A Stochastic LBFGS Algorithm for Radio Interferometric Calibration
| Autor: | H. Spreeuw, Faruk Diblen, Sarod Yatawatta, Lukas De Clercq |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science business.industry Deep learning FOS: Physical sciences Time duration Machine Learning (cs.LG) Interferometry Orders of magnitude (time) Optimization and Control (math.OC) Fine resolution Calibration FOS: Mathematics Deep neural networks Artificial intelligence Raw data business Astrophysics - Instrumentation and Methods for Astrophysics Algorithm Mathematics - Optimization and Control Instrumentation and Methods for Astrophysics (astro-ph.IM) |
| Zdroj: | DSW |
| DOI: | 10.48550/arxiv.1904.05619 |
| Popis: | We present a stochastic, limited-memory Broyden Fletcher Goldfarb Shanno (LBFGS) algorithm that is suitable for handling very large amounts of data. A direct application of this algorithm is radio interferometric calibration of raw data at fine time and frequency resolution. Almost all existing radio interferometric calibration algorithms assume that it is possible to fit the dataset being calibrated into memory. Therefore, the raw data is averaged in time and frequency to reduce its size by many orders of magnitude before calibration is performed. However, this averaging is detrimental for the detection of some signals of interest that have narrow bandwidth and time duration such as fast radio bursts (FRBs). Using the proposed algorithm, it is possible to calibrate data at such a fine resolution that they cannot be entirely loaded into memory, thus preserving such signals. As an additional demonstration, we use the proposed algorithm for training deep neural networks and compare the performance against the mainstream first order optimization algorithms that are used in deep learning. Comment: Draft, final version in IEEE Data Science Workshop 2019 proceedings |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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