Self-Supervised Learning for Modeling Gamma-ray Variability in Blazars

Blazars are active galactic nuclei with relativistic jets pointed almost directly at Earth. Blazars are characterized by strong, apparently stochastic flux variability at virtually all observed wavelengths and timescales, from minutes to years, the physical origin of which is still poorly understood. In the high-energy gamma-ray band, the Large Area Telescope aboard the Fermi space telescope (Fermi-LAT) has conducted regular monitoring of thousands of blazars since 2008. Deep learning can help uncover structure in gamma-ray blazars' complex variability patterns that traditional methods based on parametric statistical modeling or manual feature engineering may miss. In this work, we propose using a self-supervised Transformer encoder architecture to construct an effective representation of blazar gamma-ray variability. Measurement errors, upper limits, and missing data are accommodated using learned encodings. The model predicts a set of quantiles for the flux probability distribution at each time step, an architecture naturally suited for describing data generated by a stochastic process. As a proof of concept for how the model output can be analyzed to extract scientifically relevant information, a preliminary search for weekly-timescale time-reversal asymmetry in gamma-ray blazar light curves was conducted, finding no significant evidence for asymmetry.Comment: 2nd Annual AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), https://ai-2-ase.github.io/. Updated reference and typo correction in this version (H=32, not 64

Variability Signatures of a Burst Process in Flaring Gamma-ray Blazars

Blazars exhibit stochastic flux variability across the electromagnetic spectrum, often exhibiting heavy-tailed flux distributions, commonly modeled as lognormal. However, Tavecchio et al. (2020) and Adams et al. (2022) found that the high-energy gamma-ray flux distributions of several of the brightest flaring Fermi-LAT flat spectrum radio quasars (FSRQs) are well modeled by an even heavier-tailed distribution, which we show is the inverse gamma distribution. We propose an autoregressive inverse gamma variability model in which an inverse gamma flux distribution arises as a consequence of a shot-noise process. In this model, discrete bursts are individually unresolved and averaged over within time bins, as in the analysis of Fermi-LAT data. Stochastic variability on timescales longer than the time bin duration is modeled using first-order autoregressive structure. The flux distribution becomes approximately lognormal in the limiting case of many weak bursts. The fractional variability is predicted to decrease as the time bin duration increases. Using simulated light curves, we show that the proposed model is consistent with the typical gamma-ray variability properties of FSRQs and BL Lac objects. The model parameters can be physically interpreted as the average burst rate, the burst fluence, and the timescale of long-term stochastic fluctuations.Comment: 26 pages, 7 figures, accepted for publication in the Astrophysical Journa

Advancing Blazar Science with Very-High-Energy Gamma-Ray Telescopes

Blazars, active galactic nuclei with relativistic jets pointed almost directly at Earth, are powerful and highly variable sources of nonthermal electromagnetic radiation, including very-high-energy gamma rays. We can detect these gamma rays with arrays of imaging atmospheric Cherenkov telescopes (IACTs), including the Very Energetic Radiation Imaging Telescope Array System (VERITAS) and the upcoming Cherenkov Telescope Array (CTA). After reviewing the science of blazars and the methods used by IACTs, we investigate how gamma-ray variability can provide insight into blazars' physical properties while also complicating efforts to understand these sources as a population. We first present a study of three flaring blazars observed with VERITAS and analyze these sources' spectral and variability characteristics, taking into account data at other wavebands, including that of the Large Area Telescope aboard the Fermi space telescope (Fermi-LAT). Next, after laying out how observing biases and intrinsic variability can confound blazar population studies with IACTs, we propose methods to account for these effects, and use simulated data to report expectations for a blazar luminosity function measurement with VERITAS. Sophisticated new instruments and data analysis methods can further expand the frontier of gamma-ray blazar science. To that end, we design a camera software system to enable safer and more efficient operations of a next-generation IACT being developed for CTA, the prototype Schwarzschild-Couder Telescope (pSCT). Finally, we develop methods to apply deep neural networks to the analysis of IACT data and employ these methods to reject background events detected by simulated arrays of IACTs

Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes

Imaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity, gamma-ray showers must be efficiently distinguished from the dominant background of cosmic-ray showers using images from multiple telescopes. A combination of convolutional neural networks (CNNs) with a recurrent neural network (RNN) has been proposed to perform this task. Using CTLearn, an open source Python package using deep learning to analyze data from IACTs, with simulated data from the upcoming Cherenkov Telescope Array (CTA), we implement a CNN-RNN network and find no evidence that sorting telescope images by total amplitude improves background rejection performance.Comment: 4 pages, 4 figures, Proceedings of the 2019 New York Scientific Data Summit (NYSDS

A VERITAS/Breakthrough Listen Search for Optical Technosignatures

The Breakthrough Listen Initiative is conducting a program using multiple telescopes around the world to search for "technosignatures": artificial transmitters of extraterrestrial origin from beyond our solar system. The VERITAS Collaboration joined this program in 2018, and provides the capability to search for one particular technosignature: optical pulses of a few nanoseconds duration detectable over interstellar distances. We report here on the analysis and results of dedicated VERITAS observations of Breakthrough Listen targets conducted in 2019 and 2020 and of archival VERITAS data collected since 2012. Thirty hours of dedicated observations of 136 targets and 249 archival observations of 140 targets were analyzed and did not reveal any signals consistent with a technosignature. The results are used to place limits on the fraction of stars hosting transmitting civilizations. We also discuss the minimum-pulse sensitivity of our observations and present VERITAS observations of CALIOP: a space-based pulsed laser onboard the CALIPSO satellite. The detection of these pulses with VERITAS, using the analysis techniques developed for our technosignature search, allows a test of our analysis efficiency and serves as an important proof-of-principle.Comment: 15 pages, 7 figure

VERITAS discovery of very high energy gamma-ray emission from S3 1227+25 and multiwavelength observations

We report the detection of very high energy gamma-ray emission from the blazar S3 1227+25 (VER J1230+253) with the Very Energetic Radiation Imaging Telescope Array System (VERITAS). VERITAS observations of the source were triggered by the detection of a hard-spectrum GeV flare on May 15, 2015 with the Fermi-Large Area Telescope (LAT). A combined five-hour VERITAS exposure on May 16th and May 18th resulted in a strong 13$\sigma$ detection with a differential photon spectral index, $\Gamma$ = 3.8 $\pm$ 0.4, and a flux level at 9% of the Crab Nebula above 120 GeV. This also triggered target of opportunity observations with Swift, optical photometry, polarimetry and radio measurements, also presented in this work, in addition to the VERITAS and Fermi-LAT data. A temporal analysis of the gamma-ray flux during this period finds evidence of a shortest variability timescale of $\tau_{obs}$ = 6.2 $\pm$ 0.9 hours, indicating emission from compact regions within the jet, and the combined gamma-ray spectrum shows no strong evidence of a spectral cut-off. An investigation into correlations between the multiwavelength observations found evidence of optical and gamma-ray correlations, suggesting a single-zone model of emission. Finally, the multiwavelength spectral energy distribution is well described by a simple one-zone leptonic synchrotron self-Compton radiation model.Comment: 18 pages, 6 figures. Accepted for publication in the Astrophysical Journal (ApJ

Personal experience and attitudes of pain medicine specialists in Israel regarding the medical use of cannabis for chronic pain

Haggai Sharon,1–4 Noam Goldway,2 Itay Goor-Aryeh,5 Elon Eisenberg,6,7 Silviu Brill1,8 1Institute of Pain Medicine, Department of Anesthesiology and Critical Care Medicine, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; 2Center for Brain Functions, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel; 3Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel; 4Pain Management and Neuromodulation Centre, Guy’s and St Thomas’ Hospital, London, UK; 5Pain Center, Sheba Medical Center, Tel Hashomer, Israel; 6Institute for Pain Medicine, Rambam Health Care Campus, Haifa, Israel; 7Rappaport Faculty of Medicine, Technion Institute for Technology, Haifa, Israel; 8Goldman School of Medicine, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, Israel Introduction: The scientific study of the role of cannabis in pain medicine still lags far behind the growing use driven by public approval. Accumulated clinical experience is therefore an important source of knowledge. However, no study to date has targeted physicians who actually use cannabis in their daily practice. Methods: Registered, active, board-certified pain specialists in Israel (n=79) were asked to complete a Web-based survey. The survey was developed using the Qualtrics Online Survey Software. Questions were formulated as multiple-choice questions, and these addressed three areas of interest: 1) doctors’ personal experience; 2) the role of cannabis in pain medicine; and 3) cannabis medicalization and legalization. Results: Sixty-four percent of all practicing pain specialists in Israel responded. Almost all prescribe cannabis. Among them, 63% find cannabis moderately to highly effective, 56% have encountered mild or no side effects, and only 5% perceive it as significantly harmful. Common indications are neuropathic pain (65%), oncological pain (50%), arthralgias (25%), and any intractable pain (29%). Leading contraindications are schizophrenia (76%), pregnancy/breastfeeding (65%), and age <18 years (59%). Only 12% rated cannabis as more hazardous than opiates. On a personal note, 45% prefer cannabis for themselves or a family member. Lastly, 54% would like to see cannabis legalized in Israel. Conclusion: In this survey, pain clinicians experienced in prescribing cannabis over prolonged periods view it as an effective and relatively safe treatment for chronic pain, based on their own experience. Their responses suggest a possible change of paradigm from using cannabis as the last resort. Keywords: cannabis, pain, surve