Emmanuel Caux
Emmanuel Caux is a CNRS senior research scientist at the Institute of Research in Astrophysics and Planetology (IRAP/UPS-CNRS-CNES, Toulouse, France). He received his PhD at University of Toulouse. He was the PI of the AGLAE83 balloon-borne instrument, a co-I of the LWS instrument onboard the ESA Infrared Space Observatory, and a co-PI of the HIFI instrument on board the ESA Herschel Submillimeter Observatory. His fields of interest are infrared and submillimetre space instrumentation and the study of the interstellar medium and the star formation, particularly via modelling high spectral resolution observations.
Affiliation –IRAP/CNRS-UPS-CNRS, Toulouse, France
Position –Senior Researcher
Sessions
Astronomical spectra can be made up of hundreds or even thousands of emission and absorption lines. Astronomers need to identify each line in order to be able to determine the physical conditions of the objects studied as the temperature of the sources and the column densities of the observed species (molecules and/or atoms). The constant improvement of instruments in terms of sensitivity, instantaneous spectral range, and spatial resolution capabilities produces a mass of broad spectra with high spectral resolution for which handmade line identification is ineffective and even maybe impossible.
With the advent of BIG DATA, AI algorithms have proven to be very effective in solving complex problems (mainly related to classification and prediction tasks) for many different fields including astrophysics. The aim of this study is to automate the identification of species from their observed lines in rich astronomical spectra by combining methods in signal processing and machine learning with expert knowledge.
This talk will cover three solutions based on (1) wavelets transform, expert knowledge and decision trees to identify the species associated to each spectral line, (2) Artificial Neural Networks to predict if a species is present in a spectrum or not and (3) a greedy algorithm that simulate successively the presence of each species of the database (and its isotopes) in order to check its correspondance in the spectrum. Last, we combine and compare these methods to improve our results.
The results of our research, using an ALMA spectrum very rich in molecular lines combined with the use of CDMS and JPL molecular spectroscopic databases, have already allowed us to find a molecule that had never been detected in the spectrum experimented.
CASSIS and Aladin interfaced for a VO-compliant spectral data cube analysis tool
J.M Glorian, P. Fernique, T.Boch, M.Boiziot, F.Bonnarel, C.Bot, S.Bottinelli, E.Caux, A.Coutens, M.Louys, C.Vastel
Spectral cubes are becoming usual data products in astronomy. This is true in various spectral domains due to the high rate data production of large projects such as MUSE in optical, LOFAR, ALMA, VLA, NOEMA or ASKAP in radio astronomy, or Chandra and XMM in Xray. And this is only a hint of what will happen with the emergence of SKA or other Petascale projects in a near future. These cubes are generally on line and easy to be found and accessed due to the great number of VO services which distribute them. Efficient Display and Analysis of such spectral cubes is a big challenge.
In this context the CASSIS team at IRAP (http://cassis.irap.omp.eu) and the Aladin team at CDS (https://aladin.u-strasbg.fr/) decided to work together on the combination of their VO applications in order to create a tool able to explore both spatial and spectral dimensions of the cubes.
CASSIS is a java tool able to discover spectra in remote services via the SSAP protocol and analyse them. It provides functionalities such as spectrum display, spectral line identification, prediction of spectra from any telescope, comparison of spectra with various models and determination of the physical parameters of the sources.
Aladin is also a java tool able to discover images and cubes and display them (either in standard bitmap format or in the IVOA HiPS format (https://www.ivoa.net/documents/HiPS/) and catalogs available in the Virtual Observatory landscape. It allows transformation, overlays and comparison of data. Data discovery makes use of specific VO features such as MOCs (https://www.ivoa.net/documents/MOC/20210324/index.html) both in spatial and time dimension.
The focus demo will show how the two Desktop applications have been extended by a common dedicated interface in such a way that they behave together like a spectral cube analysis tool. For example CASSIS can analyse a spectrum built on the fly by Aladin after hand selection and combination of voxels in a specific area of a spectral cube. Reversely CASSIS allows to select spectral ranges on such spectra and ask Aladin to display 2D images combining the corresponding spectral planes in the cube.
The tool can work both on local data available on the user's disk or on cubes discovered via the VO registry and within VO services. We will demonstrate both modes.
More sophisticated developments will occur in the future and will be announced at the end of the demo.