Eruption of a plasma blob, associated M-class flare, and large-scale EUV wave observed by SDO

We present a multiwavelength study of the formation and ejection of a plasma blob and associated EUV waves in AR NOAA 11176, observed by SDO/AIA and STEREO on 25 March 2011. SDO/AIA images clearly show the formation and ejection of a plasma blob from the lower solar atmosphere at ~9 min prior to the onset of the M1.0 flare. This onset of the M-class flare happened at the site of the blob formation, while the blob was rising in a parabolic path with an average speed of ~300 km/s. The blob also showed twisting and de-twisting motion in the lower corona, and the blob speed varied from ~10-540 km/s. The faster and slower EUV wavefronts were observed in front of the plasma blob during its impulsive acceleration phase. The faster EUV wave propagated with a speed of ~785 to 1020 km/s, whereas the slower wavefront speed varied in between ~245 and 465 km/s. The timing and speed of the faster wave match the shock speed estimated from the drift rate of the associated type II radio burst. The faster wave experiences a reflection by the nearby AR NOAA 11177. In addition, secondary waves were observed (only in the 171 \AA channel), when the primary fast wave and plasma blob impacted the funnel-shaped coronal loops. The HMI magnetograms revealed the continuous emergence of new magnetic flux along with shear flows at the site of the blob formation. It is inferred that the emergence of twisted magnetic fields in the form of arch-filaments/"anemone-type" loops is the likely cause for the plasma blob formation and associated eruption along with the triggering of M-class flare. Furthermore, the faster EUV wave formed ahead of the blob shows the signature of fast-mode MHD wave, whereas the slower wave seems to be generated by the field line compression by the plasma blob. The secondary wave trains originated from the funnel-shaped loops are probably the fast magnetoacoustic waves.Comment: A&A (in press), 22 pages, 13 figure

High Energy Emission Processes in OJ 287 during 2009 Flare

The broadband spectrum of a BL Lac object, OJ 287, from radio to $\gamma$-rays obtained during a major $\gamma$-ray flare detected by \emph{Fermi} in 2009 are studied to understand the high energy emission mechanism during this episode. Using a simple one-zone leptonic model, incorporating synchrotron and inverse Compton emission processes, we show that the explanation of high energy emission from X-rays to $\gamma$-rays, by considering a single emission mechanism, namely, synchrotron self-Compton (SSC) or external Compton (EC) requires unlikely physical conditions. However, a combination of both SSC and EC mechanisms can reproduce the observed high energy spectrum satisfactorily. Using these emission mechanisms we extract the physical parameters governing the source and its environment. Our study suggests that the emission region of OJ 287 is surrounded by a warm infrared (IR) emitting region of $\sim 250 \, K$. Assuming this region as a spherical cloud illuminated by an accretion disk, we obtain the location of the emission region to be $\sim 9 pc$. This supports the claim that the $\gamma$-ray emission from OJ 287 during the 2009 flare arises from a location far away from the central engine as deduced from millimeter-gamma ray correlation study and very long baseline array images.Comment: 22 pages, 7 figures, 1 table, accepted for publication in MNRA

Brightest Fermi-LAT Flares of PKS 1222+216: Implications on Emission and Acceleration Processes

We present a high time resolution study of the two brightest $\gamma$-ray outbursts from a blazar PKS 1222+216 observed by the \textit{Fermi} Large Area Telescope (LAT) in 2010. The $\gamma$-ray light-curves obtained in four different energy bands: 0.1--3, 0.1--0.3, 0.3--1 and 1--3 GeV, with time bin of 6 hr, show asymmetric profiles with a similar rise time in all the bands but a rapid decline during the April flare and a gradual one during the June. The light-curves during the April flare show $\sim 2$ days long plateau in 0.1--0.3 GeV emission, erratic variations in 0.3--1 GeV emission, and a daily recurring feature in 1--3 GeV emission until the rapid rise and decline within a day. The June flare shows a monotonic rise until the peak, followed by a gradual decline powered mainly by the multi-peak 0.1--0.3 GeV emission. The peak fluxes during both the flares are similar except in the 1--3 GeV band in April which is twice the corresponding flux during the June flare. Hardness ratios during the April flare indicate spectral hardening in the rising phase followed by softening during the decay. We attribute this behavior to the development of a shock associated with an increase in acceleration efficiency followed by its decay leading to spectral softening. The June flare suggests hardening during the rise followed by a complicated energy dependent behavior during the decay. Observed features during the June flare favor multiple emission regions while the overall flaring episode can be related to jet dynamics.Comment: 17 pages, 9 figures, 4 tables, accepted for publication in Ap

Codes Detecting and Correcting Solid Burst Errors

This paper studies linear codes capable of detecting and correcting solid burst error of length b or less. The lower and upper bounds on the number of parity-check digits required for such codes are obtained. Illustrations of codes for detecting as well as correcting such errors are provided

Multi-wavelength Temporal Variability of the Blazar 3C 454.3 during 2014 Activity Phase

We present a multi-wavelength temporal analysis of the blazar 3C 454.3 during the high $\gamma$-ray active period from May-December, 2014. Except for X-rays, the period is well sampled at near-infrared (NIR)-optical by the \emph{SMARTS} facility and the source is detected continuously on daily timescale in the \emph{Fermi}-LAT $\gamma$-ray band. The source exhibits diverse levels of variability with many flaring/active states in the continuously sampled $\gamma$-ray light curve which are also reflected in the NIR-optical light curves and the sparsely sampled X-ray light curve by the \emph{Swift}-XRT. Multi-band correlation analysis of this continuous segment during different activity periods shows a change of state from no lags between IR and $\gamma$-ray, optical and $\gamma$-ray, and IR and optical to a state where $\gamma$-ray lags the IR/optical by $\sim$3 days. The results are consistent with the previous studies of the same during various $\gamma$-ray flaring and active episodes of the source. This consistency, in turn, suggests an extended localized emission region with almost similar conditions during various $\gamma$-ray activity states. On the other hand, the delay of $\gamma$-ray with respect to IR/optical and a trend similar to IR/optical in X-rays along with strong broadband correlations favor magnetic field related origin with X-ray and $\gamma$-ray being inverse Comptonized of IR/optical photons and external radiation field, respectively.Comment: 15 pages, 5 figures, 1 table, MNRAS accepte

Computing Similarity between a Pair of Trajectories

With recent advances in sensing and tracking technology, trajectory data is becoming increasingly pervasive and analysis of trajectory data is becoming exceedingly important. A fundamental problem in analyzing trajectory data is that of identifying common patterns between pairs or among groups of trajectories. In this paper, we consider the problem of identifying similar portions between a pair of trajectories, each observed as a sequence of points sampled from it. We present new measures of trajectory similarity --- both local and global --- between a pair of trajectories to distinguish between similar and dissimilar portions. Our model is robust under noise and outliers, it does not make any assumptions on the sampling rates on either trajectory, and it works even if they are partially observed. Additionally, the model also yields a scalar similarity score which can be used to rank multiple pairs of trajectories according to similarity, e.g. in clustering applications. We also present efficient algorithms for computing the similarity under our measures; the worst-case running time is quadratic in the number of sample points. Finally, we present an extensive experimental study evaluating the effectiveness of our approach on real datasets, comparing with it with earlier approaches, and illustrating many issues that arise in trajectory data. Our experiments show that our approach is highly accurate in distinguishing similar and dissimilar portions as compared to earlier methods even with sparse sampling

Crossing the Logarithmic Barrier for Dynamic Boolean Data Structure Lower Bounds

This paper proves the first super-logarithmic lower bounds on the cell probe complexity of dynamic boolean (a.k.a. decision) data structure problems, a long-standing milestone in data structure lower bounds. We introduce a new method for proving dynamic cell probe lower bounds and use it to prove a $\tilde{\Omega}(\log^{1.5} n)$ lower bound on the operational time of a wide range of boolean data structure problems, most notably, on the query time of dynamic range counting over $\mathbb{F}_2$ ([Pat07]). Proving an $\omega(\lg n)$ lower bound for this problem was explicitly posed as one of five important open problems in the late Mihai P\v{a}tra\c{s}cu's obituary [Tho13]. This result also implies the first $\omega(\lg n)$ lower bound for the classical 2D range counting problem, one of the most fundamental data structure problems in computational geometry and spatial databases. We derive similar lower bounds for boolean versions of dynamic polynomial evaluation and 2D rectangle stabbing, and for the (non-boolean) problems of range selection and range median. Our technical centerpiece is a new way of "weakly" simulating dynamic data structures using efficient one-way communication protocols with small advantage over random guessing. This simulation involves a surprising excursion to low-degree (Chebychev) polynomials which may be of independent interest, and offers an entirely new algorithmic angle on the "cell sampling" method of Panigrahy et al. [PTW10]