scholarly journals Galaxy formation and evolution using multi-wavelength, multi-resolution imaging data in the Virtual Observatory

2006 ◽  
Vol 2 (14) ◽  
pp. 592-592
Author(s):  
Paresh Prema ◽  
Nicholas A. Walton ◽  
Richard G. McMahon
Keyword(s):  
Galaxy Formation ◽  
High Redshift ◽  
Large Data ◽  
Electromagnetic Spectrum ◽  
Data Sets ◽  
Virtual Observatory ◽  
X Rays ◽  
Imaging Data ◽  
Wide Range ◽  
Multi Wavelength

Observational astronomy is entering an exciting new era with large surveys delivering deep multi-wavelength data over a wide range of the electromagnetic spectrum. The last ten years has seen a growth in the study of high redshift galaxies discovered with the method pioneered by Steidel et al. (1995) used to identify galaxies above z>1. The technique is designed to take advantage of the multi-wavelength data now available for astronomers that can extend from X-rays to radio wavelength. The technique is fast becoming a useful way to study large samples of objects at these high redshifts and we are currently designing and implementing an automated technique to study these samples of objects. However, large surveys produce large data sets that have now reached terabytes (e.g. for the Sloan Digital Sky Survey, <http://www.sdss.org>) in size and petabytes over the next 10yr (e.g., LSST, <http://www.lsst.org>). The Virtual Observatory is now providing a means to deal with this issue and users are now able to access many data sets in a quicker more useful form.

scholarly journals Mapping large-scale-structure evolution over cosmic times

2021 ◽  
Author(s):  
Marta B. Silva ◽  
Ely D. Kovetz ◽  
Garrett K. Keating ◽  
Azadeh Moradinezhad Dizgah ◽  
Matthieu Bethermin ◽  
...  
Keyword(s):  
Galaxy Formation ◽  
Large Scale ◽  
High Redshift ◽  
Formation Rate ◽  
Far Infrared ◽  
Large Scale Structure ◽  
Scale Structure ◽  
Structure Evolution ◽  
Intensity Mapping ◽  
Wide Range

AbstractThis paper outlines the science case for line-intensity mapping with a space-borne instrument targeting the sub-millimeter (microwaves) to the far-infrared (FIR) wavelength range. Our goal is to observe and characterize the large-scale structure in the Universe from present times to the high redshift Epoch of Reionization. This is essential to constrain the cosmology of our Universe and form a better understanding of various mechanisms that drive galaxy formation and evolution. The proposed frequency range would make it possible to probe important metal cooling lines such as [CII] up to very high redshift as well as a large number of rotational lines of the CO molecule. These can be used to trace molecular gas and dust evolution and constrain the buildup in both the cosmic star formation rate density and the cosmic infrared background (CIB). Moreover, surveys at the highest frequencies will detect FIR lines which are used as diagnostics of galaxies and AGN. Tomography of these lines over a wide redshift range will enable invaluable measurements of the cosmic expansion history at epochs inaccessible to other methods, competitive constraints on the parameters of the standard model of cosmology, and numerous tests of dark matter, dark energy, modified gravity and inflation. To reach these goals, large-scale structure must be mapped over a wide range in frequency to trace its time evolution and the surveyed area needs to be very large to beat cosmic variance. Only a space-borne mission can properly meet these requirements.

scholarly journals Recurrent Novae — A Review

2015 ◽  
Vol 2 (1) ◽  
pp. 246-251 ◽  
Author(s):  
K. Mukai
Keyword(s):  
Accretion Rate ◽  
Current Understanding ◽  
X Rays ◽  
Symbiotic Star ◽  
Classical Novae ◽  
Extensive Data ◽  
Star Evolution ◽  
Wide Range ◽  
Multi Wavelength ◽  
Recurrent Nova

In recent years, recurrent nova eruptions are often observed very intensely in wide range of wavelengths from radio to optical to X-rays. Here I present selected highlights from recent multi-wavelength observations. The enigma of T Pyx is at the heart of this paper. While our current understanding of CV and symbiotic star evolution can explain why certain subset of recurrent novae have high accretion rate, that of T Pyx must be greatly elevated compared to the evolutionary mean. At the same time, we have extensive data to be able to estimate how the nova envelope was ejected in T Pyx, and it turns to be a rather complex tale. One suspects that envelope ejection in recurrent and classical novae in general is more complicated than the textbook descriptions. At the end of the review, I will speculate that these two may be connected.

scholarly journals Graph representation learning: a survey

2020 ◽  
Vol 9 ◽  
Author(s):  
Fenxiao Chen ◽  
Yun-Cheng Wang ◽  
Bin Wang ◽  
C.-C. Jay Kuo
Keyword(s):  
Graph Embedding ◽  
Large Data ◽  
Representation Learning ◽  
Graph Representation ◽  
Data Sets ◽  
Graph Data ◽  
Graph Properties ◽  
Wide Range ◽  
Regular Lattices ◽  
Low Dimensional

Abstract Research on graph representation learning has received great attention in recent years since most data in real-world applications come in the form of graphs. High-dimensional graph data are often in irregular forms. They are more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several stat-of-the-art methods against small and large data sets and compare their performance. Finally, potential applications and future directions are presented.

scholarly journals Data segmentation based on the local intrinsic dimension

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Michele Allegra ◽  
Elena Facco ◽  
Francesco Denti ◽  
Alessandro Laio ◽  
Antonietta Mira
Keyword(s):  
High Dimensional Data ◽  
Large Data ◽  
Large Data Sets ◽  
High Dimensional ◽  
Data Sets ◽  
Imaging Data ◽  
Unsupervised Segmentation ◽  
Real World Data ◽  
Data Set ◽  
Intrinsic Dimension

Abstract One of the founding paradigms of machine learning is that a small number of variables is often sufficient to describe high-dimensional data. The minimum number of variables required is called the intrinsic dimension (ID) of the data. Contrary to common intuition, there are cases where the ID varies within the same data set. This fact has been highlighted in technical discussions, but seldom exploited to analyze large data sets and obtain insight into their structure. Here we develop a robust approach to discriminate regions with different local IDs and segment the points accordingly. Our approach is computationally efficient and can be proficiently used even on large data sets. We find that many real-world data sets contain regions with widely heterogeneous dimensions. These regions host points differing in core properties: folded versus unfolded configurations in a protein molecular dynamics trajectory, active versus non-active regions in brain imaging data, and firms with different financial risk in company balance sheets. A simple topological feature, the local ID, is thus sufficient to achieve an unsupervised segmentation of high-dimensional data, complementary to the one given by clustering algorithms.

scholarly journals 0062 Improved Circadian Data Ordering in the Presence of Biological and Technical Confounds

SLEEP ◽  
2020 ◽  
Vol 43 (Supplement_1) ◽  
pp. A24-A26
Author(s):  
J Hammarlund ◽  
R Anafi
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
Large Data ◽  
Data Sets ◽  
Sample Collection ◽  
Circadian Phase ◽  
Confounding Variables ◽  
Wide Range ◽  
Fully Connected ◽  
Genome Scale

Abstract Introduction We recently used unsupervised machine learning to order genome scale data along a circadian cycle. CYCLOPS (Anafi et al PNAS 2017) encodes high dimensional genomic data onto an ellipse and offers the potential to identify circadian patterns in large data-sets. This approach requires many samples from a wide range of circadian phases. Individual data-sets often lack sufficient samples. Composite expression repositories vastly increase the available data. However, these agglomerated datasets also introduce technical (e.g. processing site) and biological (e.g. age or disease) confounders that may hamper circadian ordering. Methods Using the FLUX machine learning library we expanded the CYCLOPS network. We incorporated additional encoding and decoding layers that model the influence of labeled confounding variables. These layers feed into a fully connected autoencoder with a circular bottleneck, encoding the estimated phase of each sample. The expanded network simultaneously estimates the influence of confounding variables along with circadian phase. We compared the performance of the original and expanded networks using both real and simulated expression data. In a first test, we used time-labeled data from a single-center describing human cortical samples obtained at autopsy. To generate a second, idealized processing center, we introduced gene specific biases in expression along with a bias in sample collection time. In a second test, we combined human lung biopsy data from two medical centers. Results The performance of the original CYCLOPS network degraded with the introduction of increasing, non-circadian confounds. The expanded network was able to more accurately assess circadian phase over a wider range of confounding influences. Conclusion The addition of labeled confounding variables into the network architecture improves circadian data ordering. The use of the expanded network should facilitate the application of CYCLOPS to multi-center data and expand the data available for circadian analysis. Support This work was supported by the National Cancer Institute (1R01CA227485-01)

scholarly journals A Hidden Markov Model for Investigating Recent Positive Selection through Haplotype Structure

2014 ◽  
Author(s):  
Hua Chen ◽  
Jody Hey ◽  
Montgomery Slatkin
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Positive Selection ◽  
Genetic Model ◽  
Hidden Markov ◽  
Large Data ◽  
Data Sets ◽  
Recent Positive Selection ◽  
A Genome ◽  
Wide Range

Recent positive selection can increase the frequency of an advantageous mutant rapidly enough that a relatively long ancestral haplotype will be remained intact around it. We present a hidden Markov model (HMM) to identify such haplotype structures. With HMM identified haplotype structures, a population genetic model for the extent of ancestral haplotypes is then adopted for parameter inference of the selection intensity and the allele age. Simulations show that this method can detect selection under a wide range of conditions and has higher power than the existing frequency spectrum-based method. In addition, it provides good estimate of the selection coefficients and allele ages for strong selection. The method analyzes large data sets in a reasonable amount of running time. This method is applied to HapMap III data for a genome scan, and identifies a list of candidate regions putatively under recent positive selection. It is also applied to several genes known to be under recent positive selection, including the LCT, KITLG and TYRP1 genes in Northern Europeans, and OCA2 in East Asians, to estimate their allele ages and selection coefficients.

scholarly journals Calibration of Star-Formation Rate Measurements Across the Electromagnetic Spectrum

2012 ◽  
Vol 10 (H16) ◽  
pp. 495-527
Author(s):  
V. Buat ◽  
J. Braine ◽  
D. A. Dale ◽  
A. Hornschemeier ◽  
B. Lehmer ◽  
...  
Keyword(s):  
Star Formation ◽  
Radiative Transfer ◽  
Star Formation Rate ◽  
Formation Rate ◽  
Mass Function ◽  
Initial Mass Function ◽  
Stellar Atmospheres ◽  
Electromagnetic Spectrum ◽  
X Rays ◽  
Multi Wavelength

AbstractStar-formation is one of the main processes that shape galaxies, and together with black-hole accretion activity the two agents of energy production in galaxies. It is important on a range of scales from star clusters/OB associations to galaxy-wide and even group/cluster scales. Recently, studies of star-formation in sub-galactic and galaxy-wide scales have met significant advances owing to: (a) developments in the theory of stellar evolution, stellar atmospheres, and radiative transfer in the interstellar medium; (b) the availability of more sensitive and higher resolution data; and (c) observations in previously poorly charted wavebands (e.g. Ultraviolet, Infrared, and X-rays). These data allow us to study more galaxies at ever-increasing distances and nearby galaxies in greater detail, and different modes of star formation activity such as massive star formation and low level continuous star formation in a variety of environments. In this contribution we summarize recent results in the fields of multi-wavelength calibrations of star-formation rate indicators, the Stellar Initial Mass function, and radiative transfer and modeling of the Spectrale Energy Disrtributions of galaxies.

An Atom-Probe Tomography Primer

MRS Bulletin ◽  
2009 ◽  
Vol 34 (10) ◽  
pp. 717-724 ◽  
Author(s):  
David N. Seidman ◽  
Krystyna Stiller
Keyword(s):  
Atom Probe Tomography ◽  
Organic Materials ◽  
Ion Beam ◽  
Large Data ◽  
Laser Pulses ◽  
Atom Probe ◽  
Femtosecond Laser Pulses ◽  
Large Data Sets ◽  
Data Sets ◽  
Wide Range

AbstractAtom-probe tomography (APT) is in the midst of a dynamic renaissance as a result of the development of well-engineered commercial instruments that are both robust and ergonomic and capable of collecting large data sets, hundreds of millions of atoms, in short time periods compared to their predecessor instruments. An APT setup involves a field-ion microscope coupled directly to a special time-of-flight (TOF) mass spectrometer that permits one to determine the mass-to-charge states of individual field-evaporated ions plus theirx-,y-, andz-coordinates in a specimen in direct space with subnanoscale resolution. The three-dimensional (3D) data sets acquired are analyzed using increasingly sophisticated software programs that utilize high-end workstations, which permit one to handle continuously increasing large data sets. APT has the unique ability to dissect a lattice, with subnanometer-scale spatial resolution, using either voltage or laser pulses, on an atom-by-atom and atomic plane-by-plane basis and to reconstruct it in 3D with the chemical identity of each detected atom identified by TOF mass spectrometry. Employing pico- or femtosecond laser pulses using visible (green or blue light) to ultraviolet light makes the analysis of metallic, semiconducting, ceramic, and organic materials practical to different degrees of success. The utilization of dual-beam focused ion-beam microscopy for the preparation of microtip specimens from multilayer and surface films, semiconductor devices, and for producing site-specific specimens greatly extends the capabilities of APT to a wider range of scientific and engineering problems than could previously be studied for a wide range of materials: metals, semiconductors, ceramics, biominerals, and organic materials.

scholarly journals Novel Stream Ciphering Algorithm for Big Data Images Using Zeckendorf Representation

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Liangshun Wu ◽  
Hengjin Cai
Keyword(s):  
Big Data ◽  
Stream Cipher ◽  
Large Data ◽  
Security And Privacy ◽  
Data Sets ◽  
Imaging Data ◽  
High Definition ◽  
High Data ◽  
Huge Data ◽  
Data Volume

Big data is a term used for very large data sets. Digital equipment produces vast amounts of images every day; the need for image encryption is increasingly pronounced, for example, to safeguard the privacy of the patients’ medical imaging data in cloud disk. There is an obvious contradiction between the security and privacy and the widespread use of big data. Nowadays, the most important engine to provide confidentiality is encryption. However, block ciphering is not suitable for the huge data in a real-time environment because of the strong correlation among pixels and high redundancy; stream ciphering is considered a lightweight solution for ciphering high-definition images (i.e., high data volume). For a stream cipher, since the encryption algorithm is deterministic, the only thing you can do is to make the key “look random.” This article proves that the probability that the digit 1 appears in the midsection of a Zeckendorf representation is constant, which can be utilized to generate the pseudorandom numbers. Then, a novel stream cipher key generator (ZPKG) is proposed to encrypt high-definition images that need transferring. The experimental results show that the proposed stream ciphering method, with the keystream of which satisfies Golomb’s randomness postulates, is faster than RC4 and LSFR with indistinguishable performance on hardware depletion, and the method is highly key sensitive and shows good resistance against noise attacks and statistical attacks.

scholarly journals A Lyα nebula at z ∼ 3.3

2020 ◽  
Vol 641 ◽  
pp. A32
Author(s):  
P. Hibon ◽  
F. Tang ◽  
R. Thomas
Keyword(s):  
Galaxy Formation ◽  
High Redshift ◽  
Broad Band ◽  
Observational Cosmology ◽  
Redshift Range ◽  
High Redshift Galaxies ◽  
Star Forming ◽  
Lyman Alpha ◽  
Multi Wavelength ◽  
Intense Activity

Context. Searching for high-redshift galaxies is a field of intense activity in modern observational cosmology that will continue to grow with future ground-based and sky observatories. Over the last few years, a lot has been learned about the high-z Universe. Aims. Despite extensive Lyα blobs (LAB) surveys from low to high redshifts, giant LABs over 100 kpc have been found mostly at z ∼ 2–4. This redshift range is coincident with the transition epoch of galactic gas-circulation processes from inflows to outflows at z ∼ 2.5–3. This suggests that the formation of giant LABs may be related to a combination of gas inflows and outflows. Their extreme youth makes them interesting objects in the study of galaxy formation as they provide insight into some of the youngest known highly star forming galaxies, with only modest time investments using ground-based telescopes. Methods. Systematic narrow-band Lyα nebula surveys are ongoing, but they are limited in their covered redshift range and their comoving volume. This poses a significant problem when searching for such rare sources. To address this problem, we developed a systematic searching tool, ATACAMA (A Tool for seArChing for lArge LyMan Alpha nebulae) designed to find large Lyα nebulae at any redshift within deep multi-wavelength broad-band imaging. Results. We identified a Lyα nebula candidate at zphot ∼ 3.3 covering an isophotal area of 29.4arcsec2. Its morphology shows a bright core and a faint core which coincides with the morphology of previously known Lyα blobs. A first estimation of the Lyα equivalent width and line flux agree with the values from the study led by several groups.

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