International Conference on Machine Learning for Astrophysics - ML4Astro

Session

Poster Session Day 3.2

1 Jun 2022, 17:10

Catania

81. matryoshka: A suite of neural network based emulators for the power spectrum.

Jamie Donald-McCann (University of Portsmouth)

01/06/2022, 17:10

Poster Presentation

Cosmological inference can be a computationally expensive task. Calculation of posterior distributions often requires sampling form a high dimensional parameter space, with a large number of nuisance parameters. Typically analyses require hundreds of thousands or even millions of model evaluations. Therefore, even analyses that use the most efficient perturbative models for predicting the...

119. Integration and deployment of Model Serving Framework to serve machine learning models at production scale in easy way.

Francesco Caronte

01/06/2022, 17:13

Poster Presentation

With the help of machine learning systems, we can examine data, learn from that data and make decisions. Now machine learning projects have become more relevant to various use case, but too many models are difficult to manage. For this reason several MLOps tools were born. These tools are the main platforms, hosting the full machine learning process lifecycle, starting with data management and...

116. Predictive Maintenance for a Cherenkov Telescope array

Salvatore Gambadoro (Unict-Inaf)

01/06/2022, 17:16

Poster Presentation

Cherenkov telescope arrays are equipped with a multitude of sensors
spread all over the instrumentation and collect a large volume of
housekeeping and auxiliary data coming from telescopes, weather
stations and other devices in the array site.
In this poster we will present how we intend to exploit the sensor's
information, together with the most advanced artificial...

77. Reconstruction and Particle Identification with CYGNO detector

Atul Prajapati (Gran Sasso Science Institute -INFN)

01/06/2022, 17:19

Poster Presentation

CYGNO is developing a gaseous Time Projection Chamber (TPC) for directional dark matter searches, to be hosted at Laboratori Nazionali del Gran Sasso (LNGS), Italy. CYGNO uses He:CF4 gas mixture at atmospheric pressure and relies on Gas Electron Multipliers (GEMs) stack for the charge amplification. Light is produced by the electrons avalanche thanks to the CF4 scintillation properties and is...

102. Deep neural networks for single-line event direction reconstruction in ANTARES

Juan García Méndez (Universidad Politécnica de Valencia)

01/06/2022, 17:23

Poster Presentation

ANTARES, the first large undersea neutrino telescope, has recently stopped taking data after nearly 16 years of operation. ANTARES consisted of 12 vertical lines forming a 3D array of photo-sensors, instrumenting about 10 megatons of Mediterranean seawater to detect Cherenkov light induced by secondary particles from neutrino interactions.

The event reconstruction and background...

56. Machine-learning based detector optimization of the future P-ONE neutrino telescope

Christian Haack (Technical University of Munich)

01/06/2022, 17:27

Poster Presentation

P-ONE is a planned cubic-kilometer-scale neutrino detector in the Pacific ocean. Similar to the successful IceCube Neutrino Observatory, P-ONE will measure high-energy astrophysical neutrinos to help characterize the nature of astrophysical accelerators. Using existing deep-sea infrastructure provided by Ocean Networks Canada (ONC), P-ONE will instrument the ocean with optical modules - which...

68. Cats and Dogs vs Gamma and Hadrons

Francesco Visconti (Istituto Nazionale di Astrofisica (INAF))

01/06/2022, 17:31

Poster Presentation

A simple Convolutional Neural Network architecture suitable for cats and dogs classification can discriminate gammas from hadrons on Montacarlo data for a single ASTRI telescope, much better than classic methods based on Hillas parameters.

141. Background discrimination for DSNB detection using high-resolution convolutional neural networks

David Maksimović (Johannes Gutenberg-University Mainz)

01/06/2022, 17:35

Poster Presentation

The Diffuse Supernova Neutrino Background (DSNB) is the faint signal of all core-collapse supernovae explosions on cosmic scales. A prime method for detecting the DSNB is finding its inverse beta decay (IBD) signatures in Gadolinium-loaded large water Cherenkov detectors like Super-Kamiokande (SK-GD).Here, we report on a novel machine learning method based on Convolutional Neural Networks...