Machine learning for observational cosmology

Moriwaki, Kana; Nishimichi, Takahiro; Yoshida, Naoki

doi:10.1088/1361-6633/acd2ea

Cited by 14 publications

(4 citation statements)

References 349 publications

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“…Similarly, on the right for figure 9, and in dash-dotted green for figure 10, the 'true' XRF averaged over the cluster, as well as the colormap of the flattened 'true' XRF 12 , i.e. : h(true)…”

Section: Resultsmentioning

confidence: 99%

“…In cosmology, the analysis of the cosmic microwave background has been revolutionized by deep learning (see, e.g. [11,12]. and references therein), as highlighted in recent works [13,14], which have extracted subtle cosmological signals from complex datasets more effectively than traditional methods.…”

Section: Introductionmentioning

confidence: 99%

“…www.wga.hu/frames-e.html?/html/a/attavant/heroica2.html 11. We recall here the non-Riemannian nature of colour spaces[108], which give rise to impossibilities of using metric approaches in colour space, leaving only distance-based methods 12. Here we use the quote marks on the word true, because the XRF is synthetic.…”

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confidence: 99%

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Datacube segmentation via deep spectral clustering

Bombini,

García-Avello Bofías,

Bracci

et al. 2024

Mach. Learn.: Sci. Technol.

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Extended Vision techniques are ubiquitous in physics. However, the data cubes steaming from such analysis often pose a challenge in their interpretation, due to the intrinsic difficulty in discerning the relevant information from the spectra composing the data cube.Furthermore, the huge dimensionality of data cube spectra poses a complex task in its statistical interpretation; nevertheless, this complexity contains a massive amount of statistical information that can be exploited in an unsupervised manner to outline some essential properties of the case study at hand, e.g. it is possible to obtain an image segmentation via (deep) clustering of data-cube's spectra, performed in a suitably defined low-dimensional embedding space.To tackle this topic, we explore the possibility of applying unsupervised clustering methods in encoded space, i.e. perform deep clustering on the spectral properties of datacube pixels. A statistical dimensional reduction is performed by an ad hoc trained (Variational) AutoEncoder, in charge of mapping spectra into lower dimensional metric spaces, while the clustering process is performed by a (learnable) iterative K-Means clustering algorithm.We apply this technique to two different use cases, of different physical origins: a set of Macro mapping X-Ray Fluorescence (MA-XRF) synthetic data on pictorial artworks, and a dataset of simulated astrophysical observations.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Datacube segmentation via deep spectral clustering

Bombini,

García-Avello Bofías,

Bracci

et al. 2024

Mach. Learn.: Sci. Technol.

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“…But spectroscopic observation is expensive, which has high requirements for telescopes and observation time. Therefore, spectroscopic classification can only be applied to limited objects [16]. Compared to spectroscopic observation, the photometric method has a lower accuracy but higher efficiency.…”

Section: Introductionmentioning

confidence: 99%

TLW: A Real-Time Light Curve Classification Algorithm for Transients Based on Machine Learning

Li,

Wu,

Kang

et al. 2024

Universe

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The real-time light curve classification of transients is helpful in searching for rare transients. We propose a new algorithm based on machine learning, namely the Temporary Convective Network and Light Gradient Boosting Machine Combined with Weight Module Algorithm (TLW). The TLW algorithm can classify the photometric simulation transients data in g, r, i bands provided via PLAsTiCC, typing Tidal Disruption Event (TDE), Kilonova (KN), Type Ia supernova (SNIa), and Type I Super-luminous supernova (SLSN-I). When comparing the real-time classification results of the TLW algorithm and six other algorithms, such as Rapid, we found that the TLW algorithm has the best comprehensive performance indexes and has the advantages of high precision and high efficiency. The average accuracy of TLW is 84.54%. The average implementation timings of the TLW algorithm for classifying four types of transients is 123.09 s, which is based on TensorFlow’s architecture in windows and python. We use three indicators to prove that the TLW algorithm is superior to the classical Rapid algorithm, including Confusion Matrix, PR curve, and ROC curve. We also use the TLW algorithm to classify ZTF real transients. The real-time classification results for ZTF transients show that the accuracy of the TLW algorithm is higher than the other six algorithms.

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Searching for Short M-Dwarf Flares by Machine Learning Method

Jiang

2024

Lecture Notes in Computer Science

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Machine learning for observational cosmology

Cited by 14 publications

References 349 publications

Datacube segmentation via deep spectral clustering

Datacube segmentation via deep spectral clustering

TLW: A Real-Time Light Curve Classification Algorithm for Transients Based on Machine Learning

Searching for Short M-Dwarf Flares by Machine Learning Method

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