Zobrazeno 1 - 10
of 1 164
pro vyhledávání: '"Shimmin , Chase"'
We introduce a novel machine learning method developed for the fast simulation of calorimeter detector response, adapting vector-quantized variational autoencoder (VQ-VAE). Our model adopts a two-stage generation strategy: initially compressing geome
Externí odkaz:
http://arxiv.org/abs/2405.06605
Many datasets in scientific and engineering applications are comprised of objects which have specific geometric structure. A common example is data which inhabits a representation of the group SO$(3)$ of 3D rotations: scalars, vectors, tensors, \text
Externí odkaz:
http://arxiv.org/abs/2303.11288
Autor:
Bogatskiy, Alexander, Ganguly, Sanmay, Kipf, Thomas, Kondor, Risi, Miller, David W., Murnane, Daniel, Offermann, Jan T., Pettee, Mariel, Shanahan, Phiala, Shimmin, Chase, Thais, Savannah
Physical theories grounded in mathematical symmetries are an essential component of our understanding of a wide range of properties of the universe. Similarly, in the domain of machine learning, an awareness of symmetries such as rotation or permutat
Externí odkaz:
http://arxiv.org/abs/2203.06153
A scientific instrument comprised of a global network of millions of independent, connected, remote devices presents unique data acquisition challenges. We describe the software design of a mobile application which collects data from smartphone camer
Externí odkaz:
http://arxiv.org/abs/2108.04803
A measurement of the efficiency of CMOS sensors in smartphone cameras to cosmic ray muons is presented. A coincidence in external scintillators indicates the passage of a cosmic ray muon, allowing the measurement of the efficiency of the CMOS sensor.
Externí odkaz:
http://arxiv.org/abs/2107.06332
Autor:
Shimmin, Chase
We introduce the Particle Convolution Network (PCN), a new type of equivariant neural network layer suitable for many tasks in jet physics. The particle convolution layer can be viewed as an extension of Deep Sets and Energy Flow network architecture
Externí odkaz:
http://arxiv.org/abs/2107.02908
Autor:
Nachman, Benjamin, Shimmin, Chase
The field of high-energy physics (HEP), along with many scientific disciplines, is currently experiencing a dramatic influx of new methodologies powered by modern machine learning techniques. Over the last few years, a growing body of HEP literature
Externí odkaz:
http://arxiv.org/abs/1910.08606
Our team of dance artists, physicists, and machine learning researchers has collectively developed several original, configurable machine-learning tools to generate novel sequences of choreography as well as tunable variations on input choreographic
Externí odkaz:
http://arxiv.org/abs/1907.05297
The CRAYFIS experiment proposes to use privately owned mobile phones as a ground detector array for Ultra High Energy Cosmic Rays. Upon interacting with Earth's atmosphere, these events produce extensive particle showers which can be detected by came
Externí odkaz:
http://arxiv.org/abs/1709.08605
Autor:
Shimmin, Chase, Sadowski, Peter, Baldi, Pierre, Weik, Edison, Whiteson, Daniel, Goul, Edward, Søgaard, Andreas
Publikováno v:
Phys. Rev. D 96, 074034 (2017)
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in backg
Externí odkaz:
http://arxiv.org/abs/1703.03507