At just 1.3 pounds and displaying symptoms of respiratory illness, Blueberry, a male miniature lop-eared bunny, was brought to the San Diego Humane Society (SDHS). Under the dedicated care of veterinarians and volunteers, Blueberry made a remarkable recovery, growing to a healthy weight of 3.6 pounds, and was eventually adopted. However, the adoption did not go as planned, and Blueberry was returned due to his aggressive behavior. This setback left him frustrated and resulted in a week-long period without food. After Blueberry regained his health and rebuilt trust with people, SDHS successfully matched him with another family, leading to a successful second adoption. This exemplifies SDHS’s goal of pairing pets with the right families, giving them the opportunity to settle into stable and loving homes.
San Diego Humane Society (SDHS)
In the United States, more than 70 million stray animals endure freezing temperatures during winter and suffer from heat exhaustion in the summer. Only a fortunate 10% of these animals find their way into shelters. In San Diego, the go-to shelter that comes to mind is the San Diego Humane Society. Founded in 1880, SDHS is the oldest and largest animal shelter in San Diego County. They have made a commitment to avoid euthanasia for healthy and treatable shelter animals. During the 2019-2020 period, SDHS admitted a total of 44,483 animals, including dogs, cats, bunnies, guinea pigs, and more. Once animals enter SDHS, their health is assessed through thorough medical examinations conducted by the shelter’s veterinarians. Subsequently, the animals receive vaccinations and microchips for identification. Additionally, neuter or spay surgeries are performed to prevent unplanned breeding, with SDHS having conducted 17,473 such surgeries during the 2019-2020 period. After completing the necessary pre-adoption procedures, the animals are placed on the adoption list and are ready to find their new homes.
In-person visit to find the most fitted one
When a potential owner expresses interest in a specific animal, SDHS recommends an in-person visit to one of their campuses for a face-to-face meeting with an adoption counselor. During the Covid-19 pandemic, people can opt to call SDHS and speak with an adoption counselor instead. SDHS also follows up with adopters to monitor the animal’s adjustment. Furthermore, the cost of the initial veterinary examination for adopted animals is covered by SDHS. They offer pet behavior and training classes for dogs and cats to help them acclimate to their new families. One special feature of the adoption in SDHS is the returning policy. The owners can return their adopted animals for whatever reasons. This policy greatly not only reduces the opportunity for these animals to get back on the street but also increases potential owner’s adoption willingness
A stable and loving home for every pet
SDHS helped 19,230 animals to find their new home during 2019-2020. Their main mission is to inspire compassion for people and animals. The core adoption philosophy in SDHS is “every pet has the right to a continuous and nurturing relationship with people who convey an enduring sense of love and care.” Therefore, SDHS work hard to match pets to the right families and a chance to finally settle into a stable, loving home. Hopefully, San Diego Humane Society would create a more humane San Diego.
In our paper, we use Vision Transformer (ViT) to classify galaxies from how they look like. ViT is a new tool in deep learning community. We are the first group to apply ViT in astronomy field. In this post, I will explain why we care about how galaxies look like , what ViT is, and what we learned.
Why do we care about how the galaxies look like?
How galaxy looks like in your naked eye
Galaxy is one type of deep-sky object. The only galaxy you can see with naked eyes is the Andromeda Galaxy (M31). Observing the Andromeda Galaxy with naked eyes is not a great experience. First, we need a clear night sky and great naked eyes (or a great pair of glasses). Next, we should be able to find the constellation Cassiopeia and Andromeda. Finally, after locating the Andromeda galaxy, we only see a tiny blurred clump. That’s it. This is how the galaxy looks like with your naked eyes. Thank Galileo that he invented the telescope 400 years ago. Otherwise, we would only have one classification label for the galaxy, the blurred clump.
How galaxy looks like in the telescopes
With powerful telescopes, we can resolve the details in the galaxy’s appearance. Some examples are in Figure 1. Their names reflect how they look. Their appearance serves as a first criterion for us to classify. This method sounds superficial and offensive since we are always taught not to judge something from their appearance. But, this is how scientists classify them. Disclaimer: I don’t mean scientists are superficial or offensive. They are great people, or at least most of them.
What information we learn from their appearance
Why do scientists use the galaxy’s appearance to classify? The reason is that galaxies with similar appearances have similar properties. For example, the elliptical galaxies are older, whereas the spiral galaxies are younger. This indicates that how galaxies look is a good label. Upon seeing the galaxy, we can have a basic understanding of the galaxy, such as its ages and metal abundance.
How we distinguish different galaxies
A naive answer is by the human eye. Indeed, scientists used their human eyes, aka professional human inspection, to distinguish different galaxies in early days. But, as the data went up to 10,000 or even 100,000 images, this task seemed impossible. So, how do scientists solve this problem? Find volunteers to help! This is the core idea in the Galaxy Zoo project. They invited interested people, aka citizen astronomers, to do this visual classification. Each galaxy image is inspected by many people, and the label with the most votes would win. The Galaxy Zoo project turned out to be successful. It provides scientists with many labeled images (and free labors I think) and gives the general public opportunities to participate in scientific projects.
Even though the human eye method helps us in most galaxy classification tasks, we admit this is inaccurate and inefficient. An accurate automated algorithm to perform classification would be the best candidate here.
One possible solution could be found in the deep learning community. In the deep learning community, they have spent much time studying this problem, an accurate automated algorithm. One famous example is called Convolutional Neural Network (CNN). Hopefully, we will have NPR in deep learning field someday. In our work, we use a new tool for visual classification, Vision Transformer (ViT).
What is Vision Transformer (ViT)?
Vision Transformer — A new deep learning tool in visual classification
Vision Transformer belongs to one variant of the Transformer-like architecture. Initially, people used Transformer to solve problems in natural language processing (NLP). One typical example is to let the machine do real-time translation between different languages. Transformer has made a great success in the NLP field. Then, Google first successfully applied this architecture in solving visual classification problems. They called it Vision Transformer (ViT). This success triggers the rage of ViT in the deep learning community. All these Transformer-like tools share a similar underlying mechanism — attention mechanism.
Attention Mechanism — Core in Vision Transformer
“May I have your attention please.” This is the most common airport announcement opening. It plays an essential role for people to perceive important information.
Imagine that you are in a crowded airport, such as Los Angeles International Airport (the worst airport in my opinion). Now, you get stuck in the Starbucks waiting line. A lot of sounds and information are around you. Two undergraduates in front of you are chatting about how they screwed up their finals. The Starbucks clerk is calling the name “Tim” for the third time. The baby behind you starts to cry. You are exhausted from the information bombardment. In this situation, you can easily miss the last call of your flight. This “may I have your attention please” can help you refocus and put attention on the following information.
The attention mechanism in Vision Transformer (ViT) does a similar task. It tells ViT which parts in the image are significant for classification. Then, ViT pays attention to them and classifies the image.
How ViT learns which parts in image are significant
I feel that this is an inspirational story, especially for those graduates students with irresponsible advisors. You are gonna survive and learn something novel.
What have we learned in this project?
We are the first group to use Vision Transformer in galaxy classification. For comparison, we also use the CNN model (ResNet-50) as a baseline.
The best accuracy in the ViT model is 80.55%, whereas the accuracy in the CNN model is 85.12%. Even though our ViT is not better than CNN, we investigate some interesting cases. These cases are correctly classified by ViT but failed by CNN.
We find that ViT reaches higher classification accuracy in classifying smaller and fainter galaxies. Note that these galaxies are more challenging to classify since the image quality of these samples are noisier.
This difference might come from the attention mechanism in ViT model. More future works are required to conclude. However, these preliminary results give us confidence about the future application of Vision Transformer in scientific fields. We can use ViT to study those cases which are already examined by CNN. Maybe more interesting results would pop up. We can have a deeper understanding of our nature.
Recently, scientists tested whether quantum correlation exists at energies never explored so far (~13TeV). They used top-quark pairs at the Large Hadron Collider (LHC), the world’s largest and most powerful particle accelerator. Their result showed an affirmative result of the existing quantum correlation. In the following articles, I will briefly discuss what is quantum correlation and how to show its existence with Bell Inequality.
Quantum Correlation (Quantum Entanglement)
The distinct feature in distinguishing classical and quantum physics is quantum correlation, also known as quantum entanglement. Let’s use an example to explain quantum correlation. This example is called the daily surprise package. Imagine that you and your friend, Tim, both get a daily package from a Quantum company. Quantum company creates the quantum correlation between your and Tim’s package. When opening the package, you only see either a blue or red balloon. Today, you get a red balloon. Quantum theory enables you to predict what color Tim gets with 100% precision regardless of the distance between you and Tim.
Why do we need Quantum theory? Maybe a classical lottery machine is enough.
This mysterious nonlocal property confused people, even the greatest scientists, such as Albert Einstein. To avoid accepting this strange concept of quantum correlation, Einstein instead developed a classical theory, the hidden-variables theory, and claimed this should be the underlying theory of Quantum theory. How do we understand this hidden variable in the daily surprise package example? This idea is that Quantum company actually uses lottery machines to randomly put the balloons in the package. Einstein stated that the random lottery machine exists behind quantum theory. Classical theory is enough to understand quantum correlations.
Bell Inequality
However, in 1964, John Bell showed classical hidden variables theory and Quantum theory are incompatible. He also introduced Bell Inequality to quantitatively distinguish classical and quantum correlations. In Bell Inequality, he derived a upper bound for the classical correlation from the hidden-variables theory. As for quantum theory, the resultant quantum correlation can exceed this bound. Therefore, the violation of Bell Inequality indicates the existence of quantum correlation.
Loopholes in testing Bell Inequality in real experiments
In theory side, classical hidden-variabes theory and Quantum theory result in different correlation properties between particles. How do we experimentally determine whether our system is more classical or quantum ? In principle, we have to measure these particles independently for multiple times. Based on the measurement results, we compute the correlation and test whether the Bell Inequality is satisfied. However, these procedures have several loopholes in real experiment setup. One typical loophole is the detection loophole which comes from the inefficiency in detectors. Sometimes the detector might not read the output of the measurement. These flawed data strongly affect the correlation and the Bell Inequality test. Similar loopholes also happen due to how we measure these particles. Thus, testing the Bell Inequality in real experiment requires careful experiment design and data analysis.
The Sun, though the most familiar star to us, still holds many unsolved scientific mysteries, including the sunspot cycle and the coronal heating mystery. One such mystery is the enigmatic linear polarization of the sodium D1 line. Before exploring this issue, one may wonder how scientists study the Sun given that it is impossible to land on it. The answer lies in the sunlight that carries information about the Sun’s environment. By observing it, scientists can infer what is happening on the Sun.
Mysterious Spectral Line from the Sun
This blog post discusses the curious linear polarization of the sodium D1 line. The line originates from atoms transitioning from the 3p(J=1/2) level to the 3s(J=1/2) level, as illustrated in Figure 1. According to quantum theory, the Sodium D1 line should not carry polarization. However, in 1996, scientists observed that the Sodium D1 line from the Sun was linearly polarized, sparking interest in further research.
Figure 1: The energy levels related to the sodium D1 spectral line. This figure is adapted from the paper.
In 1998, , a theoretical explanation for the paradox emerged. The polarization of the sodium D1 line requires two critical ingredients: hyperfine structure and atomic polarization. The former arises from the interaction between nuclear spin and electron motion, leading to the original energy levels splitting into several sublevels with an additional label F (shown in Figure 1). The transitions with red dashed lines generate the sodium D1 line. However, the question remains of how to polarize the sodium D1 line from these levels. Atomic polarization suggests that having different atomic populations in these energy levels leads to a polarized line. However, this population difference is sensitive to magnetic fields, with the maximum allowable magnetic field strength being only about 0.01 G, much smaller than those from other observations (around 5~15G from other observations). Therefore, a satisfactory explanation for the mysterious polarized Sodium D1 line is still lacking.
Recent theoretical breakthrough
However, a recent study published in Physical Review Letters proposed another potential mechanism for generating a polarized line from hyperfine energy levels. Instead of atomic polarization, the authors focused on the difference in radiation fields pumping the atoms to higher hyperfine energy levels. When combined with the appropriate magnetic field strength, their simulation results demonstrated a strong agreement with the observed profile.
This new research provides a resolution to the two-decades-long sodium D1 line paradox and advances our understanding of the Sun to the next level. With ongoing or future observations, we can anticipate the resolution of more paradoxes about the Sun in the near future.
Black holes are the most enigmatic objects in the universe and are being used as a valuable resource to help physicists resolve fundamental issues in physics. Scientists have made significant strides in this area over the past few years, including capturing the first image of a black hole and detecting gravitational waves from black hole mergers. These advancements have provided scientists with more tools to test theoretical predictions.
The second law of thermodynamics explains that the entropy of a system never decreases, which means the randomness of a system always increases. Entropy is typically proportional to the temperature of a system, with higher temperatures resulting in more energetic elements and increased randomness.
The “entropy” of black hole
To determine the entropy of a black hole, scientists first need to define its temperature. This temperature is determined by observing the thermal Hawking radiation emitted by the black hole, similar to how the temperature of the sun is determined. Interestingly, the black hole’s entropy is directly proportional to its surface area, and combining this with the second law of thermodynamics leads to the conclusion that the surface area of a black hole never decreases.
The study published in Physical Review Letter on July 1, 2021, analyzed the data from the first observed gravitational wave event, GW150914, from the Laser Interferometer Gravitational-Wave Observatory (LIGO). The researchers used a high-accuracy numerical simulation of a GW150914-like system to extract the area information, and their results showed that the black hole’s surface area increased after the merger with 97% credibility, confirming Hawking’s theorem.
This confirmation provides physicists with confidence in their current theoretical foundation and opens up new avenues for exploring black holes through observational tools such as gravitational waves. With the progress of these tools, we can expect a more comprehensive understanding of black holes in both theory and observation in the near future.
