Identifying lower-energy neutrinos with a liquid-argon particle detector

This is a visual display of an ArgoNeuT event showing a long trail left behind by a high energy particle traveling through the liquid argon accompanied by small blips, indicated by the arrows, caused by low energy particles.

The ArgoNeuT experiment recently demonstrated for the first time that a particular class of particle detector — those that use liquid argon — can identify signals in an energy range that particle physicists call the “MeV range.” It’s the first substantive step in confirming that researchers will be able to detect a wide energy range of neutrinos — even those at the harder-to-catch, lower energies — with the international Deep Underground Neutrino Experiment, or DUNE, hosted by Fermilab. DUNE is scheduled to start up in the mid-2020s.

By Leah Hesla

You can read the article here.

From turkeys to turn-keys

A superconducting radio-frequency accelerator cavity is mounted and connected to a cryocooler, cooling the cavity without the use of liquid helium. This new device could make it easier to produce high-average-power electron beams for industrial applications. Photo: Marty Murphy

The Illinois Accelerator Research Center (IARC) at Fermilab is on a mission to build a high-power, compact, superconducting electron beam accelerator that will serve all of these purposes.

By Charles Thangaraj

Read the article here.

Single-electron beam observed in IOTA for the first time

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Published on: May 15, 2019

Scientists and engineers stand by screens that display IOTA activity. The photo was taken on Oct. 31, 10:15 p.m., when three electrons were circulating in the machine. Aleksandr  Romanov, left, points to the computer screen on the left, where a camera image of the beam was visible. The screen on the right shows one of the first plots of the discrete steps. Photo: Giulio Stancari

Just two months ago, in September 2018, the IOTA ring was successfully commissioned, and the program of the advanced beam physics studies has since begun. One of the most interesting scientific topics at IOTA will be studies of beams made of a single electron.

By Vladimir Shiltsev and Giulio Stancari

You can read the article here.

An aside about listening to this podcast.

I have gotten some feedback that some of the articles are a little hard to follow.  Indeed, some of them are.  My suggestions to help you understand these podcasts better are:

  1. Do not speed up the podcast on your client player.  In fact, slowing them down a little will give you an opportunity to digest some of the more difficult ideas as they stream into your ears
  2. Read along!  Since you are reading this, you have found the WordPress site where this podcast originates. So you can click on the link above and read the article.

Superconducting film technology leads to record performance for low-frequency SRF cavity

A low-frequency, single-cell cavity is under preparation niobium-tin coating. Photo courtesy of Sam Posen

Superconducting radio-frequency (SRF) cavities are the “muscle” of many modern particle accelerators. By cooling these devices to cryogenic temperatures (usually around 2 Kelvin, or minus 456 degrees Fahrenheit) and inputting electric power, SRF cavities increase the energy of beams of charged particles passing through them. Making cavities out of superconducting materials dramatically increases their efficiency (represented by a cavity’s quality factor, or Q), allowing them to accelerate beams to high energies over short distances, without leaving long cool-down times between particle beam pulses.

By Sam Posen .

Read the article here.

MicroBooNE demonstrates use of convolutional neural networks on liquid-argon TPC data for first time

Categories: Intensity Frontier
Published on: May 1, 2019

This example image shows a charged-current neutrino interaction with decay gamma rays from a neutral pion (left). The label image (middle) is shown with the output of U-ResNet (right) where track and shower pixels are shown in yellow and cyan color respectively.

It is hard these days not to encounter examples of machine learning out in the world. Chances are, if your phone unlocks using facial recognition or if you’re using voice commands to control your phone, you are likely using machine learning algorithms — in particular deep neural networks.

By  Victor GentyKazuhiro Terao and Taritree Wongjirad

Read the article here.

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