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1. Searching for Diamagnetic Blob Accretion in the 74 day K2 Observation of V2400 Ophiuchi

   https://doi.org/10.3847/1538-3881/ac3010  

   Langford, Andrew ; Littlefield, Colin ; Garnavich, Peter ; Kennedy, Mark R. ; Scaringi, Simone ; Szkody, Paula ( December 2021 , The Astronomical Journal)

   Since its discovery in 1995, V2400 Ophiuchi (V2400 Oph) has stood apart from most known intermediate polar cataclysmic variables due to its proposed magnetic field strength (9–27 MG) and diskless accretion. To date, the exact accretion mechanism of the system is still unknown, and standard accretion models fail to accurately predict the peculiar behavior of its light curve. We present the K2 Campaign 11 light curve of V2400 Oph recording 74.19 days of photometric data cadenced at 1 minute. The light curve is dominated by aperiodic flickering and quasiperiodic oscillations, which make the beat and spin signals inconspicuous on short timescales. Notably, a log–log full power spectrum shows a break frequency at ∼10²cycles d⁻¹similar to some disk-fed systems. Through power-spectral analysis, the beat and spin periods are measured as 1003.4 ± 0.2 s and 927.7 ± 0.1 s, respectively. A power spectrum of the entire K2 observation demonstrates beat period dominance. However, time-resolved power spectra reveal a strong dependence between observation length and the dominant frequency of the light curve. For short observations (2–12 hr) the beat, spin, or first beat harmonic can be observed as the dominant periodic signal. Such incoherence and variability indicate a dynamical accretion systemmore »more complex than current intermediate polar theories can explain. We propose that a diamagnetic blob accretion model may serve as a plausible explanation for the accretion mechanism.

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2. The 2019 super-Eddington outburst of RX J0209.6−7427: detection of pulsations and constraints on the magnetic field strength

   https://doi.org/10.1093/mnras/staa991  

   Vasilopoulos, G ; Ray, P S ; Gendreau, K C ; Jenke, P A ; Jaisawal, G K ; Wilson-Hodge, C A ; Strohmayer, T E ; Altamirano, D ; Iwakiri, W B ; Wolff, M T ; et al ( June 2020 , Monthly Notices of the Royal Astronomical Society)

   ABSTRACT In 2019 November, MAXI detected an X-ray outburst from the known Be X-ray binary system RX J0209.6−7427 located in the outer wing of the Small Magellanic Cloud. We followed the outburst of the system with NICER, which led to the discovery of X-ray pulsations with a period of 9.3 s. We analysed simultaneous X-ray data obtained with NuSTAR and NICER, allowing us to characterize the spectrum and provide an accurate estimate of its bolometric luminosity. During the outburst, the maximum broad-band X-ray luminosity of the system reached (1–2) × 1039 erg s−1, thus exceeding by about one order of magnitude the Eddington limit for a typical 1.4 M⊙ mass neutron star (NS). Monitoring observations with Fermi/GBM and NICER allowed us to study the spin evolution of the NS and compare it with standard accretion torque models. We found that the NS magnetic field should be of the order of 3 × 1012 G. We conclude that RX J0209.6−7427 exhibited one of the brightest outbursts observed from a Be X-ray binary pulsar in the Magellanic Clouds, reaching similar luminosity level to the 2016 outburst of SMC X-3. Despite the super-Eddington luminosity of RX J0209.6−7427, the NS appears to have only a moderate magnetic field strength.
3. Magnetic braking saturates: evidence from the orbital period distribution of low-mass detached eclipsing binaries from ZTF

   https://doi.org/10.1093/mnras/stac2945  

   El-Badry, Kareem ; Conroy, Charlie ; Fuller, Jim ; Kiman, Rocio ; van Roestel, Jan ; Rodriguez, Antonio C. ; Burdge, Kevin B. ( October 2022 , Monthly Notices of the Royal Astronomical Society)

   We constrain the orbital period (Porb) distribution of low-mass detached main-sequence eclipsing binaries (EBs) with light-curves from the Zwicky Transient Facility (ZTF), which provides a well-understood selection function and sensitivity to faint stars. At short periods (Porb ≲ 2 d), binaries are predicted to evolve significantly due to magnetic braking (MB), which shrinks orbits and ultimately brings detached binaries into contact. The period distribution is thus a sensitive probe of MB. We find that the intrinsic period distribution of low-mass (0.1 ≲ M1/M⊙ < 0.9) binaries is basically flat (${\rm d}N/{\rm d}P_{\rm orb} \propto P_{\rm orb}^0$) from Porb = 10 d down to the contact limit. This is strongly inconsistent with predictions of classical MB models based on the Skumanich relation, which are widely used in binary evolution calculations and predict ${\rm d}N/{\rm d}P_{\rm orb} \propto P_{\rm orb}^{7/3}$ at short periods. The observed distributions are best reproduced by models in which the magnetic field saturates at short periods with a MB torque that scales roughly as $\dot{J}\propto P_{\rm orb}^{-1}$, as opposed to $\dot{J} \propto P_{\rm orb}^{-3}$ in the standard Skumanich law. We also find no significant difference between the period distributions of binaries containing fully and partially convective stars. Our results confirmmore »that a saturated MB law, which was previously found to describe the spin-down of rapidly rotating isolated M dwarfs, also operates in tidally locked binaries. We advocate using saturated MB models in binary evolution calculations. Our work supports previous suggestions that MB in cataclysmic variables (CVs) is much weaker than assumed in the standard evolutionary model, unless mass transfer leads to significant additional angular momentum loss in CVs.

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4. A Deep Learning-Based Real-time Seizure Detection System

   https://doi.org/10.1109/SPMB50085.2020.9353623  

   Shawki, N. ; Elseify, T. ; Cap, T. ; Shah, V. ; Obeid, I. ; Picone, J. ( December 2020 , Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad Selesnick (Ed.)

   Electroencephalography (EEG) is a popular clinical monitoring tool used for diagnosing brain-related disorders such as epilepsy [1]. As monitoring EEGs in a critical-care setting is an expensive and tedious task, there is a great interest in developing real-time EEG monitoring tools to improve patient care quality and efficiency [2]. However, clinicians require automatic seizure detection tools that provide decisions with at least 75% sensitivity and less than 1 false alarm (FA) per 24 hours [3]. Some commercial tools recently claim to reach such performance levels, including the Olympic Brainz Monitor [4] and Persyst 14 [5]. In this abstract, we describe our efforts to transform a high-performance offline seizure detection system [3] into a low latency real-time or online seizure detection system. An overview of the system is shown in Figure 1. The main difference between an online versus offline system is that an online system should always be causal and has minimum latency which is often defined by domain experts. The offline system, shown in Figure 2, uses two phases of deep learning models with postprocessing [3]. The channel-based long short term memory (LSTM) model (Phase 1 or P1) processes linear frequency cepstral coefficients (LFCC) [6] features from each EEGmore »channel separately. We use the hypotheses generated by the P1 model and create additional features that carry information about the detected events and their confidence. The P2 model uses these additional features and the LFCC features to learn the temporal and spatial aspects of the EEG signals using a hybrid convolutional neural network (CNN) and LSTM model. Finally, Phase 3 aggregates the results from both P1 and P2 before applying a final postprocessing step. The online system implements Phase 1 by taking advantage of the Linux piping mechanism, multithreading techniques, and multi-core processors. To convert Phase 1 into an online system, we divide the system into five major modules: signal preprocessor, feature extractor, event decoder, postprocessor, and visualizer. The system reads 0.1-second frames from each EEG channel and sends them to the feature extractor and the visualizer. The feature extractor generates LFCC features in real time from the streaming EEG signal. Next, the system computes seizure and background probabilities using a channel-based LSTM model and applies a postprocessor to aggregate the detected events across channels. The system then displays the EEG signal and the decisions simultaneously using a visualization module. The online system uses C++, Python, TensorFlow, and PyQtGraph in its implementation. The online system accepts streamed EEG data sampled at 250 Hz as input. The system begins processing the EEG signal by applying a TCP montage [8]. Depending on the type of the montage, the EEG signal can have either 22 or 20 channels. To enable the online operation, we send 0.1-second (25 samples) length frames from each channel of the streamed EEG signal to the feature extractor and the visualizer. Feature extraction is performed sequentially on each channel. The signal preprocessor writes the sample frames into two streams to facilitate these modules. In the first stream, the feature extractor receives the signals using stdin. In parallel, as a second stream, the visualizer shares a user-defined file with the signal preprocessor. This user-defined file holds raw signal information as a buffer for the visualizer. The signal preprocessor writes into the file while the visualizer reads from it. Reading and writing into the same file poses a challenge. The visualizer can start reading while the signal preprocessor is writing into it. To resolve this issue, we utilize a file locking mechanism in the signal preprocessor and visualizer. Each of the processes temporarily locks the file, performs its operation, releases the lock, and tries to obtain the lock after a waiting period. The file locking mechanism ensures that only one process can access the file by prohibiting other processes from reading or writing while one process is modifying the file [9]. The feature extractor uses circular buffers to save 0.3 seconds or 75 samples from each channel for extracting 0.2-second or 50-sample long center-aligned windows. The module generates 8 absolute LFCC features where the zeroth cepstral coefficient is replaced by a temporal domain energy term. For extracting the rest of the features, three pipelines are used. The differential energy feature is calculated in a 0.9-second absolute feature window with a frame size of 0.1 seconds. The difference between the maximum and minimum temporal energy terms is calculated in this range. Then, the first derivative or the delta features are calculated using another 0.9-second window. Finally, the second derivative or delta-delta features are calculated using a 0.3-second window [6]. The differential energy for the delta-delta features is not included. In total, we extract 26 features from the raw sample windows which add 1.1 seconds of delay to the system. We used the Temple University Hospital Seizure Database (TUSZ) v1.2.1 for developing the online system [10]. The statistics for this dataset are shown in Table 1. A channel-based LSTM model was trained using the features derived from the train set using the online feature extractor module. A window-based normalization technique was applied to those features. In the offline model, we scale features by normalizing using the maximum absolute value of a channel [11] before applying a sliding window approach. Since the online system has access to a limited amount of data, we normalize based on the observed window. The model uses the feature vectors with a frame size of 1 second and a window size of 7 seconds. We evaluated the model using the offline P1 postprocessor to determine the efficacy of the delayed features and the window-based normalization technique. As shown by the results of experiments 1 and 4 in Table 2, these changes give us a comparable performance to the offline model. The online event decoder module utilizes this trained model for computing probabilities for the seizure and background classes. These posteriors are then postprocessed to remove spurious detections. The online postprocessor receives and saves 8 seconds of class posteriors in a buffer for further processing. It applies multiple heuristic filters (e.g., probability threshold) to make an overall decision by combining events across the channels. These filters evaluate the average confidence, the duration of a seizure, and the channels where the seizures were observed. The postprocessor delivers the label and confidence to the visualizer. The visualizer starts to display the signal as soon as it gets access to the signal file, as shown in Figure 1 using the “Signal File” and “Visualizer” blocks. Once the visualizer receives the label and confidence for the latest epoch from the postprocessor, it overlays the decision and color codes that epoch. The visualizer uses red for seizure with the label SEIZ and green for the background class with the label BCKG. Once the streaming finishes, the system saves three files: a signal file in which the sample frames are saved in the order they were streamed, a time segmented event (TSE) file with the overall decisions and confidences, and a hypotheses (HYP) file that saves the label and confidence for each epoch. The user can plot the signal and decisions using the signal and HYP files with only the visualizer by enabling appropriate options. For comparing the performance of different stages of development, we used the test set of TUSZ v1.2.1 database. It contains 1015 EEG records of varying duration. The any-overlap performance [12] of the overall system shown in Figure 2 is 40.29% sensitivity with 5.77 FAs per 24 hours. For comparison, the previous state-of-the-art model developed on this database performed at 30.71% sensitivity with 6.77 FAs per 24 hours [3]. The individual performances of the deep learning phases are as follows: Phase 1’s (P1) performance is 39.46% sensitivity and 11.62 FAs per 24 hours, and Phase 2 detects seizures with 41.16% sensitivity and 11.69 FAs per 24 hours. We trained an LSTM model with the delayed features and the window-based normalization technique for developing the online system. Using the offline decoder and postprocessor, the model performed at 36.23% sensitivity with 9.52 FAs per 24 hours. The trained model was then evaluated with the online modules. The current performance of the overall online system is 45.80% sensitivity with 28.14 FAs per 24 hours. Table 2 summarizes the performances of these systems. The performance of the online system deviates from the offline P1 model because the online postprocessor fails to combine the events as the seizure probability fluctuates during an event. The modules in the online system add a total of 11.1 seconds of delay for processing each second of the data, as shown in Figure 3. In practice, we also count the time for loading the model and starting the visualizer block. When we consider these facts, the system consumes 15 seconds to display the first hypothesis. The system detects seizure onsets with an average latency of 15 seconds. Implementing an automatic seizure detection model in real time is not trivial. We used a variety of techniques such as the file locking mechanism, multithreading, circular buffers, real-time event decoding, and signal-decision plotting to realize the system. A video demonstrating the system is available at: https://www.isip.piconepress.com/projects/nsf_pfi_tt/resources/videos/realtime_eeg_analysis/v2.5.1/video_2.5.1.mp4. The final conference submission will include a more detailed analysis of the online performance of each module. ACKNOWLEDGMENTS Research reported in this publication was most recently supported by the National Science Foundation Partnership for Innovation award number IIP-1827565 and the Pennsylvania Commonwealth Universal Research Enhancement Program (PA CURE). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the official views of any of these organizations. REFERENCES [1] A. Craik, Y. He, and J. L. Contreras-Vidal, “Deep learning for electroencephalogram (EEG) classification tasks: a review,” J. Neural Eng., vol. 16, no. 3, p. 031001, 2019. https://doi.org/10.1088/1741-2552/ab0ab5. [2] A. C. Bridi, T. Q. Louro, and R. C. L. Da Silva, “Clinical Alarms in intensive care: implications of alarm fatigue for the safety of patients,” Rev. Lat. Am. Enfermagem, vol. 22, no. 6, p. 1034, 2014. https://doi.org/10.1590/0104-1169.3488.2513. [3] M. Golmohammadi, V. Shah, I. Obeid, and J. Picone, “Deep Learning Approaches for Automatic Seizure Detection from Scalp Electroencephalograms,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York, New York, USA: Springer, 2020, pp. 233–274. https://doi.org/10.1007/978-3-030-36844-9_8. [4] “CFM Olympic Brainz Monitor.” [Online]. Available: https://newborncare.natus.com/products-services/newborn-care-products/newborn-brain-injury/cfm-olympic-brainz-monitor. [Accessed: 17-Jul-2020]. [5] M. L. Scheuer, S. B. Wilson, A. Antony, G. Ghearing, A. Urban, and A. I. Bagic, “Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset,” J. Clin. Neurophysiol., 2020. https://doi.org/10.1097/WNP.0000000000000709. [6] A. Harati, M. Golmohammadi, S. Lopez, I. Obeid, and J. Picone, “Improved EEG Event Classification Using Differential Energy,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium, 2015, pp. 1–4. https://doi.org/10.1109/SPMB.2015.7405421. [7] V. Shah, C. Campbell, I. Obeid, and J. Picone, “Improved Spatio-Temporal Modeling in Automated Seizure Detection using Channel-Dependent Posteriors,” Neurocomputing, 2021. [8] W. Tatum, A. Husain, S. Benbadis, and P. Kaplan, Handbook of EEG Interpretation. New York City, New York, USA: Demos Medical Publishing, 2007. [9] D. P. Bovet and C. Marco, Understanding the Linux Kernel, 3rd ed. O’Reilly Media, Inc., 2005. https://www.oreilly.com/library/view/understanding-the-linux/0596005652/. [10] V. Shah et al., “The Temple University Hospital Seizure Detection Corpus,” Front. Neuroinform., vol. 12, pp. 1–6, 2018. https://doi.org/10.3389/fninf.2018.00083. [11] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011. https://dl.acm.org/doi/10.5555/1953048.2078195. [12] J. Gotman, D. Flanagan, J. Zhang, and B. Rosenblatt, “Automatic seizure detection in the newborn: Methods and initial evaluation,” Electroencephalogr. Clin. Neurophysiol., vol. 103, no. 3, pp. 356–362, 1997. https://doi.org/10.1016/S0013-4694(97)00003-9.« less
5. PNV J00444033+4113068: Early superhumps with 0.7 mag amplitude and non-red color

   https://doi.org/10.1093/pasj/psac068  

   Tampo, Yusuke ; Isogai, Keisuke ; Kojiguchi, Naoto ; Uemura, Makoto ; Kato, Taichi ; Tordai, Tamás ; Vanmunster, Tonny ; Itoh, Hiroshi ; Dubovsky, Pavol A. ; Medulka, Tomáš ; et al ( September 2022 , Publications of the Astronomical Society of Japan)

   In the first days of WZ Sge-type dwarf nova (DN) outbursts, the 2 : 1 resonance induces a spiral arm structure in the accretion disk, which is observed as early superhumps in optical light curves. We reports on our optical observations of an eclipsing WZ Sge-type DN PNV J00444033+4113068 during its 2021 superoutburst using the 3.8 m Seimei telescope and through the Variable Star Network collaboration. The eclipse analysis showed that its orbital period was 0.055425534(1) d. Our observations confirmed early superhumps with an amplitude of 0.7 mag, the largest amplitude among known WZ Sge-type DNe. More interestingly, its early superhumps became the reddest around their secondary minimum, whereas other WZ Sge-type DNe show the reddest color around the early superhump maximum. The spectrum around the peak of the outburst showed two double-peaked emission lines of He ii 4686 Å and Hα with a peak separation of ≥700 km s−1, supporting a very high-inclination system. With the early superhump mapping, the unique profile and color of the early superhump are successfully reproduced by an accretion disk with a vertically extended double arm structure. Therefore, a large amplitude and a unique color behavior of the early superhumps in PNV J00444033+4113068 can be explained by the 2 : 1 resonance model along with other WZ Sge-type DNe.