More Like this

1. GRB minimum variability timescale with Insight-HXMT and Swift: Implications for progenitor models, dissipation physics, and GRB classifications

   https://doi.org/10.1051/0004-6361/202245657  

   Camisasca, A. E.; Guidorzi, C.; Amati, L.; Frontera, F.; Song, X. Y.; Xiao, S.; Xiong, S. L.; Zhang, S. N.; Margutti, R.; Kobayashi, S.; et al (March 2023, Astronomy & Astrophysics)

   Context. There has been significant technological and scientific progress in our ability to detect, monitor, and model the physics of γ -ray bursts (GRBs) over the 50 years since their first discovery. However, the dissipation process thought to be responsible for their defining prompt emission is still unknown. Recent efforts have focused on investigating how the ultrarelativistic jet of the GRB propagates through the progenitor’s stellar envelope for different initial composition shapes, jet structures, magnetisation, and, consequently, possible energy dissipation processes. Study of the temporal variability – in particular the shortest duration of an independent emission episode within a GRB – may provide a unique way to distinguish the imprint of the inner engine activity from geometry and propagation related effects. The advent of new high-energy detectors with exquisite time resolution now makes this possible. Aims. We aim to characterise the minimum variability timescale (MVT) defined as the shortest duration of individual pulses that shape a light curve for a sample of GRBs in the keV–MeV energy range and test correlations with other key observables such as the peak luminosity, the Lorentz factor, and the jet opening angle. We compare these correlations with predictions from recent numerical simulations for a relativistic structured – possibly wobbling – jet and assess the value of temporal variability studies as probes of prompt-emission dissipation physics. Methods. We used the peak detection algorithm MEPSA to identify the shortest pulse within a GRB time history and preliminarily calibrated MEPSA to estimate the full width at half maximum duration. We then applied this framework to two sets of GRBs: Swift GRBs (from 2005 to July 2022) and Insight Hard Modulation X-ray Telescope (Insight-HXMT) GRBs (from June 2017 to July 2021, including the exceptional 221009A). We then selected 401 GRBs with measured redshift to test for correlations. Results. We confirm that, on average, short GRBs have significantly shorter MVTs than long GRBs. The MVT distribution of short GRBs with extended emission such as 060614 and 211211A is compatible only with that of short GRBs. This is important because it provides a new clue concerning the progenitor’s nature. The MVT for long GRBs with measured redshift anti-correlates with peak luminosity; our analysis includes careful evaluation of selection effects. We confirm the anti-correlation with the Lorentz factor and find a correlation with the jet opening angle as estimated from the afterglow light curve, along with an inverse correlation with the number of pulses. Conclusions. The MVT can identify the emerging putative new class of long GRBs that are suggested to be produced by compact binary mergers. For otherwise typical long GRBs, the different correlations between MVT and peak luminosity, Lorentz factor, jet opening angle, and number of pulses can be explained within the context of structured, possibly wobbling, weakly magnetised relativistic jets. 

   more » « less
2. Robot Model Identification and Learning: A Modern Perspective

   https://doi.org/10.1146/annurev-control-061523-102310  

   Lee, Taeyoon; Kwon, Jaewoon; Wensing, Patrick M; Park, Frank C (July 2024, Annual Review of Control, Robotics, and Autonomous Systems)

   In recent years, the increasing complexity and safety-critical nature of robotic tasks have highlighted the importance of accurate and reliable robot models. This trend has led to a growing belief that, given enough data, traditional physics-based robot models can be replaced by appropriately trained deep networks or their variants. Simultaneously, there has been a renewed interest in physics-based simulation, fueled by the widespread use of simulators to train reinforcement learning algorithms in the sim-to-real paradigm. The primary objective of this review is to present a unified perspective on the process of determining robot models from data, commonly known as system identification or model learning in different subfields. The review aims to illuminate the key challenges encountered and highlight recent advancements in system identification for robotics. Specifically, we focus on recent breakthroughs that leverage the geometry of the identification problem and incorporate physics-based knowledge beyond mere first-principles model parameterizations. Through these efforts, we strive to provide a contemporary outlook on this problem, bridging classical findings with the latest progress in the field. 

   more » « less
3. Forecasting the Gulf Stream Path Using Buoyancy and Wind Forcing Over the North Atlantic

   https://doi.org/10.1029/2021JC017614  

   Silver, Adrienne; Gangopadhyay, Avijit; Gawarkiewicz, Glen; Taylor, Arnold; Sanchez‐Franks, Alejandra (August 2021, Journal of Geophysical Research: Oceans)

   Abstract Fluctuations in the path of the Gulf Stream (GS) have been previously studied by primarily connecting to either the wind‐driven subtropical gyre circulation or buoyancy forcing via the subpolar gyre. Here we present a statistical model for 1 year predictions of the GS path (represented by the GS northern wall—GSNW) betweenW andW incorporating both mechanisms in a combined framework. An existing model with multiple parameters including the previous year's GSNW index, center location, and amplitude of the Icelandic Low and the Southern Oscillation Index was augmented with basin‐wide Ekman drift over the Azores High. The addition of the wind is supported by a validation of the simpler two‐layer Parsons‐Veronis model of GS separation over the last 40 years. A multivariate analysis was carried out to compare 1‐year‐in‐advance forecast correlations from four different models. The optimal predictors of the best performing model include: (a) the GSNW index from the previous year, (b) gyre‐scale integrated Ekman Drift over the past 2 years, and (c) longitude of the Icelandic Low center lagged by 3 years. The forecast correlation over the 27 years (1994–2020) is 0.65, an improvement from the previous multi‐parameter model's forecast correlation of 0.52. The improvement is attributed to the addition of the wind‐drift component. The sensitivity of forecasting the GS path after extreme atmospheric years is quantified. Results indicate the possibility of better understanding and enhanced predictability of the dominant wind‐driven variability of the Atlantic Meridional Overturning Circulation and of fisheries management models that use the GS path as a metric. 

   more » « less
4. Physics Guided Neural Networks for Time-Aware Fairness: An Application in Crop Yield Prediction

   https://doi.org/10.1609/aaai.v37i12.26664  

   He, Erhu; Xie, Yiqun; Liu, Licheng; Chen, Weiye; Jin, Zhenong; Jia, Xiaowei (June 2023, Proceedings of the AAAI Conference on Artificial Intelligence)

   This paper proposes a physics-guided neural network model to predict crop yield and maintain the fairness over space. Failures to preserve the spatial fairness in predicted maps of crop yields can result in biased policies and intervention strategies in the distribution of assistance or subsidies in supporting individuals at risk. Existing methods for fairness enforcement are not designed for capturing the complex physical processes that underlie the crop growing process, and thus are unable to produce good predictions over large regions under different weather conditions and soil properties. More importantly, the fairness is often degraded when existing methods are applied to different years due to the change of weather conditions and farming practices. To address these issues, we propose a physics-guided neural network model, which leverages the physical knowledge from existing physics-based models to guide the extraction of representative physical information and discover the temporal data shift across years. In particular, we use a reweighting strategy to discover the relationship between training years and testing years using the physics-aware representation. Then the physics-guided neural network will be refined via a bi-level optimization process based on the reweighted fairness objective. The proposed method has been evaluated using real county-level crop yield data and simulated data produced by a physics-based model. The results demonstrate that this method can significantly improve the predictive performance and preserve the spatial fairness when generalized to different years. 

   more » « less
5. Quantum simulation of fundamental particles and forces

   https://doi.org/10.1038/s42254-023-00599-8  

   Bauer, Christian W.; Davoudi, Zohreh; Klco, Natalie; Savage, Martin J. (June 2023, Nature Reviews Physics)

   Key static and dynamic properties of matter — from creation in the Big Bang to evolution into subatomic and astrophysical environments — arise from the underlying fundamental quantum fields of the standard model and their effective descriptions. However, the simulation of these properties lies beyond the capabilities of classical computation alone. Advances in quantum technologies have improved control over quantum entanglement and coherence to the point at which robust simulations of quantum fields are anticipated in the foreseeable future. In this Perspective article, we discuss the emerging area of quantum simulations of standard-model physics, outlining the challenges and opportunities for progress in the context of nuclear and high-energy physics. 

   more » « less