For several decades, broadband spectroscopy and fast X-ray timing have been the primary tools for investigating the rich and often intricate phenomenology of nonpulsating neutron stars in X-ray binaries with weak magnetic fields. However, available information has been insufficient to unambiguously disentangle the relative contributions of the different emitting regions, since different geometries and physical scenarios can produce similar signatures. Nowadays, X-ray polarimetry provides the missing, independent set of observables—polarization degree and angle—that are directly sensitive to understanding the nature and geometry of the emission regions. GX 13+1 is among the most peculiar systems of this class. We observed it with the Imaging X-ray Polarimetry Explorer (IXPE) over four distinct epochs, enabling the first polarization measurements during dips. A joint analysis of IXPE observations demonstrated that the polarization properties varied in response to intensity and spectral hardness changes associated with the dips, indicating the presence of multiple components contributing to the polarized X-ray emission. In particular, during dips, when the flux is lowest because the central region, hosting the spreading layer, boundary layer, and central neutron star regions, is obscured, the measured polarization can be attributed either to an extended accretion disk corona or to a disk wind component. Both of these emitting regions, if partially obscured, may explain the observed changes in the polarization angle. I will present the results of the IXPE observations, focusing on a recent observation conducted during the source's long periodic dip, which confirms that polarization tracks changes in the geometric configuration, thereby validating theoretical scenarios that have long been proposed solely on the basis of dipping phenomenology.

High-redshift radio-loud active galactic nuclei (AGN) are key laboratories for studying the formation of the earliest galaxies and supermassive black holes. Among them, blazars, AGNs with relativistic jets oriented close to the line of sight, appear to dominate at redshifts above four. Blazars can be used to obtain an estimate of jetted AGN. However, distinguishing blazars from misaligned radio sources remains challenging, particularly given the limited number of known high-redshift radio quasars. In general, the jet orientation can be inferred from X-ray data and milliarcsecond (mas)-scale angular resolution radio measurements. The latter can be achieved by very long-baseline interferometry (VLBI) imaging. VLBI is suitable for distinguishing between compact, high-brightness temperature radio emission of blazars and the more extended structures of misaligned jetted AGN. Previous high-resolution VLBI studies revealed that some of the radio sources among blazar candidates in fact show unbeamed radio emission on mas scales. For this reason, it is extremely important to compare the classifications obtained with different methods and from different wavelength bands. We found that the combination of multi-band (radio, optical, and X-ray) data and the combination of different classification methods is able to properly recover all the oriented sources present in high-redshift radio quasar samples.

Observations with the James Webb Space Telescope (JWST) led to the discovery of a new population of high-redshift sources, referred to as little red dots (LRDs). These are a mysterious class of objects that appear to be extremely compact in size, show excess ultraviolet emission, have a red optical continuum in the rest-frame, and exhibit broad line spectral features. They were discovered as part of an effort to characterise distant galaxies that might represent an early stage in galaxy evolution. Since their discovery, there has been significant interest in understanding the physical processes behind the observed characteristics. Are these galaxies rapidly forming stars? Do they host accreting supermassive black holes that power active galactic nuclei? Or do they represent another type of galaxy altogether? As radio observations are crucial in distinguishing between these possibilities, an extensive sample of LRDs were studied utilising radio maps from the Faint Images of the Radio Sky at Twenty-centimeters (FIRST) survey and the Very Large Array Sky Survey (VLASS). Investigating both the individual and statistical properties of LRDs in radio wavebands, the results were compared to and discussed with respect to those of known high-redshift AGN. Due to their extreme quiescence in the radio regime, we discuss various scenarios and hypoteses to explain the 'LRD phenomenon'.

Dwarf galaxies, although intrinsically faint and containing only modest stellar populations, provide an unusually sensitive testing ground for understanding how structure emerges in a cosmological context. Their shallow gravitational potentials make them particularly responsive to environmental influences and internal feedback, allowing researchers to probe physical processes that are harder to isolate in larger systems. Over the past decade, advances in numerical modeling—ranging from finely resolved hydrodynamic calculations to large-volume N-body suites—have offered increasingly detailed views of their kinematic evolution, star formation cycles, and dark matter configurations. Modern simulations now reproduce several of the empirical relationships observed in nearby dwarfs, including trends connecting mass, size, chemical enrichment, and luminosity. However, uncertainties in how feedback is implemented still produce noticeable variation among models. A long-standing tension involves the predicted shape of central dark matter profiles. Many simulations generate steep cusps, even though observations frequently point to shallower cores. Energetic stellar activity has been proposed as a mechanism for reshaping these regions, yet its effectiveness depends sensitively on resolution and feedback prescriptions. Another unresolved issue concerns the unexpectedly small number of known satellites in the Local Group compared with the abundance of low-mass halos in ΛCDM predictions. Recent work indicates that many such halos may host extremely faint systems that elude current surveys. Large simulation programs such as FIRE, APOSTLE, and NIHAO pursue these questions with differing assumptions and numerical strategies. Each captures certain aspects of dwarf galaxy evolution, but none fully replicates the diversity seen observationally. Considering results from multiple frameworks remains essential for constructing a comprehensive picture of how these small galaxies form, evolve, and interact with their environments.

Gamma-ray burst (GRB) light curves contain a remarkable level of temporal complexity, reflecting both the physics of the relativistic outflow and potential gravitational distortions caused by intervening compact objects. Gravitational microlensing, in particular, can imprint subtle yet measurable modifications on the temporal profile of a GRB. A central challenge, however, lies in reliably detecting these signatures within large GRB data sets, where the differences between lensed and non-lensed events may be difficult to identify by inspection alone.

In this study, we demonstrate that the temporal features inherently present in GRB light curves carry sufficient information to distinguish microlensed bursts from their unlensed counterparts. To explore this, we generate an extensive suite of simulated GRB light curves representing both lensed and non-lensed classes. Using the Cesium package and other feature extraction tools, we extract a comprehensive set of features that characterize key aspects of the temporal behavior, including variability patterns, statistical moments, and other descriptors of morphological evolution. These features serve as input to a machine learning (ML) pipeline designed to identify lensing-induced signatures.

We evaluate several ML algorithms on this feature space. Our results demonstrate that light curves themselves contain rich and discriminative information,and that feature-based ML approaches are capable of exploiting this information effectively. Our work highlights the potential of such methods for future observational applications, including automated searches for GRB lensing.

X-ray reflection spectroscopy is a powerful tool for exploring the innermost regions of accreting black hole systems. The X-ray spectra of black hole X-ray binaries (XRBs) typically consist of three components: thermal emission from the accretion disk, Comptonized emission from a hot corona, and a reflection component resulting from the illumination of the disk by the corona. Modeling the reflection features provides key information about the black hole’s spin and disk's parameters.

A phenomenon known as returning radiation (i.e., disk emission that is bent back onto itself by the black hole’s strong gravity) can significantly affect the reflection spectra, particularly for sources in the high-soft state. However, current XSPEC reflection models do not fully account for this effect.
We present a new reflection model that self-consistently includes returning radiation. To isolate its effects, we adopt a standard disk–corona configuration but disable the corona, allowing the reflection spectrum to arise solely from returning radiation, including higher-order reflections. Compared to widely used models such as relxillNS, our model naturally produces a harder high-energy reflection spectrum without requiring a Comptonized component. Our results demonstrate that returning radiation alone can account for the observed reflection features of XRBs in the soft state
and should therefore be considered an important component. Including returning radiation in spectra modeling also enables us to explore its impact on black hole spin measurements and test General Relativity in the strong-field regime.

Binary neutron star (BNS) mergers are among the most powerful engines in the universe, capable of producing both gravitational waves and short gamma-ray bursts (SGRBs). In this talk, I will present general relativistic magnetohydrodynamic (GRMHD) simulations that, for the first time, self-consistently capture the full sequence from merger to jet launching for the delayed collapse scenario. In our models, the merger forms a metastable massive neutron star (MNS) that eventually collapses into a black hole (BH). We explore different MNS lifetimes of about 25 ms and 50 ms—long enough to allow the emergence of magnetically driven polar outflows prior to collapse. When the BH forms, the resulting jet interacts dynamically with this earlier ejecta, producing shock heating and possible observable electromagnetic signatures. Using an unprecedentedly low-numerical-density floor that scales as r^-6, we are able to follow the jet propagation out to distances of about 1e4 km, revealing how the surrounding environment—shaped by the MNS lifetime—strongly influences jet collimation, the terminal Lorentz factor, and Poynting-flux luminosity. For comparison, I will also discuss a non-collapsing scenario, in which the long-lived MNS drives a dense, slow outflow that effectively chokes any potential relativistic jet, reinforcing the need for a BH central engine. These results provide a unified picture for the delayed collapse scenario, linking merger dynamics, outflows and jet formation, and potential impact on observable transients—an essential step toward end-to-end modeling of short gamma-ray burst progenitors in the multi-messenger era.

The electron Born self-energy (eBse) model assumes a finite-sized electron of radius R_e = 1.9 x 10^-20m, determined from electron-positron collisions at LEP. The Born self-energy U_e^B, corresponding to the energy contained in the surrounding electric field, provides a quantitative description of Dark Energy (Astrophys Space Sci 365, 64 (2020); Phys Sci Forum 2, 9 (2021)). Specifically, this model explains (i) the magnitude of DE, (ii) the occurrence of a deceleration-acceleration transition at a redshift z ~ 0.8, and (iii) possesses an equation of state w = -1 (in Quantum Electrodynamics the electron is assumed to be a point particle (R_e = 0); thus, U_e^B ~ 1/R_e is divergent and is “renormalized away” by assuming that U_e^B is contained within the electron rest mass m_e). w = -1 implies that two electron Born masses m_e^B = U_e^B/c² will experience a gravitational repulsion, whereas m_e^B and an uncharged mass will experience the normal gravitational attraction. m_e^B (~ 40 m_p) is a Dark Matter candidate that provides a good description of the Grand Rotation Curves for the Milky Way and M31 galaxies out to distances of ~ 400 kpc (Sci Rep 14, 24090 (2024)) (the difference between DE and DM, in this model, is as follows: DE arises from the time-dependent creation of m_e^B in intergalactic space due to ionization of hydrogen, whereas DM is a time-independent effect arising from the presence of a halo of electrons, along with their associated m_e^B, that surrounds a galaxy). Early in the Universe’s expansion history, for electrons and positrons of finite size, a glass transition occurs at a maximum number density of ~ 1/(2R_e)³ corresponding to physical contact between particles. This glass transition possesses properties akin to Cosmic Inflation (Sci Rep 13, 21798 (2023)). A brief summary of the eBse model and its interconnections to DE, DM, and CI will be provided in this contribution.

The Cosmic Microwave Background (CMB) provides a precise snapshot of the early Universe and encodes detailed information about the fundamental parameters governing cosmological evolution. Conventional approaches to cosmological parameter inference rely on Bayesian sampling techniques combined with numerical Boltzmann solvers, which, while powerful, often obscure the underlying functional relationships between cosmological parameters and observable features of the CMB power spectrum.

In this work, we investigate symbolic regression as an interpretable machine-learning framework for cosmological parameter inference from CMB temperature and polarization power spectra. Using the PySR algorithm, we search for compact, closed-form analytic expressions that relate cosmological parameters to features of the CMB power spectrum, enabling transparent and physically interpretable mappings between theory and observation. Unlike black-box neural networks, symbolic regression yields explicit mathematical expressions that can be directly analyzed and compared with theoretical expectations.

We evaluate the accuracy, complexity, and stability of the recovered symbolic models across multiple cosmological parameters and benchmark their performance against expressions obtained using the AI Feynman algorithm. While AI Feynman performs effectively in low-dimensional settings, we find that its performance degrades as the dimensionality and complexity of the parameter space increase. In contrast, PySR demonstrates greater robustness and flexibility in higher-dimensional regimes relevant to realistic cosmological inference problems.

Our results show that symbolic regression can recover accurate and compact analytic relationships while providing direct physical insight into the structure of the CMB parameter space. This work highlights symbolic machine learning as a promising complementary approach to traditional inference methods and contributes toward more interpretable and physics-informed analyses of cosmological data.

We study the application of Lorentz kinematic transformations deformed by critical velocity to a continuity equation. This generates a source structure for the continuity equation that is indistinguishable from a Nernst–Planck equation. In this case, the fluid flow is the source of excited states. This makes the fluid a type of self-source, where the flow is a force that compensates for its crystallization into excited states. Therefore, we can understand that the kinematic transformation adds a stress term to the fluid dynamics, which can be understood as a sort of dark energy.

We studied the effects of the currents that arise. Specifically, the dissipative and convective currents, which play a role of self-induction and mutual induction, are capable of generating local phase transitions in the fluid.

This construction is a step toward building a theory of gravity based on a theory of two self-interacting fluids. In future work, we will clarify the role of vorticity, include viscosity effects, and relate this to Helmholtz–Hodge decomposition. We will also clarify the dynamic role of critical velocity as a fundamental state and its effect on the causal structure of the fluid, in addition to investigating a possible condition of non-integrability, which should severely affect the construction of the causal structure.