Speaker 1: Welcome to Bedtime Astronomy. Explore the wonders of the cosmos
Speaker 1: with our soothing Bedtime Astronomy podcast. Each episode offers a
Speaker 1: gentle journey through the stars, planets, and beyond, perfect for
Speaker 1: unwinding after a long day. Let's travel through the mysteries
Speaker 1: of the universe as you drift off into a peaceful
Speaker 1: slumber under the night sky. This week in Astronomy, microquasar acceleration,
Speaker 1: dark matter detection, and machine learning in gravitational waves, microquasars,
Speaker 1: hidden cosmic accelerators. Earth is constantly bombarded by particles originating
Speaker 1: from outer space. While people are mostly familiar with rocky
Speaker 1: meteorites from within our Solar system, which create the brilliant
Speaker 1: streaks of light known as shooting stars, it is the
Speaker 1: much smaller particles that provide scientists with deeper insights into
Speaker 1: the nature of the universe. Among these particles are subatomic
Speaker 1: ones such as electrons and protons, which arrive from interstellar
Speaker 1: space and beyond. These high energy particles, known as cosmic rays,
Speaker 1: are among the fastest moving particles in the universe. Despite
Speaker 1: their known presence. The origins and mechanisms that accelerate the
Speaker 1: most energetic of these cosmic rays remain one of the
Speaker 1: biggest unanswered questions in astrophysics. One of the most promising
Speaker 1: locations for particle acceleration is in the relativistic jets launched
Speaker 1: from black holes. These jets, which expel matter at nearly
Speaker 1: the speed of light, are thought to provide ideal conditions
Speaker 1: for accelerating particles. However, the details of how these acceleration
Speaker 1: processes occur and under what specific conditions they take place
Speaker 1: are not yet fully understood. Within our galaxy, some of
Speaker 1: the most powerful jets are found in systems known as microquasars.
Speaker 1: These systems consist of a stellar mass black hole in
Speaker 1: close orbit with a normal star. When the two objects
Speaker 1: are near enough to each other, the black hole begins
Speaker 1: pulling in material from its stellar companion. This process fuels
Speaker 1: the formation of jets that are ejected from the region
Speaker 1: surrounding the black hole. Recent studies suggest that microcoasar jets
Speaker 1: are efficient at accelerating particles. However, it remains unclear how
Speaker 1: much these systems contribute as a group to the overall
Speaker 1: amount of cosmic rays in the Milky Way. Answering this
Speaker 1: question requires determining whether all microquasars have the capability to
Speaker 1: accelerate particles or if only certain systems possess the necessary conditions.
Speaker 1: Microquasars are typically classified into two categories based on the
Speaker 1: mass of the companion star, low mass and high mass systems.
Speaker 1: The low mass systems are far more abundant in the galaxy. However,
Speaker 1: up until now, evidence for Parkle acceleration had only been
Speaker 1: detected in high mass microquasars. One well known example is
Speaker 1: SS four thirty three, which was recently identified as one
Speaker 1: of the most powerful Parkle accelerators in the Milky Way.
Speaker 1: This system features a companion star with a mass approximately
Speaker 1: ten times that of the Sun. Because no previous observations
Speaker 1: had detected Parkle acceleration in low mass microquasars, it was
Speaker 1: widely believed that they lacked the necessary power to produce
Speaker 1: high energy gamma rays. This assumption has now been challenged
Speaker 1: by a groundbreaking discovery made by doctor Laura Olivernietto from
Speaker 1: the Max Planck Institute for Nuclear Physics in Heidelberg, Germany
Speaker 1: and doctor Gilim Marty de Vessa from the University of
Speaker 1: Trieste in Italy. Their findings published in the Astrophysical journal
Speaker 1: Letters provide the first evidence that a low mass microquasar
Speaker 1: can accelerate parkles to a sad extremely high energies. The
Speaker 1: researchers analyzed sixteen years of data collected by the Large
Speaker 1: Area Telescope LAT aboard NASA's FERMI satellite. Their study revealed
Speaker 1: a faint but significant gamma ray signal originating from GRS
Speaker 1: nineteen fifteen plus one oh five, a microquasar with a
Speaker 1: companion star that is smaller than the Sun. The detected
Speaker 1: gamma rays have energies exceeding ten gate electron volts GeV,
Speaker 1: suggesting that this system is capable of accelerating particles to
Speaker 1: even higher energy levels. The data strongly support a scenario
Speaker 1: in which protons are accelerated within the microquaser's jets. Once
Speaker 1: these protons escape the system, they interact with the surrounding gas,
Speaker 1: producing high energy gamma ray photons. To further support this theory,
Speaker 1: the researchers also used observations from the NOBIUMA forty five
Speaker 1: meter radio telescope in Japan. These data confirm that there
Speaker 1: is an ample amount of gas material surrounding GRS nineteen
Speaker 1: fifteen plus one oh five, making it possible for escaping
Speaker 1: protons to generate gamma rays in the expected manner. This
Speaker 1: discovery has major implications for astrophysics, since low mass microquasars
Speaker 1: are far more numerous than their high mass counterparts. This
Speaker 1: finding suggests that microquasars as a whole could be a
Speaker 1: much more significant source of cosmic rays in our galaxy
Speaker 1: than previously estimated. Despite this breakthrough, many questions remain. Not
Speaker 1: all microquasars appear to be efficient particle accelerators, and it
Speaker 1: is still unclear why only certain systems exhibit this behavior.
Speaker 1: More detections, along with multi wavelength studies across different types
Speaker 1: of radiation, will be necessary to understand the specific conditions
Speaker 1: that enable some microquasars to accelerate particles so efficiently while
Speaker 1: others do not. Space experiment aims to detect dark matter.
Speaker 1: Scientists are embarking on an ambitious space experiment to investigate
Speaker 1: one of the greatest unsolved mysteries of the universe, dark matter.
Speaker 1: Despite making up an estimated eighty five percent of all
Speaker 1: mass in the cosmos, this elusive substance remains invisible and
Speaker 1: undetectable through conventional observational methods. Now, a team from the
Speaker 1: University of Southampton has developed a novel approach that could
Speaker 1: advance our understanding of dark matter by measuring its potential
Speaker 1: interactions in the vacuum of space. The researchers have designed
Speaker 1: and begun testing a device that detects extremely small forces
Speaker 1: by firing lasers through levitating graphite sheets in a zero
Speaker 1: gravity environment. According to physicist Tim Fuchs, the project could
Speaker 1: lay the groundwork for future space based experiments that may
Speaker 1: finally provide direct evidence of dark matter. He highlights that
Speaker 1: although ungrous theories exist regarding its nature, no Earth based
Speaker 1: experiment has ever succeeded in detecting it. Dark matter plays
Speaker 1: a crucial role in shaping the structure of the universe,
Speaker 1: yet its presence can only be inferred through its gravitational
Speaker 1: influence on visible matter. First identified in the nineteen thirties,
Speaker 1: It does not emit, absorb, or reflect light in any
Speaker 1: meaningful way, making it impossible to observe with traditional telescopes.
Speaker 1: The motion of stars and galaxies suggests the presence of
Speaker 1: an unseen mass exerting gravitational effects, which is attributed to
Speaker 1: dark matter. The experiment proposed by the Southampton team is
Speaker 1: unlike anything attempted before. Their method involves suspending graphite particles
Speaker 1: between magnets in the absence of gravity. This setup becomes
Speaker 1: highly sensitive to even the smallest external forces. If dark
Speaker 1: matter exists at a high enough density, it could generate
Speaker 1: a subtle, yet man measurable wind gently pushing the levitated
Speaker 1: graphite particles. Detecting this movement would provide the first direct
Speaker 1: measurement of dark matter interactions. The experimental device, weighing only
Speaker 1: one point five kilograms, will be sent into space as
Speaker 1: part of the Jovian one satellite mission, a collaboration between
Speaker 1: Space south Central and the Universities of Southampton, Portsmouth and Surrey.
Speaker 1: The team is currently evaluating different launch options, with the
Speaker 1: goal of deploying the satellite early next year. Once in
Speaker 1: lowerth orbit, the device will conduct its tests over a
Speaker 1: two year period, analyzing the movements of the levitating graphite
Speaker 1: in response to potential dark matter interactions. Fuchs points out
Speaker 1: that one possible reason for the lack of success in
Speaker 1: Earth based dark matter experiments is the interaction rate hypothesis.
Speaker 1: Some theories suggest that dark matter might interact with ordinary
Speaker 1: matter so frequently that it cannot penetrate Earth's atmosphere or
Speaker 1: the underground locations where many major detectors are placed. If
Speaker 1: this is true, then space based detection could provide the
Speaker 1: breakthrough scientists have been searching for. This mission is the
Speaker 1: first of its kind to apply levitation technology in space
Speaker 1: for dark matter detection. If successful, it will serve as
Speaker 1: a proof of concept demonstrating that direct measurements of dark
Speaker 1: matter interactions are possible outside of Earth's atmosphere. This innovative
Speaker 1: approach could pave the way for future space based experiments,
Speaker 1: bringing science one step closer to solving one of the
Speaker 1: deepest enigmas of modern astrophysics, gravitational wave detection. With machine learning.
Speaker 1: Researchers at the University of California Riverside have developed a
Speaker 1: new machine learning approach to improve data analysis for LIGO,
Speaker 1: the Laser Interferometer Gravitational Wave Observatory. This method enhanced is
Speaker 1: the ability to detect patterns and reduce noise in the
Speaker 1: vast and complex data sets produced by the facility, helping
Speaker 1: scientists refine gravitational wave observations. The approach could also be
Speaker 1: applied to other large scale scientific experiments, such as Parkle
Speaker 1: accelerators and industrial systems that generate massive amounts of data.
Speaker 1: LIGO is designed to detect gravitational waves ripples in the
Speaker 1: fabric of space time caused by the movement of extremely
Speaker 1: massive objects, such as merging black holes. The observatory was
Speaker 1: the first to confirm the existence of these waves, providing
Speaker 1: key evidence for Einstein's theory of relativity. It consists of
Speaker 1: two four kilometer long interferometers in Hanford, Washington and Livingstone, Louisiana,
Speaker 1: which work together to capture these cosmic signals by measuring
Speaker 1: tiny distortions caused by passing gravitational waves. These detections offer
Speaker 1: a new way to study the universe, shedding light on
Speaker 1: the nature of black holes, cosmology in the densest forms
Speaker 1: of matter. The challenge, however, lies in distinguishing real gravitational
Speaker 1: wave signals from noise. Each LIGO detector records data from
Speaker 1: thousands of environmental sensors which track factors such as seismic activity,
Speaker 1: atmospheric disturbances, and human made vibrations. These external influences can
Speaker 1: introduce noise into the system, affecting the quality of the
Speaker 1: data and sometimes causing glitches, brief bursts of unwanted signals
Speaker 1: that interfere with gravitational wave detection. To address this issue,
Speaker 1: the UC Riverside team developed a machine learning tool capable
Speaker 1: of identifying patterns in the data without human supervision. The
Speaker 1: system was designed in collaboration with LIGO operators and engineers,
Speaker 1: ensuring its practical application to real world data. The results
Speaker 1: showed that the model was able to act accurately recognize
Speaker 1: environmental states such as earthquakes, ocean waves, and human generated
Speaker 1: noise without any prior input from researchers. This capability makes
Speaker 1: it a powerful tool for isolating and understanding noise sources,
Speaker 1: ultimately leading to improvements in ligo's sensitivity. The machine learning
Speaker 1: algorithm works by analyzing signals from over one hundred thousand
Speaker 1: auxiliary channels at the LGO sites, which include sensors like
Speaker 1: seismometers and accelerometers. These instruments monitor the surroundings of the interferometers,
Speaker 1: recording environmental conditions that might influence detections. By clustering and
Speaker 1: classifying data points, the tool can link specific noise events
Speaker 1: to external sources, helping scientists pinpoint the causes of certain glitches.
Speaker 1: The team presented their findings at the aitripole e S
Speaker 1: fifth International Workshop on Big data and AI tools, models
Speaker 1: and use case for innovative scientific discovery held in Washington, DC.
Speaker 1: Their research paper, titled Multivariate time series clustering for Environmental
Speaker 1: state Characterization of ground based gravitational wave detectors is available
Speaker 1: on the AR fourteen pre print server. One of the
Speaker 1: key breakthroughs of this research is the public release of
Speaker 1: a large data set used in the study. This data set,
Speaker 1: made available with the cooperation of the LIGO scientific collaboration,
Speaker 1: allows other scientists to validate the results and develop new
Speaker 1: data analysis methods. Given that most scientific data sets of
Speaker 1: this nature remain proprietary, this release is expected to foster
Speaker 1: interdisciplinary research in machine learning and data science. By working
Speaker 1: through all LIGO data channels for over a year, the
Speaker 1: team identified links between environmental noise and the occurrence of
Speaker 1: glitches and gravitational wave detections. This discovery could lead to
Speaker 1: strategies for preventing or mitigating noise sources, improving the accuracy
Speaker 1: of ligo's observations. The research also highlights how machine learning
Speaker 1: can assist in detecting new patterns in large scale scientific experiments.
Speaker 1: The long term vision for this tool is to enable
Speaker 1: researchers to identify unknown noise sources and guide practical improvements
Speaker 1: in Ligo's design. By recognizing patterns and environmental data, scientists
Speaker 1: can make targeted changes, such as replacing components or adjusting
Speaker 1: sensitivity settings to minimize interference. This approach paves the way
Speaker 1: for more accurate gravitational wave detections and deeper insights into
Speaker 1: the most extreme cosmic events. In addition to the UC
Speaker 1: Riverside team, the research involved contributors from the Ligo Livingstone
Speaker 1: Observatory and included distinguished physicist Barry Bearrisch, a leading figure
Speaker 1: in the field. The project represents a signal magnificant step
Speaker 1: forward in the intersection of physics, data science, and artificial intelligence,
Speaker 1: demonstrating how advanced computational techniques can enhance our ability to
Speaker 1: explore the universe m