spin dark matter.

The presence of a chiral asymmetry in a relativistic plasma opens a tachyonic instability toward the growth of a helical magnetic field. We study the transfer of energy from the chiral asymmetry into the magnetic field during the development of this chiral plasma instability. We find that there is more energy stored in the initial chiral asymmetry than goes into growing magnetic field and that the excess energy is transferred to the thermal bath. Consequently, we find that the chiral plasma instability is accompanied by a heating of the plasma, and the temperature increase is parametrically $δT \sim μ_5^2 / T$ if the ratio of chemical potential to temperature is small, i.e. $μ_5/T \ll 1$. We briefly remark on possible observable implications for early universe cosmology.

X-ray spectral modeling is a powerful tool for studying the immediate environment of accreting objects, including supermassive black holes. Several models, either phenomenological or physically driven, have been developed over the past decade to study X-ray spectra, delivering important insights into the properties of circumnuclear material of active galactic nuclei (AGN). Despite the fact that these models are able to reproduce the data well, they often lack realistic geometries, and most of them consist of simplified configurations such as a slab or a torus. We use the ray-tracing code \textsc{RefleX} to generate new spectral models that cover a wide energy range in the X-ray band, adopting a realistic configuration for the surrounding material. We introduce two new table models that are publicly available: 1) the RXToPo model, which features an X-ray source along with a dusty torus and a polar hollow cone; 2) the RXagn1 model, which includes, besides the torus and polar cone, also the accretion disk and the broad line region. Both models were applied to the X-ray spectrum of NGC 424, demonstrating their potential to study sources whose X-ray emission is dominated by reprocessed radiation.

We propose a sharpened version of Dynamical Cobordism, where the physical structure $ξ$ of the theory in question determines an allowed range $R^ξ$ for the critical exponent $δ$. We interpret a singularity with $δ\in R^ξ$ as a true transition-to-nothing, i.e., a configuration ending spacetime, while a singularity with $δ\notin R^ξ$ indicates some obstruction to such a transition, i.e., the presence of a non-trivial cobordism global charge, which is incompatible with a theory of quantum gravity. In the spirit of the original Cobordism Conjecture, this apparent inconsistency of the theory can be alleviated via the modification of the structure, for instance by introducing new degrees of freedom and associated defects. Inspired by the Gubser criterion for good singularities, we propose a way to determine $R^ξ$. As a proof-of-concept we show explicitly how the introduction of a higher-form gauge field changes the allowed range of $δ$ compared to an EFT with only scalars. We test this sharpened version of Dynamical Cobordism against several examples, such as massive IIA string theory, where it is notably compatible with the presence of O8-planes; the Janis-Newman-Winicour and Garfinkle-Horowitz-Strominger black hole solutions; and certain singular distributions of D-branes. In all these cases, the Sharpened Dynamical Cobordism Conjecture leads to results consistent with our expectations.

Heavily reddened quasars (HRQs) are luminous, dust-obscured broad-line quasars thought to represent a short-lived phase of intense black hole growth and feedback. Previous studies have been limited by small sample sizes, restricting robust statistical analysis. We expand the sample of the most luminous HRQs to enable population-level studies, connecting their spectral energy distributions (SEDs) to other quasar populations and placing them within an evolutionary sequence of massive galaxy and black hole formation. We assemble multiwavelength broadband photometry for the brightest HRQ candidates (K$_{AB}$ < 18 mag) and select AGN with red near-infrared colours (J-K)$_{AB}$ > 1.6. Using SPHEREx spectrophotometry, we confirm HRQs and determine redshifts. Detailed SED fitting allows comparison with other luminous quasars, including a control sample of hyper-luminous, unobscured Quaia quasars and luminous Hot Dust-Obscured Galaxies (Hot DOGs). We confirm 77 new HRQs with redshifts 1.5 < z < 3.9, dust-corrected optical continuum luminosities log$_{10}(λL_λ(3000A)$ [erg/s])>47.0, and line-of-sight extinctions 0.4 < E(B-V) < 1.6 (A$_V$ mag). This more than doubles the known HRQs at z > 1.5, including the first seven at z > 3. A UV excess consistent with scattered quasar emission is detected in 76% of HRQs. We show that HRQs are hot-dust poor compared to blue quasars of similar luminosity and redshift. Their 6um continuum luminosities are systematically fainter at fixed 3000A continuum luminosity relative to blue Quaia quasars, indicating deficiency in both hot and warm dust. These results support a scenario in which HRQs represent a blow-out phase, where strong feedback begins clearing obscuring material from central regions.

The cosmological redshift of a galaxy's light is inferable from its observable properties in images. Because imaging is much easier to acquire than spectroscopic observations that would allow the identification of distinct line features, this motivates the technique of photometric redshift estimation (photo-$z$). Photo-$z$ has been an early and sustained driver for the utilization of artificial intelligence (AI) in astrophysics, and conversely AI methods are underlying most of the recent advances in photo-$z$. Here we review the diversity of AI methods applied to the photo-$z$ problem over the years in a discriminative way, that is, to regress redshift from photometric observables. We argue that, besides optimization suiting specific applications, this approach has effectively converged. It is limited not by the AI methodology but by the size and substantial systematic uncertainties and selection effects in spectroscopic training samples. In order to progress, either an unobtainable quantity and quality of training data or a more principled approach in using it is required. We thus outline ongoing research of integrating AI in a Bayesian modeling of galaxy data. This comes in the form of generative models for representing the distribution of intrinsic properties and outcomes of telescope observations of the galaxy population.