GRB 091127: The cooling break race on magnetic fuel

Using high-quality, broad-band afterglow data for GRB 091127, we investigate the validity of the synchrotron fireball model for gamma-ray bursts, and infer physical parameters of the ultra-relativistic outflow. We used multi-wavelength follow-up observations obtained with GROND and the XRT onboard the Swift satellite. The resulting afterglow light curve is of excellent accuracy, and the spectral energy distribution is well-sampled over 5 decades in energy. These data present one of the most comprehensive observing campaigns for a single GRB afterglow and allow us to test several proposed emission models and outflow characteristics in unprecedented detail. Both the multi-color light curve and the broad-band SED of the afterglow of GRB 091127 show evidence of a cooling break moving from high to lower energies. The early light curve is well described by a broken power-law, where the initial decay in the optical/NIR wavelength range is considerably flatter than at X-rays. Detailed fitting of the time-resolved SED shows that the break is very smooth with a sharpness index of 2.2 +- 0.2, and evolves towards lower frequencies as a power-law with index -1.23 +- 0.06. These are the first accurate and contemporaneous measurements of both the sharpness of the spectral break and its time evolution. The measured evolution of the cooling break (nu_c propto t^-1.2) is not consistent with the predictions of the standard model, wherein nu_c propto t^-0.5 is expected. A possible explanation for the observed behavior is a time dependence of the microphysical parameters, in particular the fraction of the total energy in the magnetic field epsilon_B. This conclusion provides further evidence that the standard fireball model is too simplistic, and time-dependent micro-physical parameters may be required to model the growing number of well-sampled afterglow light curves.Comment: accepted to A&A, 13 pages, 5 figure

Harnessing Uncertainty in Radiotherapy Auto-Segmentation Quality Assurance

One of the key contributions of this study is the reappropriation of standard DL outputs as a quality indicator to identify cases that clinicians should review further. The authors achieve this by applying an empirically derived threshold to the softmax output of their DL network, computing the mean of the thresholded score map (termed the HiS metric), and correlating it with standard geometric quality indices. When juxtaposed with a mean entropy — a commonly used measure of model output uncertainty — HiS consistently demonstrated a stronger correlation with the geometric indices, suggesting its superior ability to stratify cases needing additional review. We applaud the authors\u27 efforts for their novel contributions and would like to note some potential caveats that could pave the way for future research directions

Evolving Horizons in Radiation Therapy Auto-Contouring: Distilling Insights, Embracing Data-Centric Frameworks, and Moving Beyond Geometric Quantification

Historically, clinician-derived contouring of tumors and healthy tissues has been crucial for radiation therapy (RT) planning. In recent years, advances in artificial intelligence (AI), predominantly in deep learning (DL), have rapidly improved automated contouring for RT applications, particularly for routine organs-at-risk.1, 2, 3 Despite research efforts actively promoting its broader acceptance, clinical adoption of auto-contouring is not yet standard practice. Notably, within several AI communities, there has been growing enthusiasm to shift from conventional “model-centric” AI approaches (ie, improving a model while keeping the data fixed), to “data-centric” AI approaches (ie, improving the data while keeping a model fixed).4 Although balancing both approaches is typically ideal for crafting the optimal solution for specific-use cases, most research in RT auto-contouring has prioritized algorithmic modifications aimed at enhancing quantitative contouring performance based on geometric (ie, structural overlap) indices5—a clear testament to the “model-centric” AI paradigm. In this editorial, aimed at clinician end-users and multidisciplinary research teams, we harmonize key insights in contemporary RT auto-contouring algorithmic development to promote the adoption of data-centric AI frameworks for impactful future research directions that would further facilitate clinical acceptance. Of note, the discussion herein draws primarily from literature related to head and neck cancer (HNC), showcasing it as a representative example of a complex disease site. However, these insights apply broadly to auto-contouring across disease sites

The Rapidly Flaring Afterglow of the Very Bright and Energetic GRB 070125

We report on multi-wavelength observations, ranging from the X-ray to radio wave bands, of the IPN-localized gamma-ray burst GRB 070125. Spectroscopic observations reveal the presence of absorption lines due to O I, Si II, and C IV, implying a likely redshift of z = 1.547. The well-sampled light curves, in particular from 0.5 to 4 days after the burst, suggest a jet break at 3.7 days, corresponding to a jet opening angle of ~7.0 degrees, and implying an intrinsic GRB energy in the 1 - 10,000 keV band of around E = (6.3 - 6.9)x 10^(51) erg (based on the fluences measured by the gamma-ray detectors of the IPN network). GRB 070125 is among the brightest afterglows observed to date. The spectral energy distribution implies a host extinction of Av < 0.9 mag. Two rebrightening episodes are observed, one with excellent time coverage, showing an increase in flux of 56% in ~8000 seconds. The evolution of the afterglow light curve is achromatic at all times. Late-time observations of the afterglow do not show evidence for emission from an underlying host galaxy or supernova. Any host galaxy would be subluminous, consistent with current GRB host-galaxy samples. Evidence for strong Mg II absorption features is not found, which is perhaps surprising in view of the relatively high redshift of this burst and the high likelihood for such features along GRB-selected lines of sight.Comment: 50 pages, 9 figures, 5 tables Accepted to the Astrophysical Journa

Event-by-event fluctuations in Mean pTp_T and Mean eTe_T in sqrt(s_NN) = 130 GeV Au+Au Collisions

Distributions of event-by-event fluctuations of the mean transverse momentum and mean transverse energy near mid-rapidity have been measured in Au+Au collisions at sqrt(s_NN) = 130 GeV at RHIC. By comparing the distributions to what is expected for statistically independent particle emission, the magnitude of non-statistical fluctuations in mean transverse momentum is determined to be consistent with zero. Also, no significant non-random fluctuations in mean transverse energy are observed. By constructing a fluctuation model with two event classes that preserve the mean and variance of the semi-inclusive p_T or e_T spectra, we exclude a region of fluctuations in sqrt(s_NN) = 130 GeV Au+Au collisions.Comment: 10 pages, RevTeX 3, 7 figures, 4 tables, 307 authors, submitted to Phys. Rev. C on 22 March 2002. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (will be made) publicly available at http://www.phenix.bnl.gov/phenix/WWW/run/phenix/papers.htm

Measurement of the mid-rapidity transverse energy distribution from sNN=130\sqrt{s_{NN}}=130 GeV Au+Au collisions at RHIC

The first measurement of energy produced transverse to the beam direction at RHIC is presented. The mid-rapidity transverse energy density per participating nucleon rises steadily with the number of participants, closely paralleling the rise in charged-particle density, such that E_T / N_ch remains relatively constant as a function of centrality. The energy density calculated via Bjorken's prescription for the 2% most central Au+Au collisions at sqrt(s_NN)=130 GeV is at least epsilon_Bj = 4.6 GeV/fm^3 which is a factor of 1.6 larger than found at sqrt(s_NN)=17.2 GeV (Pb+Pb at CERN).Comment: 307 authors, 6 pages, 4 figures, 1 table, submitted to PRL 4/18/2001; revised version submitted to PRL 5/24/200

Net Charge Fluctuations in Au + Au Interactions at sqrt(s_NN) = 130 GeV

Data from Au + Au interactions at sqrt(s_NN) = 130 GeV, obtained with the PHENIX detector at RHIC, are used to investigate local net charge fluctuations among particles produced near mid-rapidity. According to recent suggestions, such fluctuations may carry information from the Quark Gluon Plasma. This analysis shows that the fluctuations are dominated by a stochastic distribution of particles, but are also sensitive to other effects, like global charge conservation and resonance decays.Comment: 6 pages, RevTeX 3, 3 figures, 307 authors, submitted to Phys. Rev. Lett. on 21 March, 2002. Plain text data tables for the points plotted in figures for this and previous PHENIX publications are (will be made) publicly available at http://www.phenix.bnl.gov/phenix/WWW/run/phenix/papers.htm