Probing the Cosmic Gamma-Ray Burst Rate with Trigger Simulations of the Swift Burst Alert Telescope

The gamma-ray burst (GRB) rate is essential for revealing the connection between GRBs, supernovae and stellar evolution. Additionally, the GRB rate at high redshift provides a strong probe of star formation history in the early universe. While hundreds of GRBs are observed by Swift, it remains difficult to determine the intrinsic GRB rate due to the complex trigger algorithm of Swift. Current studies of the GRB rate usually approximate the Swift trigger algorithm by a single detection threshold. However, unlike the previously flown GRB instruments, Swift has over 500 trigger criteria based on photon count rate and additional image threshold for localization. To investigate possible systematic biases and explore the intrinsic GRB properties, we develop a program that is capable of simulating all the rate trigger criteria and mimicking the image threshold. Our simulations show that adopting the complex trigger algorithm of Swift increases the detection rate of dim bursts. As a result, our simulations suggest bursts need to be dimmer than previously expected to avoid over-producing the number of detections and to match with Swift observations. Moreover, our results indicate that these dim bursts are more likely to be high redshift events than low-luminosity GRBs. This would imply an even higher cosmic GRB rate at large redshifts than previous expectations based on star-formation rate measurements, unless other factors, such as the luminosity evolution, are taken into account. The GRB rate from our best result gives a total number of 4571^{+829}_{-1584} GRBs per year that are beamed toward us in the whole universe. SPECIAL NOTE (2015.05.16): This new version incorporates an erratum. All the GRB rate normalizations ($R_{\rm GRB}(z=0)$) should be a factor of 2 smaller than previously reported. Please refer to the Appendix for more details. We sincerely apologize for the mistake.Comment: 52 pages, 17 figures, published in ApJ 783, 24L (2014). An erratum is included. A typo in Eq. 8 is fixed in this versio

Evidence for Circumburst Extinction of Gamma-Ray Bursts with Dark Optical Afterglows and Evidence for a Molecular Cloud Origin of Gamma-Ray Bursts

First, we show that the gamma-ray bursts with dark optical afterglows (DOAs) cannot be explained by a failure to image deeply enough quickly enough, and argue that circumburst extinction is the most likely solution. If so, many DOAs will be ``revived'' with rapid follow up and NIR searches in the HETE-2 and Swift eras. Next, we consider the effects of dust sublimation and fragmentation, and show that DOAs occur in clouds of size R > 10L_{49}^{1/2} pc and mass M > 3x10^5L_{49} M_{sun}, where L is the luminosity of the optical flash. Stability considerations show that such clouds cannot be diffuse, but must be molecular. Consequently, we compute the expected column density distribution of bursts that occur in Galactic-like molecular clouds, and show that the column density measurements from X-ray spectra of afterglows, DOAs and otherwise, satisfy this expectation in the source frame.Comment: Invited Review. To appear in Procs. of Gamma-Ray Burst and Afterglow Astronomy 2001: A Workshop Celebrating the First Year of the HETE Mission, 8 pages, 8 figures, LaTe

Image Segmentation Using Weak Shape Priors

The problem of image segmentation is known to become particularly challenging in the case of partial occlusion of the object(s) of interest, background clutter, and the presence of strong noise. To overcome this problem, the present paper introduces a novel approach segmentation through the use of "weak" shape priors. Specifically, in the proposed method, an segmenting active contour is constrained to converge to a configuration at which its geometric parameters attain their empirical probability densities closely matching the corresponding model densities that are learned based on training samples. It is shown through numerical experiments that the proposed shape modeling can be regarded as "weak" in the sense that it minimally influences the segmentation, which is allowed to be dominated by data-related forces. On the other hand, the priors provide sufficient constraints to regularize the convergence of segmentation, while requiring substantially smaller training sets to yield less biased results as compared to the case of PCA-based regularization methods. The main advantages of the proposed technique over some existing alternatives is demonstrated in a series of experiments.Comment: 27 pages, 8 figure

Surprisingly different star-spot distributions on the near equal-mass equal-rotation-rate stars in the M dwarf binary GJ 65 AB

We aim to understand how stellar parameters such as mass and rotation impact the distribution of star-spots on the stellar surface. To this purpose, we have used Doppler imaging to reconstruct the surface brightness distributions of three fully convective M dwarfs with similar rotation rates. We secured high cadence spectral time series observations of the 5.5 au separation binary GJ 65, comprising GJ 65A (M5.5V, Prot = 0.24 d) and GJ 65B (M6V, Prot = 0.23 d). We also present new observations of GJ 791.2A (M4.5V, Prot = 0.31 d). Observations of each star were made on two nights with UVES, covering a wavelength range from 0.64 - 1.03μm. The time series spectra reveal multiple line distortions that we interpret as cool star-spots and which are persistent on both nights suggesting stability on the time-scale of 3 d. Spots are recovered with resolutions down to 8.3° at the equator. The global spot distributions for GJ 791.2A are similar to observations made a year earlier. Similar high latitude and circumpolar spot structure is seen on GJ 791.2A and GJ 65A. However, they are surprisingly absent on GJ 65B, which instead reveals more extensive, larger, spots concentrated at intermediate latitudes. All three stars show small amplitude latitude-dependent rotation that is consistent with solid body rotation. We compare our measurements of differential rotation with previous Doppler imaging studies and discuss the results in the wider context of other observational estimates and recent theoretical predictions

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

Distribution of chromosome 18 and X centric heterochromatin in the interphase nucleus of cultured human cells

In situ hybridization of human chromosome 18 and X-specific alphoid DNA-probes was performed in combination with three dimensional (3D) and two dimensional (2D) image analysis to study the interphase distribution of the centric heterochromatin (18c and Xc) of these chromosomes in cultured human cells. 3D analyses of 18c targets using confocal laser scanning microscopy indicated a nonrandom disposition in 73 amniotic fluid cell nuclei. The shape of these nuclei resembled rather flat cylinders or ellipsoids targets were preferentially arranged in a domain around the nuclear center, but close to or associated with the nuclear envelope. Within this domain, however, positionings of the two targets occurred independently from each other, i.e., the two targets were observed with similar frequencies at the same (upper or lower) side of the nuclear envelope as those on opposite sides. This result strongly argues against any permanent homologous association of 18c. A 2D analytical approach was used for the rapid evaluation of 18c positions in over 4000 interphase nuclei from normal male and female individuals, as well as individuals with trisomy 18 and Bloom's syndrome. In addition to epithelially derived amniotic fluid cells, investigated cell types included in vitro cultivated fibroblastoid cells established from fetal lung tissue and skin-derived fibroblasts. In agreement with the above 3D observations 18c targets were found significantly closer (P < 0.01) to the center of the 2D nuclear image (CNI) and to each other in all these cultures compared to a random distribution derived from corresponding ellipsoid or cylinder model nuclei. For comparison, a chromosome X-specific alphoid DNA probe was used to investigate the 2D distribution of chromosome X centric heterochromatin in the same cell types. Two dimensional Xc-Xc and Xc-CNI distances fit a random distribution in diploid normal and Bloom's syndrome nuclei, as well as in nuclei with trisomy X. The different distributions of 18c and Xc targets were confirmed by the simultaneous staining of these targets in different colors within individual nuclei using a double in situ hybridization approach

Gaussian mixture model based probabilistic modeling of images for medical image segmentation

In this paper, we propose a novel image segmentation algorithm that is based on the probability distributions of the object and background. It uses the variational level sets formulation with a novel region based term in addition to the edge-based term giving a complementary functional, that can potentially result in a robust segmentation of the images. The main theme of the method is that in most of the medical imaging scenarios, the objects are characterized by some typical characteristics such a color, texture, etc. Consequently, an image can be modeled as a Gaussian mixture of distributions corresponding to the object and background. During the procedure of curve evolution, a novel term is incorporated in the segmentation framework which is based on the maximization of the distance between the GMM corresponding to the object and background. The maximization of this distance using differential calculus potentially leads to the desired segmentation results. The proposed method has been used for segmenting images from three distinct imaging modalities i.e. magnetic resonance imaging (MRI), dermoscopy and chromoendoscopy. Experiments show the effectiveness of the proposed method giving better qualitative and quantitative results when compared with the current state-of-the-art. INDEX TERMS Gaussian Mixture Model, Level Sets, Active Contours, Biomedical Engineerin