arXiv subjects

Jen-Hung Wang

Publications and source records attributed to Jen-Hung Wang.

7 recordsLinked to original sources

arXiv2025

Back-End System of BURSTT

The Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) is a new-generation wide-angle radio telescope specifically designed to survey Fast Radio Bursts (FRBs), energetic millisecond-duration pulses of unknown extragalactic origin. To realize its scientific potential, which includes detecting approximately 50 FRBs per year and sub-arcsecond localization capability, the system is designed to perform real-time beamforming and pulse search over the \SI{60}{\degree} $\times$ \SI{120}{\degree} field of view. This paper provides a detailed account of the design, implementation, and performance validation of the BURSTT back-end System. The system employs an efficient multi-stage processing architecture: initial beamforming is executed on the Xilinx ZCU216 RF System-on-Chip (RFSoC) platform; data is then transferred to Intel Xeon servers, where AVX-512 and AMX instruction sets are utilized for the second stage of beamforming and channelization, achieving high computational efficiency to ensure real-time capability. A highly optimized \texttt{bonsai} de-dispersion algorithm performs a real-time pulse search and triggering across 256 beams, which, upon detection, issues commands to the distributed outrigger system to save voltage data for very-long baseline interferometry (VLBI) precise localization. System performance has been validated through beamforming tests using bright radio sources and real-time detection of known pulsars, confirming the high fidelity of the signal processing pipeline.

astro-ph.IM↗

arXiv2022

BURSTT: Bustling Universe Radio Survey Telescope in Taiwan

Fast Radio Bursts (FRBs) are bright millisecond-duration radio transients that appear about 1,000 times per day, all-sky, for a fluence threshold 5 Jy ms at 600 MHz. The FRB radio-emission physics and the compact objects involved in these events are subjects of intense active debate. To better constrain source models, the Bustling Universe Radio Survey Telescope in Taiwan (BURSTT) is optimized to discover and localize a large sample of rare, high-fluence, nearby FRBs. This is the population most amenable to multi-messenger, multi-wavelength follow-up, allowing deeper understanding of source mechanisms. BURSTT will provide horizon-to-horizon sky coverage with a half power field-of-view (FoV) of $\sim$10$^{4}$ deg$^{2}$, a 400 MHz effective bandwidth between 300-800 MHz, and sub-arcsecond localization, made possible using outrigger stations hundreds to thousands of km from the main array. Initially, BURSTT will employ 256 antennas. After tests of various antenna designs and optimization of system performance we plan to expand to 2048 antennas. We estimate that BURSTT-256 will detect and localize $\sim$100 bright ($\geq$100 Jy ms) FRBs per year. Another advantage of BURSTT's large FoV and continuous operation will be greatly enhanced monitoring of FRBs for repetition. The current lack of sensitive all-sky observations likely means that many repeating FRBs are currently cataloged as single-event FRBs.

astro-ph.IM↗

arXiv2019

A Reinforcement Learning Approach for the Multichannel Rendezvous Problem

In this paper, we consider the multichannel rendezvous problem in cognitive radio networks (CRNs) where the probability that two users hopping on the same channel have a successful rendezvous is a function of channel states. The channel states are modelled by two-state Markov chains that have a good state and a bad state. These channel states are not observable by the users. For such a multichannel rendezvous problem, we are interested in finding the optimal policy to minimize the expected time-to-rendezvous (ETTR) among the class of {\em dynamic blind rendezvous policies}, i.e., at the $t^{th}$ time slot each user selects channel $i$ independently with probability $p_i(t)$, $i=1,2, \ldots, N$. By formulating such a multichannel rendezvous problem as an adversarial bandit problem, we propose using a reinforcement learning approach to learn the channel selection probabilities $p_i(t)$, $i=1,2, \ldots, N$. Our experimental results show that the reinforcement learning approach is very effective and yields comparable ETTRs when comparing to various approximation policies in the literature.

eess.SP↗

arXiv2019

ETTR Bounds and Approximation Solutions of Blind Rendezvous Policies in Cognitive Radio Networks with Random Channel States

In this paper, we consider the multichannel rendezvous problem in cognitive radio networks (CRNs) where the probability that two users hopping on the same channel have a successful rendezvous is a function of channel states. The channel states are modeled by stochastic processes with joint distributions known to users. However, the exact state of a channel at any time is not observable. We first consider two channel models: (i) the fast time-varying channel model (where the channel states are assumed to be independent and identically distributed in each time slot), and (ii) the slow time-varying channel model (where the channel states remain unchanged over time). Among the classes of the blind rendezvous policies that randomly hop on channels according to certain channel selection probabilities, we show the optimal channel selection policy that minimizes the expected time-to-rendezvous (ETTR) is the single selection policy that hops on the ``best'' channel all the time in the fast time-varying channel model. However, for the slow time-varying channel model, it is much more difficult to find the optimal channel selection policy. By using the majorization ordering, we derive a lower bound and an upper bound for the ETTR under the assumption that the channel states are exchangeable random variables. Bases on these bounds, we then prove various approximation solutions. We then extend our results to general channel models where the joint distribution of the channel states is only assumed to be stationary in time.

cs.IT↗

arXiv2017

Machine Learning Based Real Bogus System for HSC-SSP Moving Object Detecting Pipeline

Machine learning techniques are widely applied in many modern optical sky surveys, e.q. Pan-STARRS1, PTF/iPTF and Subaru/Hyper Suprime-Cam survey, to reduce human intervention for data verification. In this study, we have established a machine learning based real-bogus system to reject the false detections in the Subaru/Hyper-Suprime-Cam StrategicSurvey Program (HSC-SSP) source catalog. Therefore the HSC-SSP moving object detection pipeline can operate more effectively due to the reduction of false positives. To train the real-bogus system, we use the stationary sources as the real training set and the "flagged" data as the bogus set. The training set contains 47 features, most of which are photometric measurements and shape moments generated from the HSC image reduction pipeline (hscPipe). Our system can reach a true positive rate (tpr) ~96% with a false positive rate (fpr) ~ 1% or tpr ~99% at fpr ~5%. Therefore we conclude that the stationary sources are decent real training samples, and using photometry measurements and shape moments can reject the false positives effectively.

astro-ph.IM↗

arXiv2017

Searching for Moving Objects in HSC-SSP: Pipeline and Preliminary Results

The Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) is currently the deepest wide- field survey in progress. The 8.2 m aperture of Subaru telescope is very powerful in detect- ing faint/small moving objects, including near-Earth objects, asteroids, centaurs and Tran- Neptunian objects (TNOs). However, the cadence and dithering pattern of the HSC-SSP are not designed for detecting moving objects, making it difficult to do so systematically. In this paper, we introduce a new pipeline for detecting moving objects (specifically TNOs) in a non-dedicated survey. The HSC-SSP catalogs are re-arranged into the HEALPix architecture. Then, the stationary detections and false positive are removed with a machine learning al- gorithm to produce a list of moving object candidates. An orbit linking algorithm and visual inspections are executed to generate the final list of detected TNOs. The preliminary results of a search for TNOs using this new pipeline on data from the first HSC-SSP data release (Mar 2014 to Nov 2015) are also presented.

astro-ph.EP↗

arXiv2016

Repetitive Patterns in Rapid Optical Variations in the Nearby Black-hole Binary V404 Cygni

How black holes accrete surrounding matter is a fundamental, yet unsolved question in astrophysics. It is generally believed that matter is absorbed into black holes via accretion disks, the state of which depends primarily on the mass-accretion rate. When this rate approaches the critical rate (the Eddington limit), thermal instability is supposed to occur in the inner disc, causing repetitive patterns of large-amplitude X-ray variability (oscillations) on timescales of minutes to hours. In fact, such oscillations have been observed only in sources with a high mass accretion rate, such as GRS 1915+105. These large-amplitude, relatively slow timescale, phenomena are thought to have physical origins distinct from X-ray or optical variations with small amplitudes and fast ($\lesssim$10 sec) timescales often observed in other black hole binaries (e.g., XTE J1118+480 and GX 339-4). Here we report an extensive multi-colour optical photometric data set of V404 Cygni, an X-ray transient source containing a black hole of nine solar masses (and a conpanion star) at a distance of 2.4 kiloparsecs. Our data show that optical oscillations on timescales of 100 seconds to 2.5 hours can occur at mass-accretion rates more than ten times lower than previously thought. This suggests that the accretion rate is not the critical parameter for inducing inner-disc instabilities. Instead, we propose that a long orbital period is a key condition for these large-amplitude oscillations, because the outer part of the large disc in binaries with long orbital periods will have surface densities too low to maintain sustained mass accretion to the inner part of the disc. The lack of sustained accretion -- not the actual rate -- would then be the critical factor causing large-amplitude oscillations in long-period systems.

astro-ph.HE↗