Self-supervised component separation for the extragalactic submillimeter sky

Bonjean, V.; Tanimura, H.; Aghanim, N.; Bonnaire, T.; Douspis, M.

doi:10.48550/arxiv.2212.02847

Cited by 3 publications

(3 citation statements)

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“…Many realizations of wide-area, high-resolution maps generated by ML methods can be used to study potential systematic errors and to evaluate covariance matrices, which are crucial for precise cosmological analysis. The use of conditional generative models are also proposed for other purposes such as removing the foreground [172], separation of each component [173], reconstruction of lensing map [174] and in-painting of masked regions [175][176][177][178]. To utilize all-sky data, one can extend a traditional 2D CNN to be applied to images on a sphere.…”

Section: Intensity Mappingmentioning

confidence: 99%

Machine Learning for Observational Cosmology

Moriwaki¹,

Nishimichi²,

Yoshida³

2023

Preprint

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An array of large observational programs using ground-based and spaceborne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires communitywide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

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Section: Intensity Mappingmentioning

confidence: 99%

Machine Learning for Observational Cosmology

Moriwaki¹,

Nishimichi²,

Yoshida³

2023

Preprint

View full text Add to dashboard Cite

show abstract

Section: Intensity Mappingmentioning

confidence: 99%

Machine learning for observational cosmology

2023

View full text Add to dashboard Cite

An array of large observational programs using ground-based and space-borne telescopes is planned in the next decade. The forthcoming wide-field sky surveys are expected to deliver a sheer volume of data exceeding an exabyte. Processing the large amount of multiplex astronomical data is technically challenging, and fully automated technologies based on machine learning and artificial intelligence are urgently needed. Maximizing scientific returns from the big data requires community-wide efforts. We summarize recent progress in machine learning applications in observational cosmology. We also address crucial issues in high-performance computing that are needed for the data processing and statistical analysis.

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“…In [54] this same technique was used to construct representations for CWoLa-based anomaly detection. In addition to these works, other self-supervised / representation learning techniques have been applied in particle physics [55,56] and in other scientific disciplines such as astrophysics [57][58][59][60]. In [53,54] the augmentations corresponded to transformations of the event to which the underlying physics should be invariant to rotations or translations, but also soft-collinear parton splittings.…”

Section: Introductionmentioning

confidence: 99%

Anomalies, Representations, and Self-Supervision

Dillon¹,

Favaro²,

Feiden³

et al. 2023

Preprint

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We develop a self-supervised method for density-based anomaly detection using contrastive learning, and test it using event-level anomaly data from CMS ADC2021. The Anomaly-CLR technique is data-driven and uses augmentations of the background data to mimic non-Standard-Model events in a model-agnostic way. It uses a permutation-invariant Transformer Encoder architecture to map the objects measured in a collider event to the representation space, where the data augmentations define a representation space which is sensitive to potential anomalous features. An AutoEncoder trained on background representations then computes anomaly scores for a variety of signals in the representation space. With AnomalyCLR we find significant improvements on performance metrics for all signals when compared to the raw data baseline.

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Self-supervised component separation for the extragalactic submillimeter sky

Cited by 3 publications

References 0 publications

Machine Learning for Observational Cosmology

Machine Learning for Observational Cosmology

Machine learning for observational cosmology

Anomalies, Representations, and Self-Supervision

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