Deep learning-based variational autoencoder for classification of quantum and classical states of light

no code implementations • 8 May 2024 • Mahesh Bhupati, Abhishek Mall, Anshuman Kumar, Pankaj K. Jha

Advancements in optical quantum technologies have been enabled by the generation, manipulation, and characterization of light, with identification based on its photon statistics.

Classification Transfer Learning +1

Holographic single particle imaging for weakly scattering, heterogeneous nanoscale objects

1 code implementation • 19 Oct 2022 • Abhishek Mall, Kartik Ayyer

Single particle imaging (SPI) at X-ray free electron lasers (XFELs) is a technique to determine the 3D structure of nanoscale objects like biomolecules from a large number of diffraction patterns of copies of these objects in random orientations.

Retrieval

Unsupervised learning approaches to characterize heterogeneous samples using X-ray single particle imaging

no code implementations • 13 Sep 2021 • Yulong Zhuang, Salah Awel, Anton Barty, Richard Bean, Johan Bielecki, Martin Bergemann, Benedikt J. Daurer, Tomas Ekeberg, Armando D. Estillore, Hans Fangohr, Klaus Giewekemeyer, Mark S. Hunter, Mikhail Karnevskiy, Richard A. Kirian, Henry Kirkwood, Yoonhee Kim, Jayanath Koliyadu, Holger Lange, Romain Letrun, Jannik Lübke, Abhishek Mall, Thomas Michelat, Andrew J. Morgan, Nils Roth, Amit K. Samanta, Tokushi Sato, Zhou Shen, Marcin Sikorski, Florian Schulz, John C. H. Spence, Patrik Vagovic, Tamme Wollweber, Lena Worbs, P. Lourdu Xavier, Oleksandr Yefanov, Filipe R. N. C. Maia, Daniel A. Horke, Jochen Küpper, N. Duane Loh, Adrian P. Mancuso, Henry N. Chapman, Kartik Ayyer

One of the outstanding analytical problems in X-ray single particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and that even identical objects can yield patterns that vary greatly when orientation is taken into consideration.