Daily Papers

For decades, researchers have developed task-specific models to address scientific challenges across diverse disciplines. Recently, large language models (LLMs) have shown enormous capabilities in handling general tasks; however, these models encounter difficulties in addressing real-world scientific problems, particularly in domains involving large-scale numerical data analysis, such as experimental high energy physics. This limitation is primarily due to BPE tokenization's inefficacy with numerical data. In this paper, we propose a task-agnostic architecture, BBT-Neutron, which employs a binary tokenization method to facilitate pretraining on a mixture of textual and large-scale numerical experimental data. We demonstrate the application of BBT-Neutron to Jet Origin Identification (JoI), a critical categorization challenge in high-energy physics that distinguishes jets originating from various quarks or gluons. Our results indicate that BBT-Neutron achieves comparable performance to state-of-the-art task-specific JoI models. Furthermore, we examine the scaling behavior of BBT-Neutron's performance with increasing data volume, suggesting the potential for BBT-Neutron to serve as a foundational model for particle physics data analysis, with possible extensions to a broad spectrum of scientific computing applications for Big Science experiments, industrial manufacturing and spacial computing. The project code is available at https://github.com/supersymmetry-technologies/bbt-neutron.

Machine learning has become an essential tool in jet physics. Due to their complex, high-dimensional nature, jets can be explored holistically by neural networks in ways that are not possible manually. However, innovations in all areas of jet physics are proceeding in parallel. We show that specially constructed machine learning models trained for a specific jet classification task can improve the accuracy, precision, or speed of all other jet physics tasks. This is demonstrated by training on a particular multiclass generation and classification task and then using the learned representation for different generation and classification tasks, for datasets with a different (full) detector simulation, for jets from a different collision system (pp versus ep), for generative models, for likelihood ratio estimation, and for anomaly detection. We consider, our OmniLearn approach thus as a jet-physics foundation model. It is made publicly available for use in any area where state-of-the-art precision is required for analyses involving jets and their substructure.

The exact location of the gamma-ray emitting region in blazar jets has long been a matter of debate. However, the location has important implications about the emission processes, geometric and physical parameters of the jet, as well as the nature of interaction of the jet with the interstellar and intergalactic medium. Diverse conclusions have been drawn by various authors based on a variety of methods applied to different data sets of many blazars, e.g., the location is less than 0.1 pc from the central engine within the broad line region (BLR) or a few or tens of pc downstream beyond the dusty torus or at some intermediate distance. Here we use a method, established in a previous work, in which the location of the GeV/optical emission is determined using the ratio of energy dissipated during contemporaneous outbursts at those wave bands. We apply it to a total of 47 multi-wavelength outbursts in 10 blazars. We find that the location of the GeV/optical emission is beyond the BLR for all cases. This result is consistent with other studies, in which the location has been determined for a large sample of blazars. We compare the location determined by our method for several GeV outbursts of multiple blazars to that obtained by other authors using different methods. We find that our results are consistent in such one-to-one comparison in most cases, for which the required data were available.

We present the outflows detected in HOPS 373SW, a protostar undergoing a modest 30% brightness increase at 850 mum. Atacama Large Millimeter/submillimeter Array (ALMA) observations of shock tracers, including SiO 8--7, CH_3OH 7_{rm k}--6_{rm k}, and ^{12}CO 3--2 emission, reveal several outflow features around HOPS 373SW. The knots in the extremely high-velocity SiO emission reveal the wiggle of the jet, for which a simple model derives a 37^circ inclination angle of the jet to the plane of the sky, a jet velocity of 90 km s^{-1}, and a period of 50 years. The slow SiO and CH_3OH emission traces U-shaped bow shocks surrounding the two CO outflows. One outflow is associated with the high-velocity jets, while the other is observed to be close to the plane of the sky. The misaligned outflows imply that previous episodic accretion events have either reoriented HOPS 373SW or that it is an unresolved protostellar binary system with misaligned outflows.

In the literature, flows produced by synthetic jets (SJ) have been studied extensively through experiments and numeric simulations. The essential physics of such a complex system has been simplified successfully to Lumped-element models in a wide range of conditions. LE models effectively predict the pressure in the cavity and the velocity in the neck of SJ. But, this does not comprise the complete dynamics of SJ. As soon as the flow starts separating from the neck of the SJ device, vortices and jets form at some distance downstream. These structures are the result of loosening the flow boundaries. Despite such a dramatic change, predictions of LE models remain unverified by measurements of the fully developed jet. We compared predictions of momentum transfer using an LE model with measurements of size and velocity of a fully developed jet/vortex detached from an SJ. Our SJ device operated with air as an active fluid. Comparing measurements and predictions, we found a constant difference for the higher sound pressures. However, the predictions and the measurements follow similar trends. Additionally, we found that the decay rate of the flow regime given by the relationship between the Reynolds and the Strouhal numbers differs significantly when the flow is studied within the neck and downstream the cavity.

We present a transformer architecture-based foundation model for tasks at high-energy particle colliders such as the Large Hadron Collider. We train the model to classify jets using a self-supervised strategy inspired by the Joint Embedding Predictive Architecture. We use the JetClass dataset containing 100M jets of various known particles to pre-train the model with a data-centric approach -- the model uses a fraction of the jet constituents as the context to predict the embeddings of the unseen target constituents. Our pre-trained model fares well with other datasets for standard classification benchmark tasks. We test our model on two additional downstream tasks: top tagging and differentiating light-quark jets from gluon jets. We also evaluate our model with task-specific metrics and baselines and compare it with state-of-the-art models in high-energy physics. Project site: https://hep-jepa.github.io/

Jet tagging is a critical yet challenging classification task in particle physics. While deep learning has transformed jet tagging and significantly improved performance, the lack of a large-scale public dataset impedes further enhancement. In this work, we present JetClass, a new comprehensive dataset for jet tagging. The JetClass dataset consists of 100 M jets, about two orders of magnitude larger than existing public datasets. A total of 10 types of jets are simulated, including several types unexplored for tagging so far. Based on the large dataset, we propose a new Transformer-based architecture for jet tagging, called Particle Transformer (ParT). By incorporating pairwise particle interactions in the attention mechanism, ParT achieves higher tagging performance than a plain Transformer and surpasses the previous state-of-the-art, ParticleNet, by a large margin. The pre-trained ParT models, once fine-tuned, also substantially enhance the performance on two widely adopted jet tagging benchmarks. The dataset, code and models are publicly available at https://github.com/jet-universe/particle_transformer.

Fast X-ray transients (FXRTs) are short-lived X-ray outbursts with diverse progenitor scenarios, including compact object mergers, stellar core-collapses and tidal disruption events. The Einstein Probe (EP) has enabled the rapid discovery and follow-up of dozens of FXRTs, revealing that while some of them overlap with traditional gamma-ray bursts (GRBs), a larger fraction of FXRTs have no associated gamma-ray counterpart down to deep limits. The origin of these gamma-ray dark FXRTs and their connection to the diverse landscape of stellar explosions remains an open question, which can be tackled through the study of their multi-wavelength counterparts and environment. In this paper, we present long-term radio observations of the gamma-ray dark EP241021a, which exhibits sustained radio emission for over 100 days, placing it among the longest-lived radio afterglows. We detect signature of interstellar scintillation in early epochs, allowing us to constrain the angular size and Lorentz factor of the emitting region. Our observations point to an outflow that is at least mildly relativistic with Lorentz factor > 4. Afterglow modeling favors a moderately relativistic and collimated outflow interacting with a low-density interstellar medium. The derived beaming-corrected kinetic energy and low radiative efficiency are consistent with a standard relativistic explosion which did not produce bright gamma-rays. Alternatively, a highly-relativistic structured jet remains consistent with our observations if seen substantially off-axis. In the latter case, the initial X-ray flare detected by EP would be caused by the slower ejecta from the lateral wings intercepting our line of sight rather than by traditional prompt-emission mechanisms within the jet core.

We present new JWST-NIRSpec IFS data for the luminous infrared galaxy NGC7469: a nearby (70.6Mpc) active galaxy with a Sy 1.5 nucleus that drives a highly ionized gas outflow and a prominent nuclear star-forming ring. Using the superb sensitivity and high spatial resolution of the JWST instrument NIRSpec-IFS, we investigate the role of the Seyfert nucleus in the excitation and dynamics of the circumnuclear gas. Our analysis focuses on the [Fe ii], H2, and hydrogen recombination lines that trace the radiation/shocked-excited molecular and ionized ISM around the AGN. We investigate the gas excitation through H2/Br{\gamma} and [Fe ii]/Paeta emission line ratios and find that photoionization by the AGN dominates within the central 300 pc of the galaxy and together with a small region show ing signatures of shock-heated gas; these shock-heated regions are likely associated with a compact radio jet. In addition, the velocity field and velocity dispersion maps reveal complex gas kinematics. Rotation is the dominant feature, but we also identify non-circular motions consistent with gas inflows as traced by the velocity residuals and the spiral pattern in the Pa{\alpha} velocity dispersion map. The inflow is consistent with the mass outflow rate and two orders of magnitude higher than the AGN accretion rate. The compact nuclear radio jet has enough power to drive the highly ionized outflow. This scenario suggests that the inflow and outflow are in a self-regulating feeding-feedback process, with a contribution from the radio jet helping to drive the outflow.

We present the RODEM Jet Datasets, a comprehensive collection of simulated large-radius jets designed to support the development and evaluation of machine-learning algorithms in particle physics. These datasets encompass a diverse range of jet sources, including quark/gluon jets, jets from the decay of W bosons, top quarks, and heavy new-physics particles. The datasets provide detailed substructure information, including jet kinematics, constituent kinematics, and track displacement details, enabling a wide range of applications in jet tagging, anomaly detection, and generative modelling.

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.

In high energy physics, self-supervised learning (SSL) methods have the potential to aid in the creation of machine learning models without the need for labeled datasets for a variety of tasks, including those related to jets -- narrow sprays of particles produced by quarks and gluons in high energy particle collisions. This study introduces an approach to learning jet representations without hand-crafted augmentations using a jet-based joint embedding predictive architecture (J-JEPA), which aims to predict various physical targets from an informative context. As our method does not require hand-crafted augmentation like other common SSL techniques, J-JEPA avoids introducing biases that could harm downstream tasks. Since different tasks generally require invariance under different augmentations, this training without hand-crafted augmentation enables versatile applications, offering a pathway toward a cross-task foundation model. We finetune the representations learned by J-JEPA for jet tagging and benchmark them against task-specific representations.

We continue our program of investigation of the proper motions of spectrally separated structures in the Herbig-Haro outflows with the aid of Fabry-Perot scanning interferometry. This work mainly focuses on the physical nature of various structures in the jets. The aim of the present study is to measure the proper motions of the previously discovered kinematically separated structures in the working surface of the HH 83 collimated outflow. We used observations from two epochs separated by 15 years, which were performed on the 6m telescope with Fabry-Perot scanning interferometer. We obtained images corresponding to different radial velocities for the two separate epochs, and used them to measure proper motions. In the course of our data analysis, we discovered a counter bow-shock of HH 83 flow with positive radial velocity, which makes this flow a relatively symmetric bipolar system. The second epoch observations confirm that the working surface of the flow is split into two structures with an exceptionally large (250 km\ s^{-1}) difference in radial velocity. The proper motions of these structures are almost equal, which suggests that they are physically connected. The asymmetry of the bow shock and the turning of proper motion vectors suggests a collision between the outflow and a dense cloud. The profile of the Halpha line for the directly invisible infrared source HH 83 IRS, obtained by integration of the data within the reflection nebula, suggests it to be of P Cyg type with a broad absorption component characteristic of the FU Ori like objects. If this object underwent an FU Ori type outburst, which created the HH 83 working surfaces, its eruption took place about 1500 years ago according to the kinematical age of the outflow.

Advances in time-resolved Particle Tracking Velocimetry (4D-PTV) techniques have been consistently revealed more accurate Lagrangian particle motions. A novel track initialisation technique as a complementary part of 4D-PTV, based on local temporal and spatial coherency of neighbour trajectories, is proposed. The proposed Lagrangian Coherent Track Initialisation (LCTI) applies physics-based Finite Time Lyapunov Exponent (FTLE) to build four frame coherent tracks. We locally determine the boundaries (i.e., ridges) of Lagrangian Coherent Structures (LCS) among neighbour trajectories by using FTLE to distinguish clusters of coherent motions. To evaluate the proposed technique, we created an open-access synthetic Lagrangian and Eulerian dataset of the wake downstream of a smooth cylinder at a Reynolds number equal to 3900 obtained from 3D Direct Numerical Simulation (DNS). The dataset is available to the public. Performance of the proposed method based on three characteristic parameters, temporal scale, particle concentration (i.e., density), and noise ratio, showed robust behaviour in finding true tracks compared to the recent initialisation algorithms. Sensitivity of LCTI to the number of untracked and wrong tracks are also discussed. We address the capability of using the proposed method as a function of a 4D-PTV scheme in the Lagrangian Particle Tracking challenge for a flow with high particle densities. Finally, the LCTI behaviour was assessed in a real jet impingement experiment. LCTI was found to be a reliable tracking tool in complex flow motions, with a strength revealed for flows with high particle concentrations.

We study various machine learning based algorithms for performing accurate jet flavor classification on field-programmable gate arrays and demonstrate how latency and resource consumption scale with the input size and choice of algorithm. These architectures provide an initial design for models that could be used for tagging at the CERN LHC during its high-luminosity phase. The high-luminosity upgrade will lead to a five-fold increase in its instantaneous luminosity for proton-proton collisions and, in turn, higher data volume and complexity, such as the availability of jet constituents. Through quantization-aware training and efficient hardware implementations, we show that O(100) ns inference of complex architectures such as deep sets and interaction networks is feasible at a low computational resource cost.

The advent of new generation radio telescopes is opening new possibilities on the classification and study of extragalactic high-energy sources, specially the underrepresented ones like radio galaxies. Among these, Giant Radio Galaxies (GRG, larger than 0.7 Mpc) are among the most extreme manifestations of the accretion/ejection processes on supermassive black holes. Our recent studies have shown that GRG can be up to four times more abundant in hard X-ray selected (i.e. from INTEGRAL/IBIS and Swift/BAT at >20 keV) samples and, most interestingly, the majority of them present signs of restarted radio activity. This makes them the ideal test-bed to study the so far unknown duty cycle of jets in active galactic nuclei. Open questions in the field include: How and when jets are restarted? How jets evolve and what's their dynamic? What is the jet's duty cycle and what triggers them? Our group has recently collected a wealth of radio data on these high-energy selected GRGs, allowing us to study their jet formation and evolution from the pc to kpc scales, across different activity epochs. In particular, thanks to our EVN large programme, we were able to probe the new radio phase in the core of these giants. Furthermore, we are devoting an effort to the exploitation of new radio surveys data for the discovery of new classes of counterparts of Fermi/LAT catalogues. In particular, we are unveiling the hidden population of radio galaxies associated with gamma-ray sources.

We present a 70ks Chandra observation of the radio galaxy 3C293. This galaxy belongs to the class of molecular hydrogen emission galaxies (MOHEGs) that have very luminous emission from warm molecular hydrogen. In radio galaxies, the molecular gas appears to be heated by jet-driven shocks, but exactly how this mechanism works is still poorly understood. With Chandra, we observe X-ray emission from the jets within the host galaxy and along the 100 kpc radio jets. We model the X-ray spectra of the nucleus, the inner jets, and the X-ray features along the extended radio jets. Both the nucleus and the inner jets show evidence of 10^7 K shock-heated gas. The kinetic power of the jets is more than sufficient to heat the X-ray emitting gas within the host galaxy. The thermal X-ray and warm H2 luminosities of 3C293 are similar, indicating similar masses of X-ray hot gas and warm molecular gas. This is consistent with a picture where both derive from a multiphase, shocked interstellar medium (ISM). We find that radio-loud MOHEGs that are not brightest cluster galaxies (BCGs), like 3C293, typically have LH2/LX~1 and MH2/MX~1, whereas MOHEGs that are BCGs have LH2/LX~0.01 and MH2/MX~0.01. The more massive, virialized, hot atmosphere in BCGs overwhelms any direct X-ray emission from current jet-ISM interaction. On the other hand, LH2/LX~1 in the Spiderweb BCG at z=2, which resides in an unvirialized protocluster and hosts a powerful radio source. Over time, jet-ISM interaction may contribute to the establishment of a hot atmosphere in BCGs and other massive elliptical galaxies.

Blueshifted absorption is the classic spectroscopic signature of an accretion disc wind in X-ray binaries and cataclysmic variables (CVs). However, outflows can also create pure emission lines, especially at optical wavelengths. Therefore, developing other outflow diagnostics for these types of lines is worthwhile. With this in mind, we construct a systematic grid of 3645 synthetic wind-formed H-alpha line profiles for CVs with the radiative transfer code SIROCCO. Our grid yields a variety of line shapes: symmetric, asymmetric, single- to quadruple-peaked, and even P-Cygni profiles. About 20% of these lines -- our `Gold' sample -- have strengths and widths consistent with observations. We use this grid to test a recently proposed method for identifying wind-formed emission lines based on deviations in the wing profile shape: the `excess equivalent width diagnostic diagram'. We find that our `Gold' sample can preferentially populate the suggested `wind regions' of this diagram. However, the method is highly sensitive to the adopted definition of the line profile `wing'. Hence, we propose a refined definition based on the full-width at half maximum to improve the interpretability of the diagnostic diagram. Furthermore, we define an approximate scaling relation for the strengths of wind-formed CV emission lines in terms of the outflow parameters. This relation provides a fast way to assess whether -- and what kind of -- outflow can produce an observed emission line. All our wind-based models are open-source and we provide an easy-to-use web-based tool to browse our full set of H-alpha spectral profiles.

Active galactic nuclei (AGNs) have been proposed as plausible sites for hosting a sizable fraction of the binary black hole (BBH) mergers measured through gravitational waves (GWs) by the LIGO-Virgo-Kagra (LVK) experiment. These GWs could be accompanied by radiation feedback due to the interaction of the BBH merger remnant with the AGN disc. We present a new predicted radiation signature driven by the passage of a kicked BBH remnant throughout a thin AGN disc. We analyse the situation of a merger occurring outside the thin disc, where the merger is of second or higher generation in a merging hierarchical sequence. The coalescence produces a kicked BH remnant that eventually plunges into the disc, accretes material, and inflates jet cocoons. We consider the case of a jet cocoon propagating quasi-parallel to the disc plane and study the outflow that results when the cocoon emerges from the disc. We calculate the transient emission of the emerging cocoon using a photon diffusion model typically employed to describe the light curves of supernovae. Depending on the parameter configuration, the flare produced by the emerging cocoon could be comparable to or exceed the AGN background emission at optical, and extreme ultraviolet wavelengths. For instance, in AGNs with central engines of sim 5times10^{6} M_odot, flares driven by BH remnants with masses of sim 100 M_odot can appear in about sim[10-100] days after the GW, lasting for few days.

We present a novel method for measuring |V_{cb}| at the LHC using an advanced boosted-jet tagger to identify "bc signatures". By associating boosted W rightarrow bc signals with bc-matched jets from top-quark decays, we enable an in-situ calibration of the tagger. This approach significantly suppresses backgrounds while reducing uncertainties in flavor tagging efficiencies -- key to improving measurement precision. Our study is enabled by the development of realistic, AI-based large- and small-radius taggers, Sophon and the newly introduced SophonAK4, validated to match ATLAS and CMS's state-of-the-art taggers. The method complements the conventional small radius jet approach and enables a ~30% improvement in |V_{cb}| precision under HL-LHC projections. As a byproduct, it enhances H^{pm} rightarrow bc search sensitivity by a factor of 2--5 over the recent ATLAS result based on Run 2 data. Our work offers a new perspective for the precision |V_{cb}| measurement and highlights the potential of using advanced tagging models to probe unexplored boosted regimes at the LHC.

We use two-dimensional particle-in-cell simulations to investigate the generation and evolution of the magnetic field associated with the propagation of a jet for various initial conditions. We demonstrate that, in general, the magnetic field is initially grown by the Weibel and Mushroom instabilities. However, the field is saturated by the Alfv'en current limit. For initially non-magnetized plasma, we show that the growth of the magnetic field is delayed when the matter density of the jet environment is lower, which are in agreement with simple analytical predictions. We show that the higher Lorentz factor (gtrsim 2) prevents rapid growth of the magnetic fields. When the initial field is troidal, the position of the magnetic filaments moves away from the jet as the field strength increases. The axial initial field helps the jet maintain its shape more effectively than the troidal initial field.

The study of jet-inflated X-ray cavities provides a powerful insight into the energetics of hot galactic atmospheres and radio-mechanical AGN feedback. By estimating the volumes of X-ray cavities, the total energy and thus also the corresponding mechanical jet power required for their inflation can be derived. Properly estimating their total extent is, however, non-trivial, prone to biases, nearly impossible for poor-quality data, and so far has been done manually by scientists. We present a novel and automated machine-learning pipeline called Cavity Detection Tool (CADET), developed to detect and estimate the sizes of X-ray cavities from raw Chandra images. The pipeline consists of a convolutional neural network trained for producing pixel-wise cavity predictions and a DBSCAN clustering algorithm, which decomposes the predictions into individual cavities. The convolutional network was trained using mock observations of early-type galaxies simulated to resemble real noisy Chandra-like images. The network's performance has been tested on simulated data obtaining an average cavity volume error of 14 % at an 89 % true-positive rate. For simulated images without any X-ray cavities inserted, we obtain a 5 % false-positive rate. When applied to real Chandra images, the pipeline recovered 91 out of 100 previously known X-ray cavities in nearby early-type galaxies and all 14 cavities in chosen galaxy clusters. Besides that, the CADET pipeline discovered 8 new cavity pairs in atmospheres of early-type galaxies and galaxy clusters (IC4765, NGC533, NGC2300, NGC3091, NGC4073, NGC4125, NGC4472, NGC5129) and a number of potential cavity candidates.

The near-infrared (NIR) emission of the youngest protostars still needs to be characterized to better understand the evolution of their accretion and ejection activity. We analyze James Webb Space Telescope NIRSpec 1.7 -- 5.3 mum observations of two deeply embedded sources in the S68N protostellar core in Serpens. The North Central (NC) source exhibits a highly obscured spectrum (A_K ~ 4.8 mag) that is modeled with a pre-main-sequence photosphere and a hot disk component. The photospheric parameters are consistent with a young, low-mass photosphere, as suggested by the low surface gravity, log g of 1.95 pm 0.15 cm s^{-2}. The hot disk suggests that accretion onto the central protostellar embryo is ongoing, although prototypical accretion-tracing emission lines HI are not detected. The South Central (SC) source, which is even more embedded (A_K ~ 8 mag; no continuum is detected shortward of 3.6 mum) appears to be driving the large-scale S68N protostellar outflow, and launches a collimated hot molecular jet detected in \Ht and CO ro-vibrational lines. Shock modeling of the \Ht (ro)vibrational lines establishes that fast C-type shocks (geq 30 km s^{-1}), with high pre-shock density (geq 10^7 cm^{-3}), and strong magnetic field (b ~ 3--10, where B = b,times,textrm{n_{H} (cm^{-3})},muG) best match the data. The bright CO fundamental line forest suggests energetic excitation, with the contribution of non-LTE effects, ie irradiation pumping. Detected OH and CH^{+} ro-vibrational lines support this hypothesis. These two Class 0 protostars seem to be in very young evolutionary stages and still have to acquire the bulk of their final stellar masses. These results demonstrate that JWST enables unprecedented diagnostics of these first stages of the protostellar evolutionary phase.

Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passive free surface. In this investigation, we collect the sound of a water jet of varying flowrate falling into a pool of water, and use this sound to predict the flowrate and flowrate trajectory involved. Two approaches are employed: one uses machine-learning models trained using audio features extracted from the collected sound to predict the flowrate (and subsequently the flowrate trajectory). In contrast, the second method directly uses acoustic parameters related to the spectral energy of the liquid-liquid interaction to estimate the flowrate trajectory. The actual flowrate, however, is determined directly using a gravimetric method: tracking the change in mass of the pooling liquid over time. We show here that the two methods agree well with the actual flowrate and offer comparable performance in accurately predicting the flowrate trajectory, and accordingly offer insights for potential real-life applications using sound.

X-ray observations are essential for understanding the multimessenger emission mechanisms of active galactic nuclei (AGN). Blazars, a subset of AGN whose X-ray emission predominantly originates from relativistic jets, have been proposed as promising high-energy neutrino sources. In this work, we study the candidate neutrino-emitting blazar 5BZB J0630-24064, which has been observed over multiple epochs with the XMM-Newton, NuSTAR, Neil Gehrels Swift-XRT, and eROSITA observatories. Analysis of the X-ray spectra in the 2.0-10.0 keV band shows significant variability, with high flux states adhering to a power-law model indicative of jet emission. However, during low-flux states, the spectrum reveals an additional component at hard-X-rays, indicating a transition from jet-dominated to multi-component X-ray emission, possibly associated with hadronic processes. To investigate this spectral evolution, we tested various models and found it to be consistent with corona emission or photoionised absorption processes typically observed in obscured AGN. The identification of the X-ray spectral variability in 5BZB J0630-24064, combined with its potential for neutrino production, opens new perspectives in multimessenger astrophysics of blazars highlighting the synergies between the mechanisms of the jet and the nuclear environment.

The Gamma-ray detection from an astrophysical object indicates the presence of an extreme environment where high-energy radiation is produced. With the continuous monitoring of the Gamma-ray sky by the Fermi Large Area Telescope (LAT), leading to deeper sensitivity, the high-energy Gamma-ray emission has now been detected from a diverse class of jetted active galactic nuclei (AGN). Here, we present the results of a multiwavelength study of the radio source DA~362, which was reported to be a blazar candidate of uncertain type. However, it was recently identified as a bona fide compact symmetric object (CSO) based on its sub-kpc, bi-polar radio morphology, and lack of radio variability. This makes DA~362 the only fourth Gamma-ray emitting object of this enigmatic class of radio-loud AGN. Using five very long baseline interferometry observations covering 1996-2018, we found the jet separation velocity to be subluminal (v_{rm app}sim 0.2c), thus supporting its CSO nature. Its Fermi-LAT observations revealed a Gamma-ray flaring activity, a phenomenon never detected from the other three Gamma-ray detected CSOs. This object is bright in the near-infrared band but extremely faint in the optical-ultraviolet filters, hinting at possible obscuration. The Swift X-Ray Telescope observation of DA 362 reveals an extremely hard X-ray spectrum, though a strong claim cannot be made due to large uncertainties. We conclude that deeper observations are needed to probe the broadband properties of this enigmatic object and to understand the origin of high-energy Gamma-ray emission.

Blazars, a class of active galactic nuclei (AGN) powered by supermassive black holes, are known for their remarkable variability across multiple timescales and wavelengths. With advancements in both ground- and space-based telescopes, our understanding of AGN central engines has significantly improved. However, the mechanisms driving this variability remain elusive, and continue to fascinate both theorists and observers alike. The primary objective of this study is to constrain the X-ray variability properties of the TeV blazar PKS 2155-304. We conduct a comprehensive X-ray spectral and timing analysis, focusing on both long-term and intra-day variability. This analysis uses data from 22 epochs of XMM-Newton EPIC-pn observations, collected over 15 years (2000-2014). To investigate the variability of the source, we applied both timing and spectral analyses. For the timing analysis, we estimated fractional variability, variability amplitude, minimum variability timescales, flux distribution, and power spectral density (PSD). In the spectral analysis, we fitted the X-ray spectra using power-law, log-parabola, and broken power-law (BPL) models to determine the best-fitting parameters. Additionally, we studied the hardness ratio (HR). We observed moderate intra-day variability in most of the light curves. Seven out of the twenty-two observations showed a clear bimodal flux distribution, indicating the presence of two distinct flux states. Our analysis revealed a variable power-law PSD slope. Most HR plots did not show significant variation with flux, except for one observation (OBSID 0124930501), where HR increased with flux (Count/s). The fitted X-ray spectra favored the BPL model for the majority of observations. The findings of this work shed light on the intraday variability of blazars, providing insights into the non-thermal jet processes that drive the observed flux variations.

We conducted 22~GHz 1" JVLA imaging of 100 radio-quiet X-ray selected AGN from the Swift-BAT survey. We find AGN-driven kiloparsec-scale radio structures inconsistent with pure star formation in 11 AGN. The host galaxies of these AGN lie significantly below the star-forming main sequence, indicating suppressed star formation. While these radio structures tend to be physically small compared to the host galaxy, the global star formation rate of the host is affected. We evaluate the energetics of the radio structures interpreted first as immature radio jets, and then as consequences of an AGN-driven radiative outflow, and compare them to two criteria for successful feedback: the ability to remove the CO-derived molecular gas mass from the galaxy gravitational potential and the kinetic energy transfer to molecular clouds leading to v_cloud > sigma_*. In most cases, the jet interpretation is insufficient to provide the energy necessary to cause the star formation suppression. Conversely, the wind interpretation provides ample energy in all but one case. We conclude that it is more likely that the observed suppression of star formation in the global host galaxy is due to ISM interactions of a radiative outflow, rather than a small-scale radio jet.

We performed variability analysis of the multiwavelength light curves for the flat-spectrum radio quasar PKS 0727-11. Using the generalized Lomb-Scargle periodogram, we identified a possible quasi-periodic oscillation (QPO) of sim 168.6 days (persisted for 6 cycles, with a significance of 3.8sigma) in the gamma-ray light curve during the flare period (MJD 54687-55738). It is the first time that periodic variations have been detected in this source, and further supported by other methods: weighted wavelet z-transform, phase dispersion minimization, REDFIT, autoregressive integrated moving average model, and structure function analysis. Cross-correlation analysis shows that there is a strong correlation between multi-band light variations, indicating that gamma-ray and radio flares may originate from the same disturbance, and the distance between the emission regions of gamma-ray and radio flares is calculated based on the time lag. We demonstrate that QPO arising from the non-ballistic helical jet motion driven by the orbital motion in a supermassive binary black hole is a plausible physical explanation. In this scenario, the estimated mass of the primary black hole is Msim3.66times10^8-5.79times10^{9}M_odot.

Foundation Models are neural networks that are capable of simultaneously solving many problems. Large Language Foundation Models like ChatGPT have revolutionized many aspects of daily life, but their impact for science is not yet clear. In this paper, we use a new Foundation Model for hadronic jets to solve three key challenges in collider physics. In particular, we show how experiments can (1) save significant computing power when developing reconstruction algorithms, (2) perform a complete uncertainty quantification for high-dimensional measurements, and (3) search for new physics with model agnostic methods using low-level inputs. In each case, there are significant computational or methodological challenges with current methods that limit the science potential of deep learning algorithms. By solving each problem, we take jet Foundation Models beyond proof-of-principle studies and into the toolkit of practitioners.

In this work, we significantly enhance masked particle modeling (MPM), a self-supervised learning scheme for constructing highly expressive representations of unordered sets relevant to developing foundation models for high-energy physics. In MPM, a model is trained to recover the missing elements of a set, a learning objective that requires no labels and can be applied directly to experimental data. We achieve significant performance improvements over previous work on MPM by addressing inefficiencies in the implementation and incorporating a more powerful decoder. We compare several pre-training tasks and introduce new reconstruction methods that utilize conditional generative models without data tokenization or discretization. We show that these new methods outperform the tokenized learning objective from the original MPM on a new test bed for foundation models for jets, which includes using a wide variety of downstream tasks relevant to jet physics, such as classification, secondary vertex finding, and track identification.

Recent progress in visual generative models enables the generation of high-quality images. To prevent the misuse of generated images, it is important to identify the origin model that generates them. In this work, we study the origin attribution of generated images in a practical setting where only a few images generated by a source model are available and the source model cannot be accessed. The goal is to check if a given image is generated by the source model. We first formulate this problem as a few-shot one-class classification task. To solve the task, we propose OCC-CLIP, a CLIP-based framework for few-shot one-class classification, enabling the identification of an image's source model, even among multiple candidates. Extensive experiments corresponding to various generative models verify the effectiveness of our OCC-CLIP framework. Furthermore, an experiment based on the recently released DALL-E 3 API verifies the real-world applicability of our solution.

In 2013, the IceCube collaboration announced the detection of a diffuse high-energy astrophysical neutrino flux. The origin of this flux is still largely unknown. The most significant individual source is the close-by Seyfert galaxy NGC 1068 at 4.2-sigma level with a soft spectral index. To identify sources based on their counterpart, IceCube releases realtime alerts corresponding to neutrinos with a high probability of astrophysical origin. We report here the spatial coincidence of two neutrino alerts, IC220424A and IC230416A, with the Seyfert galaxy NGC 7469 at a distance of 70 Mpc. We evaluate, a-posteriori, the chance probability of such a coincidence and discuss this source as a potential neutrino emitter based on its multi-wavelength properties and in comparison to NGC 1068 by performing a Goodness-of-Fit test. The test statistic is derived from a likelihood ratio that includes the neutrino angular uncertainty and the source distance. We apply this test first to a catalog of AGN sources and second to a catalog of Seyfert galaxies only. Our a-posteriori evaluation excludes the possibility of an accidental spatial coincidence of both neutrinos with the Seyfert galaxy NGC 7469 at 3.2-sigma level, leaving open the possibility that either one or both neutrinos originated from the source. To be compatible with non-detections of TeV neutrinos, the source would need to have a hard spectral index.

The X-ray polarization observations made possible with the Imaging X-ray Polarimetry Explorer (IXPE) offer new ways of probing high-energy emission processes in astrophysical jets from blazars. Here we report on the first X-ray polarization observation of the blazar S4 0954+65 in a high optical and X-ray state. During our multi-wavelength campaign on the source, we detected an optical flare whose peak coincided with the peak of an X-ray flare. This optical-X-ray flare most likely took place in a feature moving along the parsec-scale jet, imaged at 43 GHz by the Very Long Baseline Array. The 43 GHz polarization angle of the moving component underwent a rotation near the time of the flare. In the optical band, prior to the IXPE observation, we measured the polarization angle to be aligned with the jet axis. In contrast, during the optical flare the optical polarization angle was perpendicular to the jet axis; after the flare, it reverted to being parallel to the jet axis. Due to the smooth behavior of the optical polarization angle during the flare, we favor shocks as the main acceleration mechanism. We also infer that the ambient magnetic field lines in the jet were parallel to the jet position angle. The average degree of optical polarization during the IXPE observation was (14.3pm4.1)%. Despite the flare, we only detected an upper limit of 14% (at 3sigma level) on the X-ray polarization degree; although a reasonable assumption on the X-ray polarization angle results in an upper limit of 8.8% (3sigma). We model the spectral energy distribution (SED) and spectral polarization distribution (SPD) of S4 0954+65 with leptonic (synchrotron self-Compton) and hadronic (proton and pair synchrotron) models. The constraints we obtain with our combined multi-wavelength polarization observations and SED modeling tentatively disfavor hadronic models for the X-ray emission in S4 0954+65.

Energy-energy correlators are constructed by averaging the number of charged particle pairs within jets, weighted by the product of their transverse momenta, as a function of the angular separation of the particles within a pair. They are sensitive to a multitude of perturbative and nonperturbative quantum chromodynamics phenomena in high-energy particle collisions. Using lead-lead data recorded with the CMS detector, energy-energy correlators inside high transverse momentum jets are measured in heavy ion collisions for the first time. The data are obtained at a nucleon-nucleon center-of-mass energy of 5.02 TeV and correspond to an integrated luminosity of 1.70 nb^{-1}. A similar analysis is done for proton-proton collisions at the same center-of-mass energy to establish a reference. The ratio of lead-lead to proton-proton energy-energy correlators reveals significant jet substructure modifications in the quark-gluon plasma. The results are compared to different models that incorporate either color coherence or medium response effects, where the two effects predict similar substructure modifications.

The 100 MW cryogenic liquid oxygen/hydrogen multi-injector combustor BKD operated by the DLR Institute of Space Propulsion is a research platform that allows the study of thermoacoustic instabilities under realistic conditions, representative of small upper stage rocket engines. We use data from BKD experimental campaigns in which the static chamber pressure and fuel-oxidizer ratio are varied such that the first tangential mode of the combustor is excited under some conditions. We train an autoregressive Bayesian neural network model to forecast the amplitude of the dynamic pressure time series, inputting multiple sensor measurements (injector pressure/ temperature measurements, static chamber pressure, high-frequency dynamic pressure measurements, high-frequency OH* chemiluminescence measurements) and future flow rate control signals. The Bayesian nature of our algorithms allows us to work with a dataset whose size is restricted by the expense of each experimental run, without making overconfident extrapolations. We find that the networks are able to accurately forecast the evolution of the pressure amplitude and anticipate instability events on unseen experimental runs 500 milliseconds in advance. We compare the predictive accuracy of multiple models using different combinations of sensor inputs. We find that the high-frequency dynamic pressure signal is particularly informative. We also use the technique of integrated gradients to interpret the influence of different sensor inputs on the model prediction. The negative log-likelihood of data points in the test dataset indicates that predictive uncertainties are well-characterized by our Bayesian model and simulating a sensor failure event results as expected in a dramatic increase in the epistemic component of the uncertainty.

Shape optimization is essential in aerospace vehicle design, including reentry systems, and propulsion system components, as it directly influences aerodynamic efficiency, structural integrity, and overall mission success. Rapid and accurate prediction of external and internal flows accelerates design iterations. To this end, we develop a new variant of DeepONet, called Fusion-DeepONet as a fast surrogate model for geometry-dependent hypersonic and supersonic flow fields. We evaluated Fusion-DeepONet in learning two external hypersonic flows and a supersonic shape-dependent internal flow problem. First, we compare the performance of Fusion-DeepONet with state-of-the-art neural operators to learn inviscid hypersonic flow around semi-elliptic blunt bodies for two grid types: uniform Cartesian and irregular grids. Fusion-DeepONet provides comparable accuracy to parameter-conditioned U-Net on uniform grids while outperforming MeshGraphNet and Vanilla-DeepONet on irregular grids. Fusion-DeepONet requires significantly fewer trainable parameters than U-Net, MeshGraphNet, and FNO. For the second hypersonic problem, we set up Fusion-DeepONet to map from geometry and free stream Mach number to the temperature field around a reentry capsule traveling at hypersonic speed. This fast surrogate is then improved to predict the spatial derivative of the temperature, resulting in an accurate prediction of heat flux at the surfaces of the capsule. To enhance the accuracy of spatial derivative prediction, we introduce a derivative-enhanced loss term with the least computation overhead. For the third problem, we show that Fusion-DeepONet outperforms MeshGraphNet in learning geometry-dependent supersonic flow in a converging-diverging nozzle configuration. For all the problems, we used high-fidelity simulations with a high-order entropy-stable DGSEM solver to generate training datasets with limited samples.

The James Webb Space Telescope (JWST) has recently discovered a new population of objects at high redshift referred to as `Little Red Dots' (LRDs). Their nature currently remains elusive, despite their surprisingly high inferred number densities. This emerging population of red point-like sources is reshaping our view of the early Universe and may shed light on the formation of high-redshift supermassive black holes. Here we present a spectroscopically confirmed LRD CANUCS-LRD-z8.6 at z_{rm spec}=8.6319pm 0.0005 hosting an Active Galactic Nucleus (AGN), using JWST data. This source shows the typical spectral shape of an LRD (blue UV and red optical continuum, unresolved in JWST imaging), along with broad Hbeta line emission, detection of high-ionization emission lines (CIV, NIV]) and very high electron temperature indicative of the presence of AGN. This is also combined with a very low metallicity (Z<0.1 Z_odot). The presence of all these diverse features in one source makes CANUCS-LRD-z8.6 unique. We show that the inferred black hole mass of CANUCS-LRD-z8.6 (M_{rm BH}=1.0^{+0.6}_{-0.4}times 10^{8}rm ~M_odot) strongly challenges current standard theoretical models and simulations of black hole formation, and forces us to adopt `ad hoc' prescriptions. Indeed if massive seeds, or light seeds with super-Eddington accretion, are considered, the observed BH mass of CANUCS-LRD-z8.6 at z=8.6 can be reproduced. Moreover, the black hole is over-massive compared to its host, relative to the local M_{rm BH}-M_* relations, pointing towards an earlier and faster evolution of the black hole compared to its host galaxy.

We present the first detection of GeV gamma-ray emission potentially associated with the pulsar wind nebula (PWN) hosted by the young core-collapse supernova remnant G292.0+1.8, based on a detailed time-resolved analysis of Fermi-LAT data. By isolating the unpulsed component from the dominant magnetospheric radiation of PSR~J1124-5916, we successfully disentangle a candidate nebular emission in the GeV range, characterise its morphology and extract its spectrum. This identification places G292.0+1.8 among the few systems in which the pulsar and PWN contributions have been spectrally resolved at high energies, offering new insight into their respective emission mechanisms. We characterise the gamma-ray spectrum of the pulsar and model the broadband spectral energy distribution (SED) of the PWN using radio, X-ray, and GeV data. The emission is well described by a single electron population with two spectral breaks: one intrinsic to the injection spectrum and another produced by synchrotron cooling in a magnetic field of sim15~muG. Notably, the inferred magnetic field and the low TeV flux of the nebula resemble those of 3C~58, suggesting that similar low-field environments can arise in young PWNe. The high-energy portion of the SED is now tightly constrained by our GeV detection and existing TeV upper limits. Compared to our model, earlier predictions tend to underpredict the gamma-ray flux, while others that succeed in reproducing the GeV component often overpredict the TeV emission. This mismatch underscores the challenges in modelling particle acceleration and radiation processes in young PWNe and establishes G292.0+1.8 as a valuable benchmark for testing and refining such models.

We present Chandra ACIS-S imaging spectroscopy results of the extended (1.5''- 8'', 300 pc-1600 pc) hard X-ray emission of NGC 5728, the host galaxy of a Compton thick active galactic nucleus (CT AGN). We find spectrally and spatially-resolved features in the Fe Kalpha complex (5.0-7.5 keV), redward and blueward of the neutral Fe line at 6.4 keV in the extended narrow line region bicone. A simple phenomenological fit of a power law plus Gaussians gives a significance of 5.4sigma and 3.7sigma for the red and blue wings, respectively. Fits to a suite of physically consistent models confirm a significance geq3sigma for the red wing. The significance of the blue wing may be diminished by the presence of rest frame highly ionized Fe XXV and Fe XXVI lines (1.4sigma - 3.7sigma range). A detailed investigation of the Chandra ACIS-S point spread function (PSF) and comparison with the observed morphology demonstrates that these red and blue wings are radially extended (~5'', ~1 kpc) along the optical bicone axis. If the wings emission is due solely to redshifted and blueshifted high-velocity neutral Fe Kalpha then the implied line-of-sight velocities are +/- ~0.1c, and their fluxes are consistent with being equal. A symmetric high-velocity outflow is then a viable explanation. This outflow has deprojected velocities ~100 times larger than the outflows detected in optical spectroscopic studies, potentially dominating the kinetic feedback power.

The instantaneous emission from a relativistic surface endowed with a Lorentz factor Gamma that decreases away from the outflow symmetry axis can naturally explain the three phases observed by Swift/XRT in GRBs and their afterglows (GRB tail, afterglow plateau and post-plateau). We expand the analytical formalism of the "Larger-Angle Emission" model previously developed for "Power-Law" outflows to "n-Exponential" outflows (e.g. exponential with n=1 and Gaussian with n=2) and compare their abilities to account for the X-ray emission of XRT afterglows. We assume power-law Gamma-dependences of two spectral characteristics (peak-energy and peak intensity) and find that, unlike Power-Law outflows, n-Exponential outflows cannot account for plateaus with a temporal dynamical range larger than 100. To include all information existing in the Swift/XRT measurements of X-ray aferglows (0.3-10 keV unabsorbed flux and effective spectral slope), we calculate 0.3 keV and 10 keV light-curves using a broken power-law emission spectrum of peak-energy and low-and high-energy slopes that are derived from the effective slope measured by XRT. This economical peak-energy determination is found to be consistent with more expensive spectral fits. The angular distributions of the Lorentz factor, comoving frame peak-energy, and peak-intensity (Gamma (theta), E'_p (theta), i'_p(theta)) constrain the (yet-to-be determined) convolution of various features of the production of relativistic jets by solar-mass black-holes and of their propagation through the progenitor/circumburst medium, while the E'_p (Gamma) and i'_p (Gamma) dependences may constrain the GRB dissipation mechanism and the GRB emission process.

This paper presents a methodology to learn surrogate models of steady state fluid dynamics simulations on meshed domains, based on Implicit Neural Representations (INRs). The proposed models can be applied directly to unstructured domains for different flow conditions, handle non-parametric 3D geometric variations, and generalize to unseen shapes at test time. The coordinate-based formulation naturally leads to robustness with respect to discretization, allowing an excellent trade-off between computational cost (memory footprint and training time) and accuracy. The method is demonstrated on two industrially relevant applications: a RANS dataset of the two-dimensional compressible flow over a transonic airfoil and a dataset of the surface pressure distribution over 3D wings, including shape, inflow condition, and control surface deflection variations. On the considered test cases, our approach achieves a more than three times lower test error and significantly improves generalization error on unseen geometries compared to state-of-the-art Graph Neural Network architectures. Remarkably, the method can perform inference five order of magnitude faster than the high fidelity solver on the RANS transonic airfoil dataset. Code is available at https://gitlab.isae-supaero.fr/gi.catalani/aero-nepf