Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The PAU Survey: Background light estimation with deep learning
  techniques   In any imaging survey, measuring accurately the astronomical background light
is crucial to obtain good photometry. This paper introduces BKGnet, a deep
neural network to predict the background and its associated error. BKGnet has
been developed for data from the Physics of the Accelerating Universe Survey
(PAUS), an imaging survey using a 40 narrow-band filter camera (PAUCam). Images
obtained with PAUCam are affected by scattered light: an optical effect
consisting of light multiply that deposits energy in specific detector regions
contaminating the science measurements. Fortunately, scattered light is not a
random effect, but it can be predicted and corrected for. We have found that
BKGnet background predictions are very robust to distorting effects, while
still being statistically accurate. On average, the use of BKGnet improves the
photometric flux measurements by 7% and up to 20% at the bright end. BKGnet
also removes a systematic trend in the background error estimation with
magnitude in the i-band that is present with the current PAU data management
method. With BKGnet, we reduce the photometric redshift outlier rate

37
The categories of this paper are: supr-con
5
The categories of this paper are: gr-qc
12
The categories of this paper are: nucl-th
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
26
The categories of this paper are: bayes-an
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an