Deciphering dark matter: the remarkable life of Fritz Zwicky

Swart, Jaco de

doi:10.1038/d41586-019-02603-7

Cited by 4 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…During the 1930s, while investigating the movements of galaxies within the Coma Cluster, Zwicky made a groundbreaking proposition. He observed that the visible mass within the cluster failed to account for the observed velocities of the galaxies, thereby prompting him to posit the existence of an imperceptible mass component [2].…”

Section: Introductionmentioning

confidence: 99%

The State-of-art Searching Scenarios for Dark Matter Candidate: Evidence from Axion and WIMPs

2023

HSET

View full text Add to dashboard Cite

In recent years, the dark matter still remains a mysterious in astrophysics and cosmology. So far, none of the candidate of dark matter is searched, even based on the state-of-art detection scenarios and facilities. With this in mind, this study comprehensively discusses dark matter's historical origins, research significance, and an overview of recent developments in this field. To be specific, the focus is placed on two candidate particles for dark matter: axions and Weakly Interacting Massive Particles (WIMPs). The ADMX project is discussed in detail concerning axions, emphasizing the utilization of susceptible detectors and instruments for large-scale searches across various mass ranges. Additionally, experiments such as XENON and LZ are examined to provide deeper insights into the existence of WIMPs. The article also addresses the limitations of current investigations and offers prospective directions for future research endeavors in dark matter. Overall, these results shed light on guiding further exploration of dark matter searching.

show abstract

Section: Introductionmentioning

confidence: 99%

The State-of-art Searching Scenarios for Dark Matter Candidate: Evidence from Axion and WIMPs

2023

HSET

View full text Add to dashboard Cite

show abstract

Prompts for Creativity

Lee¹,

O’Mahony

Lebeck

2023

Creativity and Innovation

View full text Add to dashboard Cite

This paper describes Difference-aware Deep continuous prompt for Contrastive Sentence Embeddings (D2CSE) that learns sentence embeddings. Compared to state-of-the-art approaches, D2CSE computes sentence vectors that are exceptional to distinguish a subtle difference in similar sentences by employing a simple neural architecture for continuous prompts. Unlike existing architectures that require multiple pretrained language models (PLMs) to process a pair of the original and corrupted (subtly modified) sentences, D2CSE avoids cumbersome fine-tuning of multiple PLMs by only optimizing continuous prompts by performing multiple tasks-i.e., contrastive learning and conditional replaced token detection all done in a self-guided manner. D2CSE overloads a single PLM on continuous prompts and greatly saves memory consumption as a result. The number of training parameters in D2CSE is reduced to about 1% of existing approaches while substantially improving the quality of sentence embeddings. We evaluate D2CSE on seven Semantic Textual Similarity (STS) benchmarks, using three different metrics, namely, Spearman's rank correlation, recall@K for a retrieval task, and the anisotropy of an embedding space measured in alignment and uniformity. Our empirical results suggest that shallow (not too meticulously devised) continuous prompts can be honed effectively for multiple NLP tasks and lead to improvements upon existing state-of-the-art approaches.

show abstract