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Antiferromagnets that display very small stray magnetic field are ideal for spintronic applications. Of particular interest are non-collinear, chiral antiferromagnets of the type Mn3X (X=Sn, Ge), which display a large magnetotransport response that is correlated with their antiferromagnetic ordering. The ability to read out and manipulate this ordering is crucial for their integration into spintronic devices. These materials exhibit a tiny unbalanced magnetic moment such that a large external magnetic field can, in principle, be used to set the material into a single antiferromagnetic domain. However, in thin films of Mn3Sn, we find that such fields induce only a partial magnetic ordering. By detecting two orthogonal in-plane components of the magnetic order vector, we find that the non-switchable fraction has a unidirectional anisotropy. This also enables us to visualize switching along multiple easy axes in Mn3Sn. Studying the switching at the nanoscale allows us to correlate the pining behavior to crystal grain boundaries in the Mn3Sn nanowire structures.

Variational Physics-Informed Neural Networks often suffer from poor convergence when using stochastic gradient-descent-based optimizers. By introducing a Least Squares solver for the weights of the last layer of the neural network, we improve the convergence of the loss during training in most practical scenarios. This work analyzes the computational cost of the resulting hybrid Least-Squares/Gradient-Descent optimizer and explains how to implement it efficiently. In particular, we show that a traditional implementation based on backward-mode automatic differentiation leads to a prohibitively expensive algorithm. To remedy this, we propose using either forward-mode automatic differentiation or an ultraweak-type scheme that avoids the differentiation of trial functions in the discrete weak formulation. The proposed alternatives are up to one hundred times faster than the traditional one, recovering a computational cost-per-iteration similar to that of a conventional gradient-descent-based optimizer alone. To support our analysis, we derive computational estimates and conduct numerical experiments in one- and two-dimensional problems., Comment: 23 pages, 10 figures, preprint

The Tarantula Nebula in the Large Magellanic Cloud is known for its high star formation activity. At its center lies the young massive star cluster R136, providing a significant amount of the energy that makes the nebula shine so brightly at many wavelengths. Recently, young massive star clusters have been suggested to also efficiently produce high-energy cosmic rays, potentially beyond PeV energies. Here, we report the detection of very-high-energy $\gamma$-ray emission from the direction of R136 with the High Energy Stereoscopic System, achieved through a multicomponent, likelihood-based modeling of the data. This supports the hypothesis that R136 is indeed a very powerful cosmic-ray accelerator. Moreover, from the same analysis, we provide an updated measurement of the $\gamma$-ray emission from 30 Dor C, the only superbubble detected at TeV energies presently. The $\gamma$-ray luminosity above $0.5\,\mathrm{TeV}$ of both sources is $(2-3)\times 10^{35}\,\mathrm{erg}\,\mathrm{s}^{-1}$. This exceeds by more than a factor of 2 the luminosity of HESS J1646$-$458, which is associated with the most massive young star cluster in the Milky Way, Westerlund 1. Furthermore, the $\gamma$-ray emission from each source is extended with a significance of $>3\sigma$ and a Gaussian width of about $30\,\mathrm{pc}$. For 30 Dor C, a connection between the $\gamma$-ray emission and the nonthermal X-ray emission appears likely. Different interpretations of the $\gamma$-ray signal from R136 are discussed., Comment: 10+11 pages, 4+6 figures. Corresponding authors: L. Mohrmann, N. Komin

The anomalous Nernst effect (ANE) gives rise to an electrical response transverse to the magnetization and an applied temperature gradient in a magnetic metal. A nanoscale temperature gradient can be generated by the use of a laser beam applied to the apex of an atomic force microscope tip, thereby allowing for spatially-resolved ANE measurements beyond the optical diffraction limit. Such a method has been used previously to map in-plane magnetized magnetic textures. However, the spatial distribution of the out-of-plane temperature gradient, which is needed to fully interpret such an ANE-based imaging, was not studied. We therefore use a well-known magnetic texture, a magnetic vortex core, to demonstrate the reliability of the ANE method for the imaging of magnetic domains with nanoscale resolution. Moreover, since the ANE signal is directly proportional to the temperature gradient, we can also consider the inverse problem and deduce information about the nanoscale temperature distribution. Our results together with finite element modeling indicate that besides the out-of-plane temperature gradients, there are even larger in-plane temperature gradients. Thus we extend the ANE imaging to study out-of-plane magnetization in a racetrack nano-wire by detecting the ANE signal generated by in-plane temperature gradients. In all cases, a spatial resolution of about 80 nm is obtained. These results are significant for the rapidly growing field of thermo-electric imaging of antiferromagnetic spintronic device structures.

PSR B1259-63 is a gamma-ray binary system that hosts a pulsar in an eccentric orbit, with a 3.4 year period, around an O9.5Ve star. At orbital phases close to periastron passages, the system radiates bright and variable non-thermal emission. We report on an extensive VHE observation campaign conducted with the High Energy Stereoscopic System, comprised of ~100 hours of data taken from $t_p-24$ days to $t_p+127$ days around the system's 2021 periastron passage. We also present the timing and spectral analyses of the source. The VHE light curve in 2021 is consistent with the stacked light curve of all previous observations. Within the light curve, we report a VHE maximum at times coincident with the third X-ray peak first detected in the 2021 X-ray light curve. In the light curve -- although sparsely sampled in this time period -- we see no VHE enhancement during the second disc crossing. In addition, we see no correspondence to the 2021 GeV flare in the VHE light curve. The VHE spectrum obtained from the analysis of the 2021 dataset is best described by a power law of spectral index $\Gamma = 2.65 \pm 0.04_{\text{stat}}$ $\pm 0.04_{\text{sys}}$, a value consistent with the previous H.E.S.S. observations of the source. We report spectral variability with a difference of $\Delta \Gamma = 0.56 ~\pm~ 0.18_{\text{stat}}$ $~\pm~0.10_{\text{sys}}$ at 95% c.l., between sub-periods of the 2021 dataset. We also find a linear correlation between contemporaneous flux values of X-ray and TeV datasets, detected mainly after $t_p+25$ days, suggesting a change in the available energy for non-thermal radiation processes. We detect no significant correlation between GeV and TeV flux points, within the uncertainties of the measurements, from $\sim t_p-23$ days to $\sim t_p+126$ days. This suggests that the GeV and TeV emission originate from different electron populations., Comment: accepted to A&A

Whilst the Universal Approximation Theorem guarantees the existence of approximations to Sobolev functions -- the natural function spaces for PDEs -- by Neural Networks (NNs) of sufficient size, low-regularity solutions may lead to poor approximations in practice. For example, classical fully-connected feed-forward NNs fail to approximate continuous functions whose gradient is discontinuous when employing strong formulations like in Physics Informed Neural Networks (PINNs). In this article, we propose the use of regularity-conforming neural networks, where a priori information on the regularity of solutions to PDEs can be employed to construct proper architectures. We illustrate the potential of such architectures via a two-dimensional (2D) transmission problem, where the solution may admit discontinuities in the gradient across interfaces, as well as power-like singularities at certain points. In particular, we formulate the weak transmission problem in a PINNs-like strong formulation with interface and continuity conditions. Such architectures are partially explainable; discontinuities are explicitly described, allowing the introduction of novel terms into the loss function. We demonstrate via several model problems in one and two dimensions the advantages of using regularity-conforming architectures in contrast to classical architectures. The ideas presented in this article easily extend to problems in higher dimensions.

The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in outcomes that may be associated with changes in the risk of an event. However, existing methods for jointly modeling longitudinal and event-time outcomes are not well-equipped to handle ILD due to the high computational cost. We propose a joint longitudinal and time-to-event model suitable for analyzing ILD. In this model, we summarize a multivariate longitudinal outcome as a smaller number of time-varying latent factors. These latent factors, which are modeled using an Ornstein-Uhlenbeck stochastic process, capture the risk of a time-to-event outcome in a parametric hazard model. We take a Bayesian approach to fit our joint model and conduct simulations to assess its performance. We use it to analyze data from an mHealth study of smoking cessation. We summarize the longitudinal self-reported intensity of nine emotions as the psychological states of positive and negative affect. These time-varying latent states capture the risk of the first smoking lapse after attempted quit. Understanding factors associated with smoking lapse is of keen interest to smoking cessation researchers., Comment: Main text is 32 pages with 6 figures. Supplementary material is 21 pages

Recent multi-wavelength observations of gamma-ray burst afterglows observed in the TeV energy range challenge the simplest Synchrotron Self-Compton (SSC) interpretation of this emission, and are consistent with a single power-law component spanning over eight orders of magnitude in energy. To interpret this generic behaviour in the single-zone approximation without adding further free parameters, we perform an exhaustive parameter space study using the public, time-dependent, multi-messenger transport software AM3. This description accounts for the radiation from non-thermal protons and the lepto-hadronic cascade induced by pp- and p{\gamma}-interactions. We summarise the main scenarios which we have found (SSC, Extended-syn, Proton-syn, pp-cascade, and p{\gamma}-cascade), and discuss their advantages and limitations. We find that possible high-density environments, as may be typical for surrounding molecular cloud material, offer an alternative explanation for producing flat hard (source) spectra up to and beyond energies of 10 TeV.

Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatial phenomenon of interest. It is common for predictors to also be measured on different spatial supports than the response variables. Although there is no standard way to work with spatial data with different supports, a prevalent approach used by practitioners has been to use downscaling or interpolation to project all the variables of analysis towards a common support, and then using standard spatial models. The main disadvantage with this approach is that simple interpolation can introduce biases and, more importantly, the uncertainty associated with the change of support is not taken into account in parameter estimation. In this article, we propose a Bayesian spatial latent Gaussian model that can handle data with different rectilinear supports in both the response variable and predictors. Our approach allows to handle changes of support more naturally according to the properties of the spatial stochastic process being used, and to take into account the uncertainty from the change of support in parameter estimation and prediction. We use spatial stochastic processes as linear combinations of basis functions where Gaussian Markov random fields define the weights. Our hierarchical modelling approach can be described by the following steps: (i) define a latent model where response variables and predictors are considered as latent stochastic processes with continuous support, (ii) link the continuous-index set stochastic processes with its projection to the support of the observed data, (iii) link the projected process with the observed data. We show the applicability of our approach by simulation studies and modelling land suitability for improved grassland in Rhondda Cynon Taf, a county borough in Wales., Comment: 45 pages, 16 figures

The radio galaxy M87 is a variable very-high energy (VHE) gamma-ray source, exhibiting three major flares reported in 2005, 2008, and 2010. Despite extensive studies, the origin of the VHE gamma-ray emission is yet to be understood. In this study, we investigate the VHE gamma-ray spectrum of M87 during states of high gamma-ray activity, utilizing 20.2$\,$ hours the H.E.S.S. observations. Our findings indicate a preference for a curved spectrum, characterized by a log-parabola model with extra-galactic background light (EBL) model above 0.3$\,$TeV at the 4$\sigma$ level, compared to a power-law spectrum with EBL. We investigate the degeneracy between the absorption feature and the EBL normalization and derive upper limits on EBL models mainly sensitive in the wavelength range 12.4$\,$$\mu$m - 40$\,$$\mu$m., Comment: 10 pages, 7 figures. Accepted for publication in A&A. Corresponding authors: Victor Barbosa Martins, Rahul Cecil, Iryna Lypova, Manuel Meyer, Perri Zilberman. Supplementary material: https://zenodo.org/records/10781524

SS 433 is a microquasar, a stellar binary system with collimated relativistic jets. We observed SS 433 in gamma rays using the High Energy Stereoscopic System (H.E.S.S.), finding an energy-dependent shift in the apparent position of the gamma-ray emission of the parsec-scale jets. These observations trace the energetic electron population and indicate the gamma rays are produced by inverse-Compton scattering. Modelling of the energy-dependent gamma-ray morphology constrains the location of particle acceleration and requires an abrupt deceleration of the jet flow. We infer the presence of shocks on either side of the binary system at distances of 25 to 30 parsecs and conclude that self-collimation of the precessing jets forms the shocks, which then efficiently accelerate electrons., Comment: Submitted 20th Apr. 2023, published 25th January 2024 (accepted version)

The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural networks (VPINNs). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on approximating the dual norm of the weak residual of a PDE. This is equivalent to minimizing the energy norm of the error. To compute the dual of the weak residual norm, the DFR method employs an orthonormal spectral basis of the test space, which is known for rectangles or cuboids for multiple function spaces. In this work, we extend the DFR method with ideas of traditional domain decomposition (DD). This enables two improvements: (a) to solve problems in more general polygonal domains, and (b) to develop an adaptive refinement technique in the test space using a Dofler marking algorithm. In the former case, we show that under non-restrictive assumptions we retain the desirable equivalence between the employed loss function and the H1-error, numerically demonstrating adherence to explicit bounds in the case of the L-shaped domain problem. In the latter, we show how refinement strategies lead to potentially significant improvements against a reference, classical DFR implementation with a test function space of significantly lower dimensionality, allowing us to better approximate singular solutions at a more reasonable computational cost.

Gate-defined quantum dots are a promising candidate system to realize scalable, coupled qubit systems and serve as a fundamental building block for quantum computers. However, present-day quantum dot devices suffer from imperfections that must be accounted for, which hinders the characterization, tuning, and operation process. Moreover, with an increasing number of quantum dot qubits, the relevant parameter space grows sufficiently to make heuristic control infeasible. Thus, it is imperative that reliable and scalable autonomous tuning approaches are developed. In this report, we outline current challenges in automating quantum dot device tuning and operation with a particular focus on datasets, benchmarking, and standardization. We also present ideas put forward by the quantum dot community on how to overcome them., Comment: White paper/overview based on a workshop held at the National Institute of Standards and Technology, Gaithersburg, MD. 13 pages

In this paper, we model the configurations of a system of hard rods by viewing each rod in a cell formed by its neighbors. By minimizing the free energy in the model and performing molecular dynamics, where, in both cases, the shape of the cell is a free parameter, we obtain the equilibrium orientational order parameter, free energy and pressure of the system. Our model enables the calculation of anisotropic stresses exerted on the walls of the cell due to shape change of the rod in photoisomerization. These results are a key step towards understanding molecular shape change effects in photomechanical systems under illumination., Comment: 15 pages, 8 figures

Today's mechanical sensors are capable of detecting extremely weak perturbations while operating near the standard quantum limit. However, further improvements can be made in both sensitivity and bandwidth when we reduce the noise originating from the process of measurement itself -- the quantum-mechanical backaction of measurement -- and go below this 'standard' limit, possibly approaching the Heisenberg limit. One of the ways to eliminate this noise is by measuring a quantum nondemolition variable such as the momentum in a free-particle system. Here, we propose and characterize theoretical models for direct velocity measurement that utilize traditional electric and magnetic transducer designs to generate a signal while enabling this backaction evasion. We consider the general readout of this signal via electric or magnetic field sensing by creating toy models analogous to the standard optomechanical position-sensing problem, thereby facilitating the assessment of measurement-added noise. Using simple models that characterize a wide range of transducers, we find that the choice of readout scheme -- voltage or current -- for each mechanical detector configuration implies access to either the position or velocity of the mechanical sub-system. This in turn suggests a path forward for key fundamental physics experiments such as the direct detection of dark matter particles., Comment: 25 pages, 7 figures

Accurate modeling of noise in realistic quantum processors is critical for constructing fault-tolerant quantum computers. While a full simulation of actual noisy quantum circuits provides information about correlated noise among all qubits and is therefore accurate, it is, however, computationally expensive as it requires resources that grow exponentially with the number of qubits. In this paper, we propose an efficient systematic construction of approximate noise channels, where their accuracy can be enhanced by incorporating noise components with higher qubit-qubit correlation degree. To formulate such approximate channels, we first present a method, dubbed the cluster expansion approach, to decompose the Lindbladian generator of an actual Markovian noise channel into components based on interqubit correlation degree. We then generate a $k$-th order approximate noise channel by truncating the cluster expansion and incorporating noise components with correlations up to the $k$-th degree. We require that the approximate noise channels must be accurate and also "honest", i.e., the actual errors are not underestimated in our physical models. As an example application, we apply our method to model noise in a three-qubit quantum processor that stabilizes a [[2,0,0]] codeword, which is one of the four Bell states. We find that, for realistic noise strength typical for fixed-frequency superconducting qubits coupled via always-on static interactions, correlated noise beyond two-qubit correlation can significantly affect the code simulation accuracy. Since our approach provides a systematic noise characterization, it enables the potential for accurate, honest and scalable approximation to simulate large numbers of qubits from full modeling or experimental characterizations of small enough quantum subsystems, which are efficient but still retain essential noise features of the entire device., Comment: 13 pages, 8 figures

High-velocity stellar collisions driven by a supermassive black hole (BH) or BH-driven disruptive collisions, in dense, nuclear clusters can rival the energetics of supergiant star explosions following gravitational collapse of their iron core. Here, starting from a sample of red-giant star collisions simulated with the hydrodynamics code AREPO, we generate photometric and spectroscopic observables using the nonlocal thermodynamic equilibrium time-dependent radiative transfer code CMFGEN. Collisions from more extended giants or stronger collisions (higher velocity or smaller impact parameter) yield bolometric luminosities on the order of 1e43 erg/s at 1d, evolving on a timescale of a week to a bright plateau at ~1e41 erg/s, before plunging precipitously after 20-40d at the end of the optically-thick phase. This luminosity falls primarily in the UV in the first days, thus when it is at its maximum, and shifts to the optical thereafter. Collisions at lower velocity or from less extended stars produce ejecta that are fainter but may remain optically thick for up to 40d if they have a small expansion rate. These collision debris show a similar spectral evolution as that observed or modeled for blue-supergiant star explosions of massive stars, differing only in the more rapid transition to the nebular phase. Such BH-driven disruptive collisions should be detectable by high-cadence surveys in the UV like ULTRASAT., Comment: submitted to A&A

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.

Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen (PSA) measurements. When PSA levels rise, salvage therapies are recommended to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that PSA is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal PSA history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.

We explore the implications of the light curve of the early TeV gamma-ray afterglow of GRB221009A reported by the LHAASO collaboration. We show that the reported offset of the reference time, $T_*$, allows the determination of the relativistic jet activation time, which occurs approximately $200\,\mathrm{s}$ after the GBM trigger time and closely precedes the moment at which GBM was saturated. We find that while the LHAASO data do not exclude the homogeneous circumburst medium scenario, the progenitor wind scenario looks preferable, finding excellent agreement with the expected size of the stellar bubble. We conclude that the initial growth of the light curve is dominated by processes internal to the jet or by gamma-gamma attenuation on the photons emitted during the prompt phase. Namely, either the activation of the acceleration process or the decrease of internal gamma-gamma absorption can naturally explain the initial rapid flux increase. The subsequent slow flux growth phase observed up to $T_*+18\,\mathrm{s}$ is explained by the build-up of the synchrotron radiation -- the target for inverse Compton scattering, which is also supported by a softer TeV spectrum measured during this period. The duration of this phase allows an almost parameter-independent determination of the jet's initial Lorentz factor, $\Gamma_0\approx600$, and magnetic field strength, $B'\sim0.3\,\mathrm{G}$. These values appear to match well those previously revealed through spectral modeling of the GRB emission., Comment: 20 pages, 6 figures, submitted

We present counts-level fits to the keV-GeV data of the early afterglow of the brightest gamma-ray burst detected to date, GRB 221009A. We discuss the complexity of the data reduction due to the unprecedented brightness and the location in the Galactic plane. We find the energy spectrum to be well described as a smoothly broken power law with a break around 10 keV and no indications for additional features towards GeV energies. An interpretation as synchrotron emission from forward-shock accelerated and subsequently cooled electrons yields three possible types of solutions: (1) a slow cooling solution with low magnetic fields (few percent of a Gauss) but poorly constrained minimum injected electron energy (<100 GeV), (2) a fast cooling solution with stronger magnetic fields (few percent to few Gauss) and minimum injected electron energy 10-100 GeV or (3) the transition between both regimes with low magnetic fields and minimum injected electron energy around 100 GeV. Limited statistics at GeV energies prevent conclusions extrapolating towards a cut-off or the onset of a new component at higher energies.

In order to increase the measured phase of an atom interferometer and improve its sensitivity, researchers attempt to increase the enclosed space-time area using two methods: creating larger separations between the interferometer arms and having longer evolution times. However, increasing the evolution time reduces the bandwidth that can be sampled, whereas decreasing the evolution time worsens the sensitivity. In this paper, we attempt to address this by proposing a setup for high-bandwidth applications, with improved overall sensitivity. This is realized by accelerating and holding the atoms using optical dipole traps. We find that accelerations of up to $10^{3}$-$10^{5}$ m/s$^2$ can be achieved using acousto-optic deflectors (AODs) to move the traps. By comparing the sensitivity of our approach to acceleration as a baseline to traditional atom interferometry, we find a substantial improvement to the state of the art. In the limit of appropriate beam and optics stabilization, sensitivities approaching 10$^{-14}$ (m/s$^2$)/$\sqrt{\rm Hz}$ may be achievable at 1 Hz, while detection at 1 kHz with a sensitivity an order of magnitude better than traditional free-fall atom interferometers is possible with today's systems., Comment: 27 pages (including bibliography). 9 figures. Typos have been corrected (most importantly, unit of "Hz" may have been replaced with "rad s^{-1}" in certain places). No change to previous results

Intensive longitudinal data (ILD) collected in mobile health (mHealth) studies contain rich information on multiple outcomes measured frequently over time that have the potential to capture short-term and long-term dynamics. Motivated by an mHealth study of smoking cessation in which participants self-report the intensity of many emotions multiple times per day, we describe a dynamic factor model that summarizes the ILD as a low-dimensional, interpretable latent process. This model consists of two submodels: (i) a measurement submodel--a factor model--that summarizes the multivariate longitudinal outcome as lower-dimensional latent variables and (ii) a structural submodel--an Ornstein-Uhlenbeck (OU) stochastic process--that captures the temporal dynamics of the multivariate latent process in continuous time. We derive a closed-form likelihood for the marginal distribution of the outcome and the computationally-simpler sparse precision matrix for the OU process. We propose a block coordinate descent algorithm for estimation. Finally, we apply our method to the mHealth data to summarize the dynamics of 18 different emotions as two latent processes. These latent processes are interpreted by behavioral scientists as the psychological constructs of positive and negative affect and are key in understanding vulnerability to lapsing back to tobacco use among smokers attempting to quit., Comment: Main text is 20 pages with 5 figures and 1 table. Supplementary material is 26 pages

Relativistic electrons are an essential component in many astrophysical sources, and their radiation may dominate the high-energy bands. Inverse Compton (IC) emission is the radiation mechanism that plays the most important role in these bands. The basic properties of IC, such as the total and differential cross sections, have long been studied; the properties of the IC emission depend strongly not only on the emitting electron distribution but also on the properties of the target photons. This complicates the phenomenological studies of sources, where target photons are supplied from a broad radiation component. We study the spectral properties of IC emission generated by a power-law distribution of electrons on a power-law distribution of target photons. We approximate the resulting spectrum by a broken-power-law distribution and show that there can be up to three physically motivated spectral breaks. If the target photon spectrum extends to sufficiently low energies, $\varepsilon_{\mathrm{min}}< m_e^2c^4/E_{\mathrm{max}}$ ($m_e$ and $c$ are electron mass and speed of light, respectively; $\varepsilon_{\mathrm{min}}$ and $E_{\mathrm{max}}$ are the minimum/maximum energies of target photons and electrons, respectively), then the high energy part of the IC component has a spectral slope typical for the Thomson regime with an abrupt cutoff close to $E_{\mathrm{max}}$. The spectra typical for the Klein-Nishina regime are formed above $m_e^2c^4/\varepsilon_{\mathrm{min}}$. If the spectrum of target photons features a cooling break, i.e., a change of the photon index by $0.5$ at $\varepsilon_{\mathrm{br}}$, then the transition to the Klein-Nishina regime proceeds through an intermediate change of the photon index by $0.5$ at $m_e^2c^4/\varepsilon_{\mathrm{br}}$., Comment: 12 pages, 7 figures

In stellar-dense environments, stars can collide with each other. For collisions close to a supermassive black hole (SMBH), the collisional kinetic energy can be so large that the colliding stars can be completely destroyed, potentially releasing an amount of energy comparable to that of a supernova. Such violent collisions, which we call BH-driven disruptive collisions (BDCs), have been examined mostly analytically, with the non-linear hydrodynamical effects being left largely unstudied. Using the moving-mesh hydrodynamics code {\small AREPO}, we investigate high-velocity ($>10^{3}$ km/s) collisions between 1M$_{\odot}$ giants with varying radii, impact parameters, and initial approaching velocities, and estimate their observables. Very strong shocks across the collision surface efficiently convert $\gtrsim10\%$ of the initial kinetic energy into radiation energy. The outcome is a gas cloud expanding supersonically, homologously, and quasi-spherically, generating a flare with a peak luminosity $\simeq 10^{41}-10^{44}$ erg/s in the extreme UV band ($\simeq 10$ eV). The luminosity decreases approximately following a power-law $t^{-0.7}$ initially, then $t^{-0.4}$ after $t\simeq$10 days at which point it would be bright in the optical band ($\lesssim 1$eV). Subsequent, and possibly even brighter, emission would be generated due to the accretion of the gas cloud onto the nearby SMBH, possibly lasting up to multi-year timescales. This inevitable BH-collision product interaction can contribute to the growth of BHs at all mass scales, in particular, seed BHs at high redshifts. Furthermore, the proximity of the events to the central BH makes them a potential tool for probing the existence of dormant BHs, even very massive ones which cannot be probed by tidal disruption events., Comment: 16 pages, 14 figures, 2 tables, accepted for publication in MNRAS, comments welcome, movies here: https://www.youtube.com/playlist?list=PLxLK3qI02cQd9lyIo6DIqm1tQnx_-G3Ut

We consider a pair of quantum dot-based spin qubits that interact via microwave photons in a superconducting cavity, and that are also parametrically driven by separate external electric fields. For this system, we formulate a model for spin qubit entanglement in the presence of mutually off-resonant qubit and cavity frequencies. We show that the sidebands generated via the driving fields enable highly tunable qubit-qubit entanglement using only ac control and without requiring the qubit and cavity frequencies to be tuned into simultaneous resonance. The model we derive can be mapped to a variety of qubit types, including detuning-driven one-electron spin qubits in double quantum dots and three-electron resonant exchange qubits in triple quantum dots. The high degree of nonlinearity inherent in spin qubits renders these systems particularly favorable for parametric drive-activated entanglement. We determine multiple common resonance conditions for the two driven qubits and the cavity and identify experimentally relevant parameter regimes that enable the implementation of entangling gates with suppressed sensitivity to cavity photon occupation and decay. The parametrically driven sideband resonance approach we describe provides a promising route toward scalability and modularity in spin-based quantum information processing through drive-enabled tunability that can also be implemented in micromagnet-free electron and hole systems for spin-photon coupling., Comment: 21 pages, 3 figures

Magnetic fields in galaxies and galaxy clusters are believed to be the result of the amplification of intergalactic seed fields during the formation of large-scale structures in the universe. However, the origin, strength, and morphology of this intergalactic magnetic field (IGMF) remain unknown. Lower limits on (or indirect detection of) the IGMF can be obtained from observations of high-energy gamma rays from distant blazars. Gamma rays interact with the extragalactic background light to produce electron-positron pairs, which can subsequently initiate electromagnetic cascades. The $\gamma$-ray signature of the cascade depends on the IGMF since it deflects the pairs. Here we report on a new search for this cascade emission using a combined data set from the Fermi Large Area Telescope and the High Energy Stereoscopic System. Using state-of-the-art Monte Carlo predictions for the cascade signal, our results place a lower limit on the IGMF of $B > 7.1\times10^{-16}$ G for a coherence length of 1 Mpc even when blazar duty cycles as short as 10 yr are assumed. This improves on previous lower limits by a factor of 2. For longer duty cycles of $10^4$ ($10^7$) yr, IGMF strengths below $1.8\times10^{-14}$ G ($3.9\times10^{-14}$ G) are excluded, which rules out specific models for IGMF generation in the early universe., Comment: 20 pages, 7 figures, 4 tables. Accepted for publication in ApJ Letters. Auxiliary data is provided in electronic format at https://zenodo.org/record/8014311

The Near-Infrared Imager and Slitless Spectrograph (NIRISS) is the science module of the Canadian-built Fine Guidance Sensor (FGS) onboard the James Webb Space Telescope (JWST). NIRISS has four observing modes: 1) broadband imaging featuring seven of the eight NIRCam broadband filters, 2) wide-field slitless spectroscopy (WFSS) at a resolving power of $\sim$150 between 0.8 and 2.2 $\mu$m, 3) single-object cross-dispersed slitless spectroscopy (SOSS) enabling simultaneous wavelength coverage between 0.6 and 2.8 $\mu$m at R$\sim$700, a mode optimized for exoplanet spectroscopy of relatively bright ($J<6.3$) stars and 4) aperture masking interferometry (AMI) between 2.8 and 4.8 $\mu$m enabling high-contrast ($\sim10^{-3}-10^{-4}$) imaging at angular separations between 70 and 400 milliarcsec for relatively bright ($M<8$) sources. This paper presents an overview of the NIRISS instrument, its design, its scientific capabilities, and a summary of in-flight performance. NIRISS shows significantly better response shortward of $\sim2.5\,\mu$m resulting in 10-40% sensitivity improvement for broadband and low-resolution spectroscopy compared to pre-flight predictions. Two time-series observations performed during instrument commissioning in the SOSS mode yield very stable spectro-photometry performance within $\sim$10% of the expected noise. The first space-based companion detection of the tight binary star AB Dor AC through AMI was demonstrated.

The origin of the gamma-ray emission from M87 is currently a matter of debate. This work aims to localize the VHE (100 GeV-100 TeV) gamma-ray emission from M87 and probe a potential extended hadronic emission component in the inner Virgo Cluster. The search for a steady and extended gamma-ray signal around M87 can constrain the cosmic-ray energy density and the pressure exerted by the cosmic rays onto the intra-cluster medium, and allow us to investigate the role of the cosmic rays in the active galactic nucleus feedback as a heating mechanism in the Virgo Cluster. H.E.S.S. telescopes are sensitive to VHE gamma rays and have been utilized to observe M87 since 2004. We utilized a Bayesian block analysis to identify M87 emission states with H.E.S.S. observations from 2004 until 2021, dividing them into low, intermediate, and high states. Because of the causality argument, an extended ($\gtrsim$kpc) signal is allowed only in steady emission states. Hence, we fitted the morphology of the 120h low state data and found no significant gamma-ray extension. Therefore, we derived for the low state an upper limit of 58"(corresponding to $\approx$4.6kpc) in the extension of a single-component morphological model described by a rotationally symmetric 2D Gaussian model at 99.7% confidence level. Our results exclude the radio lobes ($\approx$30 kpc) as the principal component of the VHE gamma-ray emission from the low state of M87. The gamma-ray emission is compatible with a single emission region at the radio core of M87. These results, with the help of two multiple-component models, constrain the maximum cosmic-ray to thermal pressure ratio $X_{{CR,max.}}$$\lesssim$$0.32$ and the total energy in cosmic-ray protons (CRp) to $U_{CR}$$\lesssim$5$\times10^{58}$ erg in the inner 20kpc of the Virgo Cluster for an assumed CRp power-law distribution in momentum with spectral index $\alpha_{p}$=2.1., Comment: 15 pages, 7 figures. Accepted for publication in A&A. Corresponding authors: Victor Barbosa Martins, Stefan Ohm, Cornelia Arcaro, Natalia \.Zywucka, Mathieu de Naurois

Solving PDEs with machine learning techniques has become a popular alternative to conventional methods. In this context, Neural networks (NNs) are among the most commonly used machine learning tools, and in those models, the choice of an appropriate loss function is critical. In general, the main goal is to guarantee that minimizing the loss during training translates to minimizing the error in the solution at the same rate. In this work, we focus on the time-harmonic Maxwell's equations, whose weak formulation takes H(curl) as the space of test functions. We propose a NN in which the loss function is a computable approximation of the dual norm of the weak-form PDE residual. To that end, we employ the Helmholtz decomposition of the space H(curl) and construct an orthonormal basis for this space in two and three spatial dimensions. Here, we use the Discrete Sine/Cosine Transform to accurately and efficiently compute the discrete version of our proposed loss function. Moreover, in the numerical examples we show a high correlation between the proposed loss function and the H(curl)-norm of the error, even in problems with low-regularity solutions.