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Tidal disruption events (TDEs) of giant stars by supermassive black holes (SMBH) differ significantly from those of main sequence ones. Most (all for SMBH of more than a~ few times 10^5 m_\odot) giant-TDEs are partial: only a fraction of the envelope is torn apart. The dense stellar core and the rest of the envelope remain intact. In this work, we explore, using the stellar evolution code MESA, the fate of the remnants. We find that after a short period, comparable to the thermal time scale, the remnant returns to a giant structure with a radius comparable to the progenitor giant one, a slightly larger luminosity (as compared with a regular giant with the same mass), and a comparable lifetime until it collapses to a white dwarf. If such a giant with a mass less than approx 0.9 m_\odot is discovered, it can be identified as an outlier - a giant that is too light for the current age of the Universe. If the remnant orbit is not perturbed significantly during the encounter, the remnant will undergo successive partial tidal disruptions until its mass is $0.6-0.7 m_\odot$. We expect a few dozen to a few hundred such remnants in the Galactic nucleus.

Considered a pair of random lifetimes whose dependence is described by a Time Transformed Exponential model, we provide analytical expressions for the distribution of their sum. These expressions are obtained by using a representation of the joint distribution in terms of multivariate distortions, which is an alternative approach to the classical copula representation. Since this approach allows to obtain conditional distributions and their inverses in simple form, then it is also shown how it can be used to predict the value of the sum from the value of one of the variables (or vice versa) by using quantile regression techniques.

The coherent systems are basic concepts in reliability theory and survival analysis. They contain as particular cases the popular series, parallel and $k$-ou-of-$n$ systems (order statistics). Many results have been obtained for them by assuming that the component lifetimes are independent. In many practical cases, this assumption is unrealistic. In this paper we study them by assuming a Time Transformed Exponential (TTE) model for the joint distribution of the component lifetimes. This model is equivalent to the frailty model which assumes that they are conditionally independent given a common risk parameter (which represents the common environment risk). Under this model, we obtain explicit expressions for the system reliability functions and comparison results for the main stochastic orders. The system residual lifetime (under different assumptions) is studied as well.

Cosmic shear, galaxy clustering, and the abundance of massive halos each probe the large-scale structure of the universe in complementary ways. We present cosmological constraints from the joint analysis of the three probes, building on the latest analyses of the lensing-informed abundance of clusters identified by the South Pole Telescope (SPT) and of the auto- and cross-correlation of galaxy position and weak lensing measurements (3$\times$2pt) in the Dark Energy Survey (DES). We consider the cosmological correlation between the different tracers and we account for the systematic uncertainties that are shared between the large-scale lensing correlation functions and the small-scale lensing-based cluster mass calibration. Marginalized over the remaining $\Lambda$CDM parameters (including the sum of neutrino masses) and 52 astrophysical modeling parameters, we measure $\Omega_\mathrm{m}=0.300\pm0.017$ and $\sigma_8=0.797\pm0.026$. Compared to constraints from Planck primary CMB anisotropies, our constraints are only 15% wider with a probability to exceed of 0.22 ($1.2\sigma$) for the two-parameter difference. We further obtain $S_8\equiv\sigma_8(\Omega_\mathrm{m}/0.3)^{0.5}=0.796\pm0.013$ which is lower than the Planck measurement at the $1.6\sigma$ level. The combined SPT cluster, DES 3$\times$2pt, and Planck datasets mildly prefer a non-zero positive neutrino mass, with a 95% upper limit $\sum m_\nu<0.25~\mathrm{eV}$ on the sum of neutrino masses. Assuming a $w$CDM model, we constrain the dark energy equation of state parameter $w=-1.15^{+0.23}_{-0.17}$ and when combining with Planck primary CMB anisotropies, we recover $w=-1.20^{+0.15}_{-0.09}$, a $1.7\sigma$ difference with a cosmological constant. The precision of our results highlights the benefits of multiwavelength multiprobe cosmology., Comment: Submitted to Phys. Rev. D

In this article we briefly review the adiabatic renormalization program for spin 1/2 fields in expanding universes. We introduce the method and provide explicit expressions for the renormalized vacuum expectation value of the stress-energy tensor. Then, we discuss its application to some cosmological scenario of physical interest. We end up sketching out the proof that adiabatic and DeWitt-Schwinger point-splitting schemes provide the same renormalized expectation values of the stress-energy tensor for Dirac fields., Comment: Proceedings of the 14th Marcel Grossmann Meeting, 2816-2821 (2017)

While there is widespread interest in supporting young people to critically evaluate machine learning-powered systems, there is little research on how we can support them in inquiring about how these systems work and what their limitations and implications may be. Outside of K-12 education, an effective strategy in evaluating black-boxed systems is algorithm auditing-a method for understanding algorithmic systems' opaque inner workings and external impacts from the outside in. In this paper, we review how expert researchers conduct algorithm audits and how end users engage in auditing practices to propose five steps that, when incorporated into learning activities, can support young people in auditing algorithms. We present a case study of a team of teenagers engaging with each step during an out-of-school workshop in which they audited peer-designed generative AI TikTok filters. We discuss the kind of scaffolds we provided to support youth in algorithm auditing and directions and challenges for integrating algorithm auditing into classroom activities. This paper contributes: (a) a conceptualization of five steps to scaffold algorithm auditing learning activities, and (b) examples of how youth engaged with each step during our pilot study.

The ability to wield tools was once considered exclusive to human intelligence, but it's now known that many other animals, like crows, possess this capability. Yet, robotic systems still fall short of matching biological dexterity. In this paper, we investigate the use of Large Language Models (LLMs), tool affordances, and object manoeuvrability for non-prehensile tool-based manipulation tasks. Our novel method leverages LLMs based on scene information and natural language instructions to enable symbolic task planning for tool-object manipulation. This approach allows the system to convert the human language sentence into a sequence of feasible motion functions. We have developed a novel manoeuvrability-driven controller using a new tool affordance model derived from visual feedback. This controller helps guide the robot's tool utilization and manipulation actions, even within confined areas, using a stepping incremental approach. The proposed methodology is evaluated with experiments to prove its effectiveness under various manipulation scenarios.

Strong degeneracy exists between some modified gravity (MG) models and massive neutrinos because the enhanced structure growth produced by modified gravity can be suppressed due to the free-streaming massive neutrinos. Previous works showed this degeneracy can be broken with non-Gaussian or velocity information. Therefore in this work, we focus on the large-scale structure (LSS) in redshift space and investigate for the first time the possibility of using the non-Gaussian information and velocity information captured by the 3D scalar Minkowski functionals (MFs) and the 3D Minkowski tensors (MTs) to break this degeneracy. Based on the Quijote and Quijote-MG simulations, we find the imprints on redshift space LSS left by the Hu-Sawicki $f(R)$ gravity can be discriminated from those left by massive neutrinos with these statistics. With the Fisher information formalism, we first show how the MTs extract information with their perpendicular and parallel elements for both low- and high-density regions; then we compare constraints from the power spectrum monopole and MFs in real space with those in redshift space, and investigate how the constraining power is further improved with anisotropies captured by the quadrupole and hexadecapole of the power spectrum and the MTs; finally, we combine the power spectrum multipoles with MFs plus MTs and find the constraints from the power spectrum multipoles on $\Omega_{\mathrm{m}}, h, \sigma_8$, $M_\nu$, and $f_{R_0}$ can be improved, because they are complemented with non-Gaussian information, by a factor of 3.4, 3.0, 3.3, 3.3, and 1.9 on small scales ($k_{\rm{max}}=0.5~h\rm{Mpc}^{-1},\ R_G=5~h^{-1}\rm{Mpc}$), and 2.8, 2.2, 3.4, 3.4, and 1.5 on large scales ($k_{\rm{max}}=0.25~h\rm{Mpc}^{-1},\ R_G=10~h^{-1}\rm{Mpc}$)., Comment: 41 pages, 15 figures, submitted to JCAP

The circumgalactic medium (CGM) around massive galaxies plays a crucial role in regulating star formation and feedback. Using the CAMELS simulation suite, we develop emulators for the X-ray surface brightness profile and the X-ray luminosity--stellar mass scaling relation to investigate how stellar and AGN feedback shape the X-ray properties of the hot CGM. Our analysis shows that at CGM scales ($10^{12} \lesssim M_{\rm halo}/M_\odot \lesssim 10^{13}$, $10\lesssim r/{\rm kpc} \lesssim 400$), stellar feedback more significantly impacts the X-ray properties than AGN feedback within the parameters studied. Comparing the emulators to recent eROSITA All-Sky Survey observations, it was found that stronger feedback than currently implemented in the IllustrisTNG, SIMBA, and Astrid simulations is required to match observed CGM properties. However, adopting these enhanced feedback parameters causes deviations in the stellar-mass-halo-mass relations from observational constraints below the group mass scale. This tension suggests possible unaccounted systematics in X-ray CGM observations or inadequacies in the feedback models of cosmological simulations., Comment: 13 pages, 6 figures, submitted to ApJ

Purpose: The purpose of this paper is to examine how a clinical interview protocol with failure artifact scenarios can capture changes in high school students' explanations of troubleshooting processes in physical computing activities. We focus on physical computing since finding and fixing hardware and software bugs is a highly contextual practice that involves multiple interconnected domains and skills. Approach: We developed and piloted a "failure artifact scenarios" clinical interview protocol. Youth were presented with buggy physical computing projects over video calls and asked for suggestions on how to fix them without having access to the actual project or its code. We applied this clinical interview protocol before and after an eight-week-long physical computing (more specifically, electronic textiles) unit. We analyzed matching pre- and post-interviews from 18 students at four different schools. Findings: Our findings demonstrate how the protocol can capture change in students' thinking about troubleshooting by eliciting students' explanations of specificity of domain knowledge of problems, multimodality of physical computing, iterative testing of failure artifact scenarios, and concreteness of troubleshooting and problem solving processes. Originality: Beyond tests and surveys used to assess debugging, which traditionally focus on correctness or student beliefs, our "failure artifact scenarios" clinical interview protocol reveals student troubleshooting-related thinking processes when encountering buggy projects. As an assessment tool, it may be useful to evaluate the change and development of students' abilities over time., Comment: arXiv admin note: text overlap with arXiv:2311.17212

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of neighboring nodes are likely to be the same. In recent years, an increasing body of work has been devoted to the development of GNN architectures for heterophilic datasets, where labels do not exhibit this low-pass behavior. In this work, we create a new graph in which nodes are connected if they share structural characteristics, meaning a higher chance of sharing their labels, and then use this new graph in the GNN architecture. To do this, we compute the k-nearest neighbors graph according to distances between structural features, which are either (i) role-based, such as degree, or (ii) global, such as centrality measures. Experiments show that the labels are smoother in this newly defined graph and that the performance of GNN architectures improves when using this alternative structure., Comment: Presented as a conference paper in the Asilomar Conference on Signals, Systems, and Computers 2024

The adiabatic regularization method is likely the most direct and intuitive renormalization scheme for FLRW cosmologies. The method requires one to start with a nonvanishing mass, but massless theories can be studied by taking the massless limit at the end of the calculations. For spin-$1$, however, this limit changes the number of degrees of freedom, so it cannot be performed directly. In this work, we show a direct approach that begins with the canonical quantization of the physical degrees of freedom of a massive Proca field. We give the details of the construction and show that, in the massless limit, the renormalized stress-energy tensor of the Proca field is closely related to that of a minimally coupled scalar field. For completeness and pedagogical purposes, we also include a brief orientation to the adiabatic method for scalar fields. The construction of the adiabatic subtractions in momentum space is compared with that of the BPHZ method for renormalizing Feynman loop integrals., Comment: Accepted to appear in the Proceedings of the 17th Marcel Grossmann Meeting (MG17)

Flat elastic sheets tend to display wrinkles and folds. From pieces of clothing down to two-dimensional crystals, these corrugations appear in response to strain generated by sheet compression or stretching, thermal or mechanical mismatch with other elastic layers, or surface tension. Extensively studied in metals, polymers and, more recently, in van der Waals exfoliated layers, with the advent of thin single crystal freestanding films of complex oxides, researchers are now paying attention to novel microstructural effects induced by bending ferroelectric-ferroelastics, where polarization is strongly coupled to lattice deformation. Here we show that wrinkle undulations in BaTiO3 sheets bonded to a viscoelastic substrate transform into a buckle delamination geometry when transferred onto a rigid substrate. Using spatially resolved techniques at different scales (Raman, scanning probe and electron microscopy), we show how these delaminations in the free BaTiO3 sheets display a self-organization of ferroelastic domains along the buckle profile that strongly differs from the more studied sinusoidal wrinkle geometry. Moreover, we disclose the hierarchical distribution of a secondary set of domains induced by the misalignment of these folding structures from the preferred in-plane crystallographic orientations. Our results disclose the relevance of the morphology and orientation of buckling instabilities in ferroelectric free sheets, for the stabilization of different domain structures, pointing to new routes for domain engineering of ferroelectrics in flexible oxide sheets.

The contribution to pushing the boundaries of knowledge is a critical metric for evaluating the research performance of countries and institutions, which in many cases is not revealed by common bibliometric indicators. The Rk-index was specifically designed to assess such contributions, and the Rn-index is a variant that corrects the weakness of the Rk-index, particularly in the evaluation of countries that produce a high proportion of global advancements. This is the case of the USA and China in many technological fields. Additionally, the Rn-index is simple to calculate and understand, as it involves only summing the ratios between the local and global ranks of papers, ordered by their citation count. Moreover, the Rn-index may also be fractionally counted., Comment: 6 pages; 2 figures; 4 tables

We present a timing study of the gamma and X-ray observations and analysis of a sample of bright gamma-ray bursts (GRBs; i.e. GRB 180720B, GRB 181222B, GRB 211211A and GRB 220910A), including the very bright and long GRB 211211A (a.k.a. kilonova candidate). They have been detected and observed by the Atmosphere-Space Interactions Monitor (ASIM) installed on the International Space Station (ISS) and the Gamma-ray Burst Monitor (GBM) on-board the Fermi mission. The early (T-T0=s) and high-energy (0.3-20 MeV) ASIM High Energy Detector (HED) and (150 keV-30 GeV) Fermi (BGO) light curves show well-defined peaks with a low quasi-periodic oscillation (QPO) frequency between 2.5-3.5 Hz that could be identified with the spin of the neutron star in the binary mergers (coinciding with the orbital frequency of the binary merger) originating these GRBs. These QPOs consist on the first detection of low-frequency QPOs (<10 Hz) detected in magnetars so far. We also detect a strong QPO at 21.8-22 Hz in GRB 181222B together with its (less significant) harmonics. The low-frequency QPO would correspond to the signal of the orbiting neutron star (NS) previous to the final coalescence giving rise to the gravitational-wave (GW) signal., Comment: Submitted to MNRAS Letters (21-Nov.-2024)

We present the first analysis of Dark Matter axion detection applying neural networks for the improvement of sensitivity. The main sources of thermal noise from a typical read-out chain are simulated, constituted by resonant and amplifier noises. With this purpose, an advanced modal method employed in electromagnetic modal analysis for the design of complex microwave circuits is applied. A feedforward neural network is used for a boolean decision (there is axion or only noise), and robust results are obtained: the neural network can improve by a factor of 50 the integration time needed to reach a given signal to noise ratio. This could either significantly reduce measurement times or achieve lower sensitivities with the same exposure durations., Comment: 6 pages, 6 figures, 1 table

In sunspot umbrae, the core of some chromospheric lines exhibits periodic brightness enhancements known as umbral flashes. The consensus is that they are produced by the upward propagation of shock waves. This view has recently been challenged by the detection of downflowing umbral flashes and the confirmation of the existence of a resonant cavity above sunspots. We aim to determine waves' propagating or standing nature in the low umbral chromosphere and confirm or refute the existence of downflowing umbral flashes. Spectroscopic temporal series of Ca II 8542 \AA, Ca II H, and Halpha in a sunspot were acquired with the Swedish Solar Telescope. The Halpha velocity was inferred using bisectors. Simultaneous inversions of the Ca II 8542 \AA\ line and the Ca II H core were performed using the NICOLE code. The nature of the oscillations and insights into the resonant oscillatory pattern were determined by analyzing the phase shift between the velocity signals and examining the temporal evolution. Propagating waves in the low chromosphere are more common in regions with frequent umbral flashes, where the transition region is shifted upward, making resonant cavity signatures less noticeable. In contrast, areas with fewer umbral flashes show velocity fluctuations that align with standing oscillations. Evidence suggests dynamic changes in the location of velocity resonant nodes due to variations in transition region height. Downflowing profiles appear at the onset of some umbral flashes, but upflowing motion dominates during most of the flash. These downflowing flashes are more common in standing umbral flashes. We confirm the existence of a chromospheric resonant cavity above sunspot umbrae produced by wave reflections at the transition region. The oscillatory pattern depends on the transition region height, which exhibits spatial and temporal variations due to the impact of the waves., Comment: Accepted for publication in A&A

Predicting team dynamics from personality traits remains a fundamental challenge for the psychological sciences and team-based organizations. Understanding how team composition generates team processes can significantly advance team-based research along with providing practical guidelines for team staffing and training. Although the Input-Process-Output (IPO) model has been useful for studying these connections, the complex nature of team member interactions demands a more dynamic approach. We develop a computational model of conversational turn-taking within self-organized teams that can provide insight into the relationships between team member personality traits and team communication dynamics. We focus on turn-taking patterns between team members, independent of content, which can significantly influence team emergent states and outcomes while being objectively measurable and quantifiable. As our model is trained on conversational data from teams of given trait compositions, it can learn the relationships between individual traits and speaking behaviors and predict group-wide patterns of communication based on team trait composition alone. We first evaluate the performance of our model using simulated data and then apply it to real-world data collected from self-organized student teams. In comparison to baselines, our model is more accurate at predicting speaking turn sequences and can reveal new relationships between team member traits and their communication patterns. Our approach offers a more data-driven and dynamic understanding of team processes. By bridging the gap between individual personality traits and team communication patterns, our model has the potential to inform theories of team processes and provide powerful insights into optimizing team staffing and training., Comment: 64 pages, 9 figures

We perform the first direct cosmological and astrophysical parameter inference from the combination of galaxy luminosity functions and colours using a simulation based inference approach. Using the Synthesizer code we simulate the dust attenuated ultraviolet--near infrared stellar emission from galaxies in thousands of cosmological hydrodynamic simulations from the CAMELS suite, including the Swift-EAGLE, Illustris-TNG, Simba & Astrid galaxy formation models. For each galaxy we calculate the rest-frame luminosity in a number of photometric bands, including the SDSS $\textit{ugriz}$ and GALEX FUV & NUV filters; this dataset represents the largest catalogue of synthetic photometry based on hydrodynamic galaxy formation simulations produced to date, totalling >200 million sources. From these we compile luminosity functions and colour distributions, and find clear dependencies on both cosmology and feedback. We then perform simulation based (likelihood-free) inference using these distributions, and obtain constraints on both cosmological and astrophysical parameters. Both colour distributions and luminosity functions provide complementary information on certain parameters when performing inference. Most interestingly we achieve constraints on $\sigma_8$, describing the clustering of matter. This is attributable to the fact that the photometry encodes the star formation--metal enrichment history of each galaxy; galaxies in a universe with a higher $\sigma_8$ tend to form earlier and have higher metallicities, which leads to redder colours. We find that a model trained on one galaxy formation simulation generalises poorly when applied to another, and attribute this to differences in the subgrid prescriptions, and lack of flexibility in our emission modelling. The photometric catalogues are publicly available at: https://camels.readthedocs.io/ ., Comment: 28 pages, 20 figures, submitted to MNRAS. Comments and feedback welcome!

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensitive low-background detectors placed at the focal spot. The aim of this article is to provide an accurate quantitative description of the different components (such as the magnet, optics, and X-ray detectors) involved in the detection of axions. Our efforts have focused on developing robust and integrated software tools to model these helioscope components, enabling future assessments of modifications or upgrades to any part of the IAXO axion helioscope and evaluating the potential impact on the experiment's sensitivity. In this manuscript, we demonstrate the application of these tools by presenting a precise signal calculation and response analysis of BabyIAXO's sensitivity to the axion-photon coupling. Though focusing on the Primakoff solar flux component, our virtual helioscope model can be used to test different production mechanisms, allowing for direct comparisons within a unified framework., Comment: 36 pages, 18 figures, 4 tables, Submitted to JHEP

The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of the Universe. In this work, we present the first comprehensive analytical study of the marked power spectrum's sensitivity to primordial non-Gaussianity (PNG) of the non-local type. We extend previous effective field theory frameworks to incorporate PNG, developing a complete theoretical model that we validate against the Quijote simulation suite. Through a systematic Fisher analysis, we compare the constraining power of the marked power spectrum against traditional approaches combining the power spectrum and bispectrum (P+B). We explore different choices of mark parameters to evaluate their impact on parameter constraints, particularly focusing on equilateral and orthogonal PNG as well as neutrino masses. Our analysis shows that while marking up underdense regions yields optimal constraints in the low shot-noise regime, the marked power spectrum's performance for discrete tracers with BOSS-like number densities does not surpass that of P+B analysis at mildly non-linear scales ($k \lesssim 0.25 \,h/\text{Mpc}$). However, the marked approach offers several practical advantages, including simpler estimation procedures and potentially more manageable systematic effects. Our theoretical framework reveals how the marked power spectrum incorporates higher-order correlation information through terms resembling tree-level bispectra and power spectrum convolutions. This work establishes a robust foundation for applying marked statistics to future large-volume surveys., Comment: 18 pages, 7 figures

We assessed the validity of one of the most frequently used methods to estimate cancer incidence, on the basis of cancer mortality data and the incidence-to-mortality ratio IMR, the IMR method. Using the previous 15 year cancer mortality time series, we derived the expected yearly number of cancer cases in the period 2004 to 2013 for six cancer sites for each sex. Generalized linear mixed models, including a polynomial function for the year of death and smoothing splines for age, were adjusted. Models were fitted under a Bayesian framework based on Markov chain Monte Carlo methods. The IMR method was applied to five scenarios reflecting different assumptions regarding the behavior of the IMR. We compared incident cases estimated with the IMR method to observed cases diagnosed in 2004 to 2013 in Granada. A goodness-of-fit GOF indicator was formulated to determine the best estimation scenario. The relative differences between the observed and predicted numbers of cancer cases were less than 10 percent for most cancer sites. The constant assumption for the IMR trend provided the best GOF for colon, rectal, lung, bladder, and stomach cancers in men and colon, rectum, breast, and corpus uteri in women.

We analyze the semiclassical Schwarzschild geometry in the Boulware quantum state in the framework of two-dimensional (2D) dilaton gravity. The classical model is defined by the spherical reduction of Einstein's gravity sourced with conformal scalar fields. The expectation value of the stress-energy tensor in the Boulware state is singular at the classical horizon of the Schwarzschild spacetime, but when backreaction effects are considered, previous results have shown that the 2D geometry is horizonless and described by a non-symmetric wormhole with a curvature singularity on the other side of the throat. In this work we show that reversing the sign of the central charge of the conformal matter removes the curvature singularity of the 2D backreacted geometry, which happens to be horizonless and asymptotically flat. This result is consistent with a similar analysis recently performed for the CGHS model. We also argue the physical significance of negative central charges in conformal anomalies from a four-dimensional perspective.

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing valuable datasets obsolete. However, recreating similar datasets with newer sensor technologies is both tedious and time-consuming. Therefore, it is crucial to adapt these existing datasets for use with new setups and modalities. In response, we introduce ACROSS, a novel framework for translating data between tactile sensors by exploiting sensor deformation information. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of first converting the input signals into 3D deformation meshes. We then transition from the 3D deformation mesh of one sensor to the mesh of another, and finally convert the generated 3D deformation mesh into the corresponding output space. We demonstrate our approach to the most challenging problem of going from a low-dimensional tactile representation to a high-dimensional one. In particular, we transfer the tactile signals of a BioTac sensor to DIGIT tactile images. Our approach enables the continued use of valuable datasets and the exchange of data between groups with different setups., Comment: Paper Submitted to ICRA2025. arXiv admin note: text overlap with arXiv:2410.14310

In social sciences, researchers often face challenges when conducting large-scale experiments, particularly due to the simulations' complexity and the lack of technical expertise required to develop such frameworks. Agent-Based Modeling (ABM) is a computational approach that simulates agents' actions and interactions to evaluate how their behaviors influence the outcomes. However, the traditional implementation of ABM can be demanding and complex. Generative Agent-Based Modeling (GABM) offers a solution by enabling scholars to create simulations where AI-driven agents can generate complex behaviors based on underlying rules and interactions. This paper introduces a framework for designing reliable experiments using GABM, making sophisticated simulation techniques more accessible to researchers across various fields. We provide a step-by-step guide for selecting appropriate tools, designing the model, establishing experimentation protocols, and validating results.

The observational detection of some metastable isomers in the interstellar medium with abundances comparable to those of the most stable isomer, or even when the stable isomer is not detected, highlights the importance of non-equilibrium chemistry. This challenges our understanding of the interstellar chemistry. We present a chemical study of isomers through the sulphur isomer pair HNCS and HSCN, since HSCN has been observed in regions where its stable isomer has not been detected, and the observed HNCS/HSCN ratio seems to significantly vary from cold to warm regions. We have used the Nautilus chemical code to model the formation and destruction paths of HNCS and HSCN in different astrochemical scenarios, and the time evolution of the HNCS/HSCN ratio. We have also analysed the influence of the environmental conditions on their chemical abundances. We present an observational detection of the metastable isomer HSCN in the Class I object B1-a, but not of the stable isomer HNCS, despite HNCS lying 3200 K lower in energy than HSCN. Our results show an HNCS/HSCN ratio sensitive to the gas temperature and the evolutionary time, with the highest values obtained at early stages (t<10^4 yr) and low (Tg<20 K) temperatures. The results suggest a different efficiency of the isomerisation processes depending on the source temperature. The progressive decrease of HNCS/HSCN with gas temperature at early evolutionary times indicates that this ratio may be used as a tracer of cold young objects. This work also demonstrates the key role of grain surface chemistry in the formation of the isomer pair HNCS and HSCN in cold regions, and the importance of the ions H2NCS+ and HNCSH+ in warm/hot regions. Since most of the interstellar regions where HSCN is detected are cold regions, a larger sample including sources characterised by high temperatures are needed to corroborate the theoretical results., Comment: 10 pages, 12 figures. Accepted for publication by A&A

The structure and kinematics of the Milky Way disk are largely inferred from the solar vicinity. To gain a comprehensive understanding, it is essential to find reliable tracers in less-explored regions like the bulge and the far side of the disk. Mira variables, which are well-studied and bright standard candles, offer an excellent opportunity to trace intermediate and old populations in these complex regions. We aim to isolate a clean sample of Miras in the Vista Variables in the V\'ia L\'actea survey using Gaussian process algorithms. This sample will be used to study intermediate and old age populations in the Galactic bulge and far disk. Near- and mid-infrared time-series photometry were processed using Gaussian Process algorithms to identify Mira variables and model their light curves. We calibrated selection criteria with a visually inspected sample to create a high-purity sample of Miras, integrating multi-band photometry and kinematic data from proper motions. We present a catalog of 3602 Mira variables. By analyzing photometry, we classify them by O-rich or C-rich surface chemistry and derive selective-to-total extinction ratios of $A_{K_{s}}/E(J - K_{s}) = 0.471 \pm 0.01$ and $A_{K_{s}}/E(H - K_{s}) = 1.320 \pm 0.020$. Using the Mira period-age relation, we find evidence supporting the inside-out formation of the Milky Way disk. The distribution of proper motions and distances aligns with the Galactic rotation curve and disk kinematics. We extend the rotation curve up to R$_{\rm GC} \sim 17 \ \rm{kpc}$ and find no strong evidence of the nuclear stellar disk in our Mira sample. This study constitutes the largest catalog of variable stars on the far side of the Galactic disk to date., Comment: 20 pages, 19 figures, Accepted in A&A

This work presents a low-rank tensor model for multi-dimensional Markov chains. A common approach to simplify the dynamical behavior of a Markov chain is to impose low-rankness on the transition probability matrix. Inspired by the success of these matrix techniques, we present low-rank tensors for representing transition probabilities on multi-dimensional state spaces. Through tensor decomposition, we provide a connection between our method and classical probabilistic models. Moreover, our proposed model yields a parsimonious representation with fewer parameters than matrix-based approaches. Unlike these methods, which impose low-rankness uniformly across all states, our tensor method accounts for the multi-dimensionality of the state space. We also propose an optimization-based approach to estimate a Markov model as a low-rank tensor. Our optimization problem can be solved by the alternating direction method of multipliers (ADMM), which enjoys convergence to a stationary solution. We empirically demonstrate that our tensor model estimates Markov chains more efficiently than conventional techniques, requiring both fewer samples and parameters. We perform numerical simulations for both a synthetic low-rank Markov chain and a real-world example with New York City taxi data, showcasing the advantages of multi-dimensionality for modeling state spaces.

Discontinuous Named Entity Recognition (DNER) presents a challenging problem where entities may be scattered across multiple non-adjacent tokens, making traditional sequence labelling approaches inadequate. Existing methods predominantly rely on custom tagging schemes to handle these discontinuous entities, resulting in models tightly coupled to specific tagging strategies and lacking generalisability across diverse datasets. To address these challenges, we propose TriG-NER, a novel Triplet-Grid Framework that introduces a generalisable approach to learning robust token-level representations for discontinuous entity extraction. Our framework applies triplet loss at the token level, where similarity is defined by word pairs existing within the same entity, effectively pulling together similar and pushing apart dissimilar ones. This approach enhances entity boundary detection and reduces the dependency on specific tagging schemes by focusing on word-pair relationships within a flexible grid structure. We evaluate TriG-NER on three benchmark DNER datasets and demonstrate significant improvements over existing grid-based architectures. These results underscore our framework's effectiveness in capturing complex entity structures and its adaptability to various tagging schemes, setting a new benchmark for discontinuous entity extraction., Comment: Code will be made available upon publication

We analyze a sample of 4500 photometrically-selected Ha emitter galaxies at redshifts z~2 and z~4-6 selected from James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam) medium-band images in the Great Observatories Origins Deep Survey South (GOODS-S). The bulk (80%) of the galaxies in our sample have stellar masses lower than 10^8 Msun, with a median stellar mass of ~10^7.3 Msun. We derive Ha rest-frame equivalent widths ( EW0(Ha) ), line fluxes, and star formation rates using a robust photometric excess technique tested against spectroscopic measurements, being sensitive to EW0(Ha) > 75 A. Both EW0(Ha) and sSFR(Ha) anti-correlate with stellar mass, and at fixed stellar mass, show a steep increasing trend with redshift sSFR(Ha) ~ (1+z)^2.55. By comparing the Ha and rest-frame UV-derived SFRs, we probe the star formation histories (SFHs) of our galaxies in the past 100 Myr. The fraction of low-mass galaxies (M < 10^8 Msun) with signs of bursty star formation from their SFR(Ha)/SFR(UV) is ~ 50 %. It quickly drops to ~ 25 % for M > 10^8 Msun. This is consistent with the results from sSFR(Ha), showing 80% and 17%, respectively. SFR(Ha)/SFR(UV) is a stricter criterion than those based on the galaxy sSFR(Ha), as it only identifies the strongest starbursts, the ones at the initial phases of a bursty star-formation episode., Comment: 20 pages, 10 figures. Submitted to ApJ, comments are welcome

Modeling the intrinsic alignment (IA) of galaxies poses a challenge to weak lensing analyses. The Dark Energy Survey is expected to be less impacted by IA when limited to blue, star-forming galaxies. The cosmological parameter constraints from this blue cosmic shear sample are stable to IA model choice, unlike passive galaxies in the full DES Y3 sample, the goodness-of-fit is improved and the $\Omega_{m}$ and $S_8$ better agree with the cosmic microwave background. Mitigating IA with sample selection, instead of flexible model choices, can reduce uncertainty in $S_8$ by a factor of 1.5., Comment: Data access available at https://jamiemccullough.github.io/data/blueshear/

After reionization, neutral hydrogen (HI) traces the large-scale structure (LSS) of the Universe, enabling HI intensity mapping (IM) to capture the LSS in 3D and constrain key cosmological parameters. We present a new framework utilizing higher-order cross-correlations to study HI clustering around galaxies, tested using real-space data from the IllustrisTNG300 simulation. This approach computes the joint distributions of $k$-nearest neighbor ($k$NN) optical galaxies and the HI brightness temperature field smoothed at relevant scales (the $k$NN-field framework), providing sensitivity to all higher-order cross-correlations, unlike two-point statistics. To simulate HI data from actual surveys, we add random thermal noise and apply a simple foreground cleaning model, filtering out Fourier modes of the brightness temperature field with $k_\parallel < k_{\rm min,\parallel}$. Under current levels of thermal noise and foreground cleaning, typical of a Canadian Hydrogen Intensity Mapping Experiment (CHIME)-like survey, the HI-galaxy cross-correlation signal in our simulations, using the $k$NN-field framework, is detectable at $>30\sigma$ across $r = [3,12] \, h^{-1}$Mpc. In contrast, the detectability of the standard two-point correlation function (2PCF) over the same scales depends strongly on the foreground filter: a sharp $k_\parallel$ filter can spuriously boost detection to $8\sigma$ due to position-space ringing, whereas a less sharp filter yields no detection. Nonetheless, we conclude that $k$NN-field cross-correlations are robustly detectable across a broad range of foreground filtering and thermal noise conditions, suggesting their potential for enhanced constraining power over 2PCFs., Comment: 14 pages, 9 figures (with 2 figures included in the appendix), 2 tables. Comments are welcome

We study the abundance, radial distribution, and orbits of luminous satellites in simulations of Milky Way-mass dark halos in the $\Lambda$CDM cosmology. We follow the evolution of a halo from the Aquarius project and the formation of satellites with the GALFORM semi-analytic model of galaxy formation, in which gas cools radiatively into halos before reionization and in halos that exceed a redshift-dependent ``critical'' virial mass after that. Subhalos are prone to disruption in the tidal field of the main halo, with the number of surviving self-bound subhalos increasing with resolution. Even in the highest resolution simulation (Aq-L1, with particle mass $m_{\rm p}\sim10^3\, M_\odot$), a substantial number of subhalos are disrupted but their galaxies may survive as ``orphans''. Whether or not a satellite becomes an orphan depends primarily on its time of infall. When orphans are included, the simulations yield a converged satellite stellar mass function across different resolution levels. The total number of luminous satellites is sensitive to the assumed redshift of reionization, but the shape of the satellite stellar mass function is robust, peaking at the stellar mass ($\sim 10^3\, M_\odot$) of a halo just above the critical threshold. Most orphans are found in the central regions of the main halo and make up roughly half of all satellites in Aq-L1. When orphans are taken into account there is no need to populate subhalos below the critical mass with satellites to fit the radial distribution of Milky Way satellites, as had been argued in recent work. Our model predicts that orphans dominate the ultra-faint population and that many more satellites with small apocentric radii should be detected in upcoming deep wide-field surveys., Comment: 16 pages, 10 figures, submitted to MNRAS

The Hamiltonian Mean-Field (HMF) model is a long-range interaction model that exhibits quasi-stationary states associated with a phase transition. Its quasi-stationary states with a lifetime diverging with the number of particles in the system. These states are characterized by homogeneous or non-homogeneous structures in phase-space. There exists a phase-transition between these states that have been traditionally characterized by the their mean magnetization. However, the magnetization also exhibits fluctuations in time around its mean value, that can be an indicator of the kind of quasi-stationary state. Thus, we want to characterize the quasi-stationary states of the HMF model through the time-series of the magnetization and its fluctuations through a measure of information, i.e. the permutation entropy and the complexity-entropy plane. Permutation entropy is a measure for characterizing chaotic time series, especially in the presence of dynamic and observational noise, as it is computationally and conceptually simple. For non-homogeneous states, the permutation entropy shows that the HMF model tends towards order, while the magnetizacion fluctuations reveal reduced structures in time. On the contrary, homogeneous states tend to disorder and the structures of the magnetization fluctuations increase as the initial magnetization is larger. In all the study cases of this thesis, the HMF model is characterized by low entropy values but the highest possible complexity value. Thus, the HMF model can be described as a chaotic, deterministic and intermitent system. This aligns with previous studies of the model in the phase space. The results demonstrate that the HMF model can be understood and interpreted from the fluctuations of magnetization using permutation entropy and the complexity-entropy plane., Comment: 15 pages, 6 figures, submitted to Physical Review E

The baryonic physics shaping galaxy formation and evolution are complex, spanning a vast range of scales and making them challenging to model. Cosmological simulations rely on subgrid models that produce significantly different predictions. Understanding how models of stellar and active galactic nuclei (AGN) feedback affect baryon behavior across different halo masses and redshifts is essential. Using the SIMBA and IllustrisTNG suites from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project, we explore the effect of parameters governing the subgrid implementation of stellar and AGN feedback. We find that while IllustrisTNG shows higher cumulative feedback energy across all halos, SIMBA demonstrates a greater spread of baryons, quantified by the closure radius and circumgalactic medium (CGM) gas fraction. This suggests that feedback in SIMBA couples more effectively to baryons and drives them more efficiently within the host halo. There is evidence that different feedback modes are highly interrelated in these subgrid models. Parameters controlling stellar feedback efficiency significantly impact AGN feedback, as seen in the suppression of black hole mass growth and delayed activation of AGN feedback to higher mass halos with increasing stellar feedback efficiency in both simulations. Additionally, AGN feedback efficiency parameters affect the CGM gas fraction at low halo masses in SIMBA, hinting at complex, non-linear interactions between AGN and SNe feedback modes. Overall, we demonstrate that stellar and AGN feedback are intimately interwoven, especially at low redshift, due to subgrid implementation, resulting in halo property effects that might initially seem counterintuitive., Comment: 28 pages, 8 figures, submitted to ApJ

In an era increasingly dominated by digital platforms, the spread of misinformation poses a significant challenge, highlighting the need for solutions capable of assessing information veracity. Our research contributes to the field of Explainable Artificial Antelligence (XAI) by developing transformer-based fact-checking models that contextualise and justify their decisions by generating human-accessible explanations. Importantly, we also develop models for automatic evaluation of explanations for fact-checking verdicts across different dimensions such as \texttt{(self)-contradiction}, \texttt{hallucination}, \texttt{convincingness} and \texttt{overall quality}. By introducing human-centred evaluation methods and developing specialised datasets, we emphasise the need for aligning Artificial Intelligence (AI)-generated explanations with human judgements. This approach not only advances theoretical knowledge in XAI but also holds practical implications by enhancing the transparency, reliability and users' trust in AI-driven fact-checking systems. Furthermore, the development of our metric learning models is a first step towards potentially increasing efficiency and reducing reliance on extensive manual assessment. Based on experimental results, our best performing generative model \textsc{ROUGE-1} score of 47.77, demonstrating superior performance in generating fact-checking explanations, particularly when provided with high-quality evidence. Additionally, the best performing metric learning model showed a moderately strong correlation with human judgements on objective dimensions such as \texttt{(self)-contradiction and \texttt{hallucination}, achieving a Matthews Correlation Coefficient (MCC) of around 0.7.}, Comment: Forthcoming in Engineering Applications of Artificial Intelligence

Understanding the relationships between geometry and topology is a central theme in Riemannian geometry. We establish two results on the fundamental groups of open (complete and noncompact) $n$-manifolds with nonnegative Ricci curvature and linear volume growth. First, we show that the fundamental group of such a manifold contains a subgroup $\mathbb{Z}^k$ of finite index, where $0\le k\le n-1$. Second, we prove that if the Ricci curvature is positive everywhere, then the fundamental group is finite. The proofs are based on an analysis of the equivariant asymptotic geometry of successive covering spaces and a plane/halfplane rigidity result for RCD spaces.

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

Thermal effects in a one-dimensional Su-Schrieffer-Hegger (SSH) topological insulator are studied. Particularly, we focus on quantum information processing (QIP) capacity for thermal ensembles. To evaluate QIP an optimized quantum Fisher information (OQFI) is introduced as a quantifier of entanglement and topological phases are calculated by a definition in real space for the electric polarization of mixture states. For the thermal ensemble, there is a relationship between the Fisher metric and the electric polarization in such a way that in the topological region, there is more entanglement, therefore, these generate more robustness and protection in the quantum information due to thermal effects. Also, long-range hopping effects are studied and it is found that in this case, the OQFI captures these topological phase transitions in the limit of low temperature by this formalism in real space.

Tactile perception is essential for human interaction with the environment and is becoming increasingly crucial in robotics. Tactile sensors like the BioTac mimic human fingertips and provide detailed interaction data. Despite its utility in applications like slip detection and object identification, this sensor is now deprecated, making many existing datasets obsolete. This article introduces a novel method for translating data between tactile sensors by exploiting sensor deformation information rather than output signals. We demonstrate the approach by translating BioTac signals into the DIGIT sensor. Our framework consists of three steps: first, converting signal data into corresponding 3D deformation meshes; second, translating these 3D deformation meshes from one sensor to another; and third, generating output images using the converted meshes. Our approach enables the continued use of valuable datasets., Comment: Extended Abstract. Accepted in ICRA@40 (40th Anniversary of the IEEE International Conference on Robotics and Automation) 23-26 September, 2024 Rotterdam, Netherlands

The increased brightness temperature of young rocky protoplanets during their magma ocean epoch makes them potentially amenable to atmospheric characterization to distances from the solar system far greater than thermally equilibrated terrestrial exoplanets, offering observational opportunities for unique insights into the origin of secondary atmospheres and the near surface conditions of prebiotic environments. The Large Interferometer For Exoplanets (LIFE) mission will employ a space-based mid-infrared nulling interferometer to directly measure the thermal emission of terrestrial exoplanets. Here, we seek to assess the capabilities of various instrumental design choices of the LIFE mission concept for the detection of cooling protoplanets with transient high-temperature magma ocean atmospheres, in young stellar associations in particular. Using the LIFE mission instrument simulator (LIFEsim) we assess how specific instrumental parameters and design choices, such as wavelength coverage, aperture diameter, and photon throughput, facilitate or disadvantage the detection of protoplanets. We focus on the observational sensitivities of distance to the observed planetary system, protoplanet brightness temperature using a blackbody assumption, and orbital distance of the potential protoplanets around both G- and M-dwarf stars. Our simulations suggest that LIFE will be able to detect (S/N $\geq$ 7) hot protoplanets in young stellar associations up to distances of $\approx$100 pc from the solar system for reasonable integration times (up to $\sim$hours). Detection of an Earth-sized protoplanet orbiting a solar-sized host star at 1 AU requires less than 30 minutes of integration time. M-dwarfs generally need shorter integration times. The contribution from wavelength regions $<$6 $\mu$m is important for decreasing the detection threshold and discriminating emission temperatures., Comment: 18 pages, 19 figures; accepted for publication in A&A

Over the last decades, evolutionary population synthesis models have powered an unmatched leap forward in our understanding of galaxies. From dating the age of the first galaxies in the Universe to detailed measurements of the chemical composition of nearby galaxies, the success of this approach built upon simple stellar population (SSP) spectro-photometric models is unquestionable. However, the internal constraints inherent to the construction of SSP models may hinder our ability to analyze the integrated spectra of galaxies in situations where the SSP assumption does not sufficiently hold. Thus, here we revisit the possibilities of fitting galaxy spectra as a linear combination of stellar templates without assuming any a priori knowledge on stellar evolution. We showcase the sensitivity of this alternative approach to changes in the stellar population properties, in particular the direct connection to variations in the stellar initial mass function, as well as its advantages when dealing with non-canonical integrated populations and semi-resolved observations. Furthermore, our analysis demonstrates that the absorption spectra of galaxies can be used to independently constrain tellar evolution theory beyond the limited conditions of the solar neighborhood., Comment: 5 pages, 4 figures. Accepted for publication in Astronomy & Astrophysics Letters

Large Language Models~(LLMs) have demonstrated capabilities across various applications but face challenges such as hallucination, limited reasoning abilities, and factual inconsistencies, especially when tackling complex, domain-specific tasks like question answering~(QA). While Knowledge Graphs~(KGs) have been shown to help mitigate these issues, research on the integration of LLMs with background KGs remains limited. In particular, user accessibility and the flexibility of the underlying KG have not been thoroughly explored. We introduce AGENTiGraph (Adaptive Generative ENgine for Task-based Interaction and Graphical Representation), a platform for knowledge management through natural language interaction. It integrates knowledge extraction, integration, and real-time visualization. AGENTiGraph employs a multi-agent architecture to dynamically interpret user intents, manage tasks, and integrate new knowledge, ensuring adaptability to evolving user requirements and data contexts. Our approach demonstrates superior performance in knowledge graph interactions, particularly for complex domain-specific tasks. Experimental results on a dataset of 3,500 test cases show AGENTiGraph significantly outperforms state-of-the-art zero-shot baselines, achieving 95.12\% accuracy in task classification and 90.45\% success rate in task execution. User studies corroborate its effectiveness in real-world scenarios. To showcase versatility, we extended AGENTiGraph to legislation and healthcare domains, constructing specialized KGs capable of answering complex queries in legal and medical contexts., Comment: 30 pages, 7 figures; Submitted to COLING 2025 System Demonstrations Track

We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties--such as X-ray surface brightness, gas density, temperature, metallicity, and Compton-y profiles--can be used to predict parameters within the 28-dimensional parameter space of the IllustrisTNG model. Through neural networks, we achieve a high correlation coefficient of 0.97 or above for all cosmological parameters, including $\Omega_{\rm m}$, $H_0$, and $\sigma_8$, and over 0.90 for the remaining astrophysical parameters, showcasing the effectiveness of these profiles for parameter inference. We investigate the impact of different radial cuts, with bins ranging from $0.1R_{200c}$ to $0.7R_{200c}$, to simulate current observational constraints. Additionally, we perform a noise sensitivity analysis, adding up to 40\% Gaussian noise (corresponding to signal-to-noise ratios as low as 2.5), revealing that key parameters such as $\Omega_{\rm m}$, $H_0$, and the IMF slope remain robust even under extreme noise conditions. We also compare the performance of full radial profiles against integrated quantities, finding that profiles generally lead to more accurate parameter inferences. Our results demonstrate that stacked galaxy cluster profiles contain crucial information on both astrophysical processes within groups and clusters and the underlying cosmology of the universe. This underscores their significance for interpreting the complex data expected from next-generation surveys and reveals, for the first time, their potential as a powerful tool for parameter inference., Comment: Submitted to ApJ

The efficient release of magnetic energy in astrophysical plasmas, such as during solar flares, can in principle be achieved through magnetic diffusion, at a rate determined by the associated electric field. However, attempts at measuring electric fields in the solar atmosphere are scarce, and none exist for sites where the magnetic energy is presumably released. Here, we present observations of an energetic event using the National Science Foundation's Daniel K. Inouye Solar Telescope, where we detect the polarization signature of electric fields associated with magnetic diffusion. We measure the linear and circular polarization across the hydrogen H-epsilon Balmer line at 397 nm at the site of a brightening event in the solar chromosphere. Our spectro-polarimetric modeling demonstrates that the observed polarization signals can only be explained by the presence of electric fields, providing conclusive evidence of magnetic diffusion, and opening a new window for the quantitative study of this mechanism in space plasmas., Comment: 24 pages, 11 figures, published in Nature Communications

Rapid search and rescue is critical to maximizing survival rates following natural disasters. However, these efforts are challenged by the need to search large disaster zones, lack of reliability in the communications infrastructure, and a priori unknown numbers of objects of interest (OOIs), such as injured survivors. Aerial robots are increasingly being deployed for search and rescue due to their high mobility, but there remains a gap in deploying multi-robot autonomous aerial systems for methodical search of large environments. Prior works have relied on preprogrammed paths from human operators or are evaluated only in simulation. We bridge these gaps in the state of the art by developing and demonstrating a decentralized active search system, which biases its trajectories to take additional views of uncertain OOIs. The methodology leverages stochasticity for rapid coverage in communication denied scenarios. When communications are available, robots share poses, goals, and OOI information to accelerate the rate of search. Extensive simulations and hardware experiments in Bloomingdale, OH, are conducted to validate the approach. The results demonstrate the active search approach outperforms greedy coverage-based planning in communication-denied scenarios while maintaining comparable performance in communication-enabled scenarios.

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time., Comment: Corresponding author: S. Doeleman

We aim to trace the evolution of eight edge-on star-forming disc galaxies through the analysis of stellar population properties of their thin and thick discs. These galaxies have relatively low stellar masses (4 $\times$ 10$^9$ to 6 $\times$ 10$^{10}$ $M_{\odot}$). We use Multi-Unit Spectroscopic Explorer (MUSE) observations and full-spectrum fitting to produce spatially resolved maps of ages, metallicities and [Mg/Fe]-abundances and extract the star formation histories of stellar discs. Our maps show thick discs that are on average older, more metal-poor and more [Mg/Fe]-enhanced than thin discs. However, age differences between thin and thick discs are small (around 2 Gyr) and the thick discs are younger than previously observed in more massive and more quiescent galaxies. Both thin and thick discs show mostly sub-solar metallicities, and the vertical metallicity gradient is milder than previously observed in similar studies. [Mg/Fe] differences between thick and thin discs are not sharp. The star formation histories of thick discs extend down to recent times, although most of the mass in young stars was formed in thin discs. Our findings show thick discs that are different from old quiescent thick discs previously observed in galaxies of different morphologies and/or different masses. We propose that thick discs in these galaxies did not form quickly at high redshift, but slowly in an extended time. Also, the thin discs formed slowly, however, a larger mass fraction was created at very recent times., Comment: 20 pages, 9 figures, submitted to A&A

(Abridged) We explore the chemistry of the most abundant C, O, S, and N bearing species in molecular clouds, in the context of the IRAM 30 m Large Programme Gas phase Elemental abundances in Molecular Clouds (GEMS). In this work, we aim to assess the limitations introduced in the observational works when a uniform density is assumed along the line of sight for fitting the observations, developing a very simple numerical model of a turbulent box. We perform a MHD simulation in order to reproduce the turbulent steady-state of a turbulent box with properties typical of a molecular filament before collapse. We post-process the results of the MHD simulation with a chemical code to predict molecular abundances, and then post-process this cube with a radiative transfer code to create synthetic emission maps for a series of rotational transitions observed during the GEMS project. From the chemical point of view, we find that turbulence produces variations on the predicted abundances, but they are more or less critical depending on the chosen transition and the chemical age. When compared to real observations, the results from the turbulent simulation provides a better fit than when assuming a uniform gas distribution along the line of sight. In the view of our results, we conclude that taking into account turbulence when fitting observations might significantly improve the agreement with model predictions. This is especially important for sulfur bearing species that are very sensitive to the variations of density produced by turbulence at early times (0.1 Myr). The abundance of CO is also quite sensitive to turbulence when considering the evolution beyond a few 0.1 Myr., Comment: Published in Astronomy and Astrophysics

In the pursuit of alternative and less invasive medical treatments, magnetic nanoparticles (NPs) have gained significant relevance. Iron carbides NPs stand out for their higher saturation magnetizations compared to iron oxides, while maintaining a suitable biocompatibility. In this work, high control is achieved over the composition and morphology of Fe3C/C encapsulated core/shell nanoparticles through fine-tuning of the sol-gel synthesis parameters. Specifically, the impact of decreasing each surfactant concentration added, nt, the same both for oleylamine (ON) and oleic acid (OA), has been explored. A minimum value for such parameter denoted by nt,min. was required to produce pure Fe3C@C NP-composites. For nt < 4 mmol, some minor {\alpha}-Fe impurities arise, and the effective carburization becomes unstable due to insufficient carbon. The magnetic properties of the materials prepared were optimized by reducing the excess carbon from surfactants, resulting in saturation magnetization values of 86 emu/g. (for pure Fe3C at nt = 5 mmol) and 102 emu/g (for Fe3C and <2 % w.t. of {\alpha}-Fe impurity at nt = 4 mmol). In view of this, several cytotoxicity studies for different Fe3C@C samples were conducted, exhibiting excellent biocompatibility in cell-based assays, which could lead to potential application at the forefront of biomedical fields.