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Hubble Space Telescope Hubble Home Overview About Hubble The History of Hubble Hubble Timeline Why Have a Telescope in Space? Hubble by the Numbers At the Museum FAQs Impact & Benefits Hubble’s Impact & Benefits Science Impacts Cultural Impact Technology Benefits Impact on Human Spaceflight Astro Community Impacts Science Hubble Science Science Themes Science Highlights Science Behind Discoveries Hubble’s Partners in Science Universe Uncovered Explore the Night Sky Observatory Hubble Observatory Hubble Design Mission Operations Missions to Hubble Hubble vs Webb Team Hubble Team Career Aspirations Hubble Astronauts News Hubble News Hubble News Archive Social Media Media Resources Multimedia Multimedia Images Videos Sonifications Podcasts e-Books Online Activities Lithographs Fact Sheets Glossary Posters Hubble on the NASA App More 35th Anniversary 2 min read
Hubble Captures Young Stars Changing Their Environments
This NASA/ESA Hubble Space Telescope image features the nearest star-forming region to Earth, the Orion Nebula (Messier 42, M42), located some 1,500 light-years away. ESA/Hubble, NASA, and T. Megeath This NASA/ESA Hubble Space Telescope image peers into the dusty recesses of the nearest massive star-forming region to Earth, the Orion Nebula (Messier 42, M42). Just 1,500 light-years away, the Orion Nebula is visible to the unaided eye below the three stars that form the ‘belt’ in the constellation Orion. The nebula is home to hundreds of newborn stars including the subject of this image: the protostars HOPS 150 and HOPS 153.
These protostars get their names from the Herschel Orion Protostar Survey, conducted with ESA’s Herschel Space Observatory. The object visible in the upper-right corner of this image is HOPS 150: it’s a binary star system where two young protostars orbit each other. Each star has a small, dusty disk of material surrounding it. These stars gather material from their respective dust disks, growing in the process. The dark line that cuts across the bright glow of these protostars is a cloud of gas and dust falling in on the pair of protostars. It is over 2,000 times wider than the distance between Earth and the Sun. Based on the amount of infrared light HOPS 150 is emitting, as compared to other wavelengths it emits, the protostars are mid-way down the path to becoming mature stars.
Extending across the left side of the image is a narrow, colorful outflow called a jet. This jet comes from the nearby protostar HOPS 153, which is out of the frame. HOPS 153 is significantly younger than its neighbor. That stellar object is still deeply embedded in its birth nebula and enshrouded by a cloud of cold, dense gas. While Hubble cannot penetrate this gas to see the protostar, the jet HOPS 153 emitted is brightly and clearly visible as it plows into the surrounding gas and dust of the Orion Nebula.
The transition from tightly swaddled protostar to fully fledged star will dramatically affect HOPS 153’s surroundings. As gas falls onto the protostar, its jets spew material and energy into interstellar space, carving out bubbles and heating the gas. By stirring up and warming nearby gas, HOPS 153 may regulate the formation of new stars in its neighborhood and even slow its own growth.
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Three-Year Study of Young Stars with NASA’s Hubble Enters New Chapter
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Media Contact:
Claire Andreoli (claire.andreoli@nasa.gov)
NASA’s Goddard Space Flight Center, Greenbelt, MD
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Last Updated Jan 16, 2025 Editor Andrea Gianopoulos Location NASA Goddard Space Flight Center Related Terms
Astrophysics Astrophysics Division Goddard Space Flight Center Hubble Space Telescope Nebulae Protostars Stars The Universe Keep Exploring Discover More Topics From Hubble
Hubble Space Telescope
Since its 1990 launch, the Hubble Space Telescope has changed our fundamental understanding of the universe.
Exploring the Birth of Stars
Hubble’s Night Sky Challenge
Hubble Focus: The Lives of Stars
This e-book highlights the mission’s recent discoveries and observations related to the birth, evolution, and death of stars.
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By NASA
Firefly Aerospace’s Blue Ghost lander getting encapsulated in SpaceX’s rocket fairing ahead of the planned liftoff for 1:11 a.m. EST Jan. 15 from Launch Complex 39A at NASA’s Kennedy Space Center in FloridaSpaceX As part of NASA’s CLPS (Commercial Lunar Payload Services) initiative and Artemis campaign, the agency is preparing to fly ten instruments aboard Firefly Aerospace’s first delivery to the Moon. These science payloads and technology demonstrations will help advance our understanding of the Moon and planetary processes, while paving the way for future crewed missions on the Moon and beyond, for the benefit of all.
Firefly’s lunar lander, named Blue Ghost, is scheduled to launch on a SpaceX Falcon 9 rocket Wednesday, Jan.15, from Launch Complex 39A at NASA’s Kennedy Space Center in Florida. After a 45-day cruise phase, Blue Ghost is targeted to land near a volcanic feature called Mons Latreille within Mare Crisium, a basin approximately 340 miles wide (550 kilometers) located in the northeast quadrant of the Moon’s near side.
How can we enable more precise navigation on the Moon? How do spacecraft interact with the lunar surface? How does Earth’s magnetic field influence the effects of space weather on our home planet? NASA’s instruments on this flight will conduct first-of-their-kind demonstrations to help answer these questions and more, including testing regolith sampling technologies, lunar subsurface drilling capabilities, increasing precision of positioning and navigation abilities, testing radiation tolerant computing, and learning how to mitigate lunar dust during lunar landings.
The ten NASA payloads aboard Firefly’s Blue Ghost lander include:
Lunar Instrumentation for Subsurface Thermal Exploration with Rapidity (LISTER) will measure heat flow from the Moon’s interior by measuring the thermal gradient, or changes in temperature at various depths, and thermal conductivity, or the subsurface material’s ability to let heat pass through it. LISTER will take several measurements up to 10 feet deep using pneumatic drilling technology with a custom heat flow needle instrument at its tip. Data from LISTER will help scientists retrace the Moon’s thermal history and understand how it formed and cooled. Lead organization: Texas Tech University
Lunar PlanetVac (LPV) is designed to collect regolith samples from the lunar surface using a burst of compressed gas to drive the regolith into a sample chamber (sieving) for collection and analysis by various instruments. Additional instrumentation will then transmit the results back to Earth. The LPV payload is designed to help increase the science return from planetary missions by testing low-cost technologies for collecting regolith samples in-situ. Lead organization: Honeybee Robotics
Next Generation Lunar Retroreflector (NGLR) serves as a target for lasers on Earth to precisely measure the distance between Earth and the Moon by reflecting very short laser pulses from Earth-based Lunar Laser Ranging Observatories. The laser pulse transit time to the Moon and back is used to determine the distance. Data from NGLR could improve the accuracy of our lunar coordinate system and contribute to our understanding of the inner structure of the Moon and fundamental physics questions. Lead organization: University of Maryland
Regolith Adherence Characterization (RAC) will determine how lunar regolith sticks to a range of materials exposed to the Moon’s environment throughout the lunar day. RAC will measure accumulation rates of lunar regolith on surfaces (for example, solar cells, optical systems, coatings, and sensors) through imaging to determine their ability to repel or shed lunar dust. The data captured will help test, improve, and protect spacecraft, spacesuits, and habitats from abrasive regolith. Lead organization: Aegis Aerospace
Radiation Tolerant Computer (RadPC) will demonstrate a computer that can recover from faults caused by ionizing radiation. Several RadPC prototypes have been tested aboard the International Space Station and Earth-orbiting satellites, but this flight will provide the biggest trial yet by demonstrating the computer’s ability to withstand space radiation as it passes through Earth’s radiation belts, while in transit to the Moon, and on the lunar surface. Lead organization: Montana State University
Electrodynamic Dust Shield (EDS) is an active dust mitigation technology that uses electric fields to move and prevent hazardous lunar dust accumulation on surfaces. EDS is designed to lift, transport, and remove particles from surfaces with no moving parts. Multiple tests will demonstrate the feasibility of the self-cleaning glasses and thermal radiator surfaces on the Moon. In the event the surfaces do not receive dust during landing, EDS has the capability to re-dust itself using the same technology. Lead organization: NASA’s Kennedy Space Center
Lunar Environment heliospheric X-ray Imager (LEXI) will capture a series of X-ray images to study the interaction of solar wind and Earth’s magnetic field that drives geomagnetic disturbances and storms. Deployed and operated on the lunar surface, this instrument will provide the first global images showing the edge of Earth’s magnetic field for critical insights into how space weather and other cosmic forces surrounding our planet impact Earth. Lead organizations: Boston University, NASA’s Goddard Space Flight Center, and Johns Hopkins University
Lunar Magnetotelluric Sounder (LMS) will characterize the structure and composition of the Moon’s mantle by measuring electric and magnetic fields. This investigation will help determine the Moon’s temperature structure and thermal evolution to understand how the Moon has cooled and chemically differentiated since it formed. Lead organization: Southwest Research Institute
Lunar GNSS Receiver Experiment (LuGRE) will demonstrate the possibility of acquiring and tracking signals from GNSS (Global Navigation Satellite System) constellations, specifically GPS and Galileo, during transit to the Moon, during lunar orbit, and on the lunar surface. If successful, LuGRE will be the first pathfinder for future lunar spacecraft to use existing Earth-based navigation constellations to autonomously and accurately estimate their position, velocity, and time. Lead organizations: NASA Goddard, Italian Space Agency
Stereo Camera for Lunar Plume-Surface Studies (SCALPSS) will use stereo imaging photogrammetry to capture the impact of the rocket exhaust plume on lunar regolith as the lander descends on the Moon’s surface. The high-resolution stereo images will aid in creating models to predict lunar regolith erosion, which is an important task as bigger, heavier spacecraft and hardware are delivered to the Moon in close proximity to each other. This instrument also flew on Intuitive Machines’ first CLPS delivery. Lead organization: NASA’s Langley Research Center
Through the CLPS initiative, NASA purchases lunar landing and surface operations services from American companies. The agency uses CLPS to send scientific instruments and technology demonstrations to advance capabilities for science, exploration, or commercial development of the Moon. By supporting a robust cadence of lunar deliveries, NASA will continue to enable a growing lunar economy while leveraging the entrepreneurial innovation of the commercial space industry.
Learn more about CLPS and Artemis at: http://www.nasa.gov/clps
Alise Fisher
Headquarters, Washington
202-358-2546
alise.m.fisher@nasa.gov
Natalia Riusech / Nilufar Ramji
Johnson Space Center, Houston
281-483-5111
natalia.s.riusech@nasa.gov / nilufar.ramji@nasa.gov
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By NASA
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Preparations for Next Moonwalk Simulations Underway (and Underwater)
In-person participants L-R standing: Dave Francisco, Joanne Kaouk, Dr. Richard Moon, Dr. Tony Alleman, Dr. Sean Hardy, Sarah Childress, Kristin Coffey, Dr. Ed Powers, Dr. Doug Ebersole, Dr. Steven Laurie, Dr. Doug Ebert; L-R seated: Dr. Alejandro Garbino, Dr. Robert Sanders, Dr. Kristi Ray, Dr. Mike Gernhardt, Dr. Joseph Dervay, Dr. Matt Makowski). Not pictured: Dr. Caroline Fife In June 2024, the NASA Office of the Chief Health and Medical Officer (OCHMO) Standards Team hosted an independent assessment working group to review the status and progress of research and clinical activities intended to mitigate the risk of decompression sickness (DCS) related to patent foramen ovale (PFO) during spaceflight and associated ground testing and human subject studies.
Decompression sickness (DCS) is a condition which results from dissolved gases (primarily nitrogen) forming bubbles in the bloodstream and tissues. It is usually experienced in conditions where there are rapid decreases in ambient pressure, such as in scuba divers, high-altitude aviation, or other pressurized environments. The evolved gas bubbles have various physiological effects and can obstruct the blood vessels, trigger inflammation, and damage tissue, resulting in symptoms of DCS. NASA presently classifies DCS into two categories: Type I DCS, which is less severe, typically leads to musculoskeletal symptoms including pain in the joints or muscles, or skin rash. Type II DCS is more severe and commonly results in neurological, inner ear, and cardiopulmonary symptoms. The risk of DCS in spaceflight presents during extravehicular activities (EVAs) in which astronauts perform mission tasks outside the spaceflight vehicle while wearing a pressurized suit at a lower pressure than the cabin pressure. DCS mitigation protocols based on strategies to reduce systemic nitrogen load are implemented through the combination of habitat environmental parameters, EVA suit pressure, and breathing gas procedures (prebreathe protocols) to achieve safe and effective mission operations. The pathophysiology of DCS has still not been fully elucidated since cases occur despite the absence of detected gas bubbles but includes right to left shunting of venous gas emboli (VGE) via several potential mechanisms, one of which is a Patent Foramen Ovale (PFO).
From: Dr. Schochet & Dr. Lie, Pediatric Pulmonologists
Reference OCHMO-TB-037 Decompression Sickness (DCS) Risk Mitigation technical brief for additional information.
A PFO is a shunt between the right atrium and the left atrium of the heart, which is a persisting remnant of a physiological communication present in the fetal heart. Post-natal increases in left atrial pressure usually force the inter-septal valve against the septum secundum and within the first 2 years of life, the septae permanently fuse due to the development of fibrous adhesions. Thus, all humans are born with a PFO and approximately 75% of PFOs fuse following childbirth. For the 25% of the population’s whose PFOs do not fuse, ~6% have what is considered by some to be a large PFO (> 2 mm). PFO diameter can increase with age. The concern with PFOs is that with a right to left shunt between the atria, venous emboli gas may pass from the right atrium (venous) to the left atrium (arterial) (“shunt”), thus by-passing the normal lung filtration of venous emboli which prevent passage to the arterial system. Without filtration, bubbles in the arterial system may lead to a neurological event such as a stroke. Any activity that increases the right atrium/venous pressure over the left atrium/arterial pressure (such as a Valsalva maneuver, abdominal compression) may further enable blood and/or emboli across a PFO/shunt.
From: Nuffield Department of Clinical Neurosciences
The purpose of this working group was to review and provide analysis on the status and progress of research and clinical activities intended to mitigate the risk of PFO and DCS issues during spaceflight. Identified cases of DCS during NASA exploration atmosphere ground testing conducted in pressurized chambers led to the prioritization of the given topic for external review. The main goals of the working group included:
Quantification of any increased risk associated with the presence of a PFO during decompression protocols utilized in ground testing and spaceflight EVAs, as well as unplanned decompressions (e.g., cabin depressurization, EVA suit leak). Describe risks and benefits of PFO screening in astronaut candidates, current crewmembers, and chamber test subjects. What are potential risk reduction measures that could be considered if a person was believed to be at increased risk of DCS due to a PFO? What research and/or technology development is recommended that could help inform and/or mitigate PFO-related DCS risk? The working group took place over two days at NASA’s Johnson Space Center and included NASA subject matter experts and stakeholders, as well as invited external reviewers from areas including cardiology, hypobaric medicine, spaceflight medicine, and military occupational health. During the working group, participants were asked to review past reports and evidence related to PFOs and risk of DCS, materials and information regarding NASA’s current experience and practices, and case studies and subsequent decision-making processes. The working group culminated in an open-forum discussion where recommendations for current and future practices were conferred and subsequently summarized in a final summary report, available on the public NASA OCHMO Standards Team website.
The following key findings are the main take-aways from the OCHMO independent assessment:
In an extreme exposure/high-risk scenario, excluding individuals with a PFO and treating PFOs does not necessarily decrease the risk of DCS or create a ‘safe’ environment. It may create incremental differences and slightly reduce overall risk but does not make the risk zero. There are other physiological factors that also contribute to the risk of DCS that may have a larger impact (see 7.0 Other Physiological Factors in the findings section). Based on the available evidence and the risk of current decompression exposures (based on current NASA protocols and NASA-STD-3001 requirements to limit the risk of DCS), it is not recommended to screen for PFOs in any spaceflight or ground testing participants. The best strategy to reduce the risk of DCS is to create as safe an environment as possible in every scenario, through effective prebreathe protocols, safety, and the capability to rapidly treat DCS should symptoms occur. Based on opinion, no specific research is required at this time to further characterize PFOs with DCS and altitude exposure, due to the low risk and preference to institute adequate safe protocols and ensuring treatment availability both on the ground and in spaceflight. For engineering protocols conducted on the ground, it should be ensured that the same level of treatment capability (treatment chamber in the immediate vicinity of the testing) is provided as during research protocols. The ability to immediately treat a DCS case is critical in ensuring the safety of the test subjects. The full summary report includes detailed background information, discussion points from the working group, and conclusions and recommendations. The findings from the working group and resulting summary report will help to inform key stakeholders in decision-making processes for future ground testing and spaceflight operations with the main goal of protecting crew health and safety to ensure overall mission success.
Summary Report About the Author
Sarah D. Childress
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Last Updated Dec 31, 2024 Related Terms
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Ken Freeman (center) receives the ATCA Award for ATM-X Digital Information Platform (DIP) from Rachel Jackson, Chair ATCA Board of Directors (left) and Carey Fagan, President and CEO ATCA (right).NASA Air Traffic Control Association (ATCA) Award to the NASA ATM-X Digital Information Platform (DIP) Team
In November 2024, the Digital Information Platform (DIP) team received the prestigious Industry Award from the Air Traffic Control Association (ATCA) at the annual ATCA Connect Conference in Washington, DC. The award recognized the team’s efforts in supporting NASA’s Sustainable Flight National Partnership (SFNP), which aims for net-zero carbon emissions from aviation by 2050. The DIP sub-project focuses on increasing access to digital aviation information to enable efficient and sustainable airspace operations. DIP team has been conducting live operational demonstrations in North Texas Metroplex environment since 2022 with commercial airlines on the Collaborative Digital Departure Reroute (CDDR) tool that applies machine learning to make predictions on runway availability, departure times, and arrival times. DIP has signed Space Act Agreements with five major US airlines to carryout operational evaluation of CDDR in complex metroplex environments and is now deploying the CDDR capability to Houston. CDDR machine learning algorithm intelligently provides re-routing options to the operators by using real time weather and operational data reducing delays, fuel burn and carbon emissions. DIP is part of the Air Traffic Management – eXploration (ATM-X) project, which is focused on transforming the air traffic management system to accommodate new air vehicles. More information on the ATCA award is at: https://www.atca.org/detail-pages/news/2024/11/15/atca-presents-annual-awards-at-atca-connect-recognizing-exceptional-efforts-made-to-the-worldwide-air-traffic-control-and-airspace-system.
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By NASA
Download PDF: Statistical Analysis Using Random Forest Algorithm Provides Key Insights into Parachute Energy Modulator System
Energy modulators (EM), also known as energy absorbers, are safety-critical components that are used to control shocks and impulses in a load path. EMs are textile devices typically manufactured out of nylon, Kevlar® and other materials, and control loads by breaking rows of stitches that bind a strong base webbing together as shown in Figure 1. A familiar EM application is a fall-protection harness used by workers to prevent injury from shock loads when the harness arrests a fall. EMs are also widely used in parachute systems to control shock loads experienced during the various stages of parachute system deployment.
Random forest is an innovative algorithm for data classification used in statistics and machine learning. It is an easy to use and highly flexible ensemble learning method. The random forest algorithm is capable of modeling both categorical and continuous data and can handle large datasets, making it applicable in many situations. It also makes it easy to evaluate the relative importance of variables and maintains accuracy even when a dataset has missing values.
Random forests model the relationship between a response variable and a set of predictor or independent variables by creating a collection of decision trees. Each decision tree is built from a random sample of the data. The individual trees are then combined through methods such as averaging or voting to determine the final prediction (Figure 2). A decision tree is a non-parametric supervised learning algorithm that partitions the data using a series of branching binary decisions. Decision trees inherently identify key features of the data and provide a ranking of the contribution of each feature based on when it becomes relevant. This capability can be used to determine the relative importance of the input variables (Figure 3). Decision trees are useful for exploring relationships but can have poor accuracy unless they are combined into random forests or other tree-based models.
The performance of a random forest can be evaluated using out-of-bag error and cross-validation techniques. Random forests often use random sampling with replacement from the original dataset to create each decision tree. This is also known as bootstrap sampling and forms a bootstrap forest. The data included in the bootstrap sample are referred to as in-the-bag, while the data not selected are out-of-bag. Since the out-of-bag data were not used to generate the decision tree, they can be used as an internal measure of the accuracy of the model. Cross-validation can be used to assess how well the results of a random forest model will generalize to an independent dataset. In this approach, the data are split into a training dataset used to generate the decision trees and build the model and a validation dataset used to evaluate the model’s performance. Evaluating the model on the independent validation dataset provides an estimate of how accurately the model will perform in practice and helps avoid problems such as overfitting or sampling bias. A good model performs well on
both the training data and the validation data.
The complex nature of the EM system made it difficult for the team to identify how various parameters influenced EM behavior. A bootstrap forest analysis was applied to the test dataset and was able to identify five key variables associated with higher probability of damage and/or anomalous behavior. The identified key variables provided a basis for further testing and redesign of the EM system. These results also provided essential insight to the investigation and aided in development of flight rationale for future use cases.
For information, contact Dr. Sara R. Wilson. sara.r.wilson@nasa.gov
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