ALBERT

Description: We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and galaxy samples. As a use case we apply our method to CFHTLS galaxies. The ordinal classification algorithm treats distinct redshift bins as ordered values, which improves the quality of photometric redshift PDFs, compared with non-ordinal classification architectures. We also propose a new single value point estimate of the galaxy redshift, which can be used to estimate the full redshift PDF of a galaxy sample. This method is competitive in terms of accuracy with contemporary algorithms, which stack the full redshift PDFs of all galaxies in the sample, but requires orders of magnitude less storage space. The methods described in this paper greatly improve the log-likelihood of individual object redshift PDFs, when compared with a popular neural network code ( annz ). In our use case, this improvement reaches 50 per cent for high-redshift objects ( z  ≥ 0.75). We show that using these more accurate photometric redshift PDFs will lead to a reduction in the systematic biases by up to a factor of 4, when compared with less accurate PDFs obtained from commonly used methods. The cosmological analyses we examine and find improvement upon are the following: gravitational lensing cluster mass estimates, modelling of angular correlation functions and modelling of cosmic shear correlation functions.

Description: We present an analysis of anomaly detection for machine learning redshift estimation. Anomaly detection allows the removal of poor training examples, which can adversely influence redshift estimates. Anomalous training examples may be photometric galaxies with incorrect spectroscopic redshifts, or galaxies with one or more poorly measured photometric quantity. We select 2.5 million ‘clean’ SDSS DR12 galaxies with reliable spectroscopic redshifts, and 6730 ‘anomalous’ galaxies with spectroscopic redshift measurements which are flagged as unreliable. We contaminate the clean base galaxy sample with galaxies with unreliable redshifts and attempt to recover the contaminating galaxies using the Elliptical Envelope technique. We then train four machine learning architectures for redshift analysis on both the contaminated sample and on the preprocessed ‘anomaly-removed’ sample and measure redshift statistics on a clean validation sample generated without any preprocessing. We find an improvement on all measured statistics of up to 80 per cent when training on the anomaly removed sample as compared with training on the contaminated sample for each of the machine learning routines explored. We further describe a method to estimate the contamination fraction of a base data sample.

Description: We report on the identification of the new Galactic Centre (GC) transient Swift J174540.7–290015 as a likely low-mass X-ray binary located at only 16 arcsec from Sgr A * . This transient was detected on 2016 February 6, during the Swift GC monitoring, and it showed long-term spectral variations compatible with a hard- to soft-state transition. We observed the field with XMM–Newton on February 26 for 35 ks, detecting the source in the soft state, characterized by a low level of variability and a soft X-ray thermal spectrum with a high energy tail (detected by INTEGRAL up to ~50 keV), typical of either accreting neutron stars or black holes. We observed: (i) a high column density of neutral absorbing material, suggesting that Swift J174540.7–290015 is located near or beyond the GC and; (ii) a sub-Solar iron abundance, therefore we argue that iron is depleted into dust grains. The lack of detection of Fe K absorption lines, eclipses or dipping suggests that the accretion disc is observed at a low inclination angle. Radio (Very Large Array) observations did not detect any radio counterpart to Swift J174540.7–290015. No evidence for X-ray or radio periodicity is found. The location of the transient was observed also in the near-infrared (near-IR) with gamma-ray burst optical near-IR detector at MPG/European Southern Observatory La Silla 2.2 m telescope and VLT/ NaCo pre- and post-outburst. Within the Chandra error region, we find multiple objects that display no significant variations.

Description: Domestication and breeding have influenced the genetic structure of plant populations due to selection for adaptation from natural habitats to agro-ecosystems. Here, we investigate the effects of selection on the contents of 51 primary kernel metabolites and their relationships in three Triticum turgidum L. subspecies (i.e., wild emmer, emmer, durum wheat) that represent the major steps of tetraploid wheat domestication. We present a methodological pipeline to identify the signature of selection for molecular phenotypic traits (e.g., metabolites and transcripts). Following the approach, we show that a reduction in unsaturated fatty acids was associated with selection during domestication of emmer (primary domestication). We also show that changes in the amino acid content due to selection mark the domestication of durum wheat (secondary domestication). These effects were found to be partially independent of the associations that unsaturated fatty acids and amino acids have with other domestication-related kernel traits. Changes in contents of metabolites were also highlighted by alterations in the metabolic correlation networks, indicating wide metabolic restructuring due to domestication. Finally, evidence is provided that wild and exotic germplasm can have a relevant role for improvement of wheat quality and nutritional traits.

Description: We present an analysis of importance feature selection applied to photometric redshift estimation using the machine learning architecture Decision Trees with the ensemble learning routine adaboost (hereafter RDF). We select a list of 85 easily measured (or derived) photometric quantities (or ‘features’) and spectroscopic redshifts for almost two million galaxies from the Sloan Digital Sky Survey Data Release 10. After identifying which features have the most predictive power, we use standard artificial Neural Networks (aNNs) to show that the addition of these features, in combination with the standard magnitudes and colours, improves the machine learning redshift estimate by 18 per cent and decreases the catastrophic outlier rate by 32 per cent. We further compare the redshift estimate using RDF with those from two different aNNs, and with photometric redshifts available from the Sloan Digital Sky Survey (SDSS). We find that the RDF requires orders of magnitude less computation time than the aNNs to obtain a machine learning redshift while reducing both the catastrophic outlier rate by up to 43 per cent, and the redshift error by up to 25 per cent. When compared to the SDSS photometric redshifts, the RDF machine learning redshifts both decreases the standard deviation of residuals scaled by 1/(1+ z ) by 36 per cent from 0.066 to 0.041, and decreases the fraction of catastrophic outliers by 57 per cent from 2.32 to 0.99 per cent.

Description: We present optical observations of the peculiar Type Ibn supernova (SN Ibn) OGLE-2012-SN-006, discovered and monitored by the Optical Gravitational Lensing Experiment-IV survey, and spectroscopically followed by Public ESO Spectroscopic Survey of Transient Objects (PESSTO) at late phases. Stringent pre-discovery limits constrain the explosion epoch with fair precision to JD = 245 6203.8 ± 4.0. The rise time to the I -band light-curve maximum is about two weeks. The object reaches the peak absolute magnitude M I  = –19.65 ± 0.19 on JD = 245 6218.1 ± 1.8. After maximum, the light curve declines for about 25 d with a rate of 4 mag (100 d) –1 . The symmetric I -band peak resembles that of canonical Type Ib/c supernovae (SNe), whereas SNe Ibn usually exhibit asymmetric and narrower early-time light curves. Since 25 d past maximum, the light curve flattens with a decline rate slower than that of the 56 Co– 56 Fe decay, although at very late phases it steepens to approach that rate. However, other observables suggest that the match with the 56 Co decay rate is a mere coincidence, and the radioactive decay is not the main mechanism powering the light curve of OGLE-2012-SN-006. An early-time spectrum is dominated by a blue continuum, with only a marginal evidence for the presence of He i lines marking this SN type. This spectrum shows broad absorptions bluewards than 5000 Å, likely O ii lines, which are similar to spectral features observed in superluminous SNe at early epochs. The object has been spectroscopically monitored by PESSTO from 90 to 180 d after peak, and these spectra show the typical features observed in a number of SN 2006jc-like events, including a blue spectral energy distribution and prominent and narrow ( v FWHM   1900 km s –1 ) He i emission lines. This suggests that the ejecta are interacting with He-rich circumstellar material. The detection of broad (10 4 km s –1 ) O i and Ca ii features likely produced in the SN ejecta (including the [O i ] 6300,6364 doublet in the latest spectra) lends support to the interpretation of OGLE-2012-SN-006 as a core-collapse event.

Description: We present analyses of data augmentation for machine learning redshift estimation. Data augmentation makes a training sample more closely resemble a test sample, if the two base samples differ, in order to improve measured statistics of the test sample. We perform two sets of analyses by selecting 800 000 (1.7 million) Sloan Digital Sky Survey Data Release 8 (Data Release 10) galaxies with spectroscopic redshifts. We construct a base training set by imposing an artificial r -band apparent magnitude cut to select only bright galaxies and then augment this base training set by using simulations and by applying the k-correct package to artificially place training set galaxies at a higher redshift. We obtain redshift estimates for the remaining faint galaxy sample, which are not used during training. We find that data augmentation reduces the error on the recovered redshifts by 40 per cent in both sets of analyses, when compared to the difference in error between the ideal case and the non-augmented case. The outlier fraction is also reduced by at least 10 per cent and up to 80 per cent using data augmentation. We finally quantify how the recovered redshifts degrade as one probes to deeper magnitudes past the artificial magnitude limit of the bright training sample. We find that at all apparent magnitudes explored, the use of data augmentation with tree-based methods provide an estimate of the galaxy redshift with a low value of bias, although the error on the recovered redshifts increases as we probe to deeper magnitudes. These results have applications for surveys which have a spectroscopic training set which forms a biased sample of all photometric galaxies, for example if the spectroscopic detection magnitude limit is shallower than the photometric limit.

Description: We study the mid-egress eclipse timing data gathered for the cataclysmic binary HU Aquarii during the years 1993–2014. The (O–C) residuals were previously attributed to a single ~7 Jupiter mass companion in ~5 au orbit or to a stable two-planet system with an unconstrained outermost orbit. We present 22 new observations gathered between 2011 June and 2014 July with four instruments around the world. They reveal a systematic deviation of ~60–120 s from the older ephemeris. We re-analyse the whole set of the timing data available. Our results provide an erratum to the previous HU Aqr planetary models, indicating that the hypothesis for a third and fourth body in this system is uncertain. The dynamical stability criterion and a particular geometry of orbits rule out coplanar two-planet configurations. A putative HU Aqr planetary system may be more complex, e.g. highly non-coplanar. Indeed, we found examples of three-planet configurations with the middle planet in a retrograde orbit, which are stable for at least 1 Gyr, and consistent with the observations. The (O–C) may be also driven by oscillations of the gravitational quadrupole moment of the secondary, as predicted by the Lanza et al. modification of the Applegate mechanism. Further systematic, long-term monitoring of HU Aqr is required to interpret the (O–C) residuals.

Description: Motivation: In recent years, gene expression studies have increasingly made use of high-throughput sequencing technology. In turn, research concerning the appropriate statistical methods for the analysis of digital gene expression (DGE) has flourished, primarily in the context of normalization and differential analysis. Results: In this work, we focus on the question of clustering DGE profiles as a means to discover groups of co-expressed genes. We propose a Poisson mixture model using a rigorous framework for parameter estimation as well as the choice of the appropriate number of clusters. We illustrate co-expression analyses using our approach on two real RNA-seq datasets. A set of simulation studies also compares the performance of the proposed model with that of several related approaches developed to cluster RNA-seq or serial analysis of gene expression data. Availability and and implementation: The proposed method is implemented in the open-source R package HTSCluster , available on CRAN. Contact: andrea.rau@jouy.inra.fr Supplementary information: Supplementary data are available at Bioinformatics online.