Context.
Despite over 50 years of research, many open questions remain about the origin and nature of gamma-ray bursts (GRBs). Linear polarization measurements of the prompt emission of these extreme ...phenomena have long been thought to be key to answering a range of these questions. The POLAR detector was designed to produce the first set of detailed and reliable linear polarization measurements in the 50 − 500 keV energy range. During late 2016 and early 2017, POLAR detected a total of 55 GRBs. The analysis results of 5 of these GRBs have been reported, and were found to be consistent with a low or unpolarized flux. However, previous reports by other collaborations found high levels of linear polarization, including some as high as 90%.
Aims.
We study the linear polarization for the 14 GRBs observed by POLAR for which statistically robust inferences are possible. Additionally, time-resolved polarization studies are performed on GRBs with sufficient apparent flux.
Methods.
A publicly available polarization analysis tool, developed within the Multi-Mission Maximum Likelihood framework (
3ML
), was used to produce statistically robust results. The method allows spectral and polarimetric data from POLAR to be combined with spectral data from the
Fermi
Gamma-ray Burst Monitor (
Fermi
-GBM) and the
Neil Gehrels Swift
Observatory to jointly model the spectral and polarimetric parameters.
Results.
The time-integrated analysis finds all results to be compatible with low or zero polarization with the caveat that, when time-resolved analysis is possible within individual pulses, we observe moderate linear polarization with a rapidly changing polarization angle. Therefore, time-integrated polarization results, while pointing to lower polarization, are potentially an artifact of summing over the changing polarization signal and thus washing out the true moderate polarization. We therefore caution against overinterpretation of any time-integrated results inferred herein and encourage the community to wait for more detailed polarization measurements from forthcoming missions such as POLAR-2 and LEAP.
We present a catalog of quasars with their corresponding redshifts derived from the photometric Kilo-Degree Survey (KiDS) Data Release 4. We achieved it by training machine learning (ML) models, ...using optical
u
g
ri
and near-infrared
Z
Y
J
H
K
s
bands, on objects known from Sloan Digital Sky Survey (SDSS) spectroscopy. We define inference subsets from the 45 million objects of the KiDS photometric data limited to 9-band detections, based on a feature space built from magnitudes and their combinations. We show that projections of the high-dimensional feature space on two dimensions can be successfully used, instead of the standard color-color plots, to investigate the photometric estimations, compare them with spectroscopic data, and efficiently support the process of building a catalog. The model selection and fine-tuning employs two subsets of objects: those randomly selected and the faintest ones, which allowed us to properly fit the bias versus variance trade-off. We tested three ML models: random forest (RF), XGBoost (XGB), and artificial neural network (ANN). We find that XGB is the most robust and straightforward model for classification, while ANN performs the best for combined classification and redshift. The ANN inference results are tested using number counts,
Gaia
parallaxes, and other quasar catalogs that are external to the training set. Based on these tests, we derived the minimum classification probability for quasar candidates which provides the best purity versus completeness trade-off:
p
(QSO
cand
) > 0.9 for
r
< 22 and
p
(QSO
cand
) > 0.98 for 22 <
r
< 23.5. We find 158 000 quasar candidates in the safe inference subset (
r
< 22) and an additional 185 000 candidates in the reliable extrapolation regime (22 <
r
< 23.5). Test-data purity equals 97% and completeness is 94%; the latter drops by 3% in the extrapolation to data fainter by one magnitude than the training set. The photometric redshifts were derived with ANN and modeled with Gaussian uncertainties. The test-data redshift error (mean and scatter) equals 0.009 ± 0.12 in the safe subset and −0.0004 ± 0.19 in the extrapolation, averaged over a redshift range of 0.14 <
z
< 3.63 (first and 99th percentiles). Our success of the extrapolation challenges the way that models are optimized and applied at the faint data end. The resulting catalog is ready for cosmology and active galactic nucleus (AGN) studies.
Hidden Administration of Drugs Benedetti, F; Carlino, E; Pollo, A
Clinical pharmacology and therapeutics,
November 2011, Letnik:
90, Številka:
5
Journal Article
Recenzirano
In placebo‐controlled trials, the placebo component of treatments is usually assessed by simulating a therapy through the administration of a dummy treatment (placebo) in order to eliminate the ...specific effects of the therapy. Recently, a radically different approach to the analysis of placebo responses has been implemented in which placebo responses are assessed without placebo groups. To do this, the placebo (psychological) component is eliminated by conducting hidden (unexpected) administrations of the active treatment. Compelling experimental evidence now shows that when the psychological component is eliminated through the administration of therapies unbeknownst to the patient, the effects of a variety of treatments are significantly reduced. Overall, the experimental data show that the action of different pharmacological agents can be modulated by cognitive and affective factors that can increase or decrease the effects of drugs. This experimental approach is thus a window into the complex interactions between psychology and pharmacodynamics.
Clinical Pharmacology & Therapeutics (2011); 90 5, 651–661. doi:10.1038/clpt.2011.206
Wide-angle photometric surveys of previously uncharted sky areas or wavelength regimes will always bring in unexpected sources – novelties or even anomalies – whose existence and properties cannot be ...easily predicted from earlier observations. Such objects can be efficiently located with novelty detection algorithms. Here we present an application of such a method, called one-class support vector machines (OCSVM), to search for anomalous patterns among sources preselected from the mid-infrared AllWISE catalogue covering the whole sky. To create a model of expected data we train the algorithm on a set of objects with spectroscopic identifications from the SDSS DR13 database, present also in AllWISE. The OCSVM method detects as anomalous those sources whose patterns – WISE photometric measurements in this case – are inconsistent with the model. Among the detected anomalies we find artefacts, such as objects with spurious photometry due to blending, but more importantly also real sources of genuine astrophysical interest. Among the latter, OCSVM has identified a sample of heavily reddened AGN/quasar candidates distributed uniformly over the sky and in a large part absent from other WISE-based AGN catalogues. It also allowed us to find a specific group of sources of mixed types, mostly stars and compact galaxies. By combining the semi-supervised OCSVM algorithm with standard classification methods it will be possible to improve the latter by accounting for sources which are not present in the training sample, but are otherwise well-represented in the target set. Anomaly detection adds flexibility to automated source separation procedures and helps verify the reliability and representativeness of the training samples. It should be thus considered as an essential step in supervised classification schemes to ensure completeness and purity of produced catalogues.
We present a catalog of quasars selected from broad-band photometric ugri data of the Kilo-Degree Survey Data Release 3 (KiDS DR3). The QSOs are identified by the random forest (RF) supervised ...machine learning model, trained on Sloan Digital Sky Survey (SDSS) DR14 spectroscopic data. We first cleaned the input KiDS data of entries with excessively noisy, missing or otherwise problematic measurements. Applying a feature importance analysis, we then tune the algorithm and identify in the KiDS multiband catalog the 17 most useful features for the classification, namely magnitudes, colors, magnitude ratios, and the stellarity index. We used the t-SNE algorithm to map the multidimensional photometric data onto 2D planes and compare the coverage of the training and inference sets. We limited the inference set to r < 22 to avoid extrapolation beyond the feature space covered by training, as the SDSS spectroscopic sample is considerably shallower than KiDS. This gives 3.4 million objects in the final inference sample, from which the random forest identified 190 000 quasar candidates. Accuracy of 97% (percentage of correctly classified objects), purity of 91% (percentage of true quasars within the objects classified as such), and completeness of 87% (detection ratio of all true quasars), as derived from a test set extracted from SDSS and not used in the training, are confirmed by comparison with external spectroscopic and photometric QSO catalogs overlapping with the KiDS footprint. The robustness of our results is strengthened by number counts of the quasar candidates in the r band, as well as by their mid-infrared colors available from the Wide-field Infrared Survey Explorer (WISE). An analysis of parallaxes and proper motions of our QSO candidates found also in Gaia DR2 suggests that a probability cut of pQSO > 0.8 is optimal for purity, whereas pQSO > 0.7 is preferable for better completeness. Our study presents the first comprehensive quasar selection from deep high-quality KiDS data and will serve as the basis for versatile studies of the QSO population detected by this survey.
Observations of distant supernovae indicate that the Universe is now in a phase of accelerated expansion the physical cause of which is a mystery. Formally, this requires the inclusion of a term ...acting as a negative pressure in the equations of cosmic expansion, accounting for about 75 per cent of the total energy density in the Universe. The simplest option for this 'dark energy' corresponds to a 'cosmological constant', perhaps related to the quantum vacuum energy. Physically viable alternatives invoke either the presence of a scalar field with an evolving equation of state, or extensions of general relativity involving higher-order curvature terms or extra dimensions. Although they produce similar expansion rates, different models predict measurable differences in the growth rate of large-scale structure with cosmic time. A fingerprint of this growth is provided by coherent galaxy motions, which introduce a radial anisotropy in the clustering pattern reconstructed by galaxy redshift surveys. Here we report a measurement of this effect at a redshift of 0.8. Using a new survey of more than 10,000 faint galaxies, we measure the anisotropy parameter = 0.70 ± 0.26, which corresponds to a growth rate of structure at that time of f = 0.91 ± 0.36. This is consistent with the standard cosmological-constant model with low matter density and flat geometry, although the error bars are still too large to distinguish among alternative origins for the accelerated expansion. The correct origin could be determined with a further factor-of-ten increase in the sampled volume at similar redshift.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Context.
Low-surface-brightness galaxies (LSBGs), which are defined as galaxies that are fainter than the night sky, play a crucial role in our understanding of galaxy evolution and in cosmological ...models. Upcoming large-scale surveys, such as
Rubin
Observatory Legacy Survey of Space and Time and
Euclid
, are expected to observe billions of astronomical objects. In this context, using semiautomatic methods to identify LSBGs would be a highly challenging and time-consuming process, and automated or machine learning-based methods are needed to overcome this challenge.
Aims.
We study the use of transformer models in separating LSBGs from artefacts in the data from the Dark Energy Survey (DES) Data Release 1. Using the transformer models, we then search for new LSBGs from the DES that the previous searches may have missed. Properties of the newly found LSBGs are investigated, along with an analysis of the properties of the total LSBG sample in DES.
Methods.
We created eight different transformer models and used an ensemble of these eight models to identify LSBGs. This was followed by a single-component Sérsic model fit and a final visual inspection to filter out false positives.
Results.
Transformer models achieved an accuracy of ~94% in separating the LSBGs from artefacts. In addition, we identified 4083 new LSBGs in DES, adding an additional ~17% to the LSBGs already known in DES. This also increased the number density of LSBGs in DES to 5.5 deg
−2
. The new LSBG sample consists of mainly blue and compact galaxies. We performed a clustering analysis of the LSBGs in DES using an angular two-point auto-correlation function and found that LSBGs cluster more strongly than their high-surface-brightness counterparts. This effect is driven by the red LSBG. We associated 1310 LSBGs with galaxy clusters and identified 317 ultradiffuse galaxies among them. We found that these cluster LSBGs are getting bluer and larger in size towards the edge of the clusters when compared with those in the centre.
Conclusions.
Transformer models have the potential to be equivalent to convolutional neural networks as state-of-the-art algorithms in analysing astronomical data. The significant number of LSBGs identified from the same dataset using a different algorithm highlights the substantial impact of our methodology on our capacity to discover LSBGs. The reported number density of LSBGs is only a lower estimate and can be expected to increase with the advent of surveys with better image quality and more advanced methodologies.
Context. The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. Classifying them reliably is, however, a challenging task owing to ...degeneracies in WISE multicolour space and low levels of detection in its two longest-wavelength bandpasses. Simple colour cuts are often not sufficient; for satisfactory levels of completeness and purity, more sophisticated classification methods are needed. Aims. Here we aim to obtain comprehensive and reliable star, galaxy, and quasar catalogues based on automatic source classification in full-sky WISE data. This means that the final classification will employ only parameters available from WISE itself, in particular those which are reliably measured for the majority of sources. Methods. For the automatic classification we applied a supervised machine learning algorithm, support vector machines (SVM). It requires a training sample with relevant classes already identified, and we chose to use the SDSS spectroscopic dataset (DR10) for that purpose. We tested the performance of two kernels used by the classifier, and determined the minimum number of sources in the training set required to achieve stable classification, as well as the minimum dimension of the parameter space. We also tested SVM classification accuracy as a function of extinction and apparent magnitude. Thus, the calibrated classifier was finally applied to all-sky WISE data, flux-limited to 16 mag (Vega) in the 3.4 μm channel. Results. By calibrating on the test data drawn from SDSS, we first established that a polynomial kernel is preferred over a radial one for this particular dataset. Next, using three classification parameters (W1 magnitude, W1−W2 colour, and a differential aperture magnitude) we obtained very good classification efficiency in all the tests. At the bright end, the completeness for stars and galaxies reaches ~95%, deteriorating to ~80% at W1 = 16 mag, while for quasars it stays at a level of ~95% independently of magnitude. Similar numbers are obtained for purity. Application of the classifier to full-sky WISE data and appropriate a posteriori cleaning allowed us to obtain catalogues of star and galaxy candidates that appear reliable. However, the sources flagged by the classifier as “quasars” are in fact dominated by dusty galaxies; they also exhibit contamination from sources located mainly at low ecliptic latitudes, consistent with solar system objects.
Context. How the quiescent galaxies evolve with redshift and the factors that impact their evolution are still debated. It is still unclear what the dominant mechanisms of passive galaxy growth are ...and what role is played by the environment in shaping their evolutionary paths over cosmic time. Aims. The population of quiescent galaxies is altered over time by several processes that can affect their mean properties. Our aim is to study the mass–size relation (MSR) of the quiescent population and to understand how the environment shapes the MSR at intermediate redshift. Methods. We used the VIMOS Public Extragalactic Redshift Survey (VIPERS), a large spectroscopic survey of ∼90 000 galaxies in the redshift range 0.5 ≤ z ≤ 1.2. We selected a mass-complete sample of 4786 passive galaxies based on the NUVrK diagram and refined it using the D n 4000 spectral index to study the MSR of the passive population over 0.5 ≤ z ≤ 0.9. The impact of the environment on the MSR and on the growth of the quiescent population is studied through the density contrast. Results. The slope and the intercept of the MSR, α = 0.62 ± 0.04 and log( A ) = 0.52 ± 0.01, agree well with values from the literature at the same redshift. The intercept decreases with redshift, R e ( z ) = 8.20 × (1 + z ) −1.70 , while the slope remains roughly constant, and the same trend is observed in the low-density (LD) and high-density (HD) environments. Thanks to the largest spectroscopic sample at 0.5 ≤ z ≤ 0.9, these results are not prone to redshift uncertainties from photometric measurements. We find that the average size of the quiescent population in the LD and HD environments are identical within 3 σ and this result is robust against a change in the definition of the LD and HD environments or a change in the selection of quiescent galaxies. In the LD and HD environments, ∼30 and ∼40% of the population have experienced a minor merger process between 0.5 ≤ z ≤ 0.9. However, minor mergers account only for 30–40% of the size evolution in this redshift range, the remaining evolution likely being due to the progenitor bias.
ABSTRACT
Red ultra-compact massive galaxies, called red nuggets were formed at high redshifts (z ∼ 2–3). Survivors of red nuggets, known as relics, observed at lower redshifts (z < 2) are believed to ...remain almost unchanged since their formation. For the first time, we verify the environmental properties of red nuggets at intermediate redshift (0.5 < z < 0.9) using 42 red, massive (log(Mstar/M⊙) ≥ 10.9), and ultra compact (Re < 1.5 kpc) from the VIMOS Public Extragalactic Redshift Survey (VIPERS). We found that the increasing fraction of red galaxies, when moving to denser environments, is driven by the red massive normal-size galaxies. Red nuggets, similarly to red intermediate-mass (10.4 ≲ log (Mstar/M⊙) < 10.9) ultra-compact galaxies, are found in various types of environments, with consistent (within 1σ) fractions across all local densities. Analysis of red nugget stellar ages suggests that relics are preferably found in high-density regions while quiescent red nuggets are overabundant in low-density environments. We speculate that red nuggets have survived to lower redshifts via two channels: i) in low-density environments, where the fraction of red nuggets decreases as time passes due to (very) limited merger activity, ii) in high-density environments, where the number of red nuggets drops at higher redshift due to merger activity and is preserved at lower redshift as the high velocities of clusters prevent them from being cannibalized. Even more, the fraction of red nuggets in clusters may increase due to the addition of red massive normal-size galaxies deprived of their envelopes with cosmic time.
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