Integral field spectroscopy (IFS) provides spatially resolved spectra, enabling detailed studies that address the physical and kinematic properties of the interstellar medium. A critical step in analyzing IFS data is the decomposition of emission lines, where different velocity components are often modeled with Gaussian profiles. However, conventional fitting methods that treat each spectrum independently often yield spatial discontinuities in the fitting results. Here, we present Emission Line Fitting Optimization (ELFO), a Python package for IFS spectral fitting. ELFO uses the results of neighboring spectra to determine multiple initial guesses and selects the result that exhibits spatial smoothness. We tested ELFO on IFS data of two quasars obtained from the Multi-Unit Spectroscopic Explorer, where it successfully corrected anomalous fits, revealed previously unresolved substructures, and made large-scale kinematic structures more evident. With minor modifications, this method can also be easily adapted to other IFS data and different emission lines.
Nikola Herceg, Nikola Konjik, A. Naveena Kumara
et al.
Abstract Noncommutative (NC) geometry may open an alternative route to quantum gravity. We study the signatures that quantum structure of spacetime may leave on Dirac quasinormal mode spectrum in the setting defined by a common astrophysical background. For that purpose we examine the influence of spacetime noncommutativity on the Dirac quasinormal modes in modified Reissner–Nordström black hole spacetime. The framework for the latter study is provided by the effective model of NC gravity coupled to fermions introduced in Dimitrijević Ćirić et al. (Eur Phys J C 83:387, 2023). This model describes a classical Dirac field coupled to a modified Reissner–Nordström geometry where the corresponding metric acquires an additional nonvanishing $$r-\varphi $$ r - φ component. As the earlier study shows, this model appears to be equivalent to a model of semiclassical NC gauge theory in which a NC gauge field is coupled to a NC fermion field on the one side and the classical Reissner–Nordström background on the other. We compute the resulting Dirac quasinormal modes and compare them with those of the undeformed Reissner–Nordström spacetime. The results show that spacetime noncommutativity modifies both the oscillation frequencies and damping rates, and induces features in the effective potential and quasinormal mode spectrum reminiscent of a Zeeman-like splitting. Since such geometric modifications are expected to become relevant only near the Planck scale, these effects are more naturally associated with microscopic rather than astrophysical black holes.
Astrophysics, Nuclear and particle physics. Atomic energy. Radioactivity
The apparent slipping motion of flare loops is regarded as a key feature of the 3D magnetic reconnection in the solar flares. The slippage with a super-Alfvénic speed could be defined as slipping–running reconnection, while the slippage with a sub-Alfvénic speed is called slipping reconnection. Due to the limitation of the observational instrument temporal resolution, the apparent slippage of the flare loop footpoints along the flare ribbons with super-Alfvénic speed is quite rare to our knowledge. In this Letter, we report a unique event that exhibits not only the sub-Alfvénic slippage but also the quasiperiodic super-Alfvénic slippage of ribbon substructures during a C3.4-class flare (SOL2023-01–18-T15:23), using the high-temporal-resolution observations of the Interface Region Imaging Spectrograph (∼2 s). The super-Alfvénic slippage with a speed of up to ∼1688 km s ^−1 is directly observed in this study. The calculated period of the apparent super-Alfvénic slippage in both ribbons is between 8.4 and 11.9 s. This work provides the first observational evidence of the periodicity for the slipping–running magnetic reconnection.
The confined space of coal mines characterized by curved tunnels with rough surfaces and a variety of deployed production equipment induces severe signal attenuation and interruption, which significantly degrades the accuracy of conventional channel estimation algorithms applied in coal mine wireless communication systems. To address these challenges, we propose a modified Bilinear Generalized Approximate Message Passing (mBiGAMP) algorithm enhanced by intelligent reflecting surface (IRS) technology to improve channel estimation accuracy in coal mine scenarios. Due to the presence of abundant coal-carrying belt conveyors, we establish a hybrid channel model integrating both fast-varying and quasi-static components to accurately model the unique propagation environment in coal mines. Specifically, the fast-varying channel captures the varying signal paths affected by moving conveyors, while the quasi-static channel represents stable direct links. Since this hybrid structure necessitates an augmented factor graph, we introduce two additional factor nodes and variable nodes to characterize the distinct message-passing behaviors and then rigorously derive the mBiGAMP algorithm. Simulation results demonstrate that the proposed mBiGAMP algorithm achieves superior channel estimation accuracy in dynamic conveyor-affected coal mine scenarios compared with other state-of-the-art methods, showing significant improvements in both separated and cascaded channel estimation. Specifically, when the NMSE is <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>3</mn></mrow></msup></semantics></math></inline-formula>, the SNR of mBiGAMP is improved by approximately 5 dB, 6 dB, and 14 dB compared with the Dual-Structure Orthogonal Matching Pursuit (DS-OMP), Parallel Factor (PARAFAC), and Least Squares (LS) algorithms, respectively. We also verify the convergence behavior of the proposed mBiGAMP algorithm across the operational signal-to-noise ratios range. Furthermore, we investigate the impact of the number of pilots on the channel estimation performance, which reveals that the proposed mBiGAMP algorithm consumes fewer number of pilots to accurately recover channel state information than other methods while preserving estimation fidelity.
A simple model for the X-ray weakness of James Webb Space Telescope–selected broad-line active galactic nuclei (AGNs) is proposed under the assumption that the majority of these sources are fed at super-Eddington accretion rates. In these conditions, the hot inner corona above the geometrically thin disk that is responsible for the emission of X-rays in “normal” AGNs will be embedded instead in a funnel-like reflection geometry. The coronal plasma will Compton upscatter optical/UV photons from the underlying thick disk as well as the surrounding funnel walls, and the high soft-photon energy density will cool down the plasma to temperatures in the range 30–40 keV. The resulting X-ray spectra are predicted to be extremely soft, with power-law photon indices Γ ≃ 2.8–4.0, making high- z super-Eddington AGNs largely undetectable by Chandra.
There are various magnetic field waves with frequencies ranging from mHz to thousands of Hz in the Earth's magnetosphere. These waves can be categorized into three classes depending on their period: ULF (mHz to ~ Hz), ELF (~ Hz to hundreds of Hz), and VLF (hundreds of Hz to thousands of Hz). The regular and continuous ultra-low-frequency (ULF) waves in the magneto-sphere, ranging from 1 mHz to a few Hz, are important to geomagnetic micropulsations. Recently, whistler mode waves generated by lightning and extremely low-frequency (ELF) bursts, which can be attributed to earthquakes, were detected near the surface; their frequencies range from several Hz to a few hundred Hz. The research on the characteristics of ionospheric plasma disturbance caused by the known ground-based very low frequency (VLF) transmitters, whose frequencies range from a few hundred to a few thousand Hz, is of great significance for analyzing changes in the ionospheric environment. These magnetic field waves are crucial for studying various space physical phenomena. As the wave monitoring equipment of global geomagnetic stations measures relative changes and a lack of unified calibration, they cannot conduct joint observations from high to low latitudes and unified comparative studies of the observational data from multiple sensors. The magnetoresistance sensor (ULF: 0.1 mHz–2 Hz), giant magneto-inductance sensor (ELF: 0.2 Hz–2 kHz), and coil sensor (VLF: 0.2–10 kHz) is used to develop a new generation of broadband geomagnetic wave monitors, which are placed on the geomagnetic stations in typical areas such as Mohe (high latitude), Beijing's Ming Tombs (middle latitude), and Sanya Ledong (low latitude), near the 120° meridian chain. Combined with the data of near-Earth space satellites such as FY-3E and SMILE, the observation ability of various wave phenomena in the Earth's magnetosphere will be comprehensively improved. The performance test experiment shows that the developed wave monitor can detect the fluctuating magnetic field at a particular frequency (1 mHz–10 kHz); magnetic field detection ranges of: ± 65000 nT (ULF frequency band), ± 1000 nT (ELF frequency band), and ± 100 pT (VLF band); with low nonlinear errors: ULF frequency band ≤ 0.0446 %, ELF frequency band ≤0.51 %, and VLF frequency band ≤ 1.18 %; and low noise levels: RMS ≤0.5554 nT (ULF frequency band), NPS ≤0.028 nT /√Hz (ELF frequency band), and NPS ≤0.24 pT/√Hz (VLF band). These characteristics enable the proposed broadband geomagnetic wave monitor to meet the Phase II of Chinese Meridian Project magnetic field detection requirements.
This paper provides a perspective on applying the concepts of information thermodynamics, developed recently in non-equilibrium statistical physics, to problems in theoretical neuroscience. Historically, information and energy in neuroscience have been treated separately, in contrast to physics approaches, where the relationship of entropy production with heat is a central idea. It is argued here that also in neural systems, information and energy can be considered within the same theoretical framework. Starting from basic ideas of thermodynamics and information theory on a classic Brownian particle, it is shown how noisy neural networks can infer its probabilistic motion. The decoding of the particle motion by neurons is performed with some accuracy, and it has some energy cost, and both can be determined using information thermodynamics. In a similar fashion, we also discuss how neural networks in the brain can learn the particle velocity and maintain that information in the weights of plastic synapses from a physical point of view. Generally, it is shown how the framework of stochastic and information thermodynamics can be used practically to study neural inference, learning, and information storing.
The light of the first astrophysical objects is expected to leave an imprint on the global 21-cm signal as it heats, excites, and ionizes neutral hydrogen. This dependence on early astrophysics introduces significant uncertainties in modeling the 21-cm signal during Cosmic Dawn (CD). Here we show that a combination of observables including high-redshift UV luminosity functions, the cosmic X-ray background, the optical depth to reionization, and hydrogen absorption lines in quasar spectra, can be used to mitigate the astrophysical uncertainties assuming minimal modeling. Beyond its implications to standard astrophysics, we demonstrate how applying this procedure can improve sensitivity to new physics signatures in the global 21-cm signal. Taking the scenario of fractional millicharged dark matter (DM) as an example, we address astrophysical systematics to produce interesting predictions for upcoming experiments.
In this paper, we present the derivation of Jeffreys divergence, generalized Fisher divergence, and the corresponding De Bruijn identities for space–time random field. First, we establish the connection between Jeffreys divergence and generalized Fisher information of a single space–time random field with respect to time and space variables. Furthermore, we obtain the Jeffreys divergence between two space–time random fields obtained by different parameters under the same Fokker–Planck equations. Then, the identities between the partial derivatives of the Jeffreys divergence with respect to space–time variables and the generalized Fisher divergence are found, also known as the De Bruijn identities. Later, at the end of the paper, we present three examples of the Fokker–Planck equations on space–time random fields, identify their density functions, and derive the Jeffreys divergence, generalized Fisher information, generalized Fisher divergence, and their corresponding De Bruijn identities.
Utilizing the unprecedented high-resolution Magnetospheric Multiscale mission data from 2015 September to 2017 December, we perform a statistical study of electron vortexes in the turbulent terrestrial magnetosheath. On the whole, 506 electron vortex events are successfully selected. Electron vortexes can occur at four known types of magnetic structures, including 78, 42, 26, and 39 electron vortexes observed during the crossings of the current sheets, magnetic holes, magnetic peaks, and flux ropes, respectively. Except for the four types of structures, the rest of the electron vortexes are in the “Others” category, defined as unknown structures. The electron vortexes mainly occur in the subsolar region, with only a few in the flank region. The total occurrence rate of all electron vortexes is 4.86 hr ^–1 , with, on average, 3.65 events hr ^−1 in the X-Y plane and 3.26 events hr ^−1 in the X-Z plane. The durations of most of the electron vortexes concentrate within 0.5–1.5 s and are 1.09 s on average. The electron vortexes are ion-scale structures owing to the average scale of 2.05 ion gyroradius. In addition, the means, medians, and maxima of the energy dissipation J · E ′ in the electron vortexes are almost positive, implying that the electron vortex may be a potential coherent structure or channel for turbulent energy dissipation. All these results reveal the statistical characteristics of electron vortexes in the magnetosheath and improve our understanding of energy dissipation in astrophysical and space plasmas.
Astronomy and Astrophysics is an observational science dealing with celestial objects. Aryabhatta Research Institute of Observational Sciences (ARIES) is one of the premier institutions in astronomy and astrophysics and has contributed significantly in this field. No doubt, India is a part of several mega-science projects in the domain of Astronomy and Astrophysics, such as the Thirty Meter Telescope (TMT); Square Kilometer Array (SKA) and Laser Interferometer Gravitational-wave Observatory (LIGO) projects. Growing engagement of India with mega-science projects has brought a positive impact on its science and technology landscape. A few such collaborations are mentioned to demonstrate that international cooperation are necessary in the field of Astrophysical sciences.
Daphne E. Pedersen, Alena Kubátová, Rebecca B. Simmons
The Undergraduate Scholarships with Mathematics and Science Training, Exploration, and Research Program (US MASTER) is a STEM scholarship program funded by the United States National Science Foundation. It was implemented at an upper-Midwest institution to target and provide structured support to low-income, academically talented undergraduates in biology, chemistry, geography and geographic information science (GISc), environmental sciences, and physics and astrophysics. In addition to providing financial support, the program features an integrated approach to mentorship and advising consisting of an ongoing seminar course in which students engage in collaborative projects, research experiences with a faculty mentor, and targeted academic advising. As part of our assessment efforts, we interviewed student participants regarding their experiences. A consistent theme emerged regarding mentorship: in addition to providing access to professional socialization experiences and the facilitation of competency and performance, students reported that it was the ability to form close relationships based on personal authenticity and feelings of psychological safety and trust that provided the best scaffolding for success in a challenging STEM environment.
The frequency-hopping spread spectrum (FHSS) technique is widely used in secure communications. In this technique, the signal carrier frequency hops over a large band. The conventional non-compressed receiver must sample the signal at high rates to catch the entire frequency-hopping range, which is unfeasible for wide frequency-hopping ranges. In this paper, we propose an efficient adaptive compressed method to measure and detect the FHSS signals non-cooperatively. In contrast to the literature, the FHSS signal-detection method proposed in this paper is achieved directly with compressed sampling rates. The measurement kernels (the non-zero coefficients in the measurement matrix) are designed adaptively, using continuously updated knowledge from the compressed measurement. More importantly, in contrast to the iterative optimizations of the measurement matrices in the literature, the deep neural networks are trained once using task-specific information optimization and repeatedly implemented for measurement kernel design, enabling efficient adaptive detection of the FHSS signals. Simulations show that the proposed method provides stably low missing detection rates, compared to the compressed detection with random measurement kernels and the recently proposed method. Meanwhile, the measurement design in the proposed method is shown to provide improved efficiency, compared to the commonly used recursive method.
A heuristic description of the spin-rotation-gravity coupling is presented and the implications of the corresponding gravitomagnetic Stern–Gerlach force are briefly mentioned. It is shown, within the framework of linearized general relativity, that the gravitomagnetic Stern–Gerlach force reduces in the appropriate correspondence limit to the classical Mathisson spin-curvature force.
We present a pioneering estimate of the global yearly greenhouse gas emissions of a large-scale Astrophysics experiment over several decades: the Giant Array for Neutrino Detection (GRAND). The project aims at detecting ultra-high energy neutrinos with a 200,000 radio antenna array over 200,000\,km$^2$ as of the 2030s. With a fully transparent methodology based on open source data, we calculate the emissions related to three unavoidable sources: travel, digital technologies and hardware equipment. We find that these emission sources have a different impact depending on the stages of the experiment. Digital technologies and travel prevail for the small-scale prototyping phase (GRANDProto300), whereas hardware equipment (material production and transportation) and data transfer/storage largely outweigh the other emission sources in the large-scale phase (GRAND200k). In the mid-scale phase (GRAND10k), the three sources contribute equally. This study highlights the considerable carbon footprint of a large-scale astrophysics experiment, but also shows that there is room for improvement. We discuss various lines of actions that could be implemented. The GRAND project being still in its prototyping stage, our results provide guidance to the future collaborative practices and instrumental design in order to reduce its carbon footprint.
Rabah Abdul Khalek, Stefano Forte, Thomas Gehrmann
et al.
Abstract We present a systematic investigation of jet production at hadron colliders from a phenomenological point of view, with the dual aim of providing a validation of theoretical calculations and guidance to future determinations of parton distributions (PDFs). We account for all available inclusive jet and dijet production measurements from ATLAS and CMS at 7 and 8 TeV by including them in a global PDF determination, and comparing to theoretical predictions at NNLO QCD supplemented by electroweak (EW) corrections. We assess the compatibility of the PDFs, specifically the gluon, obtained before and after inclusion of the jet data. We compare the single-inclusive jet and dijet observables in terms of perturbative behaviour upon inclusion of QCD and EW corrections, impact on the PDFs, and global fit quality. In the single-inclusive case, we also investigate the role played by different scale choices and the stability of the results upon changes in modelling of the correlated experimental systematics.
Astrophysics, Nuclear and particle physics. Atomic energy. Radioactivity
Despite its importance in cardiovascular diseases and engineering applications, turbulence in pulsatile pipe flow remains little comprehended. Important advances have been made in the recent years in understanding the transition to turbulence in such flows, but the question remains of how turbulence behaves once triggered. In this paper, we explore the spatiotemporal intermittency of turbulence in pulsatile pipe flows at fixed Reynolds and Womersley numbers (<inline-formula><math display="inline"><semantics><mrow><mi>R</mi><mspace width="-1.00006pt"></mspace><mi>e</mi><mo>=</mo><mn>2400</mn></mrow></semantics></math></inline-formula>, <inline-formula><math display="inline"><semantics><mrow><mi>W</mi><mspace width="-1.00006pt"></mspace><mi>o</mi><mo>=</mo><mn>8</mn></mrow></semantics></math></inline-formula>) and different pulsation amplitudes. Direct numerical simulations (DNS) were performed according to two strategies. First, we performed DNS starting from a statistically steady pipe flow. Second, we performed DNS starting from the laminar Sexl–Womersley flow and disturbed with the optimal helical perturbation according to a non-modal stability analysis. Our results show that the optimal perturbation is unable to sustain turbulence after the first pulsation period. Spatiotemporally intermittent turbulence only survives for multiple periods if puffs are triggered. We find that puffs in pulsatile pipe flow do not only take advantage of the self-sustaining lift-up mechanism, but also of the intermittent stability of the mean velocity profile.