WEBVTT

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<v Speaker 1>Welcome to Bedtime Astronomy. Explore the wonders of the cosmos

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<v Speaker 1>with our soothing Bedtime Astronomy podcast. Each episode offers a

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<v Speaker 1>gentle journey through the stars, planets, and beyond, perfect for

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<v Speaker 1>unwinding after a long day. Let's travel through the mysteries

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<v Speaker 1>of the universe as you drift off into a peaceful

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<v Speaker 1>slumber under the night sky. This week in Astronomy, microquasar acceleration,

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<v Speaker 1>dark matter detection, and machine learning in gravitational waves, microquasars,

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<v Speaker 1>hidden cosmic accelerators. Earth is constantly bombarded by particles originating

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<v Speaker 1>from outer space. While people are mostly familiar with rocky

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<v Speaker 1>meteorites from within our Solar system, which create the brilliant

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<v Speaker 1>streaks of light known as shooting stars, it is the

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<v Speaker 1>much smaller particles that provide scientists with deeper insights into

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<v Speaker 1>the nature of the universe. Among these particles are subatomic

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<v Speaker 1>ones such as electrons and protons, which arrive from interstellar

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<v Speaker 1>space and beyond. These high energy particles, known as cosmic rays,

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<v Speaker 1>are among the fastest moving particles in the universe. Despite

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<v Speaker 1>their known presence. The origins and mechanisms that accelerate the

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<v Speaker 1>most energetic of these cosmic rays remain one of the

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<v Speaker 1>biggest unanswered questions in astrophysics. One of the most promising

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<v Speaker 1>locations for particle acceleration is in the relativistic jets launched

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<v Speaker 1>from black holes. These jets, which expel matter at nearly

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<v Speaker 1>the speed of light, are thought to provide ideal conditions

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<v Speaker 1>for accelerating particles. However, the details of how these acceleration

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<v Speaker 1>processes occur and under what specific conditions they take place

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<v Speaker 1>are not yet fully understood. Within our galaxy, some of

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<v Speaker 1>the most powerful jets are found in systems known as microquasars.

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<v Speaker 1>These systems consist of a stellar mass black hole in

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<v Speaker 1>close orbit with a normal star. When the two objects

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<v Speaker 1>are near enough to each other, the black hole begins

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<v Speaker 1>pulling in material from its stellar companion. This process fuels

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<v Speaker 1>the formation of jets that are ejected from the region

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<v Speaker 1>surrounding the black hole. Recent studies suggest that microcoasar jets

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<v Speaker 1>are efficient at accelerating particles. However, it remains unclear how

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<v Speaker 1>much these systems contribute as a group to the overall

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<v Speaker 1>amount of cosmic rays in the Milky Way. Answering this

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<v Speaker 1>question requires determining whether all microquasars have the capability to

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<v Speaker 1>accelerate particles or if only certain systems possess the necessary conditions.

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<v Speaker 1>Microquasars are typically classified into two categories based on the

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<v Speaker 1>mass of the companion star, low mass and high mass systems.

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<v Speaker 1>The low mass systems are far more abundant in the galaxy. However,

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<v Speaker 1>up until now, evidence for Parkle acceleration had only been

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<v Speaker 1>detected in high mass microquasars. One well known example is

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<v Speaker 1>SS four thirty three, which was recently identified as one

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<v Speaker 1>of the most powerful Parkle accelerators in the Milky Way.

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<v Speaker 1>This system features a companion star with a mass approximately

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<v Speaker 1>ten times that of the Sun. Because no previous observations

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<v Speaker 1>had detected Parkle acceleration in low mass microquasars, it was

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<v Speaker 1>widely believed that they lacked the necessary power to produce

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<v Speaker 1>high energy gamma rays. This assumption has now been challenged

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<v Speaker 1>by a groundbreaking discovery made by doctor Laura Olivernietto from

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<v Speaker 1>the Max Planck Institute for Nuclear Physics in Heidelberg, Germany

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<v Speaker 1>and doctor Gilim Marty de Vessa from the University of

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<v Speaker 1>Trieste in Italy. Their findings published in the Astrophysical journal

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<v Speaker 1>Letters provide the first evidence that a low mass microquasar

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<v Speaker 1>can accelerate parkles to a sad extremely high energies. The

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<v Speaker 1>researchers analyzed sixteen years of data collected by the Large

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<v Speaker 1>Area Telescope LAT aboard NASA's FERMI satellite. Their study revealed

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<v Speaker 1>a faint but significant gamma ray signal originating from GRS

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<v Speaker 1>nineteen fifteen plus one oh five, a microquasar with a

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<v Speaker 1>companion star that is smaller than the Sun. The detected

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<v Speaker 1>gamma rays have energies exceeding ten gate electron volts GeV,

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<v Speaker 1>suggesting that this system is capable of accelerating particles to

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<v Speaker 1>even higher energy levels. The data strongly support a scenario

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<v Speaker 1>in which protons are accelerated within the microquaser's jets. Once

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<v Speaker 1>these protons escape the system, they interact with the surrounding gas,

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<v Speaker 1>producing high energy gamma ray photons. To further support this theory,

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<v Speaker 1>the researchers also used observations from the NOBIUMA forty five

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<v Speaker 1>meter radio telescope in Japan. These data confirm that there

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<v Speaker 1>is an ample amount of gas material surrounding GRS nineteen

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<v Speaker 1>fifteen plus one oh five, making it possible for escaping

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<v Speaker 1>protons to generate gamma rays in the expected manner. This

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<v Speaker 1>discovery has major implications for astrophysics, since low mass microquasars

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<v Speaker 1>are far more numerous than their high mass counterparts. This

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<v Speaker 1>finding suggests that microquasars as a whole could be a

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<v Speaker 1>much more significant source of cosmic rays in our galaxy

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<v Speaker 1>than previously estimated. Despite this breakthrough, many questions remain. Not

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<v Speaker 1>all microquasars appear to be efficient particle accelerators, and it

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<v Speaker 1>is still unclear why only certain systems exhibit this behavior.

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<v Speaker 1>More detections, along with multi wavelength studies across different types

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<v Speaker 1>of radiation, will be necessary to understand the specific conditions

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<v Speaker 1>that enable some microquasars to accelerate particles so efficiently while

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<v Speaker 1>others do not. Space experiment aims to detect dark matter.

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<v Speaker 1>Scientists are embarking on an ambitious space experiment to investigate

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<v Speaker 1>one of the greatest unsolved mysteries of the universe, dark matter.

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<v Speaker 1>Despite making up an estimated eighty five percent of all

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<v Speaker 1>mass in the cosmos, this elusive substance remains invisible and

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<v Speaker 1>undetectable through conventional observational methods. Now, a team from the

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<v Speaker 1>University of Southampton has developed a novel approach that could

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<v Speaker 1>advance our understanding of dark matter by measuring its potential

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<v Speaker 1>interactions in the vacuum of space. The researchers have designed

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<v Speaker 1>and begun testing a device that detects extremely small forces

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<v Speaker 1>by firing lasers through levitating graphite sheets in a zero

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<v Speaker 1>gravity environment. According to physicist Tim Fuchs, the project could

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<v Speaker 1>lay the groundwork for future space based experiments that may

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<v Speaker 1>finally provide direct evidence of dark matter. He highlights that

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<v Speaker 1>although ungrous theories exist regarding its nature, no Earth based

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<v Speaker 1>experiment has ever succeeded in detecting it. Dark matter plays

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<v Speaker 1>a crucial role in shaping the structure of the universe,

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<v Speaker 1>yet its presence can only be inferred through its gravitational

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<v Speaker 1>influence on visible matter. First identified in the nineteen thirties,

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<v Speaker 1>It does not emit, absorb, or reflect light in any

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<v Speaker 1>meaningful way, making it impossible to observe with traditional telescopes.

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<v Speaker 1>The motion of stars and galaxies suggests the presence of

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<v Speaker 1>an unseen mass exerting gravitational effects, which is attributed to

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<v Speaker 1>dark matter. The experiment proposed by the Southampton team is

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<v Speaker 1>unlike anything attempted before. Their method involves suspending graphite particles

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<v Speaker 1>between magnets in the absence of gravity. This setup becomes

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<v Speaker 1>highly sensitive to even the smallest external forces. If dark

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<v Speaker 1>matter exists at a high enough density, it could generate

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<v Speaker 1>a subtle, yet man measurable wind gently pushing the levitated

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<v Speaker 1>graphite particles. Detecting this movement would provide the first direct

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<v Speaker 1>measurement of dark matter interactions. The experimental device, weighing only

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<v Speaker 1>one point five kilograms, will be sent into space as

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<v Speaker 1>part of the Jovian one satellite mission, a collaboration between

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<v Speaker 1>Space south Central and the Universities of Southampton, Portsmouth and Surrey.

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<v Speaker 1>The team is currently evaluating different launch options, with the

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<v Speaker 1>goal of deploying the satellite early next year. Once in

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<v Speaker 1>lowerth orbit, the device will conduct its tests over a

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<v Speaker 1>two year period, analyzing the movements of the levitating graphite

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<v Speaker 1>in response to potential dark matter interactions. Fuchs points out

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<v Speaker 1>that one possible reason for the lack of success in

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<v Speaker 1>Earth based dark matter experiments is the interaction rate hypothesis.

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<v Speaker 1>Some theories suggest that dark matter might interact with ordinary

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<v Speaker 1>matter so frequently that it cannot penetrate Earth's atmosphere or

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<v Speaker 1>the underground locations where many major detectors are placed. If

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<v Speaker 1>this is true, then space based detection could provide the

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<v Speaker 1>breakthrough scientists have been searching for. This mission is the

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<v Speaker 1>first of its kind to apply levitation technology in space

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<v Speaker 1>for dark matter detection. If successful, it will serve as

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<v Speaker 1>a proof of concept demonstrating that direct measurements of dark

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<v Speaker 1>matter interactions are possible outside of Earth's atmosphere. This innovative

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<v Speaker 1>approach could pave the way for future space based experiments,

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<v Speaker 1>bringing science one step closer to solving one of the

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<v Speaker 1>deepest enigmas of modern astrophysics, gravitational wave detection. With machine learning.

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<v Speaker 1>Researchers at the University of California Riverside have developed a

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<v Speaker 1>new machine learning approach to improve data analysis for LIGO,

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<v Speaker 1>the Laser Interferometer Gravitational Wave Observatory. This method enhanced is

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<v Speaker 1>the ability to detect patterns and reduce noise in the

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<v Speaker 1>vast and complex data sets produced by the facility, helping

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<v Speaker 1>scientists refine gravitational wave observations. The approach could also be

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<v Speaker 1>applied to other large scale scientific experiments, such as Parkle

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<v Speaker 1>accelerators and industrial systems that generate massive amounts of data.

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<v Speaker 1>LIGO is designed to detect gravitational waves ripples in the

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<v Speaker 1>fabric of space time caused by the movement of extremely

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<v Speaker 1>massive objects, such as merging black holes. The observatory was

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<v Speaker 1>the first to confirm the existence of these waves, providing

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<v Speaker 1>key evidence for Einstein's theory of relativity. It consists of

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<v Speaker 1>two four kilometer long interferometers in Hanford, Washington and Livingstone, Louisiana,

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<v Speaker 1>which work together to capture these cosmic signals by measuring

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<v Speaker 1>tiny distortions caused by passing gravitational waves. These detections offer

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<v Speaker 1>a new way to study the universe, shedding light on

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<v Speaker 1>the nature of black holes, cosmology in the densest forms

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<v Speaker 1>of matter. The challenge, however, lies in distinguishing real gravitational

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<v Speaker 1>wave signals from noise. Each LIGO detector records data from

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<v Speaker 1>thousands of environmental sensors which track factors such as seismic activity,

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<v Speaker 1>atmospheric disturbances, and human made vibrations. These external influences can

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<v Speaker 1>introduce noise into the system, affecting the quality of the

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<v Speaker 1>data and sometimes causing glitches, brief bursts of unwanted signals

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<v Speaker 1>that interfere with gravitational wave detection. To address this issue,

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<v Speaker 1>the UC Riverside team developed a machine learning tool capable

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<v Speaker 1>of identifying patterns in the data without human supervision. The

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<v Speaker 1>system was designed in collaboration with LIGO operators and engineers,

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<v Speaker 1>ensuring its practical application to real world data. The results

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<v Speaker 1>showed that the model was able to act accurately recognize

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<v Speaker 1>environmental states such as earthquakes, ocean waves, and human generated

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<v Speaker 1>noise without any prior input from researchers. This capability makes

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<v Speaker 1>it a powerful tool for isolating and understanding noise sources,

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<v Speaker 1>ultimately leading to improvements in ligo's sensitivity. The machine learning

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<v Speaker 1>algorithm works by analyzing signals from over one hundred thousand

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<v Speaker 1>auxiliary channels at the LGO sites, which include sensors like

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<v Speaker 1>seismometers and accelerometers. These instruments monitor the surroundings of the interferometers,

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<v Speaker 1>recording environmental conditions that might influence detections. By clustering and

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<v Speaker 1>classifying data points, the tool can link specific noise events

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<v Speaker 1>to external sources, helping scientists pinpoint the causes of certain glitches.

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<v Speaker 1>The team presented their findings at the aitripole e S

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<v Speaker 1>fifth International Workshop on Big data and AI tools, models

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<v Speaker 1>and use case for innovative scientific discovery held in Washington, DC.

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<v Speaker 1>Their research paper, titled Multivariate time series clustering for Environmental

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<v Speaker 1>state Characterization of ground based gravitational wave detectors is available

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<v Speaker 1>on the AR fourteen pre print server. One of the

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<v Speaker 1>key breakthroughs of this research is the public release of

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<v Speaker 1>a large data set used in the study. This data set,

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<v Speaker 1>made available with the cooperation of the LIGO scientific collaboration,

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<v Speaker 1>allows other scientists to validate the results and develop new

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<v Speaker 1>data analysis methods. Given that most scientific data sets of

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<v Speaker 1>this nature remain proprietary, this release is expected to foster

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<v Speaker 1>interdisciplinary research in machine learning and data science. By working

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<v Speaker 1>through all LIGO data channels for over a year, the

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<v Speaker 1>team identified links between environmental noise and the occurrence of

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<v Speaker 1>glitches and gravitational wave detections. This discovery could lead to

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<v Speaker 1>strategies for preventing or mitigating noise sources, improving the accuracy

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<v Speaker 1>of ligo's observations. The research also highlights how machine learning

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<v Speaker 1>can assist in detecting new patterns in large scale scientific experiments.

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<v Speaker 1>The long term vision for this tool is to enable

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<v Speaker 1>researchers to identify unknown noise sources and guide practical improvements

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<v Speaker 1>in Ligo's design. By recognizing patterns and environmental data, scientists

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<v Speaker 1>can make targeted changes, such as replacing components or adjusting

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<v Speaker 1>sensitivity settings to minimize interference. This approach paves the way

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<v Speaker 1>for more accurate gravitational wave detections and deeper insights into

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<v Speaker 1>the most extreme cosmic events. In addition to the UC

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<v Speaker 1>Riverside team, the research involved contributors from the Ligo Livingstone

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<v Speaker 1>Observatory and included distinguished physicist Barry Bearrisch, a leading figure

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<v Speaker 1>in the field. The project represents a signal magnificant step

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<v Speaker 1>forward in the intersection of physics, data science, and artificial intelligence,

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<v Speaker 1>demonstrating how advanced computational techniques can enhance our ability to

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<v Speaker 1>explore the universe m