1.5k citations · 2.9k across the 28 of their papers we have counts for
6 papers · 1 filter
Self-supervised similarity search for large scientific datasets
George Stein, Peter Harrington, Jacqueline Blaum +2
We present the use of self-supervised learning to explore and exploit large unlabeled datasets. Focusing on 42 million galaxy images from the latest data release of the Dark Energy…
Mining for Strong Gravitational Lenses with Self-supervised Learning
George Stein, Jacqueline Blaum, Peter Harrington +2
We employ self-supervised representation learning to distill information from 76 million galaxy images from the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys' Data Re…
Superclustering with the Atacama Cosmology Telescope and Dark Energy Survey: I. Evidence for thermal energy anisotropy using oriented stacking
M. Lokken, R. Hložek, A. van Engelen +104
The cosmic web contains filamentary structure on a wide range of scales. On the largest scales, superclustering aligns multiple galaxy clusters along inter-cluster bridges, visible…
The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics
Gregor Kasieczka, Benjamin Nachman, David Shih +44
A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. I…
Estimating Galactic Distances From Images Using Self-supervised Representation Learning
Md Abul Hayat, Peter Harrington, George Stein +2
We use a contrastive self-supervised learning framework to estimate distances to galaxies from their photometric images. We incorporate data augmentations from computer vision as w…
Statistical exploration of halo anisotropic clustering and intrinsic alignments with the mass-Peak Patch algorithm
Bruno Regaldo-Saint Blancard, Sandrine Codis, J. Richard Bond +1
The anisotropy or triaxiality of massive dark matter haloes largely defines the structure of the cosmic web, in particular the filaments that join the haloes together. Here we inve…