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Vicens Gaitan

Publications and source records attributed to Vicens Gaitan.

4 recordsLinked to original sources

Scientific CMOS sensors in Astronomy: IMX455 and IMX411

Scientific complementary metal-oxide-semiconductor (CMOS) detectors have developed quickly in recent years thanks to their low cost and high availability. They also have some advantages over charge-coupled devices (CCDs), such as high frame rate or typically lower readout noise. These sensors started to be used in astronomy following the development of the first back-illuminated models. Therefore, it is worth studying their characteristics, advantages, and weaknesses. One of the most widespread CMOS sensors are those from the Sony IMX series, which are included in large astronomical survey projects based on small and fast telescopes because of their low cost, and capability for wide-field and high-cadence surveys. In this paper, we aim to characterize the IMX455M and IMX411M sensors, which are integrated into the QHY600 and QHY411 cameras, respectively, for use in astronomical observations. These are large (36 $\times$ 24 and 54 $\times$ 40 mm) native 16 bit sensors with 3.76 $μ$m pixels and are sensitive in the optical range. We present the results of the laboratory characterization of both cameras. They showed a very low dark current of 0.011 and 0.007 e$^{-}$ px$^{-1}$ s$^{-1}$ @$-$10 C for the QHY600 and QHY411 cameras, respectively. They also show the presence of warm pixels, $\sim$0.024% in the QHY600 and 0.005% in the QHY411. Warm pixels proved to be stable and linear with exposure time, and are therefore easily corrected using dark frames. Pixels affected by the Salt \& Pepper noise are $\sim$2% of the total and a method to correct for this effect is presented. Both cameras were attached to night telescopes and several on-sky tests were performed to prove their capabilities. On-sky tests demonstrate that these CMOS behave as well as CCDs of similar characteristics and (for example) they can attain photometric accuracies of a few milli-magnitudes.

astro-ph.IM

Exhaustive Neural Importance Sampling applied to Monte Carlo event generation

The generation of accurate neutrino-nucleus cross-section models needed for neutrino oscillation experiments require simultaneously the description of many degrees of freedom and precise calculations to model nuclear responses. The detailed calculation of complete models makes the Monte Carlo generators slow and impractical. We present Exhaustive Neural Importance Sampling (ENIS), a method based on normalizing flows to find a suitable proposal density for rejection sampling automatically and efficiently, and discuss how this technique solves common issues of the rejection algorithm.

hep-ex

Likelihood-free inference of experimental Neutrino Oscillations using Neural Spline Flows

In machine learning, likelihood-free inference refers to the task of performing an analysis driven by data instead of an analytical expression. We discuss the application of Neural Spline Flows, a neural density estimation algorithm, to the likelihood-free inference problem of the measurement of neutrino oscillation parameters in Long Baseline neutrino experiments. A method adapted to physics parameter inference is developed and applied to the case of the disappearance muon neutrino analysis at the T2K experiment.

hep-ph

Efficient sampling generation from explicit densities via Normalizing Flows

For many applications, such as computing the expected value of different magnitudes, sampling from a known probability density function, the target density, is crucial but challenging through the inverse transform. In these cases, rejection and importance sampling require suitable proposal densities, which can be evaluated and sampled from efficiently. We will present a method based on normalizing flows, proposing a solution for the common problem of exploding reverse Kullback-Leibler divergence due to the target density having values of 0 in regions of the flow transformation. The performance of the method will be demonstrated using a multi-mode complex density function.

cs.LG