Ultraviolet and Optical Observations of OB Associations and Field Stars in the Southwest Region of the Large Magellanic Cloud

Using photometry from the Ultraviolet Imaging Telescope (UIT) and photometry and spectroscopy from three ground-based optical datasets we have analyzed the stellar content of OB associations and field areas in and around the regions N 79, N 81, N 83, and N 94 in the LMC. We compare data for the OB association Lucke-Hodge 2 (LH 2) to determine how strongly the initial mass function (IMF) may depend on different photometric reductions and calibrations. We also correct for the background contribution of field stars, showing the importance of correcting for field star contamination in determinations of the IMF of star formation regions. It is possible that even in the case of an universal IMF, the variability of the density of background stars could be the dominant factor creating the differences between calculated IMFs for OB associations. We have also combined the UIT data with the Magellanic Cloud Photometric Survey to study the distribution of the candidate O-type stars in the field. We find a significant fraction, roughly half, of the candidate O-type stars are found in field regions, far from any obvious OB associations. These stars are greater than 2 arcmin (30 pc) from the boundaries of existing OB associations in the region, which is a distance greater than most O-type stars with typical dispersion velocities will travel in their lifetimes. The origin of these massive field stars (either as runaways, members of low-density star-forming regions, or examples of isolated massive star formation) will have to be determined by further observations and analysis.Comment: 16 pages, 10 figures (19 PostScript files), tabular data + header file for Table 1 (2 ASCII files). File format is LaTeX/AASTeX v.502 using the emulateapj5 preprint style (included). Also available at http://www.boulder.swri.edu/~joel/papers.html . To appear in the February 2001 issue of the Astronomical Journa

The Low End of the Initial Mass Function in Young LMC Clusters: I. The Case of R136

We report the result of a study in which we have used very deep broadband V and I WFPC2 images of the R136 cluster in the Large Magellanic Cloud from the HST archive, to sample the luminosity function below the detection limit of 2.8 Mo previously reached. In these new deeper images, we detect stars down to a limiting magnitude of m_F555W = 24.7 (~ 1 magnitude deeper than previous works), and identify a population of red stars evenly distributed in the surrounding of the R136 cluster. A comparison of our color-magnitude diagram with recentely computed evolutionary tracks indicates that these red objects are pre-main sequence stars in the mass range 0.6 - 3 Mo. We construct the initial mass function (IMF) in the 1.35 - 6.5 Mo range and find that, after correcting for incompleteness, the IMF shows a definite flattening below ~ 2 Mo. We discuss the implications of this result for the R136 cluster and for our understanding of starburst galaxies formation and evolution in general.Comment: 29 pages, 6 tables, 11 figures included + 3 external files, accepted for publication by Ap.

Spitzer bright, UltraVISTA faint sources in COSMOS: the contribution to the overall population of massive galaxies at z=3-7

We have analysed a sample of 574 Spitzer 4.5 micron-selected galaxies with [4.5]24 (AB) over the UltraVISTA ultra-deep COSMOS field. Our aim is to investigate whether these mid-IR bright, near-IR faint sources contribute significantly to the overall population of massive galaxies at redshifts z>=3. By performing a spectral energy distribution (SED) analysis using up to 30 photometric bands, we have determined that the redshift distribution of our sample peaks at redshifts z~2.5-3.0, and ~32% of the galaxies lie at z>=3. We have studied the contribution of these sources to the galaxy stellar mass function (GSMF) at high redshifts. We found that the [4.5]24 galaxies produce a negligible change to the GSMF previously determined for Ks_auto<24 sources at 3=<z<4, but their contribution is more important at 4=~50% of the galaxies with stellar masses Mst>~6 x 10^10 Msun. We also constrained the GSMF at the highest-mass end (Mst>~2 x 10^11 Msun) at z>=5. From their presence at 5=<z<6, and virtual absence at higher redshifts, we can pinpoint quite precisely the moment of appearance of the first most massive galaxies as taking place in the ~0.2 Gyr of elapsed time between z~6 and z~5. Alternatively, if very massive galaxies existed earlier in cosmic time, they should have been significantly dust-obscured to lie beyond the detection limits of current, large-area, deep near-IR surveys.Comment: 18 pages, 15 figures, 4 tables. Updated to match version in press at the Ap

Finding Statistically Significant Interactions between Continuous Features

The search for higher-order feature interactions that are statistically significantly associated with a class variable is of high relevance in fields such as Genetics or Healthcare, but the combinatorial explosion of the candidate space makes this problem extremely challenging in terms of computational efficiency and proper correction for multiple testing. While recent progress has been made regarding this challenge for binary features, we here present the first solution for continuous features. We propose an algorithm which overcomes the combinatorial explosion of the search space of higher-order interactions by deriving a lower bound on the p-value for each interaction, which enables us to massively prune interactions that can never reach significance and to thereby gain more statistical power. In our experiments, our approach efficiently detects all significant interactions in a variety of synthetic and real-world datasets.Comment: 13 pages, 5 figures, 2 tables, accepted to the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019

Error Correction Algorithms for Genomic Sequencing Data

University of Technology Sydney. Faculty of Engineering and Information Technology.The rapid development of high-throughput next-generation sequencing (NGS) platforms has produced massive sets of genomic reads under low costs for a wide range of biomedical applications (e.g., de novo genome assembly, read alignment, resequencing, and Single-nucleotide polymorphism discovery). A serious concern over these datasets is that machine-made sequencing data suffers from lots of random errors (such as substitutions, insertions and deletions). To the best of our knowledge, all the existing methods suffer limitations. This work aims to rectify as many errors as possible by designing strategies adapted to specific cases. Three novel error correction algorithms are designed to providing high-quality sequencing data. This first novel instance-based error correction method is designed to provide high-quality reads for any given instance case and implemented as a tool named InsEC. The instance-based strategy makes it possible to make use of data traits only related to an instance, which guarantees that we can approach the ground truth of the instance case and then achieve better error correction performance. The second method is the first miRNA read error correction. A novel lattice structure combining kmers, (k-1)mers, and (k+1)mers is proposed to rectify errors, which is particularly effective for correcting indel errors. Extensive tests on datasets having known ground truth of errors demonstrate that the method is able to remove almost all of the errors, without introducing any new error, to improve the data quality from every-50-reads containing one error to every- 1300-reads containing one error. The third method is the first small RNA error correction which supports substitution, insertion, and deletion error rectification. This method is more robust and also supports all kinds of small RNA sequencing reads (read length from 20-200 nucleotides). Furthermore, we improve the three-layer lattice structure and combine it by reads with the same length, length plus one and length minus one, which dramatically increases the method’s efficiency. Finally, we consider RNA’s isoform and propose to do correction proportionally to make a fine correction. All designed algorithms achieved high performance and provided high-quality sequencing data for all downstream analyses

Guided Proofreading of Automatic Segmentations for Connectomics

Automatic cell image segmentation methods in connectomics produce merge and split errors, which require correction through proofreading. Previous research has identified the visual search for these errors as the bottleneck in interactive proofreading. To aid error correction, we develop two classifiers that automatically recommend candidate merges and splits to the user. These classifiers use a convolutional neural network (CNN) that has been trained with errors in automatic segmentations against expert-labeled ground truth. Our classifiers detect potentially-erroneous regions by considering a large context region around a segmentation boundary. Corrections can then be performed by a user with yes/no decisions, which reduces variation of information 7.5x faster than previous proofreading methods. We also present a fully-automatic mode that uses a probability threshold to make merge/split decisions. Extensive experiments using the automatic approach and comparing performance of novice and expert users demonstrate that our method performs favorably against state-of-the-art proofreading methods on different connectomics datasets.Comment: Supplemental material available at http://rhoana.org/guidedproofreading/supplemental.pd

A Public Ks-selected Catalog in the COSMOS/UltraVISTA Field: Photometry, Photometric Redshifts and Stellar Population Parameters

We present a catalog covering 1.62 deg^2 of the COSMOS/UltraVISTA field with PSF-matched photometry in 30 photometric bands. The catalog covers the wavelength range 0.15um - 24um including the available GALEX, Subaru, CFHT, VISTA and Spitzer data. Catalog sources have been selected from the DR1 UltraVISTA Ks band imaging that reaches a depth of K_{s,tot} = 23.4 AB (90% completeness). The PSF-matched catalog is generated using position-dependent PSFs ensuring accurate colors across the entire field. Also included is a catalog of photometric redshifts (z_phot) for all galaxies computed with the EAZY code. Comparison with spectroscopy from the zCOSMOS 10k bright sample shows that up to z ~ 1.5 the z_phot are accurate to dz/(1 + z) = 0.013, with a catastrophic outlier fraction of only 1.6%. The z_phot also show good agreement with the z_phot from the NEWFIRM Medium Band Survey (NMBS) out to z ~ 3. A catalog of stellar masses and stellar population parameters for galaxies determined using the FAST spectral energy distribution fitting code is provided for all galaxies. Also included are rest-frame U-V and V-J colors, L_2800 and L_IR. The UVJ color-color diagram confirms that the galaxy bi-modality is well-established out to z ~ 2. Star-forming galaxies also obey a star forming "main sequence" out to z ~ 2.5, and this sequence evolves in a manner consistent with previous measurements. The COSMOS/UltraVISTA Ks-selected catalog covers a unique parameter space in both depth, area, and multi-wavelength coverage and promises to be a useful tool for studying the growth of the galaxy population out to z ~ 3 - 4.Comment: 20 pages, 14 figures. Accepted to the ApJSS. Catalog data products available for download here: http://www.strw.leidenuniv.nl/galaxyevolution/ULTRAVISTA