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Register a new userIntegrating Temporal and Spectral Features of Astronomical Data Using Wavelet Analysis for Source Classification
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Temporal and spectral information extracted from a stream of photons received from astronomical sources is the foundation on which we build understanding of various objects and processes in the Universe. Typically astronomers fit a number of models separately to light curves and spectra to extract relevant features. These features are then used to classify, identify, and understand the nature of the sources. However, these feature extraction methods may not be optimally sensitive to unknown properties of light curves and spectra. One can use the raw light curves and spectra as features to train classifiers, but this typically increases the dimensionality of the problem, often by several orders of magnitude. We overcome this problem by integrating light curves and spectra to create an abstract image and using wavelet analysis to extract important features from the image. Such features incorporate both temporal and spectral properties of the astronomical data. Classification is then performed on those abstract features. In order to demonstrate this technique, we have used gamma-ray burst (GRB) data from the NASAs Swift mission to classify GRBs into high- and low-redshift groups. Reliable selection of high-redshift GRBs is of considerable interest in astrophysics and cosmology.
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We propose SAINT+, a successor of SAINT which is a Transformer based knowledge tracing model that separately processes exercise information and student response information. Following the architecture of SAINT, SAINT+ has an encoder-decoder structure where the encoder applies self-attention layers to a stream of exercise embeddings, and the decoder alternately applies self-attention layers and encoder-decoder attention layers to streams of response embeddings and encoder output. Moreover, SAINT+ incorporates two temporal feature embeddings into the response embeddings: elapsed time, the time taken for a student to answer, and lag time, the time interval between adjacent learning activities. We empirically evaluate the effectiveness of SAINT+ on EdNet, the largest publicly available benchmark dataset in the education domain. Experimental results show that SAINT+ achieves state-of-the-art performance in knowledge tracing with an improvement of 1.25% in area under receiver operating characteristic curve compared to SAINT, the current state-of-the-art model in EdNet dataset.
The analysis of individual X-ray sources that appear in a crowded field can easily be compromised by the misallocation of recorded events to their originating sources. Even with a small number of sources, that nonetheless have overlapping point spread functions, the allocation of events to sources is a complex task that is subject to uncertainty. We develop a Bayesian method designed to sift high-energy photon events from multiple sources with overlapping point spread functions, leveraging the differences in their spatial, spectral, and temporal signatures. The method probabilistically assigns each event to a given source. Such a disentanglement allows more detailed spectral or temporal analysis to focus on the individual component in isolation, free of contamination from other sources or the background. We are also able to compute source parameters of interest like their locations, relative brightness, and background contamination, while accounting for the uncertainty in event assignments. Simulation studies that include event arrival time information demonstrate that the temporal component improves event disambiguation beyond using only spatial and spectral information. The proposed methods correctly allocate up to 65% more events than the corresponding algorithms that ignore event arrival time information. We apply our methods to two stellar X-ray binaries, UV Cet and HBC515 A, observed with Chandra. We demonstrate that our methods are capable of removing the contamination due to a strong flare on UV Cet B in its companion approximately 40 times weaker during that event, and that evidence for spectral variability at timescales of a few ks can be determined in HBC515 Aa and HBC515 Ab.
AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the fields immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless
Most of the X-ray binary systems containing neutron stars classified as Atoll sources show two different spectral states, called soft and hard. Moreover, a large number of these systems show a reflection component relativistically smeared in their spectra, which gives information on the innermost region of the system. Our aim is to investigate the poorly studied broadband spectrum of the low mass X-ray binary system 4U 1702-429, which was recently analysed combining XMM-Newton and INTEGRAL data. The peculiar value of the reflection fraction brought us to analyse further broadband spectra of 4U 1702-429. We re-analysed the spectrum of the XMM-Newton/INTEGRAL observation of 4U 1702-429 in the 0.3-60 keV energy range and we extracted three 0.1-100 keV spectra of the source analysing three observations collected with the BeppoSAX satellite. We find that the XMM-Newton/INTEGRAL spectrum is well fitted using a model composed of a disc blackbody plus a Comptonised component and a smeared reflection component. We used the same spectral model for the BeppoSAX spectra, finding out that the addition of a smeared reflection component is statistically significant. The best-fit values of the parameters are compatible to each other for the BeppoSAX spectra. We find that the reflection fraction is $0.05^{+0.03}_{-0.01}$ for the XMM-Newton/INTEGRAL spectrum and between 0.15 and 0.4 for BeppoSAX ones. The relative reflection fraction and the ionisation parameter are incompatible between the XMM-Newton/INTEGRAL and the BeppoSAX observations and the characteristics of the Comptonising corona suggest that the source was in a soft state in the former observation and in a hard state in the latter.
We report on detailed analysis of the hard X-ray and GeV gamma-ray spectra of LS 5039, one of the brightest gamma-ray binary system in the Galaxy. The NuSTAR observation covering its entire orbit in 2016 allowed us for the first time to study the orbital variability of the spectrum above 10 keV. The hard X-ray spectrum is well described with a single power-law component up to 78 keV. The X-ray flux showed a slight deviation from those observed previously with Suzaku in 2007. The fast X-ray brightening observed with Suzaku, around the inferior conjunction, was not observed in this observation. We also analyzed 11 years of Fermi Large Area Telescope data of LS 5039. The GeV spectrum around the inferior conjunction was well described with two non-thermal components; a power law with a photon index of $sim 3$ and a cut-off power law with a cutoff energy of $sim 2$ GeV. The orbital flux variability also changed gradually around a few GeV. These results indicate that there are two emission components in the GeV band, and the dominant component above $sim 1$ GeV does not depend on the orbital phase. By combining these results, we update the spectral energy distribution of LS 5039 with the highest available statistics. Theoretical models proposed so far cannot explain the obtained multi-wavelength spectrum, especially the emission from $sim$ 1 MeV to $sim$ 400 MeV, and we discuss a possibility that particle acceleration in LS 5039 is different from the shock acceleration.
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