Photometric redshift estimation based on data mining with PhotoRApToR

Photometric redshifts (photo-z) are crucial to the scientific exploitation of modern panchromatic digital surveys. In this paper we present PhotoRApToR (Photometric Research Application To Redshift): a Java/C++ based desktop application capable to solve non-linear regression and multi-variate classification problems, in particular specialized for photo-z estimation. It embeds a machine learning algorithm, namely a multilayer neural network trained by the Quasi Newton learning rule, and special tools dedicated to pre- and postprocessing data. PhotoRApToR has been successfully tested on several scientific cases. The application is available for free download from the DAME Program web site.Comment: To appear on Experimental Astronomy, Springer, 20 pages, 15 figure

Tuning target selection algorithms to improve galaxy redshift estimates

We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as calculated on an independent sample, while requiring less targets to be observed. We compare the ML targeting algorithms with the Sloan Digital Sky Survey (SDSS) target order, and with a random targeting algorithm. The ML inspired algorithms are constructed iteratively by estimating which of the remaining target galaxies will be most difficult for the machine learning methods to accurately estimate redshifts using the previously observed data. This is performed by predicting the expected redshift error and redshift offset (or bias) of all of the remaining target galaxies. We find that the predicted values of bias and error are accurate to better than 10-30% of the true values, even with only limited training sample sizes. We construct a hypothetical follow-up survey and find that some of the ML targeting algorithms are able to obtain the same redshift predictive power with 2-3 times less observing time, as compared to that of the SDSS, or random, target selection algorithms. The reduction in the required follow up resources could allow for a change to the follow-up strategy, for example by obtaining deeper spectroscopy, which could improve ML redshift estimates for deeper test data.Comment: 16 pages, 9 figures, updated to match MNRAS accepted version. Minor text changes, results unchange

Deep Learning Applied to the Asteroseismic Modeling of Stars with Coherent Oscillation Modes

We develop a novel method based on machine learning principles to achieve optimal initiation of CPU-intensive computations for forward asteroseismic modeling in a multi-D parameter space. A deep neural network is trained on a precomputed asteroseismology grid containing about 62 million coherent oscillation-mode frequencies derived from stellar evolution models. These models are representative of the core-hydrogen burning stage of intermediate-mass and high-mass stars. The evolution models constitute a 6D parameter space and their predicted low-degree pressure- and gravity-mode oscillations are scanned, using a genetic algorithm. A software pipeline is created to find the best fitting stellar parameters for a given set of observed oscillation frequencies. The proposed method finds the optimal regions in the 6D parameters space in less than a minute, hence providing the optimal starting point for further and more detailed forward asteroseismic modeling in a high-dimensional context. We test and apply the method to seven pulsating stars that were previously modeled asteroseismically by classical grid-based forward modeling based on a $\chi^2$ statistic and obtain good agreement with past results. Our deep learning methodology opens up the application of asteroseismic modeling in +6D parameter space for thousands of stars pulsating in coherent modes with long lifetimes observed by the $Kepler$ space telescope and to be discovered with the TESS and PLATO space missions, while applications so far were done star-by-star for only a handful of cases. Our method is open source and can be used by anyone freely.Comment: Accepted for publication in PASP Speciale Volume on Machine Learnin

Using machine learning to classify the diffuse interstellar bands

Using over a million and a half extragalactic spectra we study the correlations of the Diffuse Interstellar Bands (DIBs) in the Milky Way. We measure the correlation between DIB strength and dust extinction for 142 DIBs using 24 stacked spectra in the reddening range E(B-V) < 0.2, many more lines than ever studied before. Most of the DIBs do not correlate with dust extinction. However, we find 10 weak and barely studied DIBs with correlations that are higher than 0.7 with dust extinction and confirm the high correlation of additional 5 strong DIBs. Furthermore, we find a pair of DIBs, 5925.9A and 5927.5A which exhibits significant negative correlation with dust extinction, indicating that their carrier may be depleted on dust. We use Machine Learning algorithms to divide the DIBs to spectroscopic families based on 250 stacked spectra. By removing the dust dependency we study how DIBs follow their local environment. We thus obtain 6 groups of weak DIBs, 4 of which are tightly associated with C2 or CN absorption lines.Comment: minor changes, MNRAS accepte

A Census of Large-Scale (≥\ge 10 pc), Velocity-Coherent, Dense Filaments in the Northern Galactic Plane: Automated Identification Using Minimum Spanning Tree

Large-scale gaseous filaments with length up to the order of 100 pc are on the upper end of the filamentary hierarchy of the Galactic interstellar medium. Their association with respect to the Galactic structure and their role in Galactic star formation are of great interest from both observational and theoretical point of view. Previous "by-eye" searches, combined together, have started to uncover the Galactic distribution of large filaments, yet inherent bias and small sample size limit conclusive statistical results to be drawn. Here, we present (1) a new, automated method to identify large-scale velocity-coherent dense filaments, and (2) the first statistics and the Galactic distribution of these filaments. We use a customized minimum spanning tree algorithm to identify filaments by connecting voxels in the position-position-velocity space, using the Bolocam Galactic Plane Survey spectroscopic catalog. In the range of $7.^{\circ}5 \le l \le 194^{\circ}$, we have identified 54 large-scale filaments and derived mass ($\sim 10^3 - 10^5 \, M_\odot$), length (10-276 pc), linear mass density (54-8625 $M_\odot \, \rm{pc}^{-1}$), aspect ratio, linearity, velocity gradient, temperature, fragmentation, Galactic location and orientation angle. The filaments concentrate along major spiral arms. They are widely distributed across the Galactic disk, with 50% located within $\pm$20 pc from the Galactic mid-plane and 27% run in the center of spiral arms (aka "bones"). An order of 1% of the molecular ISM is confined in large filaments. Massive star formation is more favorable in large filaments compared to elsewhere. This is the first comprehensive catalog of large filaments useful for a quantitative comparison with spiral structures and numerical simulations.Comment: Accepted to ApJS. 20 pages (in aastex6 compact format), 6 figures, 1 table. See http://www.eso.org/~kwang/MSTpaper for (1) a preprint with full resolution Fig 6, (2) filaments catalog (Table 1) in ASCII format, and (3) a DS9 region file for the coordinates of the filament