activity
20182021
most citedEffectively using unsupervised machine learning in next generation astronomical surveys

1 citations · 1 across the 1 of their papers we have counts for

collaborators

5 papers

astro-ph.CO2021

The subtlety of Ly-a photons: changing the expected range of the 21-cm signal

Itamar Reis, Anastasia Fialkov, Rennan Barkana

We present the evolution of the 21-cm signal from cosmic dawn and the epoch of reionization (EoR) in an upgraded model including three subtle effects of Ly-a radiation: Ly-a heatin…

astro-ph.CO2020

High-redshift radio galaxies: a potential new source of 21-cm fluctuations

Itamar Reis, Anastasia Fialkov, Rennan Barkana

Radio sources are expected to have formed at high redshifts, producing an excess radiation background above the cosmic microwave background (CMB) at low frequencies. Their effect o…

astro-ph.IM20191 cited

Effectively using unsupervised machine learning in next generation astronomical surveys

Itamar Reis, Michael Rotman, Dovi Poznanski +2

In recent years many works have shown that unsupervised Machine Learning (ML) can help detect unusual objects and uncover trends in large astronomical datasets, but a few challenge…

astro-ph.IM2018

Probabilistic Random Forest: A machine learning algorithm for noisy datasets

Itamar Reis, Dalya Baron, Sahar Shahaf

Machine learning (ML) algorithms become increasingly important in the analysis of astronomical data. However, since most ML algorithms are not designed to take data uncertainties i…

astro-ph.IM2018

Redshifted broad absorption line quasars found via machine-learned spectral similarity

Itamar Reis, Dovi Poznanski, Patrick B. Hall

We report the discovery of 31 new redshifted broad absorption line quasars (RSBALs) from the Sloan Digital Sky Survey (SDSS). The number of previously known such objects is 19. The…