Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Deeper Wider Faster program: chasing the fastest bursts in the Universe
82
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We present the Deeper Wider Faster (DWF) program that coordinates more than 30 multi-wavelength and multi-messenger facilities worldwide and in space to detect and study fast transients (millisecond-to-hours duration). DWF has four main components, (1) simultaneous observations, where about 10 major facilities, from radio to gamma-ray, are coordinated to perform deep, wide-field, fast-cadenced observations of the same field at the same time. Radio telescopes search for fast radio bursts while optical imagers and high-energy instruments search for seconds-to-hours timescale transient events, (2) real-time (seconds to minutes) supercomputer data processing and candidate identification, along with real-time (minutes) human inspection of candidates using sophisticated visualisation technology, (3) rapid-response (minutes) follow-up spectroscopy and imaging and conventional ToO observations, and (4) long-term follow up with a global network of 1-4m-class telescopes. The principal goals of DWF are to discover and study counterparts to fast radio bursts and gravitational wave events, along with millisecond-to-hour duration transients at all wavelengths.
rate research
Read More
Identification of anomalous light curves within time-domain surveys is often challenging. In addition, with the growing number of wide-field surveys and the volume of data produced exceeding astronomers ability for manual evaluation, outlier and anomaly detection is becoming vital for transient science. We present an unsupervised method for transient discovery using a clustering technique and the Astronomaly package. As proof of concept, we evaluate 85553 minute-cadenced light curves collected over two 1.5 hour periods as part of the Deeper, Wider, Faster program, using two different telescope dithering strategies. By combining the clustering technique HDBSCAN with the isolation forest anomaly detection algorithm via the visual interface of Astronomaly, we are able to rapidly isolate anomalous sources for further analysis. We successfully recover the known variable sources, across a range of catalogues from within the fields, and find a further 7 uncatalogued variables and two stellar flare events, including a rarely observed ultra fast flare (5 minute) from a likely M-dwarf.
We present our 500 pc distance-limited study of stellar fares using the Dark Energy Camera as part of the Deeper, Wider, Faster Program. The data was collected via continuous 20-second cadence g band imaging and we identify 19,914 sources with precise distances from Gaia DR2 within twelve, ~3 square-degree, fields over a range of Galactic latitudes. An average of ~74 minutes is spent on each field per visit. All light curves were accessed through a novel unsupervised machine learning technique designed for anomaly detection. We identify 96 flare events occurring across 80 stars, the majority of which are M dwarfs. Integrated are energies range from $sim 10^{31}-10^{37}$ erg, with a proportional relationship existing between increased are energy with increased distance from the Galactic plane, representative of stellar age leading to declining yet more energetic are events. In agreement with previous studies we observe an increase in flaring fraction from M0 -> M6 spectral types. Furthermore, we find a decrease in the flaring fraction of stars as vertical distance from the galactic plane is increased, with a steep decline present around ~100 pc. We find that ~70% of identified flares occur on short timescales of ~8 minutes. Finally we present our associated are rates, finding a volumetric rate of $2.9 pm 0.3 times 10^{-6}$ flares pc$^{-3}$ hr$^{-1}$.
More transformer blocks with residual connections have recently achieved impressive results on various tasks. To achieve better performance with fewer trainable parameters, recent methods are proposed to go shallower by parameter sharing or model compressing along with the depth. However, weak modeling capacity limits their performance. Contrastively, going wider by inducing more trainable matrixes and parameters would produce a huge model requiring advanced parallelism to train and inference. In this paper, we propose a parameter-efficient framework, going wider instead of deeper. Specially, following existing works, we adapt parameter sharing to compress along depth. But, such deployment would limit the performance. To maximize modeling capacity, we scale along model width by replacing feed-forward network (FFN) with mixture-of-experts (MoE). Across transformer blocks, instead of sharing normalization layers, we propose to use individual layernorms to transform various semantic representations in a more parameter-efficient way. To evaluate our plug-and-run framework, we design WideNet and conduct comprehensive experiments on popular computer vision and natural language processing benchmarks. On ImageNet-1K, our best model outperforms Vision Transformer (ViT) by $1.5%$ with $0.72 times$ trainable parameters. Using $0.46 times$ and $0.13 times$ parameters, our WideNet can still surpass ViT and ViT-MoE by $0.8%$ and $2.1%$, respectively. On four natural language processing datasets, WideNet outperforms ALBERT by $1.8%$ on average and surpass BERT using factorized embedding parameterization by $0.8%$ with fewer parameters.
The cosmological power spectrum of the coherent matter flow is measured exploiting an improved prescription for the apparent anisotropic clustering pattern in redshift space. New statistical analysis is presented to provide an optimal observational platform to link the improved redshift distortion theoretical model to future real datasets. The statistical power as well as robustness of our method are tested against 60 realizations of 8 Gpc/h^3 dark matter simulation maps mocking the precision level of upcoming wide--deep surveys. We showed that we can accurately extract the velocity power spectrum up to quasi linear scales of k~0.1 h/Mpc at z = 0.35 and up to k~0.15 h/Mpc at higher redshifts within a couple of percentage precision level. Our understanding of redshift space distortion is proved to be appropriate for precision cosmology, and our statistical method will guide us to righteous path to meet the real world.
Research over the past three decades has revolutionized the field of cosmology while supporting the standard cosmological model. However, the cosmological principle of Universal homogeneity and isotropy has always been in question, since structures as large as the survey size have always been found as the survey size has increased. Until now, the largest known structure in our Universe is the Sloan Great Wall (SGW), which is more than 400 Mpc long and located approximately one billion light-years away. Here we report the discovery of a structure at least six times larger than the Sloan Great Wall that is suggested by the distribution of gamma-ray bursts (GRBs). Gamma-ray bursts are the most energetic explosions in the Universe. They are associated with the stellar endpoints of massive stars and are found in and near distant galaxies. Therefore, they are very good indicators of the dense part of the Universe containing normal matter. As of July 2012, 283 GRB redshifts have been measured. If one subdivides this GRB sample into nine radial parts and compares the sky distributions of these subsamples (each containing 31 GRBs), one can observe that the fourth subsample (1.6 < z < 2.1) differs significantly from the others in that many of the GRBs are concentrated in the same angular area of the sky. Using the two-dimensional Kolmogorov-Smirnov test, the significance of this observation is found to be less than 0.05 per cent. Fourteen out of the 31 Gamma-Ray Bursts in this redshift band are concentrated in approximately 1/8 of the sky. The binomial probability to find such a deviation is p=0.0000055. This huge structure lies ten times farther away than the Sloan Great Wall, at a distance of approximately ten billion light-years. The size of the structure defined by these GRBs is about 2000-3000 Mpc, or more than six times the size of the largest known object (SGW) in the Universe.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
