MIT Technology Review > How frozen atoms could help us learn more from gravitational waves – We’ve seen ripples in spacetime only when the universe’s biggest events occur. Now there might be a way to spot them ahead of time by Neel V. Patel (Oct 21, 2019).

]]>The key to getting more out of these [gravitational wave] signals might come from a new experiment taking shape deep in a 100-meter (320-foot) vertical shaft at Fermilab in Batavia, Illinois. This is MAGIS-100, a project designed to see whether

shooting frozen atoms with laserscan be used to observe ultra-sensitive signals that might be stretching through spacetime. If successful, it could help usher in a new era of “atom interferometry” that could reveal some of the secrets of gravitational waves, dark matter, quantum mechanics, and other heady topics.

Stanford Universityphysicist Jason Hogan, one of the leads for the project, likens the technology behind MAGIS-100 to a hybrid of an interferometer and an atomic clock. “These [strontium] atoms basically act like extremely good stopwatches that keep time on the propagation of light and look for fluctuations caused by other signals,” he says.

Scientist Dr Katie Bouman, 29, was a key leader on the team that captured the first ever image of a black hole earlier this year. The celebratory image she posted online ended up on the receiving end of misogynistic trolling – but her team rallied round to support her. A video by Angelica Casas and Lu Yang for BBC 100 Women.

Regarding filling in gaps in data streams, Bouman uses an analogy, comparing measurements to musical notes: like hearing a tune being played on a piano that has a lot of broken keys but still being able to identify the underlying song.

]]>Fermilab > Department of Energy awards Fermilab funding for next-generation dark matter research (October 18, 2019 | edited by Leah Hesla)

]]>Earlier this month, the Department of Energy announced that it has awarded scientists at its Fermi National Accelerator Laboratory funding to boost research on dark matter, the mysterious substance that makes up an astounding 85% of the matter in the universe.

The award will fund two Fermilab projects focused on

searching for dark matter particles of low mass— less than the mass of a proton.The Fermilab-led initiatives funded through the DOE Basic Research Needs for Dark Matter New Initiatives grants are:

1. Extending the search for axions with ADMX

Collaborating institutions: Lawrence Livermore National Laboratory, Pacific Northwest National Laboratory, Los Alamos National Laboratory, University of Florida, University of Washington, Washington University, St. Louis, University of California, Berkeley and University of Western Australia.

2. Toward unprecedented sensitivity with skipper CCDs

One way to hunt for dark matter is to catch it in

the act of bumping into a particle of ordinary matter, such as an electron [and detect energy-transfer signals].Collaborating institutions: Pacific Northwest National Laboratory, Stony Brook University, University of Chicago, University of Washington.

Towards Data Science > “Probability Theory 101 for Dummies like Me” by Sangeet Moy Das (Oct 13, 2019)

In the

Classical interpretationProbability is the measure of the likelihood that an event will occur in a Random Experiment; In other words, the frequency of the event occurring.Probability is often associated with at least one event. This event can be anything. Toy examples of events include rolling a die or pulling a colored ball out of a bag. In these examples the outcome of the event is random (you can’t be sure of the value that the die will show when you roll it), so the variable that represents the outcome of these events is called a random variable …

The article discusses these concepts:

**Probability** (as likelihood in tossing a coin, expressed as percents or fractions).

**Probability (frequentist)** – frequencies of outcomes in large samples or repeated trials over time.

**Probability (subjective)** – a measure of belief.

**Random variable** – a variable whose value is determined by chance (typically within some range of values).

**Discrete random variable** – a random varible whose values are specific and countable and not continuous, e.g., the outcome of a coin toss is either 0 (tails) or 1 (heads). Values are quantized.

**Continuous random variable** – values can vary continuously, so that probabilities can be related to the area under the curve of a probability density function.

**Sample space** – the collection of all possible outcomes.

**Complement** – the probability of the complement of A includes the sum of all probabilities in the sample space that is not A.

**Mutually exclusive events** (disjoint events).

**Independent events**.

**Dependent events**.

And the **rules or axioms of probabiity** (sometimes using a deck of cards and lastly a game of Roulette):

The range of probabilties and summation of those outcomes.

Arithmetic on probabiities.

The Law of Large Numbers.

The **types of probability**: marginal, joint (cf. Venn Diagram), conditional.

Bayes’ Theorem (for conditional probabilities).

And the article closes with a discussion of **the difference between probability and statistics**.

Probabilitydeals withpredictingthe likelihood of future events, whilestatisticsinvolves theanalysis of the frequency of past events.Probability is primarily a

theoreticalbranch of mathematics, which studies the consequences of mathematical definitions. Statistics is primarily anapplied branchof mathematics, which tries to make sense of observations in the real world.This distinction will perhaps become clearer if we trace the thought process of a mathematician encountering his first craps game:

If this mathematician were a probabilist, he would see the dice and think, “Six-sided dice? Presumably, each face of the dice is equally likely to land face up. Now assuming that each face comes up with probability 1/6, I can figure out what my chances of crapping out are.”

If instead, a statistician wandered by, he would see the dice and think, “Those dice may look OK, but how do I know that they are not loaded? I’ll watch a while, and keep track of how often each number comes up. Then I can decide if my observations are consistent with the assumption of equal-probability faces. Once I’m confident enough that the dice are fair, I’ll call a probabilist to tell me how to play.’’

In summary, probability theory enables us to find the consequences of a given

ideal world, while statistical theory enables us tomeasure the extent to which our world is ideal.

**Frequentist vs. Bayesian View**

]]>One thing that is worth mentioning is that in the introduction of this post I made a statement regarding the

classic interpretation of probability. Specifically, this classic interpretation is referred to as thefrequentist view of probability. In this view, probabilities are based purely on objective, random experiments with the assumption that given enough trials (long-run) the relative frequency of event x will equal to the true probability of x. Notice how all of the probabilities we reported in this post were based purely on the frequency.If you’ve done any statistics or analytics, you’ll likely have come across the term

Bayesian statistics. In brief, Bayesian statistics differ from the frequentists view in that it incorporatessubjective probability which is the degree of belief in an event. This degree of belief is calledthe prior probability distribution and is incorporated along with the data from random experiments when determining probabilities.

Quanta Magazine > “Where Quantum Probability Comes From” by Sean Carroll, Contributing Columnist (September 9, 2019) > *There are many different ways to think about probability. Quantum mechanics embodies them all.*

Laplace’s demon[as portrayed in A Philosophical Essay on Probabilities (1814) by Pierre-Simon Laplace] was never supposed to be a practical thought experiment; the imagined intelligence would have to be essentially as vast as the universe itself. And in practice, chaotic dynamics can amplify tiny imperfections in the initial knowledge of the system into complete uncertainty later on. But in principle, Newtonian mechanics is deterministic.Researchers continue to argue over the best way to think about quantum mechanics. There are competing schools of thought, which are sometimes referred to as “interpretations” of quantum theory but are better thought of as distinct physical theories that give the same predictions in the regimes we have tested so far. All of them share the feature that they lean on the idea of probability in a fundamental way. Which raises the question: What is “probability,” really?

Like many subtle concepts, probability starts out with a seemingly straightforward, commonsensical meaning, which becomes trickier the closer we look at it.The

“objective” or “physical” viewtreats probability as a fundamental feature of a system, the best way we have to characterize physical behavior. An example of an objective approach to probability isfrequentism, which defines probability as the frequency with which things happen over many trials, as in … coin-tossing …Alternatively, there are

“subjective” or “evidential” views, which treat probability as personal, a reflection of an individual’s credence, or degree of belief, about what is true or what will happen. … Bayesians [as an example] imagine that rational creatures in states of incomplete information walk around with credences for every proposition …In contrast with frequentism, in Bayesianism it makes perfect sense to attach probabilities to one-shot events, …Quantum mechanics as it is currently understood doesn’t really help us choose between competing conceptions of probability, as every conception has a home in some quantum formulation or other.

Each of these [conceptions of probability] represents a way of solving

the measurement problemof quantum mechanics.

Dynamical-collapse theoriesoffer perhaps the most straightforward resolution to the measurement problem. They posit that there is a truly random component to quantum evolution, according to which every particle usually obeys the Schrödinger equation, but occasionally its wave function will spontaneously localize at some position in space. … All the particles in a large system will be entangled with each other, so that when just one of them localizes in space, the rest are brought along for the ride [so, no superpositioned Schrödinger’s cat].Dynamical-collapse theories fit perfectly into an old-fashioned frequentist view of probability. What happens next is unknowable, and all we can say is what the long-term frequency of different outcomes will be. Laplace’s demon wouldn’t be able to exactly predict the future, even if it knew the present state of the universe exactly.

…

pilot-wave theoriesbring us back to the clockwork universe of classical mechanics, but with an important twist … It characterizes our knowledge, ……

many-worlds… is my personal favorite approach to quantum mechanics [Carroll: the simplest formulation of all the alternatives.], but it’s also the one for which it is most challenging to pinpoint how and why probability enters the game.In many-worlds, we can know the wave function exactly, and it evolves deterministically. There is nothing unknown or unpredictable. Laplace’s demon could predict the entire future of the universe with perfect confidence.

An answer [as to how probability is involved in this view] is provided by the idea of “self-locating,” or “indexical,” uncertainty. [Carroll: Self-locating uncertainty is a different kind of epistemic uncertainty from that featured in pilot-wave models. … it requires a bit of work to convince yourself that there’s a reasonable way to assign numbers to your belief.]

… the wave function branches incredibly fast, on timescales of 10−21 seconds or less. That’s far quicker than a signal can even reach your brain. [In the many-worlds view] there will always be some period of time when you’re on a certain branch of the wave function, but you don’t know which one.

Can we resolve this uncertainty in a sensible way? Yes, we can, as Charles Sebens and I have argued, … Sebens and I needed to make a new assumption, which we called the “

epistemic separability principle”: Whatever predictions you make for experimental outcomes, they should be unaltered if we only change the wave function for completely separate parts of the system.

References

The Born rule is one of the key principles of quantum mechanics.

]]>Bayesian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.

The Bayesian interpretation of probability can be seen as an extension of

propositional logic that enables reasoning with hypotheses.That is to say, propositions whose truth or falsity is uncertain. In the Bayesian view,a probability is assigned to a hypothesis, whereas under frequentist inference, a hypothesis is typically tested without being assigned a probability.

]]>A new video from NASA shows how a supernova explosion morphs and changes during a 13-year period, The growing debris field, known as Cassiopeia A or Cas A, likely was generated after a star explosion in 1680. New data from

NASA’s Chandra X-Ray Observatoryshows that even an old explosion can change in subtle ways during a human lifetime.

The video combines X-ray data from Chandra with observations from the Hubble Space Telescope, which observes in visual and infrared light. Hubble’s data was held constant to emphasize the changes Chandra observed over time, according to Chandra personnel.

]]>Scientists at the Department of Energy’s Fermilab have announced that they achieved

the highest magnetic field strength ever recorded for an accelerator steering magnet, setting a world record of 14.1 teslas, with the magnet cooled to 4.5 kelvins or minus 450 degrees Fahrenheit. The previous record of 13.8 teslas, achieved at the same temperature, was held for 11 years by Lawrence Berkeley National Laboratory.That’s more than a thousand times stronger magnet than the refrigerator magnet that’s holding your grocery list to your refrigerator.

The project is supported by the Department of Energy Office of Science. It is a key part of the U.S. Magnet Development Program, which includes Fermilab, Brookhaven National Laboratory, Lawrence Berkeley National Laboratory and the National High Magnetic Field Laboratory.

]]>How do some neutron stars become the strongest magnets in the universe? A German-British team of astrophysicists has found a possible answer to the question of how magnetars form. They used large computer simulations to demonstrate how the merger of two stars creates strong magnetic fields. If such stars explode in supernovae, magnetars can result. Scientists from Heidelberg University, the Max Planck Society, the Heidelberg Institute for Theoretical Studies, and the University of Oxford were involved in the research. The results were published in Nature.

Remember that experiment (or video of a demonstration) in physics lab showing what happens to the speed of light when a laser beam is split into two paths, one through air (or vacuum) and one through water? (See this Fermilab YouTube video for an explanation of the actual physics.)

… why, … was there a recent story claiming that gamma-ray jets, where gamma-rays themselves are a high-energy form of light, can travel faster-than-light?

When you pass light through a medium, … its electric and magnetic fields interact with the particles in the medium, and the light is forced to move at a slower speed: the speed of light in that particular medium. … the amount that light slows down by depends on the light’s wavelength.

[The radiation in the story] is not moving faster than c, the speed of light in a vacuum, but v, the speed of light in the particle-filled medium [matter-rich environment] surrounding the source of these gamma rays.

What the researchers did was introduce a new, simple model that would explain the bizarre properties seen in pulsing gamma-ray bursts. They model the gamma-ray emissions as originating from a jet of fast-moving particles, which is consistent with what we know. But they then introduce

a fast-moving impactor wave that runs into this expanding jet, and as the density (and other properties) of the medium changes, that wave then accelerates from moving slower-than-light to moving faster-than-light in that medium.[The radiation is] slower than light through the medium for one part of the journey and faster than light through the medium for another part of the journey [and is observed as separate pulses] …

References

UCLA Physics > YouTube video (2013) “How to Measure the Speed of Light”

]]>]]>SpaceX CEO Elon Musk has now given four presentations about his company’s Starship rocket, but all of those updates mostly focused on the vehicle’s external stats. Musk has barely touched on

the technologies needed to keep people alive and healthy while on Starship— technologies that need to be developed relatively soon if the spacecraft has any hope of carrying people to deep-space destinations like the Moon and Mars in the near future.Musk says only 5 percent of SpaceX’s resources are being used to develop Starship at the moment, which may explain why the rocket is the sole focus. At some point, the humans will need to be addressed, though. It’s just a matter of when.