Search

There is a shortage of multi-wavelength and spectroscopic followup capabilities given the number of transient and variable astrophysical events discovered through wide-field, optical surveys such as the upcoming Vera C. Rubin Observatory. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC dataset to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real time, multivariate and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (~95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II and Type Ibc supernovae) including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow up studies in the era of the Rubin Observatory., Comment: 13 pages,9 figures, submitted to AAS Journals. Comments welcome!

The observation of very-high-energy (VHE, $\rm >10~GeV$) $\gamma$-ray emission from $\rm \gamma$-ray bursts (GRBs), especially in the prompt phase, will provide critical information for understanding many aspects of their nature including the physical environment, the relativistic bulk motion, the mechanisms of particle acceleration of GRBs and for studying Lorentz invariance violation, etc. For the afterglow phase, the highest energy photons detected to date by the imaging atmospheric Cherenkov telescopes extend to the TeV regime. However, for the prompt phase, years of efforts in searching for the VHE emission has yielded no statistically significant detections. A wide field-of-view (FOV) and large effective area above tens of GeV are essential for detecting the VHE emissions from GRBs in the prompt phase. The High Altitude Detection of Astronomical Radiation (HADAR) experiment has such merits. In this paper, we report the estimates of its expected annual GRB detection rate, which are obtained by combining the performance of the HADAR instrument with the theoretical calculations based on a phenomenological model to generate the pseudo-GRB population. The expected detectable gamma-ray signal from GRBs above the background is then obtained to give the detection rate. In the spectral model, an extra component is assigned to every GRB event in addition to the Band function. The results indicate that if the energy of the cutoff due to internal absorption is higher than 50 GeV, the detection rate for GRBs for the HADAR experiment is approximately two or three GRBs per year, which varies slightly depending upon the characteristics of the extra component., Comment: accepted by ApJ

We propose a novel defensive mechanism based on a generative adversarial network (GAN) framework to defend against adversarial attacks in end-to-end communications systems. Specifically, we utilize a generative network to model a powerful adversary and enable the end-to-end communications system to combat the generative attack network via a minimax game. We show that the proposed system not only works well against white-box and black-box adversarial attacks but also possesses excellent generalization capabilities to maintain good performance under no attacks. We also show that our GAN-based end-to-end system outperforms the conventional communications system and the end-to-end communications system with/without adversarial training., Comment: 5 pages letter

As a basic characteristic of cosmic ray (CR) propagation, the diffusive halo can advance our understanding of many CR-related studies and indirect dark matter. The method to derive the halo size usually has degeneracy problems thus affected by large uncertainties. The diffusion gamma ray from high-latitude clouds might shed light on the halo size independently. Since the spatially dependent propagation (SDP) model has a better agreement with the observed CRs, compared with conventional propagation model, in this work, we investigate the halo thickness based on SDP model with Fermi-LAT $\rm\gamma$-ray observation on the high- and intermediate-velocity clouds. As a result, in order not to exceed the relative $\gamma$-ray emissivity in the high-latitude clouds, halo thickness should be in the range of $\rm ~3.3\sim9~ kpc$. Moreover, the spatial morphology of $\rm\gamma$-rays estimated based on SDP model under different values of halo thickness are distinctive, which provides us a tool to determine the halo size. We hope that our model could be tested and tuned by multi-wavelength observations in the future., Comment: 5 pages, 3 figures

Let $\frak g$ be a reductive Lie algebra over an algebraically closed field of characteristic $p>0$. In this paper, we study the representations of $\frak g$ with a $p$-character $\chi$ of standard Levi form associated with a given subset $I$ of the simple root system $\Pi$ of $\frak g$. Let $U_\chi({\frak g})$ be the reduced enveloping algebra of $\frak g$. A notion "quasi-simple module" (denoted by $\mathcal L_\chi(\lambda)$) is introduced. The properties of such a module turn out to be better than those of the corresponding simple module $\widehat L_\chi(\lambda)$. It enables us to investigate the $U_\chi({\frak g})$-modules from a new point of view, and correspondingly gives rise new consequences. First, we show that the first self extension of $\mathcal L_\chi(\lambda)$ is zero, and the projective dimension of $\mathcal L_\chi(\lambda)$ is finite when $\lambda$ is $p$-regular. These properties make it significant to rewrite the formula of Lusztig's Hope (Lusztig's conjecture on the irreducible characters in the category of $U_\chi({\frak g})$-modules) by replacing $\widehat L_\chi(\lambda)$ by $\mathcal L_\chi(\lambda)$. Second, with the aid of quasi-simple modules, we get a formula on the Loewy lengths of standard modules and proper standard modules over $U_\chi({\frak g})$. And by studying some examples, we formulate some conjectures on the Loewy lengths of indecomposable projective $\frak g$-modules, standard modules and proper standard modules., Comment: There are some errors

The spatial-dependent propagation (SDP) model with a nearby source works well to reproduce the co-evolving features of both cosmic ray (CR) nuclei spectra and anisotropy. However, it is well known that the Sun is offset from the Galactic plane. This will lead to a dominating anisotropy in perpendicular direction, which is discrepant with observations. Thus it is necessary to reboot further investigation into the effect of the solar offset. In this work, for the first time the combined studies of the solar offset, nuclei spectra and anisotropy are performed based on the SDP model. As a result, to reproduce CR spectra and anisotropy, the thickness of the halo $\rm (\xi z_h)$ increases linearly with the displacement of the Sun. When the offset is $\rm \sim8~pc$ as estimated from the matter-borne methods, $\rm \xi z_h$ is about 0.9 kpc, which is a much thicker halo than usually. Moreover, the PeV anisotropy could estimate the value of diffusion coefficient, thus breaking the degeneracy of diffusion coefficient and halo thickness. Therefore it is a good messenger to constrain the halo thickness. On the other hand, the anisotropy in PeV energy region, as a new probe, might also shed new light to constrain the solar offset. We hope that the anisotropy at the energies of $\rm \sim TeV$ to $\rm PeV$ can be finely measured by LHAASO experiment, leading to a better understanding about the thick halo., Comment: 7 pages, 7 figures

Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ ., Comment: Published in the Journal of Open Source Software

In this paper, we construct a class of non-weight modules over the affine-Virasoro algebra of type $A_1$ by taking tensor products of irreducibles defined in [Q. Chen, J. Han, Non-weight modules over the affine-Virasoro algebra of type $A_1$, J. Math. Phys. 60, 071707 (2019)] with irreducible highest weight modules. The irreducibility and the isomorphism classes of these modules are determined. Moreover, we show that these tensor product modules are different from the known non-weight modules. Finally, we realize some tensor product modules as induced modules from modules over certain subalgebras of the affine-Virasoro algebra of type $A_1$, and give sufficient and necessary conditions for these induced modules to be reducible., Comment: 19 pages

Any graded restricted simple Lie algebra of Cartan type contains a subalgebra isomorphic to the Witt algebra over a field of prime characteristic. As some analogue of study on branching rules for restricted non-classical Lie algebras, it is shown that each graded restricted simple Lie algebra of Cartan type can be decomposed into a direct sum of restricted baby Verma modules and simple modules as an adjoint module over the Witt algebra. In particular, the composition factors are precisely determined., Comment: 13 pages

An algebraic group is called semi-reductive if it is a semi-direct product of a reductive subgroup and the unipotent radical. Such a semi-reductive algebraic group naturally arises and also plays a key role in the study of modular representations of non-classical finite-dimensional simple Lie algebras in positive characteristic, and some other cases. Let $G$ ba a connected semi-reductive algebraic group over an algebraically closed field $\mathbb{F}$ and $\mathfrak{g}=Lie(G)$. It turns out that $G$ has many same properties as reductive groups, such as the Bruhat decomposition. In this note, we obtain an analogue of classical Chevalley restriction theorem for $\mathfrak{g}$, which says that the $G$-invariant ring $\mathbb{F}[\mathfrak{g}]^G$ is a polynomial ring if $\mathfrak{g}$ satisfies a certain "posivity" condition suited for lots of cases we are interested in. As applications, we further investigate the nilpotent cones and resolutions of singularities for semi-reductive Lie algebras., Comment: 15 pages. arXiv admin note: text overlap with arXiv:2005.13152

Autonomous vehicles require fleet-wide data collection for continuous algorithm development and validation. The Smart Black Box (SBB) intelligent event data recorder has been proposed as a system for prioritized high-bandwidth data capture. This paper extends the SBB by applying anomaly detection and action detection methods for generalized event-of-interest (EOI) detection. An updated SBB pipeline is proposed for the real-time capture of driving video data. A video dataset is constructed to evaluate the SBB on real-world data for the first time. SBB performance is assessed by comparing the compression of normal and anomalous data and by comparing our prioritized data recording with a FIFO strategy. Results show that SBB data compression can increase the anomalous-to-normal memory ratio by ~25%, while the prioritized recording strategy increases the anomalous-to-normal count ratio when compared to a FIFO strategy. We compare the real-world dataset SBB results to a baseline SBB given ground-truth anomaly labels and conclude that improved general EOI detection methods will greatly improve SBB performance., Comment: Submitted to Algorithms

In this paper, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject-wise generative models. Specifically, we focus on the case where the subject-wise generative model is a dynamic causal model (DCM) for fMRI and clusters are defined in terms of effective brain connectivity. While an attractive approach for detecting mechanistically interpretable subgroups in heterogeneous populations, inverting such a hierarchical model represents a particularly challenging case, since DCM is often characterized by high posterior correlations between its parameters. In this context, standard MCMC schemes exhibit poor performance and extremely slow convergence. In this paper, we investigate the properties of hierarchical clustering which lead to the observed failure of standard MCMC schemes and propose a solution designed to improve convergence but preserve computational complexity. Specifically, we introduce a class of proposal distributions which aims to capture the interdependencies between the parameters of the clustering and subject-wise generative models and helps to reduce random walk behaviour of the MCMC scheme. Critically, these proposal distributions only introduce a single hyperparameter that needs to be tuned to achieve good performance. For validation, we apply our proposed solution to synthetic and real-world datasets and also compare it, in terms of computational complexity and performance, to Hamiltonian Monte Carlo (HMC), a state-of-the-art Monte Carlo. Our results indicate that, for the specific application domain considered here, our proposed solution shows good convergence performance and superior runtime compared to HMC.

We consider topological protection mechanisms in dissipative quantum systems in the presence of quenched disorder, with the intent to prolong coherence times of qubits. The physical setting is a network of qubits and dissipative cavities whose coupling parameters are tunable, such that topological edge states can be stabilized. The evolution of a fiducial qubit is entirely determined by a non-Hermitian Hamiltonian which thus emerges from a bona-fide physical process. It is shown how even in the presence of disorder winding numbers can be defined and evaluated in real space, as long as certain symmetries are preserved. Hence we can construct the topological phase diagrams of noisy open quantum models, such as the non-Hermitian disordered Su-Schrieffer- Heeger dimer model and a trimer model that includes longer-range couplings. In the presence of competing disorder parameters, interesting re-entrance phenomena of topologically non-trivial sectors are observed. This means that in certain parameter regions, increasing disorder drastically increases the coherence time of the fiducial qubit.

The aerosol mixing state significantly affects the climate and health impacts of atmospheric aerosol particles. Simplified aerosol mixing state assumptions, common in Earth System models, can introduce errors in the prediction of these aerosol impacts. The aerosol mixing state index, a metric to quantify aerosol mixing state, is a convenient measure for quantifying these errors. Global estimates of aerosol mixing state indices have recently become available via supervised learning models, but require regionalization to ease spatiotemporal analysis. Here we developed a simple but effective unsupervised learning approach to regionalize predictions of global aerosol mixing state indices. We used the monthly average of aerosol mixing state indices global distribution as the input data. Grid cells were then clustered into regions by the k-means algorithm without explicit spatial information as input. This approach resulted in eleven regions over the globe with specific spatial aggregation patterns. Each region exhibited a unique distribution of mixing state indices and aerosol compositions, showing the effectiveness of the unsupervised regionalization approach. This study defines "aerosol mixing state zones" that could be useful for atmospheric science research., Comment: AI for Earth Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020)

Supermassive black holes (SMBHs) are ubiquitously found at the centers of most galaxies. Measuring SMBH mass is important for understanding the origin and evolution of SMBHs. However, traditional methods require spectral data which is expensive to gather. To solve this problem, we present an algorithm that weighs SMBHs using quasar light time series, circumventing the need for expensive spectra. We train, validate, and test neural networks that directly learn from the Sloan Digital Sky Survey (SDSS) Stripe 82 data for a sample of $9,038$ spectroscopically confirmed quasars to map out the nonlinear encoding between black hole mass and multi-color optical light curves. We find a 1$\sigma$ scatter of 0.35 dex between the predicted mass and the fiducial virial mass based on SDSS single-epoch spectra. Our results have direct implications for efficient applications with future observations from the Vera Rubin Observatory., Comment: 5 pages, 3 figures, 1 table. Accepted to the Machine Learning and the Physical Sciences Workshop at NeurIPS 2020

We investigate the use of approximate Bayesian neural networks (BNNs) in modeling hundreds of time-delay gravitational lenses for Hubble constant ($H_0$) determination. Our BNN was trained on synthetic HST-quality images of strongly lensed active galactic nuclei (AGN) with lens galaxy light included. The BNN can accurately characterize the posterior PDFs of model parameters governing the elliptical power-law mass profile in an external shear field. We then propagate the BNN-inferred posterior PDFs into ensemble $H_0$ inference, using simulated time delay measurements from a plausible dedicated monitoring campaign. Assuming well-measured time delays and a reasonable set of priors on the environment of the lens, we achieve a median precision of $9.3$\% per lens in the inferred $H_0$. A simple combination of 200 test-set lenses results in a precision of 0.5 $\textrm{km s}^{-1} \textrm{ Mpc}^{-1}$ ($0.7\%$), with no detectable bias in this $H_0$ recovery test. The computation time for the entire pipeline -- including the training set generation, BNN training, and $H_0$ inference -- translates to 9 minutes per lens on average for 200 lenses and converges to 6 minutes per lens as the sample size is increased. Being fully automated and efficient, our pipeline is a promising tool for exploring ensemble-level systematics in lens modeling for $H_0$ inference., Comment: 21 pages (+2 appendix), 17 figures. Published in ApJ. Code at https://github.com/jiwoncpark/h0rton. Datasets, trained models, and inference results at https://zenodo.org/record/4300382

Nature provides a way to understand physics with reinforcement learning since nature favors the economical way for an object to propagate. In the case of classical mechanics, nature favors the object to move along the path according to the integral of the Lagrangian, called the action $\mathcal{S}$. We consider setting the reward/penalty as a function of $\mathcal{S}$, so the agent could learn the physical trajectory of particles in various kinds of environments with reinforcement learning. In this work, we verified the idea by using a Q-Learning based algorithm on learning how light propagates in materials with different refraction indices, and show that the agent could recover the minimal-time path equivalent to the solution obtained by Snell's law or Fermat's Principle. We also discuss the similarity of our reinforcement learning approach to the path integral formalism., Comment: 4 pages, 4 figures, preprint. Comments welcome!!!

Dark matter substructures are interesting since they can reveal the properties of dark matter. Collisionless N-body simulations of cold dark matter show more substructures compared with the population of dwarf galaxy satellites observed in our local group. Therefore, understanding the population and property of subhalos at cosmological scale would be an interesting test for cold dark matter. In recent years, it has become possible to detect individual dark matter subhalos near images of strongly lensed extended background galaxies. In this work, we discuss the possibility of using deep neural networks to detect dark matter subhalos, and showing some preliminary results with simulated data. We found that neural networks not only show promising results on detecting multiple dark matter subhalos, but also learn to reject the subhalos on the lensing arc of a smooth lens where there is no subhalo., Comment: Accepted by Machine Learning and the Physical Sciences Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), 2019

Supernovae mark the explosive deaths of stars and enrich the cosmos with heavy elements. Future telescopes will discover thousands of new supernovae nightly, creating a need to flag astrophysically interesting events rapidly for followup study. Ideally, such an anomaly detection pipeline would be independent of our current knowledge and be sensitive to unexpected phenomena. Here we present an unsupervised method to search for anomalous time series in real time for transient, multivariate, and aperiodic signals. We use a RNN-based variational autoencoder to encode supernova time series and an isolation forest to search for anomalous events in the learned encoded space. We apply this method to a simulated dataset of 12,159 supernovae, successfully discovering anomalous supernovae and objects with catastrophically incorrect redshift measurements. This work is the first anomaly detection pipeline for supernovae which works with online datastreams., Comment: 6 pages, 2 figures, written for non-astronomers, submitted to the NeurIPS workshop Machine Learning and the Physical Sciences. Comments welcome!!

As a result of the worldwide transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has evolved into an unprecedented pandemic. Currently, with unavailable pharmaceutical treatments and vaccines, this novel coronavirus results in a great impact on public health, human society, and global economy, which is likely to last for many years. One of the lessons learned from the COVID-19 pandemic is that a long-term system with non-pharmaceutical interventions for preventing and controlling new infectious diseases is desirable to be implemented. Internet of things (IoT) platform is preferred to be utilized to achieve this goal, due to its ubiquitous sensing ability and seamless connectivity. IoT technology is changing our lives through smart healthcare, smart home, and smart city, which aims to build a more convenient and intelligent community. This paper presents how the IoT could be incorporated into the epidemic prevention and control system. Specifically, we demonstrate a potential fog-cloud combined IoT platform that can be used in the systematic and intelligent COVID-19 prevention and control, which involves five interventions including COVID-19 Symptom Diagnosis, Quarantine Monitoring, Contact Tracing & Social Distancing, COVID-19 Outbreak Forecasting, and SARS-CoV-2 Mutation Tracking. We investigate and review the state-of-the-art literatures of these five interventions to present the capabilities of IoT in countering against the current COVID-19 pandemic or future infectious disease epidemics., Comment: 12 pages; Submitted to IEEE Internet of Things Journal