Search

Evidence has emerged for a stochastic signal correlated among 67 pulsars within the 15-year pulsar-timing data set compiled by the NANOGrav collaboration. Similar signals have been found in data from the European, Indian, Parkes, and Chinese PTAs. This signal has been interpreted as indicative of the presence of a nanohertz stochastic gravitational wave background. To explore the internal consistency of this result we investigate how the recovered signal strength changes as we remove the pulsars one by one from the data set. We calculate the signal strength using the (noise-marginalized) optimal statistic, a frequentist metric designed to measure correlated excess power in the residuals of the arrival times of the radio pulses. We identify several features emerging from this analysis that were initially unexpected. The significance of these features, however, can only be assessed by comparing the real data to synthetic data sets. After conducting identical analyses on simulated data sets, we do not find anything inconsistent with the presence of a stochastic gravitational wave background in the NANOGrav 15-year data. The methodologies developed here can offer additional tools for application to future, more sensitive data sets. While this analysis provides an internal consistency check of the NANOGrav results, it does not eliminate the necessity for additional investigations that could identify potential systematics or uncover unmodeled physical phenomena in the data., Comment: 21 pages, 11 figures, 2 tables

Pulsar timing array observations have found evidence for an isotropic gravitational wave background with the Hellings-Downs angular correlations, expected from general relativity. This interpretation hinges on the measured shape of the angular correlations, which is predominately quadrupolar under general relativity. Here we explore a more flexible parameterization: we expand the angular correlations into a sum of Legendre polynomials and use a Bayesian analysis to constrain their coefficients with the 15-year pulsar timing data set collected by the North American Nanohertz Observatory for Gravitational Waves (NANOGrav). When including Legendre polynomials with multipoles $\ell \geq 2$, we only find a significant signal in the quadrupole with an amplitude consistent with general relativity and non-zero at the $\sim 95\%$ confidence level and a Bayes factor of 200. When we include multipoles $\ell \leq 1$, the Bayes factor evidence for quadrupole correlations decreases by more than an order of magnitude due to evidence for a monopolar signal at approximately 4 nHz which has also been noted in previous analyses of the NANOGrav 15-year data. Further work needs to be done in order to better characterize the properties of this monopolar signal and its effect on the evidence for quadrupolar angular correlations., Comment: 15 pages, 6 figures

Indirect dark matter detection methods are used to observe the products of dark matter annihilations or decays originating from astrophysical objects where large amounts of dark matter are thought to accumulate. With neutrino telescopes, an excess of neutrinos is searched for in nearby dark matter reservoirs, such as the Sun and the Galactic Centre, which could potentially produce a sizeable flux of Standard Model particles. The KM3NeT infrastructure, currently under construction, comprises the ARCA and ORCA undersea \v{C}erenkov neutrino detectors located at two different sites in the Mediterranean Sea, offshore of Italy and France, respectively. The two detector configurations are optimised for the detection of neutrinos of different energies, enabling the search for dark matter particles with masses ranging from a few GeV/c$^2$ to hundreds of TeV/c$^2$. In this work, searches for dark matter annihilations in the Galactic Centre and the Sun with data samples taken with the first configurations of both detectors are presented. No significant excess over the expected background was found in either of the two analyses. Limits on the velocity-averaged self-annihilation cross section of dark matter particles are computed for five different primary annihilation channels in the Galactic Centre. For the Sun, limits on the spin-dependent and spin-independent scattering cross sections of dark matter with nucleons are given for three annihilation channels.

The history of gas assembly in early galaxies is reflected in their complex kinematics. While a considerable fraction of galaxies at z~5 are consistent with rotating disks, current studies indicate that the dominant galaxy assembly mechanism corresponds to mergers. Despite the important progress, the dynamical classification of galaxies at these epochs is still limited by observations' resolution. We present a detailed morphological and kinematic analysis of the far-infrared bright main sequence galaxy HZ10 at z=5.65, making use of new high-resolution ($\lesssim0.3$") [CII] 158$\mu$m ALMA and rest-frame optical JWST/NIRSpec observations. These observations reveal a previously unresolved complex morphology and kinematics of the HZ10. We confirm that HZ10 is not a single galaxy but consists of at least three components in close projected separation along the east-to-west direction. We find a [CII] bright central component (C), separated by 1.5 and 4 kpc from the east (E) and west (W) components, respectively. Our [CII] observations resolve the HZ10-C component resulting in a velocity gradient, produced by either rotation or a close-in merger. We test the rotating disk possibility using DysmalPy kinematic modeling and propose three dynamical scenarios for the HZ10 system: (i) a double merger, in which the companion galaxy HZ10-W merges with the disturbed clumpy rotation disk formed by the HZ10-C and E components; (ii) a triple merger, where the companion galaxies, HZ10-W and HZ10-E, merge with the rotation disk HZ10-C; and (iii) a quadruple merger, in which the companion galaxies HZ10-W and HZ10-E merge with the close double merger HZ10-C. Comparing [CII] with JWST/NIRSpec data, we find that [CII] emission closely resembles the broad [OIII] 5007{\AA} emission. The latter reflects the interacting nature of the system and suggests that ionized and neutral gas phases in HZ10 are well mixed., Comment: Submitted to A&A, 16 pages, 17 figures, 1 table

Generative AI (GenAI) technologies possess unprecedented potential to reshape our world and our perception of reality. These technologies can amplify traditionally human-centered capabilities, such as creativity and complex problem-solving in socio-technical contexts. By fostering human-AI collaboration, GenAI could enhance productivity, dismantle communication barriers across abilities and cultures, and drive innovation on a global scale. Yet, experts and the public are deeply divided on the implications of GenAI. Concerns range from issues like copyright infringement and the rights of creators whose work trains these models without explicit consent, to the conditions of those employed to annotate vast datasets. Accordingly, new laws and regulatory frameworks are emerging to address these unique challenges. Others point to broader issues, such as economic disruptions from automation and the potential impact on labor markets. Although history suggests that society can adapt to such technological upheavals, the scale and complexity of GenAI's impact warrant careful scrutiny. This paper, however, highlights a subtler, yet potentially more perilous risk of GenAI: the creation of $\textit{personalized synthetic realities}$. GenAI could enable individuals to experience a reality customized to personal desires or shaped by external influences, effectively creating a "filtered" worldview unique to each person. Such personalized synthetic realities could distort how people perceive and interact with the world, leading to a fragmented understanding of shared truths. This paper seeks to raise awareness about these profound and multifaceted risks, emphasizing the potential of GenAI to fundamentally alter the very fabric of our collective reality., Comment: HUMANS Lab -- Working Paper No. 2024.2 -- The 2024 Election Integrity Initiative -- University of Southern California

Free electrons in the interstellar medium refract and diffract radio waves along multiple paths, resulting in angular and temporal broadening of radio pulses that limits pulsar timing precision. We determine multifrequency, multi-epoch scattering times for the large dispersion measure millisecond pulsar J1903+0327 by developing a three component model for the emitted pulse shape that is convolved with a best fit pulse broadening function (PBF) identified from a family of thin-screen and extended-media PBFs. We show that the scattering time, $\tau$, at a fiducial frequency of 1500 MHz changes by approximately 10% over a 5.5yr span with a characteristic timescale of approximately 100 days. We also constrain the spectral index and inner scale of the wavenumber spectrum of electron density variations along this line of sight. We find that the scaling law for $\tau$ vs. radio frequency is strongly affected by any mismatch between the true and assumed PBF or between the true and assumed intrinsic pulse shape. We show using simulations that refraction is a plausible cause of the epoch dependence of $\tau$, manifesting as changes in the PBF shape and $1/e$ time scale. Finally, we discuss the implications of our scattering results on pulsar timing including time of arrival delays and dispersion measure misestimation., Comment: 16 pages, 14 figures, submitted to ApJ

The detection of a stochastic gravitational wave background by pulsar timing arrays suggests the presence of a supermassive black hole binary population. Although the observed spectrum generally aligns with predictions from orbital evolution driven by gravitational wave emission in circular orbits, there is a discernible preference for a turnover at the lowest observed frequencies. This turnover could indicate a significant hardening phase, transitioning from early environmental influences to later stages predominantly influenced by gravitational wave emission. In the vicinity of these binaries, the ejection of stars or dark matter particles through gravitational three-body slingshots efficiently extracts orbital energy, leading to a low-frequency turnover in the spectrum. By analyzing the NANOGrav 15-year data, we assess how the gravitational wave spectrum depends on the initial inner galactic profile prior to disruption by binary ejections, accounting for a range of initial binary eccentricities. Our findings suggest a parsec-scale galactic center density around $10^6\,M_\odot/\textrm{pc}^3$ across most of the parameter space, offering insights into the environmental effects on black hole evolution and combined matter density near galaxy centers., Comment: 15 pages, 5 figures

Human writers plan, then write. For large language models (LLMs) to play a role in longer-form article generation, we must understand the planning steps humans make before writing. We explore one kind of planning, source-selection in news, as a case-study for evaluating plans in long-form generation. We ask: why do specific stories call for specific kinds of sources? We imagine a generative process for story writing where a source-selection schema is first selected by a journalist, and then sources are chosen based on categories in that schema. Learning the article's plan means predicting the schema initially chosen by the journalist. Working with professional journalists, we adapt five existing schemata and introduce three new ones to describe journalistic plans for the inclusion of sources in documents. Then, inspired by Bayesian latent-variable modeling, we develop metrics to select the most likely plan, or schema, underlying a story, which we use to compare schemata. We find that two schemata: stance and social affiliation best explain source plans in most documents. However, other schemata like textual entailment explain source plans in factually rich topics like "Science". Finally, we find we can predict the most suitable schema given just the article's headline with reasonable accuracy. We see this as an important case-study for human planning, and provides a framework and approach for evaluating other kinds of plans. We release a corpora, NewsSources, with annotations for 4M articles., Comment: 9 pages

The presence of supermassive black holes (SMBHs, $M_{\bullet}\sim 10^{6-10}~M_{\odot}$) in the first cosmic Gyr ($z\gtrsim 6$) challenges current models of BH formation and evolution. We propose a novel mechanism for the formation of early SMBH seeds based on primordial black holes (PBHs). We assume a non-Gaussian primordial power spectrum as expected in inflationary models; these scenarios predict that PBHs are initially clustered and preferentially formed in the high-$\sigma$ fluctuations of the large-scale density field, out of which dark matter (DM) halos are originated. Our model accounts for (i) PBH accretion and feedback, (ii) DM halo growth, and (iii) gas dynamical friction. PBHs lose angular momentum due to gas dynamical friction, sink into a dense core, where BH binaries form and undergo a runaway merger, eventually leading to the formation of a single, massive seed. This mechanism starts at $z\sim 20-40$ in rare halos ($M_h\sim 10^7\ M_\odot$ corresponding to $\sim 5-7\sigma$ fluctuations), and provides massive ($\sim 10^{4-5}~ M_{\odot}$) seeds by $z\sim 10-30$. We derive a physically-motivated seeding prescription that provides the mass of the seed, $ M_{\rm seed}(z)=3.1\times 10^{5}\ { M_{\odot}}[(1+z)/10]^{-1.2}$, and seeded halo, $ M_{h}(z)=2\times 10^{9}\ {M_{\odot}}[(1+z)/10]^{-2}e^{-0.05z}$ as a function of redshift. This seeding mechanism requires that only a small fraction of DM is constituted by PBHs, namely $f_{\rm PBH}\sim 3 \times 10^{-6}$. We find that $z\sim 6-7$ quasars can be explained with $6\times 10^4 M_{\rm \odot}$ seeds planted at $z\sim 32$, and growing at sub-Eddington rates, $\langle\lambda_{\rm E}\rangle\sim 0.55$. The same scenario reproduces the BH mass of GNz11 at $z=10.6$, while UHZ1 ($z=10.1$) and GHZ9 ($z=10$) data favour instead slightly later ($z\sim 20-25$), more massive ($10^5~M_{\rm \odot}$) seeds. [Abridged], Comment: Submitted to JCAP. Comments are welcome.8

Approximately 50% of tweets in X's user timelines are personalized recommendations from accounts they do not follow. This raises a critical question: what political content are users exposed to beyond their established networks, and how might this influence democratic discourse online? Due to the black-box nature and constant evolution of social media algorithms, much remains unknown about this aspect of users' content exposure, particularly as it pertains to potential biases in algorithmic curation. Prior research has shown that certain political groups and media sources are amplified within users' in-network tweets. However, the extent to which this amplification affects out-of-network recommendations remains unclear. As the 2024 U.S. Election approaches, addressing this question is essential for understanding the influence of algorithms on online political content consumption and its potential impact on users' perspectives. In this paper, we conduct a three-week audit of X's algorithmic content recommendations using a set of 120 sock-puppet monitoring accounts that capture tweets in their personalized ``For You'' timelines. Our objective is to quantify out-of-network content exposure for right- and left-leaning user profiles and to assess any potential biases in political exposure. Our findings indicate that X's algorithm skews exposure toward a few high-popularity accounts across all users, with right-leaning users experiencing the highest level of exposure inequality. Both left- and right-leaning users encounter amplified exposure to accounts aligned with their own political views and reduced exposure to opposing viewpoints. Additionally, we observe a right-leaning bias in exposure for new accounts within their default timelines., Comment: HUMANS Lab -- Working Paper No. 2024.9 -- The 2024 Election Integrity Initiative -- University of Southern California

Truth Social, launched as a social media platform with a focus on free speech, has become a prominent space for political discourse, attracting a user base with diverse, yet often conservative, viewpoints. As an emerging platform with minimal content moderation, Truth Social has facilitated discussions around contentious social and political issues but has also seen the spread of conspiratorial and hyper-partisan narratives. In this paper, we introduce and release a comprehensive dataset capturing activity on Truth Social related to the upcoming 2024 U.S. Presidential Election, including posts, replies, user interactions, content and media. This dataset comprises 1.5 million posts published between February, 2024 and October 2024, and encompasses key user engagement features and posts metadata. Data collection began in June 2024, though it includes posts published earlier, with the oldest post dating back to February 2022. This offers researchers a unique resource to study communication patterns, the formation of online communities, and the dissemination of information within Truth Social in the run-up to the election. By providing an in-depth view of Truth Social's user dynamics and content distribution, this dataset aims to support further research on political discourse within an alt-tech social media platform. The dataset is publicly available at https://github.com/kashish-s/TruthSocial_2024ElectionInitiative, Comment: HUMANS Lab -- Working Paper No. 2024.8 -- The 2024 Election Integrity Initiative -- University of Southern California

In this paper, we introduce the first release of a large-scale dataset capturing discourse on $\mathbb{X}$ (a.k.a., Twitter) related to the upcoming 2024 U.S. Presidential Election. Our dataset comprises 22 million publicly available posts on X.com, collected from May 1, 2024, to July 31, 2024, using a custom-built scraper, which we describe in detail. By employing targeted keywords linked to key political figures, events, and emerging issues, we aligned data collection with the election cycle to capture evolving public sentiment and the dynamics of political engagement on social media. This dataset offers researchers a robust foundation to investigate critical questions about the influence of social media in shaping political discourse, the propagation of election-related narratives, and the spread of misinformation. We also present a preliminary analysis that highlights prominent hashtags and keywords within the dataset, offering initial insights into the dominant themes and conversations occurring in the lead-up to the election. Our dataset is available at: url{https://github.com/sinking8/usc-x-24-us-election

The KM3NeT Collaboration has tackled a common challenge faced by the astroparticle physics community, namely adapting the experiment-specific simulation software to work with the CORSIKA air shower simulation output. The proposed solution is an extension of the open-source code gSeaGen, allowing for the transport of muons generated by CORSIKA to a detector of any size at an arbitrary depth. The gSeaGen code was not only extended in terms of functionalities but also underwent a thorough redesign of the muon propagation routine, resulting in a more accurate and efficient simulation. This paper presents the capabilities of the new gSeaGen code as well as prospects for further developments., Comment: 27 pages, 13 figures, submitted to Computer Physics Communications

With its lenient moderation policies and long-standing associations with potentially unlawful activities, Telegram has become an incubator for problematic content, frequently featuring conspiratorial, hyper-partisan, and fringe narratives. In the political sphere, these concerns are amplified by reports of Telegram channels being used to organize violent acts, such as those that occurred during the Capitol Hill attack on January 6, 2021. As the 2024 U.S. election approaches, Telegram remains a focal arena for societal and political discourse, warranting close attention from the research community, regulators, and the media. Based on these premises, we introduce and release a Telegram dataset focused on the 2024 U.S. Presidential Election, featuring over 30,000 chats and half a billion messages, including chat details, profile pictures, messages, and user information. We constructed a network of chats and analyzed the 500 most central ones, examining their shared messages. This resource represents the largest public Telegram dataset to date, offering an unprecedented opportunity to study political discussion on Telegram in the lead-up to the 2024 U.S. election. We will continue to collect data until the end of 2024, and routinely update the dataset released at: https://github.com/leonardo-blas/usc-tg-24-us-election, Comment: HUMANS Lab -- Working Paper No. 2024.5 -- The 2024 Election Integrity Initiative -- University of Southern California

Thanks to the great interest posed by researchers and companies, recommendation systems became a cornerstone of machine learning applications. However, concerns have arisen recently about the need for reproducibility, making it challenging to identify suitable pipelines. Several frameworks have been proposed to improve reproducibility, covering the entire process from data reading to performance evaluation. Despite this effort, these solutions often overlook the role of data management, do not promote interoperability, and neglect data analysis despite its well-known impact on recommender performance. To address these gaps, we propose DataRec, which facilitates using and manipulating recommendation datasets. DataRec supports reading and writing in various formats, offers filtering and splitting techniques, and enables data distribution analysis using well-known metrics. It encourages a unified approach to data manipulation by allowing data export in formats compatible with several recommendation frameworks.

Coordinated information operations remain a persistent challenge on social media, despite platform efforts to curb them. While previous research has primarily focused on identifying these operations within individual platforms, this study shows that coordination frequently transcends platform boundaries. Leveraging newly collected data of online conversations related to the 2024 U.S. Election across $\mathbb{X}$ (formerly, Twitter), Facebook, and Telegram, we construct similarity networks to detect coordinated communities exhibiting suspicious sharing behaviors within and across platforms. Proposing an advanced coordination detection model, we reveal evidence of potential foreign interference, with Russian-affiliated media being systematically promoted across Telegram and $\mathbb{X}$. Our analysis also uncovers substantial intra- and cross-platform coordinated inauthentic activity, driving the spread of highly partisan, low-credibility, and conspiratorial content. These findings highlight the urgent need for regulatory measures that extend beyond individual platforms to effectively address the growing challenge of cross-platform coordinated influence campaigns., Comment: HUMANS Lab -- Working Paper No. 2024.7 -- The 2024 Election Integrity Initiative -- University of Southern California

The properties of luminous, blue (a.k.a. Blue Monsters), super-early galaxies at redshift $z>10$ have been successfully explained by the attenuation-free model (AFM) in which dust is pushed to kpc-scales by radiation-driven outflows. As an alternative to AFM, here we assess whether *attenuation-free* conditions can be replaced by a *dust-free* scenario in which dust is produced in very limited amounts and/or later destroyed in the interstellar medium. To this aim we compare the predicted values of the dust-to-stellar mass ratio, $\xi_d$, with those measured in 15 galaxies at $z>10$ from JWST spectra, when outflows are not included. Our model constrains $\xi_d$ as a function of several parameters by allowing wide variations in the IMF, dust/metal production, and dust destruction for a set of SN progenitor models and explosion energies. We find $\log \xi_d \approx -2.2$ for all systems, indicative of the dominant role of SN dust production over destruction in these early galaxies. Such value is strikingly different from the data, which instead indicate $\log \xi_d < -4$. We conclude that dust destruction alone can hardly explain the transparency of Blue Monsters. Other mechanisms, such as outflows, might be required., Comment: Submitted. Comments welcome

Interstellar dust links the formation of the first stars to the rocky planet we inhabit by playing a pivotal role in the cooling and fragmentation of molecular clouds, and catalyzing the formation of water and organic molecules. Despite its central role, the origin of dust and its formation timescale remain unknown. Some models favor rapid production in supernova ejecta as the primary origin of dust, while others invoke slower production by evolved asymptotic giant branch stars or grain growth in the interstellar medium (ISM). The dust content of young early-universe galaxies is highly sensitive to the dust formation timescales. Here, we evaluate the dust content of 631 galaxies at $3 < z_{\rm spec} < 14$ based on rest-UV to optical spectroscopy obtained with JWST NIRSpec. We find that dust appears rapidly. Attenuation immediately follows star formation on timescales shorter than $\sim 30$ Myr, favoring dust production by supernovae. The degree of attenuation is $\sim 30$ times lower than expected if the entire supernova dust yield were preserved in the ISM, and had Milky Way-like grain properties. This can be reconciled if the early-universe dust is composed mostly of silicate or grains much larger than those in the Milky Way, and if significant dust destruction or ejection by outflows takes place., Comment: Submitted!

This paper introduces a general method for the exploration of equivalence classes in the input space of Transformer models. The proposed approach is based on sound mathematical theory which describes the internal layers of a Transformer architecture as sequential deformations of the input manifold. Using eigendecomposition of the pullback of the distance metric defined on the output space through the Jacobian of the model, we are able to reconstruct equivalence classes in the input space and navigate across them. We illustrate how this method can be used as a powerful tool for investigating how a Transformer sees the input space, facilitating local and task-agnostic explainability in Computer Vision and Natural Language Processing tasks., Comment: 11 pages, 4 figures

Neutrinos described as an open quantum system may interact with the environment which introduces stochastic perturbations to their quantum phase. This mechanism leads to a loss of coherence along the propagation of the neutrino $-$ a phenomenon commonly referred to as decoherence $-$ and ultimately, to a modification of the oscillation probabilities. Fluctuations in space-time, as envisaged by various theories of quantum gravity, are a potential candidate for a decoherence-inducing environment. Consequently, the search for decoherence provides a rare opportunity to investigate quantum gravitational effects which are usually beyond the reach of current experiments. In this work, quantum decoherence effects are searched for in neutrino data collected by the KM3NeT/ORCA detector from January 2020 to November 2021. The analysis focuses on atmospheric neutrinos within the energy range of a few GeV to $100\,\mathrm{GeV}$. Adopting the open quantum system framework, decoherence is described in a phenomenological manner with the strength of the effect given by the parameters $\Gamma_{21}$ and $\Gamma_{31}$. Following previous studies, a dependence of the type $\Gamma_{ij} \propto (E/E_0)^n$ on the neutrino energy is assumed and the cases $n = -2,-1$ are explored. No significant deviation with respect to the standard oscillation hypothesis is observed. Therefore, $90\,\%$ CL upper limits are estimated as $\Gamma_{21} < 4.6\cdot 10^{-21}\,$GeV and $\Gamma_{31} < 8.4\cdot 10^{-21}\,$GeV for $n = -2$, and $\Gamma_{21} < 1.9\cdot 10^{-22}\,$GeV and $\Gamma_{31} < 2.7\cdot 10^{-22}\,$GeV for $n = -1$, respectively., Comment: 17 pages, 5 figures

We report the first successful ALMA follow-up observations of a secure $z > 10$ JWST-selected galaxy, by robustly detecting ($6.6\sigma$) the [OIII]$_{88\mu m}\,$ line in JADES-GS-z14-0 (hereafter GS-z14). The ALMA detection yields a spectroscopic redshift of $z=14.1793\pm0.0007$, and increases the precision on the prior redshift measurement of $z=14.32_{-0.20}^{+0.08}$ from NIRSpec by $\gtrsim$180$\times$. Moreover, the redshift is consistent with that previously determined from a tentative detection ($3.6\sigma$) of CIII]$_{1907,1909}$ ($z=14.178\pm0.013$), solidifying the redshift determination via multiple line detections. We measure a line luminosity of $L_\mathrm{[OIII]88} = (2.1 \pm 0.5)\times10^8\,L_\odot$, placing GS-z14 at the lower end, but within the scatter of, the local $L_\mathrm{[OIII]88}$-star formation rate relation. No dust continuum from GS-z14 is detected, suggesting an upper limit on the dust-to-stellar mass ratio of $< 2 \times 10^{-3}$, consistent with dust production from supernovae with a yield $y_d < 0.3\,M_\odot$. Combining a previous JWST/MIRI photometric measurement of the [OIII]$\lambda\lambda$4959,5007$\mathrm{\mathring{A}}$ and H$\beta$ lines with Cloudy models, we find GS-z14 to be surprisingly metal-enriched ($Z\sim0.05 - 0.2\,Z_\odot$) a mere $300\,\mathrm{Myr}$ after the Big Bang. The detection of a bright oxygen line in GS-z14 thus reinforces the notion that galaxies in the early Universe undergo rapid evolution., Comment: 11 Pages, 6 Figures, 1 Table. Submitted to The Astrophysical Journal

The abundance and blue color of super-early (redshift $z>10$), luminous galaxies discovered by JWST can be explained if radiation-driven outflows have ejected their dust on kpc-scales. To test this hypothesis, we predict the ALMA detectability of such extended dust component. Given the observed properties of the galaxy, its observed continuum flux at 88 $\mu$m, $F_{88}$, depends on the dust-to-stellar mass ratio, $\xi_d$, and extent of the dust distribution, $r_d$. Once applied to the most distant galaxy known, GS-z14-0 at $z=14.32$, the fiducial model ($\xi_d = 1/529$) predicts $F_{88}^{\rm fid} = 14.9\, \mu$Jy, and a dust extent $r_d=1.4$ kpc. If the galaxy is very dust-rich ($\xi_d =1/40$), $F_{88}^{\rm max} = 40.1\, \mu$Jy. These values are smaller ($F_{88}^{\rm fid} = 9.5\, \mu$Jy) if the dust is predominantly made of large grains as those formed in SN ejecta. Forthcoming ALMA observations might come very close to constraining the fiducial predictions of the outflow-based attenuation-free model. Other super-early galaxies are predicted to be fainter at 88 $\mu$m, mostly because of their lower SFR compared to GS-z14-0, with fiducial fluxes in the range $2-5.2\ \mu$Jy., Comment: 9 pages, 2 figures, submitted to A&A, comments welcome

Boron Neutron Capture Therapy (BNCT) is an innovative binary form of radiation therapy with high selectivity towards cancer tissue based on the neutron capture reaction 10B(n,$\alpha$)7Li, consisting in the exposition of patients to neutron beams after administration of a boron compound with preferential accumulation in cancer cells. The high linear energy transfer products of the ensuing reaction deposit their energy at cell level, sparing normal tissue. Although progress in accelerator-based BNCT has led to renewed interest in this cancer treatment modality, in vivo dose monitoring during treatment still remains not feasible and several approaches are under investigation. While Compton imaging presents various advantages over other imaging methods, it typically requires long reconstruction times, comparable with BNCT treatment duration. This study aims to develop deep neural network models to estimate the dose distribution by using a simulated dataset of BNCT Compton camera images. The models pursue the avoidance of the iteration time associated with the maximum-likelihood expectation-maximization algorithm (MLEM), enabling a prompt dose reconstruction during the treatment. The U-Net architecture and two variants based on the deep convolutional framelets framework have been used for noise and artifacts reduction in few-iterations reconstructed images, leading to promising results in terms of reconstruction accuracy and processing time., Comment: 16 pages, 12 figures, preprint

Information Operations (IOs) pose a significant threat to the integrity of democratic processes, with the potential to influence election-related online discourse. In anticipation of the 2024 U.S. presidential election, we present a study aimed at uncovering the digital traces of coordinated IOs on $\mathbb{X}$ (formerly Twitter). Using our machine learning framework for detecting online coordination, we analyze a dataset comprising election-related conversations on $\mathbb{X}$ from May 2024. This reveals a network of coordinated inauthentic actors, displaying notable similarities in their link-sharing behaviors. Our analysis shows concerted efforts by these accounts to disseminate misleading, redundant, and biased information across the Web through a coordinated cross-platform information operation: The links shared by this network frequently direct users to other social media platforms or suspicious websites featuring low-quality political content and, in turn, promoting the same $\mathbb{X}$ and YouTube accounts. Members of this network also shared deceptive images generated by AI, accompanied by language attacking political figures and symbolic imagery intended to convey power and dominance. While $\mathbb{X}$ has suspended a subset of these accounts, more than 75% of the coordinated network remains active. Our findings underscore the critical role of developing computational models to scale up the detection of threats on large social media platforms, and emphasize the broader implications of these techniques to detect IOs across the wider Web., Comment: First Monday 29(11), 2024

We present a new method to determine the star formation rate (SFR) density of the Universe at $z \gtrsim 5$ that includes the contribution of dust-obscured star formation. For this purpose, we use a [CII] (158 $\mu$m) selected sample of galaxies serendipitously identified in the fields of known $z\gtrsim 4.5$ objects to characterize the fraction of obscured SFR. The advantage of a [CII] selection is that our sample is SFR-selected, in contrast to a UV-selection that would be biased towards unobscured star formation. We obtain a sample of 23 [CII] emitters near star-forming (SF) galaxies and QSOs -- three of which we identify for the first time -- using previous literature and archival ALMA data. 18 of these serendipitously identified galaxies have sufficiently deep rest-UV data and are used to characterize the obscured fraction of the star formation in galaxies with SFRs $\gtrsim 30\ \text{M}_{\odot} \ \text{yr}^{-1}$. We find that [CII] emitters identified around SF galaxies have $\approx$63\% of their SFR obscured, while [CII] emitters around QSOs have $\approx$93\% of their SFR obscured. By forward modeling existing wide-area UV luminosity function (LF) determinations, we derive the intrinsic UV LF using our characterization of the obscured SFR. Integrating the intrinsic LF to $M_{UV}$ = $-$20 we find that the obscured SFRD contributes to $>3\%$ and $>10\%$ of the total SFRD at $z \sim 5$ and $z \sim 6$ based on our sample of companions galaxies near SFGs and QSOs, respectively. Our results suggest that dust obscuration is not negligible at $z\gtrsim 5$, further underlining the importance of far-IR observations of the $z\gtrsim 5$ Universe., Comment: Accepted for publication in MNRAS. 24 pages, 14 figures, 8 tables (including appendices)

Using a combination of HST, JWST, and ALMA data, we perform spatially resolved spectral energy distributions (SED) fitting of fourteen 4

We present a spatially resolved analysis of four star-forming galaxies at $z = 4.44-5.64$ using data from the JWST PRIMER and ALMA-CRISTAL surveys to probe the stellar and inter-stellar medium properties on the sub-kpc scale. In the $1-5\,\mu{\rm m}$ JWST NIRCam imaging we find that the galaxies are composed of multiple clumps (between $2$ and $\sim 8$) separated by $\simeq 5\,{\rm kpc}$, with comparable morphologies and sizes in the rest-frame UV and optical. Using BAGPIPES to perform pixel-by-pixel SED fitting to the JWST data we show that the SFR ($\simeq 25\,{\rm M}_{\odot}/{\rm yr}$) and stellar mass (${\rm log}_{10}(M_{\star}/{\rm M}_{\odot}) \simeq 9.5$) derived from the resolved analysis are in close ($ \lesssim 0.3\,{\rm dex}$) agreement with those obtained by fitting the integrated photometry. In contrast to studies of lower-mass sources, we thus find a reduced impact of outshining of the older (more massive) stellar populations in these normal $z \simeq 5$ galaxies. Our JWST analysis recovers bluer rest-frame UV slopes ($\beta \simeq -2.1$) and younger ages ($\simeq 100\,{\rm Myr}$) than archival values. We find that the dust continuum from ALMA-CRISTAL seen in two of these galaxies correlates, as expected, with regions of redder rest-frame UV slopes and the SED-derived $A_{\rm V}$, as well as the peak in the stellar mass map. We compute the resolved IRX-$\beta$ relation, showing that the IRX is consistent with the local starburst attenuation curve and further demonstrating the presence of an inhomogeneous dust distribution within the galaxies. A comparison of the CRISTAL sources to those from the FirstLight zoom-in simulation of galaxies with the same $M_{\star}$ and SFR reveals similar age and colour gradients, suggesting that major mergers may be important in the formation of clumpy galaxies at this epoch., Comment: 16 pages, 8 figures, 3 tables, plus 4 page appendix. Submitted to MNRAS

Memristive systems exhibit dynamics that depend on their past states, making them useful as memory units. Due to their functional resemblance to real synapses, memristors can also constitute the building blocks of neural networks for a variety of tasks. Recently, quantum memristor models have been proposed and notably, a photonic quantum memristor (PQM) has been experimentally proven. In this work, we explore and characterize various quantum properties that emerge from this specific model of PQM. Firstly, we find that a single PQM displays memristive dynamics on its quantum coherence. Secondly, we show that a network made of two independent PQMs can manifest memory effects on the dynamics of both entanglement and coherence of correlated photons traveling through the network, regardless of their distance. Additionally, we build and run a circuit-model of the PQM on a real qubit-based quantum computer (IBM-Q), showing that: (i) this system can effectively be used for non-linear quantum computing under specific conditions, and (ii) digital quantum simulations can reproduce the dynamics of a memristive quantum system in a non-Markovian regime., Comment: 9 pages, 7 figures

Generative adversarial network (GAN) is a strong deep learning model that has shown its value in practical applications such as image processing and data enhancement. Here, we propose a quantum topological code decoder based on GAN and we apply it to optimize the fault-tolerant quantum teleportation system. We construct the generator and discriminator networks of GAN, train the network using the eigenvalue dataset of the topological code, and obtain an optimized decoder with high decoding threshold. The decoding experiments at code distances $d=3$ and $d=5$ show that the error correction success rate of this model reaches 99.895\%. In the experiment, the fidelity threshold of this GAN decoder is about $P=0.2108$, which is significantly improved compared with the threshold $P=0.1099$ of the classical decoding model. In addition, the quantum teleportation system, optimized for noise resistance under $d=3$ topological code, shows a noticeable fidelity improvement within the non-polarized noise threshold range of $P<0.06503$, while under $d=5$ topological code optimization, there is a significant fidelity improvement within the non-polarized noise threshold range of $P<0.07512$. The proposed GAN model supplies a novel approach for topological code decoders and its principles can be applied to different kinds of noise processing., Comment: 12 pages, 11 figures

We present a complete analysis of Fermi Large Area Telescope (LAT) data of GRB 221009A, the brightest Gamma-Ray Burst (GRB) ever detected. The burst emission above 30 MeV detected by the LAT preceded by 1 s the low-energy (< 10 MeV) pulse that triggered the Fermi Gamma-Ray Burst Monitor (GBM), as has been observed in other GRBs. The prompt phase of GRB 221009A lasted a few hundred seconds. It was so bright that we identify a Bad Time Interval (BTI) of 64 seconds caused by the extremely high flux of hard X-rays and soft gamma rays, during which the event reconstruction efficiency was poor and the dead time fraction quite high. The late-time emission decayed as a power law, but the extrapolation of the late-time emission during the first 450 seconds suggests that the afterglow started during the prompt emission. We also found that high-energy events observed by the LAT are incompatible with synchrotron origin, and, during the prompt emission, are more likely related to an extra component identified as synchrotron self-Compton (SSC). A remarkable 400 GeV photon, detected by the LAT 33 ks after the GBM trigger and directionally consistent with the location of GRB 221009A, is hard to explain as a product of SSC or TeV electromagnetic cascades, and the process responsible for its origin is uncertain. Because of its proximity and energetic nature, GRB 221009A is an extremely rare event., Comment: 60 pages, 38 figures, 9 tables

Quasi-periodic Eruptions (QPEs) are luminous bursts of soft X-rays from the nuclei of galaxies, repeating on timescales of hours to weeks. The mechanism behind these rare systems is uncertain, but most theories involve accretion disks around supermassive black holes (SMBHs), undergoing instabilities or interacting with a stellar object in a close orbit. It has been suggested that this disk could be created when the SMBH disrupts a passing star, implying that many QPEs should be preceded by observable tidal disruption events (TDEs). Two known QPE sources show long-term decays in quiescent luminosity consistent with TDEs, and two observed TDEs have exhibited X-ray flares consistent with individual eruptions. TDEs and QPEs also occur preferentially in similar galaxies. However, no confirmed repeating QPEs have been associated with a spectroscopically confirmed TDE or an optical TDE observed at peak brightness. Here we report the detection of nine X-ray QPEs with a mean recurrence time of approximately 48 hours from AT2019qiz, a nearby and extensively studied optically-selected TDE. We detect and model the X-ray, ultraviolet and optical emission from the accretion disk, and show that an orbiting body colliding with this disk provides a plausible explanation for the QPEs.