اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدOrigin and physics of the highest energy cosmic rays: What can we learn from Radio Astronomy?
99
0
0.0
(
0
)
تأليف
Peter L. Biermann
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
Here in this lecture we will touch on two aspects, one the new radio methods to observe the effects of high energy particles, and second the role that radio galaxies play in helping us understand high energy cosmic rays. We will focus here on the second topic, and just review the latest developments in the first. Radio measurements of the geosynchrotron radiation produced by high energy cosmic ray particles entering the atmosphere of the Earth as well as radio v{C}erenkov radiation coming from interactions in the Moon are another path; radio observations of interactions in ice at the horizon in Antarctica is a related attempt. Radio galaxy hot spots are prime candidates to produce the highest energy cosmic rays, and the corresponding shock waves in relativistic jets emanating from nearly all black holes observed. We will review the arguments and the way to verify the ensuing predictions. This involves the definition of reliable samples of active sources, such as black holes, and galaxies active in star formation. The AUGER array will probably decide within the next few years, where the highest energy cosmic rays come from, and so frame the next quests, on very high energy neutrinos and perhaps other particles.
قيم البحث
اقرأ أيضاً
We investigate the effects of multi-task learning using the recently introduced task of semantic tagging. We employ semantic tagging as an auxiliary task for three different NLP tasks: part-of-speech tagging, Universal Dependency parsing, and Natural
Language Inference. We compare full neural network sharing, partial neural network sharing, and what we term the learning what to share setting where negative transfer between tasks is less likely. Our findings show considerable improvements for all tasks, particularly in the learning what to share setting, which shows consistent gains across all tasks.
Learning problems form an important category of computational tasks that generalizes many of the computations researchers apply to large real-life data sets. We ask: what concept classes can be learned privately, namely, by an algorithm whose output
does not depend too heavily on any one input or specific training example? More precisely, we investigate learning algorithms that satisfy differential privacy, a notion that provides strong confidentiality guarantees in contexts where aggregate information is released about a database containing sensitive information about individuals. We demonstrate that, ignoring computational constraints, it is possible to privately agnostically learn any concept class using a sample size approximately logarithmic in the cardinality of the concept class. Therefore, almost anything learnable is learnable privately: specifically, if a concept class is learnable by a (non-private) algorithm with polynomial sample complexity and output size, then it can be learned privately using a polynomial number of samples. We also present a computationally efficient private PAC learner for the class of parity functions. Local (or randomized response) algorithms are a practical class of private algorithms that have received extensive investigation. We provide a precise characterization of local private learning algorithms. We show that a concept class is learnable by a local algorithm if and only if it is learnable in the statistical query (SQ) model. Finally, we present a separation between the power of interactive and noninteractive local learning algorithms.
It has been argued that the observations of cosmic particles with energies in excess of $10^8$ TeV represent a puzzle. Its solution requires new astrophysics or new particle physics. We show that the latter is unlikely given that the scale associated
with a new particle physics threshold must be of order 1 GeV, not TeV and above, in order to resolve the problem. In most cases such new physics should have been revealed by accelerator experiments. We examine the possibility that the highest energy cosmic rays are initiated by non-standard interactions of neutrinos in the atmosphere. We show that proposals in this direction either violate s-wave unitarity or fall short of producing a sizeable effect by several orders of magnitude.
Introducing a simple Galactic wind model patterned after the solar wind we show that back-tracing the orbits of the highest energy cosmic events suggests that they may all come from the Virgo cluster, and so probably from the active radio galaxy M87.
This confirms a long standing expectation. Those powerful radio galaxies that have their relativistic jets stuck in the interstellar medium of the host galaxy, such as 3C147, will then enable us to derive limits on the production of any new kind of particle, expected in some extensions of the standard model in particle physics. New data from HIRES will be crucial in testing the model proposed here.
In this study, we investigated the employment status of recent University of Ottawa physics MSc and PhD graduates, finding that 94% of graduates are either employed or pursuing further physics education one year post-graduation. Our database was popu
lated from the public online repository of MSc and PhD theses submitted between the academic years of 2011 to 2019, with employment information collected in 2020 from the professional social media platform LinkedIn. Our results highlight that graduates primarily find employment quickly and in their field of study, with most graduates employed in either academia or physics-related industries. We also found that a significant portion of employed graduates, 20%, find employment in non-traditional physics careers, such as business management and healthcare. Graduates with careers in academia tend to have lower online connectivity compared to graduates with careers in industry or non-traditional fields, suggesting a greater importance for online networking for students interested in non-academic careers.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
