Cherenkov Telescope Array Status Report

Mangano, S.

doi:10.48550/arxiv.1705.07805

Cited by 3 publications

(2 citation statements)

References 20 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Important sensitivity improvements are also expected, within the next decade, in direct and indirect dark matter detection experiments. On the indirect detection front, the most significant one will likely come from CTA [105], the Cherenkov Telescope Array. With more than 100 telescopes located in both hemispheres, CTA will play a crucial role in indirect (via gamma-rays) dark matter searches [54].…”

Section: Current Limits and Future Sensitivitiesmentioning

confidence: 99%

On the role of neutrinos telescopes in the search for Dark Matter annihilations in the Sun

Fornengo

Masiero

Queiroz

et al. 2017

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

The observation of GeV neutrinos coming from the Sun would be an unmistakable signal of dark matter. Current neutrino detectors have so far failed to detect such a signal, however, and bounds from direct and indirect dark matter searches may significantly restrict the possibility of observing it in future experiments such as Hyper-Kamiokande or IceCube-Gen2. In this work we assess, in the light of current data and of expected experimental sensitivities, the prospects for the detection of a neutrino signal from dark matter annihilations in the Sun. To be as general as possible, equilibrium between the capture and the annihilation rates in the Sun is not assumed in our analysis; instead, the dark matter scattering and annihilation cross sections are taken as free and independent parameters. We consider capture via both spin-dependent and spin-independent interactions, and annihilations into three representative final states: bb, W + W − , and τ + τ − . We find that when the capture in the Sun is dominated by spin-independent interactions, current direct detection bounds already preclude the observation of a neutrino signal in future experiments. For capture via spin-dependent interactions, a strong complementarity is observed, over most of the parameter space, between future neutrino detectors and planned direct and indirect dark matter detection experiments, such as PICO-500 and CTA. In this case, we also identify some regions of the parameter space that can be probed, via the neutrino flux from the Sun, only by future neutrino experiments.

show abstract

Section: Current Limits and Future Sensitivitiesmentioning

confidence: 99%

On the role of neutrinos telescopes in the search for Dark Matter annihilations in the Sun

Fornengo

Masiero

Queiroz

et al. 2017

J. Cosmol. Astropart. Phys.

View full text Add to dashboard Cite

show abstract

“…The Cherenkov Telescope Array [19,20] (CTA) will be the next generation ground-based gamma-ray observatory to study very high energy processes in the Universe. The main goal of CTA is to identify and study high energy gamma-ray sources, including objects such as supernova remnants, pulsars, binary stars and active galaxies.…”

Section: Introductionmentioning

confidence: 99%

Extracting Gamma-Ray Information from Images with Convolutional Neural Network Methods on Simulated Cherenkov Telescope Array Data

Mangano

Delgado

Bernardos

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The Cherenkov Telescope Array (CTA) will be the world's leading ground-based gamma-ray observatory allowing us to study very high energy phenomena in the Universe. CTA will produce huge data sets, of the order of petabytes, and the challenge is to find better alternative data analysis methods to the already existing ones. Machine learning algorithms, like deep learning techniques, give encouraging results in this direction. In particular, convolutional neural network methods on images have proven to be effective in pattern recognition and produce data representations which can achieve satisfactory predictions. We test the use of convolutional neural networks to discriminate signal from background images with high rejections factors and to provide reconstruction parameters from gamma-ray events. The networks are trained and evaluated on artificial data sets of images. The results show that neural networks trained with simulated data can be useful to extract gamma-ray information. Such networks would help us to make the best use of large quantities of real data coming in the next decades.

show abstract

Artificial Neural Networks in Pattern Recognition

Mangano¹,

Delgado²,

Bernardos³

et al. 2018

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Bounded rationality investigates utility-optimizing decisionmakers with limited information-processing power. In particular, information theoretic bounded rationality models formalize resource constraints abstractly in terms of relative Shannon information, namely the Kullback-Leibler Divergence between the agents' prior and posterior policy. Between prior and posterior lies an anytime deliberation process that can be instantiated by sample-based evaluations of the utility function through Markov Chain Monte Carlo (MCMC) optimization. The most simple model assumes a fixed prior and can relate abstract informationtheoretic processing costs to the number of sample evaluations. However, more advanced models would also address the question of learning, that is how the prior is adapted over time such that generated prior proposals become more efficient. In this work we investigate generative neural networks as priors that are optimized concurrently with anytime sample-based decision-making processes such as MCMC. We evaluate this approach on toy examples.

show abstract

Cherenkov Telescope Array Status Report

Cited by 3 publications

References 20 publications

On the role of neutrinos telescopes in the search for Dark Matter annihilations in the Sun

On the role of neutrinos telescopes in the search for Dark Matter annihilations in the Sun

Extracting Gamma-Ray Information from Images with Convolutional Neural Network Methods on Simulated Cherenkov Telescope Array Data

Artificial Neural Networks in Pattern Recognition

Contact Info

Product

Resources

About