The First Radio-bright Off-nuclear Tidal Disruption Event AT 2024tvd Reveals the Fastest-evolving Double-peaked Radio Emission
The Astrophysical Journal Letters American Astronomical Society 992:2 (2025) L18
Abstract:
We present the first multiepoch broadband radio and millimeter monitoring of an off-nuclear tidal disruption event (TDE) using the Very Large Array, the Atacama Large Millimeter/submillimeter Array, the Allen Telescope Array, the Arcminute Microkelvin Imager Large Array, and the Submillimeter Array. The off-nuclear TDE AT 2024tvd exhibits double-peaked radio light curves and the fastest-evolving radio emission observed from a TDE to date. With respect to the optical discovery date, the first radio flare rises faster than Fν ∼ t9 at Δt = 88–131 days and then decays as fast as Fν ∼ t−6. The emergence of a second radio flare is observed at Δt ≈ 194 days with an initial fast rise of Fν ∼ t18 and an optically thin decline of Fν ∼ t−12. We interpret these observations in the context of a self-absorbed and free–free absorbed synchrotron spectrum, while accounting for both synchrotron and inverse Compton cooling. We find that a single prompt outflow cannot easily explain these observations and that it is likely that either there is only one outflow that was launched at Δt ∼ 80 days or there are two distinct outflows, with the second launched at Δt ∼ 170–190 days. The nature of these outflows, whether sub-, mildly, or ultrarelativistic, is still unclear, and we explore these different scenarios. Finally, we find a temporal coincidence between the launch time of the first radio-emitting outflow and the onset of a power-law component in the X-ray spectrum, attributed to inverse Compton scattering of thermal photons.New Metrics for Identifying Variables and Transients in Large Astronomical Surveys
The Astrophysical Journal American Astronomical Society 992:1 (2025) 109
Abstract:
A key science goal of large sky surveys such as those conducted by the Vera C. Rubin Observatory and precursors to the Square Kilometre Array is the identification of variable and transient objects. One approach is analyzing time series of the changing brightness of sources, namely, light curves. However, finding adequate statistical representations of light curves is challenging because of the sparsity of observations, irregular sampling, and nuisance factors inherent in astronomical data collection. The wide diversity of objects that a large-scale survey will observe also means that making parametric assumptions about the shape of light curves is problematic. We present a Gaussian process (GP) regression approach for characterizing light-curve variability that addresses these challenges. Our approach makes no assumptions about the shape of a light curve and, therefore, is general enough to detect a range of variable and transient source types. In particular, we propose using the joint distribution of GP amplitude hyperparameters to distinguish variable and transient candidates from nominally stable ones and apply this approach to 6394 radio light curves from the ThunderKAT survey. We compare our results with two variability metrics commonly used in radio astronomy, namely ην and Vν, and show that our approach has better discriminatory power and interpretability. Finally, we conduct a rudimentary search for transient sources in the ThunderKAT data set to demonstrate how our approach might be used as an initial screening tool. Computational notebooks in Python and R are available to help deploy this framework to other surveys.Radio emission from a nearby M dwarf binary
Monthly Notices of the Royal Astronomical Society Oxford University Press 543:3 (2025) 1935-1944
Abstract:
We present the detection of the binary system 2MASS J02132062+3648506 AB using the Karl G. Jansky Very Large Array archive data observed at 4–8 GHz. The system is a triple consisting of a tight binary () of two M dwarfs of spectral types M4.5 and M6.5 and a wide T3 brown dwarf companion (16.4 arcsec). The binary displays coronal and chromospheric activity as traced by previously measured X-ray flux and H emission. We detect the unresolved binary at a peak flux density of at a signal-to-noise ratio (SNR) of and determine a radio luminosity of . The radio emission is quiescent, polarized at a mean circular polarization fraction % and exhibits a spectral index . We probe the binary using the Enhanced Multi-Element Remotely Linked Interferometer Network (e-MERLIN) with an angular resolution of mas at 5 GHz and detect a component at a peak flux density of Jy at a SNR . We propose a gyrosynchrotron origin for the radio emission and estimate a magnetic field strength G, an emitting region of size times the radius of the M4.5 primary and a plasma number density . The brown dwarf companion is not detected. Additionally, we have analysed observations of 2MASS J04183483+213127, a chromospherically active L5 brown dwarf which is also not detected and can only place flux density upper limits at Jy and Jy for Stokes I and V, respectively.Erratum: “A Novel Technosignature Search in the Breakthrough Listen Green Bank Telescope Archive” (2025, AJ, 169, 222)
The Astronomical Journal American Astronomical Society 170:3 (2025) 194
Silent Speech Recognition with Wearable Magnetometers
bioRxiv preprint 2025.08:04.668236 (2025)
Abstract:
Next-generation human-computer interaction (HCI) is moving towards more seamless, intuitive,
and personal modes of communication, redefining how we interact with technology and one another.
Within this landscape, silent speech recognition (SSR) offers a powerful new interaction paradigm,
enabling hands-free, private interaction while supporting individuals with speech impairments and
enabling communication in noisy or sensitive environments. Recent advances in miniaturized sensors and artificial intelligence (AI) have accelerated the development of more sophisticated wearable
SSR systems, driven by growing demand for effortless and accessible communication. Although
electrophysiological (ExG) modalities, particularly electromyography (EMG), have dominated early
efforts in developing wearable SSR, critical challenges remain. Limited generalizability across
users, sensor-skin interface issues, and difficulties with the comfort of use are all current roadblocks
to reliable, high-fidelity signals in a wearable form factor. We propose that magnetometers offer
a promising alternative to ExG and have the potential to unlock more robust, generalizable, and
user-friendly SSR systems. We demonstrate that magnetometers embedded in a headphone form
factor achieve a per-user SSR accuracy of 86%, significantly outperforming previously reported
state-of-the-art wearable headphones combining ExG and inertial measurement units (IMUs). In
addition, we show that wearable magnetometry enables generalization across individuals for SSR.
Extending beyond headphones, we also introduce a necklace form factor with magnetometers that
is capable of decoding both silent and overt speech in ambient conditions, further showcasing the
versatility of magnetometers across different wearable designs in real-world conditions.
and personal modes of communication, redefining how we interact with technology and one another.
Within this landscape, silent speech recognition (SSR) offers a powerful new interaction paradigm,
enabling hands-free, private interaction while supporting individuals with speech impairments and
enabling communication in noisy or sensitive environments. Recent advances in miniaturized sensors and artificial intelligence (AI) have accelerated the development of more sophisticated wearable
SSR systems, driven by growing demand for effortless and accessible communication. Although
electrophysiological (ExG) modalities, particularly electromyography (EMG), have dominated early
efforts in developing wearable SSR, critical challenges remain. Limited generalizability across
users, sensor-skin interface issues, and difficulties with the comfort of use are all current roadblocks
to reliable, high-fidelity signals in a wearable form factor. We propose that magnetometers offer
a promising alternative to ExG and have the potential to unlock more robust, generalizable, and
user-friendly SSR systems. We demonstrate that magnetometers embedded in a headphone form
factor achieve a per-user SSR accuracy of 86%, significantly outperforming previously reported
state-of-the-art wearable headphones combining ExG and inertial measurement units (IMUs). In
addition, we show that wearable magnetometry enables generalization across individuals for SSR.
Extending beyond headphones, we also introduce a necklace form factor with magnetometers that
is capable of decoding both silent and overt speech in ambient conditions, further showcasing the
versatility of magnetometers across different wearable designs in real-world conditions.