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Machine learning for big data in galactic archaeology


par Loana Ramuz
Université de Strasbourg - Master 2 Astrophysique  2020
  

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1.2 The challenge of distances in Astrophysics

Astrophysics is the discipline within science that focuses on the understanding of our Universe. To get a glimpse of the fabric of its gigantic structure, it is important to determine the place of the objects within it. Various types of coordinates were developed to locate objects depending on their place in our sky, but the crucial and most difficult parameter to estimate is their heliocentric distance.

Spectroscopy provides useful data to estimate those distances. In fact, spectral analysis of stars can provide information on their absolute magnitude. The apparent magnitude of an object is a measure of its luminosity when observed from Earth, on an inverse logarithmic scale. These apparent magnitudes are biased by extinction from dust located in the interstellar medium. The absolute magnitude of an object is its apparent magnitude if it were observed from a distance of 10 parsecs, and corrected for extinction, so it is distance-independent. Thus, using those two magnitudes, it is possible to extract distances. This technique is called spectroscopic parallax, in contrast to true parallax which is a geometric measurement. Recently, the European Space Agency's Gaia mission has published over a billion (true) parallaxes of stars in the sky, providing a huge leap in our understanding of the structure of the Milky Way.

However, the accuracy of Gaia's parallax measurements decrease rapidly with distance, and it is useful to consider alternative methods of distance estimation. To estimate absolute magnitude, a metallicity-based technique was developed by Iveziéet al. [2].

In Astrophysics, every element heavier than hydrogen or helium is referred to as a metal. This comes from the fact that those metals are really rare compared to hydrogen and helium which constitute 98% of the mass of our baryonic Universe. That is why astrophysicists use the shorthand of metallicity to refer to chemical composition. It is often quantified by the [Fe/H] ratio defined as in Equation 1.2.1, where Nx are the abundances in element x and the e index refers to the values of the Sun. The spectrum of an object varies substantially as a function of its metallicity, since the elements composing it produce absorption rays. Hence Iveziéet al.'s approach of specifically taking into account the metallicity of an object when computing its magnitude is a reasonable data analysis strategy.

[Fe/H] = log10

(NFe/NH)e

NFe

-- log10 (1.2.1)
(NFe)e

NFe/NH

2

One issue with spectrometric data is that it is very costly in instruments and time to acquire it. In fact, spectroscopy requires the observatory to target a specific object to measure its parameters and the exposure times can be huge for faint objects.

An alternative to real spectroscopy is to build up a very low-resolution spectrum based on multi-band photometric data. Those are much quicker to obtain since imaging cameras can record numerous objects (up to many millions) at the same time. Indeed, photometric data are obtain by using cameras with various filters (hence the "multi-band" qualification) which allows one to cover all the wavelengths tackled in spectroscopy. Nowadays, with various surveys like Gaia, Pan-STARRS or SDSS and others, a huge amount of photometric data is available. This takes astrophysics into the world of big data, which

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is of no surprise since the number of objects in the Universe itself is tremendous. And with big data it is natural to consider using techniques like machine learning.

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