Gamma-ray Bursts as distance indicators through a machine learning approach. (arXiv:1907.05074v1 [astro-ph.HE])

Gamma-ray Bursts as distance indicators through a machine learning approach. (arXiv:1907.05074v1 [astro-ph.HE])
<a href="http://arxiv.org/find/astro-ph/1/au:+Dainotti_M/0/1/0/all/0/1">Maria Dainotti</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Petrosian_V/0/1/0/all/0/1">Vah&#xe9; Petrosian</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bogdan_M/0/1/0/all/0/1">Malgorzata Bogdan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Miasojedow_B/0/1/0/all/0/1">Blazej Miasojedow</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nagataki_S/0/1/0/all/0/1">Shigehiro Nagataki</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hastie_T/0/1/0/all/0/1">Trevor Hastie</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Nuyngen_Z/0/1/0/all/0/1">Zooey Nuyngen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gilda_S/0/1/0/all/0/1">Sankalp Gilda</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hernandez_X/0/1/0/all/0/1">Xavier Hernandez</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Krol_D/0/1/0/all/0/1">Dominika Krol</a>

Gamma-ray bursts (GRBs) are spectacularly energetic events, with the
potential to inform on the early universe and its evolution, once their
redshifts are known. Unfortunately, determining redshifts is a painstaking
procedure requiring detailed follow-up multi-wavelength observations often
involving various astronomical facilities, which have to be rapidly pointed at
these serendipitous events. Here we use Machine Learning algorithms to infer
redshifts from a collection of observed temporal and spectral features of GRBs.
We obtained a very high correlation coefficient ($0.96$) between the inferred
and the observed redshifts, and a small dispersion (with a mean square error of
$0.003$) in the test set. The addition of plateau afterglow parameters improves
the predictions by $61.4%$ compared to previous results. The GRB luminosity
function and cumulative density rate evolutions, obtained from predicted and
observed redshift are in excellent agreement indicating that GRBs are effective
distance indicators and a reliable step for the cosmic distance ladder.

Gamma-ray bursts (GRBs) are spectacularly energetic events, with the
potential to inform on the early universe and its evolution, once their
redshifts are known. Unfortunately, determining redshifts is a painstaking
procedure requiring detailed follow-up multi-wavelength observations often
involving various astronomical facilities, which have to be rapidly pointed at
these serendipitous events. Here we use Machine Learning algorithms to infer
redshifts from a collection of observed temporal and spectral features of GRBs.
We obtained a very high correlation coefficient ($0.96$) between the inferred
and the observed redshifts, and a small dispersion (with a mean square error of
$0.003$) in the test set. The addition of plateau afterglow parameters improves
the predictions by $61.4%$ compared to previous results. The GRB luminosity
function and cumulative density rate evolutions, obtained from predicted and
observed redshift are in excellent agreement indicating that GRBs are effective
distance indicators and a reliable step for the cosmic distance ladder.

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