Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Huang, Yafang; Ren, Yihui; Yoo, Shinjae; Huang, Jin

doi:10.1109/icmla52953.2021.00179

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…AE based compression of scientific data has shown promising results for multiple fields of study such as meteorology, cosmology, computational fluid dynamics, crystallography etc. [13][14][15][16][17][18][19]. The use of AEs for data compression in High Energy Physics (HEP) has also shown promising results in previous studies [20][21][22][23].…”

Section: Autoencoders For Lossy Data Compressionmentioning

confidence: 99%

Baler - Machine Learning Based Compression of Scientific Data

Bengtsson Folkesson,

Doglioni,

Ekman

et al. 2024

EPJ Web of Conf.

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Section: Autoencoders For Lossy Data Compressionmentioning

confidence: 99%

Baler - Machine Learning Based Compression of Scientific Data

Bengtsson Folkesson,

Doglioni,

Ekman

et al. 2024

EPJ Web of Conf.

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show abstract

“…Despite their recent appearance in the physics literature, AEs have already been studied for several interesting HEP applications. On one hand, AEs can be used to compress data by storing the much lighter latent space representation [4][5][6]. On the other hand, AEs average out the noise from different data examples during training and, therefore, they have great potential to denoise signals [7][8][9].…”

Section: Autoencodersmentioning

confidence: 99%

Data-driven detector signal characterization with constrained bottleneck autoencoders

Jesús-Valls¹,

Lux²,

Sánchez³

2022

J. Inst.

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A common technique in high energy physics is to characterize the response of a detector by means of models tuned to data which build parametric maps from the physical parameters of the system to the expected signal of the detector. When the underlying model is unknown it is difficult to apply this method, and often, simplifying assumptions are made introducing modeling errors. In this article, using a waveform toy model we present how deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The results show that excellent performance results can be achieved even when the signals are significantly affected by random noise. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.

show abstract

Section: Autoencodersmentioning

confidence: 99%

Data-driven detector signal characterization with constrained bottleneck autoencoders

Jesús-Valls,

Lux,

Sánchez

2022

Preprint

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A: A common technique in high energy physics is to characterize the response of a detector by means of models tuned to data which build parametric maps from the physical parameters of the system to the expected signal of the detector. When the underlying model is unknown it is difficult to apply this method, and often, simplifying assumptions are made introducing modeling errors. In this article, using a waveform toy model we present how deep learning in the form of constrained bottleneck autoencoders can be used to learn the underlying unknown detector response model directly from data. The results show that excellent performance results can be achieved even when the signals are significantly affected by random noise. The trained algorithm can be used simultaneously to perform estimations on the physical parameters of the model, simulate the detector response with high fidelity and to denoise detector signals.

show abstract

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Cited by 5 publications

References 24 publications

Baler - Machine Learning Based Compression of Scientific Data

Baler - Machine Learning Based Compression of Scientific Data

Data-driven detector signal characterization with constrained bottleneck autoencoders

Data-driven detector signal characterization with constrained bottleneck autoencoders

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