From Dark Matter to Galaxies

Since I just got back from the From Dark Matter to Galaxies conference in Xi’an, China, I figured I’d tell you about it. I took this photo in front of our conference venue:
photo 2

Xi’an is an important historical place, since it was one of the ancient capitals of the country (not just the Shaanxi province) and dates back to the 11th century BCE, during the Zhou dynasty. Xi’an is also the home of the terra cotta warriors, horses, and chariots, which (along with a mausoleum) were constructed during the reign of the first emperor, Qin Shi Huang. The terra cotta warriers were first discovered in 1974 by local farmers when they were digging a well, and they are still being painstakingly excavated today.


Back to the conference. This was the 10th Sino-German Workshop in Galaxy Formation and Cosmology, organized by the Chinese Academy of Sciences and the Max Planck Gesellschaft and especially by my friends and colleagues Kang Xi and Andrea Macciò. This one was a very international conference, with people coming from Japan, Korea, Iran, Mexico, US, UK, Italy, Austria, Australia, and other places.

Now scientific conferences aren’t really political exactly, unlike other things I’ve written about on this blog, though this conference did include debates about the nature of dark matter particles and perspectives on dark energy (which is relevant to this post). I should be clear that dark matter is much better understood and determined by observations though, such as by measurements of galaxy rotation curves, masses of galaxy clusters, gravitational lensing, anisotropies in the cosmic microwave background radiation, etc. (On a historical note, one conference speaker mentioned that the CMB was first discovered fifty years ago, on 20 May 1964, by Penzias and Wilson, who later won the Nobel Prize.) In contrast, the constraints on dark energy (and therefore our understanding of it) are currently rather limited.

the main points

I’ll start with the main points and results people presented at the conference. First, I thought there were some interesting and controversial talks about proposed dark matter (DM) particles and alternate dark energy cosmologies. (The currently favored view or standard “paradigm” is ΛCDM, or cold dark matter with a cosmological constant.) People are considering various cold dark matter particles (WIMPS, axions), warm dark matter (sterile neutrino), and self-interacting dark matter. (Warm dark matter refers to particles with a longer free-streaming length than CDM, which results in the same large-scale structure but in different small-scale behavior such as cored density profiles of dark matter haloes.) The jury is still out, as they say, about which kind of particle makes up the bulk of the dark matter in the universe. There were interesting talks on these subjects by Fabio Fontanot, Veronica Lora, Liang Gao, and others.

Second, people showed impressive results on simulations and observations of our Milky Way (MW) galaxy the “Local Group”, which includes the dwarf galaxy satellites of the MW and the Andromeda (M31) galaxy’s system. Astrophysicists are studying the abundance, mass, alignment of satellite galaxies as well as the structure and stellar populations of the MW. Some of these analyses can even be used to tell us something about dark matter and cosmology, because once we know the MW dark matter halo’s mass, we can predict the number and masses of the satellites based on a CDM or WDM. (Current constraints put the MW halo’s mass at about one to two trillion solar masses.) There were some interesting debates between Carlos Frenk, Aldo Rodriguez-Puebla, and others about this.

The third subject many people discussed involves models, and observations of the large-scale structure of the universe and the formation and evolution of galaxies. There are many statistical methods to probe large-scale structure (LSS), but there is still a relatively wide range of model predictions and observational measurements at high redshift, allowing for different interpretations of galaxy evolution. In addition, simulations are making progress in producing realistic disk and elliptical galaxies, though different types of simulations disagree about the detailed physical processes (such as the treatment of star formation and stellar winds) that are implemented in them.

There were many interesting talks, including reviews by Rashid Sunyaev (famous for the Sunyaev-Zel’dovich effect), Houjun Mo, Joachim Wambsganss, Eva Grebel, Volker Springel, Darren Croton, and others. Mo spoke about impressive work on reconstructing the density field of the local universe, Springel spoke about the Illustris simulation, and Wambsganss gave a nice historical review of studies of gravitational lensing. I won’t give more details about the talks here unless people express interest in learning more about them.

my own work

In my unbiased opinion, one of the best talks was my own, which was titled “Testing Galaxy Formation with Clustering Statistics and ΛCDM Halo Models at 0<z<1.” (My slides are available here, if you’re interested.) I spoke about work-in-progress as well as results in this paper and this one. The former included a model of the observed LSS of galaxies, and you can see a slice from the modeled catalog in this figure:

I also talked about galaxy clustering statistics, which are among the best methods for analyzing LSS and for bridging between the observational surveys of galaxies and numerical simulations of dark matter particles, whose behavior can be predicted based on knowledge of cosmology and gravity. I’m currently applying a particular set of models to measurements of galaxy clustering out to redshift z=1 and beyond, which includes about the last eight billion years of cosmic time. I hope that these new results (which aren’t published yet) will tell us more about how galaxies evolve within the “cosmic web” and about how galaxy growth is related to the assembly of dark matter haloes.

International Collaborations

(I actually wrote this post a week ago while I was in China, but many social media sites are blocked in China. Sites for books, beer, and boardgames weren’t blocked though—so they must be less subversive?)

Since I’m having fun on a trip to Nanjing and Xi’an now, seeing old friends and colleagues and attending a conference (From Dark Matter to Galaxies), I figured I’d write a lighter post about international collaborations. By the way, for you Star Trek fans, this month it’s been twenty years since the end of The Next Generation, which had the ultimate interplanetary collaboration. (And this image is from the “The Chase” episode.)


In physics and astrophysics, and maybe in other fields as well, scientific collaborations are becoming increasingly larger and international. (The international aspect sometimes poses difficulties for conference calls over many timezones.) These trends are partly due to e-mail, wiki pages, Dropbox, SVN repositories, Github, remote observing, and online data sets (simulations and observations). Also, due to the increasing number of scientists, especially graduate students and postdoctoral researchers, many groups of people work on related subjects and can mutually benefit from collaborating.

On a related note, the number of authors on published papers is increasing (see this paper, for example). Single-author papers are less common than they used to be, and long author lists for large collaborations, such as Planck and the Sloan Digital Sky Survey, are increasingly common. Theory papers still have fewer authors than observational ones, but they too have longer author lists than before. (I’ll probably write more about scientific publishing in more detail in another post.)

Of course, conferences, workshops, collaboration meetings and the like are important for discussing and debating scientific results. They’re also great for learning about and exposing people to new developments, ideas, methods, and perspectives. Sometimes, someone may present a critical result or make a provocative argument that happens to catch on. Furthermore, conferences are helpful for advancing the career of graduate students and young scientists, since they can advertise their own work and meet experts in their field. When looking for their next academic position (such as a postdoctoral one or fellowship), it helps to have personally met potential employers. Working hard and producing research is not enough; everyone needs to do some networking.

Also, note that for international conferences and meetings, English has become the lingua franca, and this language barrier likely puts some non-native English speakers at a disadvantage, unfortunately. I’m not sure how this problem could be solved. I’m multilingual but I only know how to talk about science in English, and I’d have no confidence trying to talk about my research in Farsi or German. We’ve talked about privilege before, and certainly we should consider this a form of privilege as well.

Finally, I’ll make a brief point about the carbon footprint of scientists and the impact of (especially overseas) travel. For astrophysicists, the environmental impact of large telescopes and observatories in Hawaii and Chile, for example, is relatively small; it’s the frequent travel that takes a toll. I enjoy traveling, but we should work more on “sustainability” and reducing our carbon footprint. There are doubts about the effectiveness of carbon-offset programs (see the book Green Gone Wrong), so what needs to be done is to reduce travel. Since conferences and workshops are very important, we should attempt to organize video conferences more often. In order for video conferences and other such organized events to be useful though, I think more technological advances need to be made, and people need to be willing to adapt to them. Another advantage to these is that they’re beneficial for people who have family, children, or other concerns and for people from outside the top-tier institutions who have smaller budgets. In other words, video conferences could potentially help to “level the playing field,” as they say.

Frontiers of Citizen Science

Since some colleagues and I recently submitted a proposal for a symposium on citizen science at a conference next year, I thought this would be a good time to write some more about citizen science and what people are doing with it. I previously gave a brief introduction to the “citizen science” phenomenon (also called “crowd science”, “crowd-sourced science”, “networked science”, “civic science”, “massively-collaborative science”, etc.) in an earlier post. The presence of massive online datasets and the availability of high-speed internet access and social media provide many opportunities for citizen scientists to work on projects analyzing and interpreting data for research.

Citizen science (CS) is an increasingly popular activity, it’s produced impressive achievements already, and it clearly has potential for more. (It also even has a meme!) You don’t have to look hard to see accomplishments of CS projects in the news. A quick online search brought up citizen scientists studying bumblebees, bird nests, weather events, plankton, and other projects. The growing phenomenon of CS has drawn the interest of social scientists as well, and I’ll say more about their research later in this post.


I’m particularly familiar with the Zooniverse, a platform that hosts projects in a variety of fields. It began in 2007 with the Galaxy Zoo project, which I’ll say more about below, and its other astronomy/astrophysics projects include Disk Detective, Planet Hunters, Moon Zoo, and Space Warps. To give other examples, outside of astronomy, there are projects in zoology, such as Snapshot Serengeti to study animals and their behavior with “camera trap” photos (the graph above describes herbivores they’ve cataloged, from a recent blog post); in biology/medicine, such as Cell Slider to identify cancer cells and aid research; and in climate science, there is Old Weather, which examines ship’s logs to study historical weather patterns. In addition, people at Adler Planetarium and elsewhere are working on producing educational resources and public outreach programs.


Galaxy Zoo (GZ) invites volunteers to visually classify the shapes and structures of galaxies seen in images from optical surveys. The project resulted in catalogs of hundreds of thousands of visually classified galaxies—much much better than anything achieved before—allowing for novel statistical analyses and the identification of rare objects and subtle trends. If you’re interested in my own research, I’m leading clustering and astrostatistical analyses of GZ catalogs to study the spatial distribution of galaxies and determine how their morphologies are related to the dark matter distribution and large-scale structure of the universe. For example, with more and better data than pre-GZ studies, my colleagues and I obtained statistically significant evidence that galaxies with stellar bars tend to reside in denser environments (see this paper). In the figure above, you can see examples of barred galaxies (lower panels) and unbarred ones (upper panels). In 2009, we used the impressive GZ datasets to disentangle the environmental dependence of galaxy color and morphology, since we tend to see redder and elliptical galaxies in denser regions (see this paper). Time permitting, I’d like to extend this work by using those results with detailed dark matter halo models, and we could potentially compare our results to galaxies in the Illustris simulation (which has been getting a lot of media attention and was misleadingly described as “the first realistic model of the universe“).

Galaxy Zoo scientists have many other achievements and interesting research. For example, a Dutch schoolteacher, Hanny van Arkel, discovered a unique image of a quasar light echo, which was dubbed “Hanny’s Voorwerp” (Lintott et al. 2009). GZ volunteers also identified galaxies that appeared to look like “green peas”, and most of them turned out to be small, compact, star-bursting galaxies (Cardamone et al. 2009). In addition, Laura Trouille is leading the Galaxy Zoo Quench project, in which participants contribute to the whole research process by classifying images, analyzing data, discussing results, and writing a paper about them.

Citizen science is related to “big data” and data-driven science (see also this article), and in particular to data mining and machine learning. According to a new astrostatistics book by Ivezic, Connolly, VanderPlas, & Gray, data mining is “a set of techniques for analyzing and describing structured data, for example, finding patterns in large data sets. Common methods include density estimation, unsupervised classification, clustering, principal component analysis, locally linear embedding, and projection pursuit.” Machine learning is a “term for a set of techniques for interpreting data by comparing them to models for data behavior (including the so-called nonparametric models), such as various regression methods, supervised classification methods, maximum likelihood estimators, and the Bayesian method.” Kaggle has data prediction competitions for machine learning, and their most recent one involved challenging people to develop automated algorithms to classify GZ galaxy morphologies like as well as the “crowd-sourced” classifications, and the winning codes performed rather well. Nothing beats numerous visual classifications, but there is clearly much to be learned along these lines.

Finally, sociologists, political scientists, economists and other social scientists have been studying CS, such as the organization and efficacy of CS projects, motivations of participants, and applications to industry and policy making. For example, Amy Freitag has written about how citizen science programs define “success” and their rigorous data collection. The sociologist Anne Holohan has written a book Community, Competition and Citizen Science on collaborative computing projects around the world. Eugenia Rodrigues is studying the views and experiences of participants in CS initiatives, and Hauke Riesch has written on this subject as well. (This is also related to the work by Galaxy Zoo scientists in Raddick et al. on participants’ motivations.)

In a recent interesting article, Chiara Franzoni & Henry Sauermann analyze the organizational features, dimensions of openness, and benefits of CS research. As case studies, they examine GZ, Foldit (an online computer game about protein folding), and Polymath (involving many mathematicians collectively solving problems). They argue that the open participation and open disclosure of inputs, which they mention is also characteristic of open source software, distinguish CS from traditional “Mertonian” science. (Robert Merton was a sociologist who emphasized—perhaps too much—social and cultural factors in science, such as scientists’ desire for peer recognition and career benefits, disputes between scientists, etc. I ended up not discussing him in my post on “paradigm shifts“.) They also discuss knowledge-related and motivational benefits, and they point out that CS projects that involve subjects less popular than astronomy or ornithology, for example, or that address very narrow and specific questions may face challenges in recruiting volunteers. Finally, they discuss organizational challenges, such as division of labor and the need for project leadership and infrastructure. If you’re interested, Bonney et al. in Science magazine is another shorter article about organizational challenges and developments in citizen science.

Diversity in Science

Diversity in science and diversity in STEM fields (science, technology, engineering, and math) in general are important issues, and I’d like to write more about them. This post is related to my previous post, in which I discussed work-life balance issues. I’ll only review some of the relevant issues here, and I plan to follow up and write more about them in later posts. On diversity in astronomy and astrophysics, I recommend checking out the Women in Astronomy blog, the American Astronomical Society’s Committee on the Status of Women in Astronomy (CSWA), and the STEM Women blog. I focus on the situation in the United States here, but these are issues that people are seeking to address internationally, though the specific situation and possible solutions vary among different countries. (For statistics on members of the International Astronomical Union, see this paper.)

By diversity, I mean the distributions of people in the scientific workforce, including graduate students, postdocs, and tenure-track and tenured faculty as a function of gender, race, ethnicity, and sexual orientation don’t reflect their distribution in the overall population. In other words, the disproportionate majority of the scientific workforce is composed of white men. Of course, this phenomenon is not limited to STEM fields; we also see a lack of diversity in the media, law, among policy-makers, the tech industry, corporate boardrooms, etc. I will take it as given that diversity is an important goal: it’s important for equality, and everyone benefits when work environments are more diverse.


To give some specific numbers, according to the CSWA nearly half of undergraduate students who obtain bachelors of science degrees are women, but only a third of astronomy graduate students and ~30% of Ph.D. recipients are. Women compose ~25-30% of postdocs and lower-level faculty, and this drops by half (to ~15%) of tenured faculty. The female participation rate drops as you look up the “ladder”. Unfortunately, the situation is worse in non-astronomy physics, engineering, and math. For example, only 18% of physics Ph.D.s are women. Among the physical sciences, biology is doing the best on gender diversity. The following figure (taken from this Scientific American article) shows the breakdown among these sciences.


While I’m focusing on gender in this post, the lack of diversity by race and ethnicity is also a major problem. Fewer than 3% of US citizens receiving Ph.D’s are African-American and Hispanic, though together they represent more than a quarter of the US population. Clearly much more work needs to be done to rectify this situation and improve racial diversity. People are working on the whole “pipeline”, from elementary school students to people holding faculty and other senior positions. I should also make the obvious point that there generally appears to be a lack of class diversity as well within higher education and graduate programs. In addition, according to the US Census Bureau, the income discrepancy between the working class and the professional class with the higher academic degrees is growing.

These are not encouraging numbers, but at least they are an improvement over the situation a decade ago and much better than two decades ago. Nonetheless, the rate of improvement is very slow, and the problem will be not be solved simply by waiting for the pipeline to flow (i.e., the senior people retire and the more diverse lower ranks are gradually promoted). Even if overt sexism and racism did not exist at all in departments and hiring decisions, some inequality would persist in STEM fields. Many people are asking what is causing this and what can be done about it.

These are issues with which physical scientists could benefit from the research and input of social scientists. For example, consider these articles by Aanerud et al. (2007) and Timmers et al. (2010), which I found from a quick search of the literature. Aanerud et al. argue that in order to understand women’s tenure status, we should widen our lens and consider the role of labor market alternatives to academic careers; consequently, “we must be cautious about women’s favorable tenure ratio in fields with interpreting gender strong alternatives to academic tenure as indicating academic gender equity.” (In other words, in physics and astronomy we may not necessarily want to emulate the policies used in more diverse fields.) Timmers et al. argue that “Three sets of factors explain women’s low shares at higher job levels, notably individual, cultural, and structural or institutional perspectives, and policies to increase the proportion of women therefore should address these factors.” Policy measures that address the cultural perspective (such as expressing responsibility for applying a gender equality policy at department levels and women in selection committees) and structural perspective (such as accounting for the recruitment of women, adapting job advertisements, and bonuses for hiring women) appear to work effectively in combination.

Also, sociology professor Crystal Fleming has a well-written blog post on privilege and the importance of resilience in the face of rejection and failure while working in academia. This is important because scientists and academics, but more female than male ones, are affected by “imposter syndrome.” (Personally, I can tell you that I’ve had to work hard applying for numerous positions, fellowships, research grants, etc., and it’s difficult to be rejected by the vast majority of them.) As one more example, Adam Burgasser, a faculty in my department at UCSD, has worked with social scientists recently to better recruit and retain diverse graduate students. They’ve found that it’s important to follow-through and continue mentoring students through their graduate career.

What other kinds of things are being done or can be done to improve the situation? It’s important to encourage and mentor undergrads, grad students, and postdocs, and also to talk to them about alternative career paths outside academia, as there are many possible careers for scientists. As important, it’s good to reach girls and boys in primary and secondary school about science, and it’s important to try to reduce currently prevalent cultural stereotypes. For example, when many people think of typical scientists, they imagine a nerdy white man (such as in the TV show “Big Bang Theory”) in a lab coat. There are many ways to encourage girls’ interest in science, though some ways may be more effective than others (see this Slate article, for example). Members of university departments and other organizations have developed a wide range of outreach programs targeting girls and children of color, including numerous programs at UC San Diego that bring secondary school students and underprivileged youth to UCSD for interactive demonstrations, labs, lectures, and other activities aimed at enhancing their interest in science.

There are other cultural issues to deal with as well. For example, some people have stereotypes about their own bosses, but women are great leaders (maybe better than men) and people’s views about female bosses vis-à-vis male bosses are improving in some ways. It’s also difficult for people of color; an African American leader who appear authoritative or offers criticism of particular policies, for example, can be stereotyped as an “angry black man” or “angry black woman.” Whites and males should be more aware of gender and race privilege, and this is something that should be more frequently and openly discussed. (See this excellent blog post by Caitlin Casey.) In addition, some people, inadvertently or not, might act in a sexist or racist manner at work, and this should be challenged and called out. And we have to be very careful about “unconscious bias”: for example, both men and women surprisingly have the same biases against women in male-dominated fields, though the biases are reduced when people are aware of them and when diverse committees make decisions about hiring and leadership decisions (see this article by Meg Urry).

It seems like the literature on “confidence gap” issues has been growing rapidly, encouraging women to “lean in” and be more confident and self-assured at work. All of this is great and important. In my opinion, however, it’s also potentially misleading. We can’t neglect persistent structural problems, power relations, pernicious cultural frames, and we can’t forget gender and race inequality.

Increasing diversity is an important goal, but it’s also a means to an end. In my opinion, we need to set our sights higher than merely having a few more women and people of color among faculty members. We need *paid* maternity leave, universal or child-care options, better dual-career policies, and paternal leave should be expected. There should be no benefit for men who continue working full-time rather than spending time with their new children, and we still have a long way to go until men share housework equally with women. This is related to work-life balance issues, which are definitely not a “women’s problem”. (See also these NY Times articles continuing the struggle for gender equality.) We’re gradually making progress, but much more work remains to be done.