Dot Astronomy Day 1

These are my reflections on .Astronomy 7.   They are not meant to be a complete as there was too much to cover but it is a few of m view about how my day was.   If you would like a full review of the day, please see Becky Smethurst fantastic live blog of the first day of .Astronomy 7.  My day zero live blog is available here.

This is my first time attending .Astronomy.   I heard about the concept years ago from Carolina Ödman and I have been meaning to attend ever since she told me about it.   So I’ll admit, my expectations were high, but I really had no idea what to expect.   I am arguable an experience python programmer, and I have dabbled in a number of different web platforms and have experience teaching, doing outreach, and working at a major observatory.   Saying all that, I am amazed by how much I have learned in the past two days.   I am not even going to be able to express how much useful information has been crammed into my head in the last 24 hours. More importantly, I can’t express how much I have been inspired by the people I have met.   Everyone I have come into contact so far has inspired me in some way and made me think about new things.

1. Amanda Bauer started off the day with a traditional greeting and remembrance of the elders of the Gadigal people.   She told a beautiful story of the Emu in the Sky, where the Coalsack nebular represents the head of the Emu and the body traces the Milky Way.    The seasons can be judged from the position of the Emu throughout the year.

Something that came up in later discussions was the importance of cultural sensitivity when engaging with different audiences (and this came up in many different forms).   I thought Amanda gave a great example of this.  As an astronomer, I feel like I have had greater success in collaborations and engagement when I do have a better appreciation of people’s culture.

2.   Alice Williamson gave a wonderful talk on the Open Source Malaria project.   Regardless of how much astronomers like to say we are studying a fundamental science, most lives we save are pretty far off in the future.   Whereas scientists designing new drugs are saving lives today.   Both works are important, but the tangible results from astronomy are sometimes harder to define.   So I am always impressed to listen to talks by people saving lives today.   The Open Source Malaria project is a great example of open science and they have adopted six rules:

  • All data are open and all ideas are shared for others to use, modify and share
  • Anyone can take part at any level
  • There will be no patents
  • Suggestions are the best form of criticism
  • Public discussion is much more valuable than private email
  • An open project is bigger than, and is not owned by, any given lab

3.  I’m not going to talk about astropy (I’ve done that enough), but I will mention Tom Robitaille’s (@astrofrog)  open development revolution.  This is what really has made things like astropy possible.  It’s the ease at which people can now collaborate and share.  It’s such a great concept.

4.   I love reading about   Seriously though, it looks like a great outreach resource that is beautifully designed and built on a simple set of tools like jekyll, github, flask, and heroku.

5.  A big issue both at ADASS and at .Astronomy is how do we raise the profile of people doing important work that enables astronomy but that is so often overlooked or under appreciated.  There is no one magic bullet here, but there are some useful tools already available like ASCL and Zenodo to make it easy for your work to be citable.

6.  I saw an idea described, defined, criticized, fleshed out, argued over, and made potentially possible over the course of an afternoon.  I think a number of us are excited over the possibility.   Can we implement it in a one day hack?  I don’t know, but I’m willing to help.

7. I led an unconference on building diversity.    This was the first time I had tried to lead something like this, and though I might not have known what I was doing, I think it is incredible important to discuss these issues.  There are many reasons astronomy and open source projects are predominately European descent and male.   Nonetheless, we came up with some concrete suggestions for how to encourage a more diverse representation in open source projects.  These included reaching out to different groups and offering tutorials to bridge programs, mentorship to beginners, tutorials from a diverse collection of people, affinity groups, and promoting diversity throughout the package.   I really appreciated the advice, suggestions, and experiences shared by everyone in the group.

Final Day of ADASS

These are my opinions and thoughts about the fourth day of ADASS.   It isn’t necessarily a summary of the day or complete but  just my reflections on the day along with some random commentary or thoughts.  Also these thoughts might not be in chronological order.  See here for Day #1Day #2, and Day #3.  And also, this is why you don’t wait a few days to blog.  

1. The day started off with Sarah Kendrew’s talk about .Astronomy. It was a very good talk and I’m excited about attending my first .Astronomy.   There were so many interesting hacks (see the end of Arna Karnick’s Day Zero .Astronomy guide  or her post from .Astronomy 6 ).    I’m going to wait to say more about .Astronomy for after this week, but it will be interesting to compare the two meetings.   My initial thought is that they will be very complimentary.

2.   The international virtual observatory.   There was so much buzz in the early years of the VO that most people were eventually disappointed with it not doing everything.   Nonetheless, in the time that people were disappointed, people dedicated to standards continued to work at and produce documents and descriptions for a wide range of astronomical data.    Much of the work done by the IVOA is hidden from the end user’s sight, but implemented by the people writing archives and services that we all use.    I know I sometimes find the IVOA documentation impenetrable, but I also know how much more is possible if interoperability between data sets is enabled.    And I really appreciate people doing the hard work that no one else wants to, and so it was really great to see Christophe Arviset’s talk about what VO has made possible today.

3.  The SKA is coming, the SKA is coming, and there is a lot of data that will be coming with it as well.  I think this is very exciting and terrifying and fun to listen to the different ways people are trying to cope with the data and how to reduce it, analyze it, and distribute it.

4. Hierarchical Progressive Survey.  I don’t fully understand it yet, but it sure looks like a useful way for exploring through different surveys.

5.  Amazon web services.  The most impressive thing was the number of groups that have just farmed out their data hosting/data distribution to Amazon.   Many talks quoted numbers that indicated that it was cheaper/easier then maintaining your own servers.   Stop buying servers, Cloud services are now a commodity.

6.  Nothing to see here but some cool Montage images.

7.  I enjoyed the impromptu poster lightning session and appreciated a few posters that I hadn’t read close enough.   I always think lightning talks should be kept to a limited number as I start to zone out after too many, but this was pretty ideal number and I hope it becomes a regular part of the program.

8.  The Hubble Source Catalog is a great idea that I’ve often wondered why it didn’t exist already.   However, it apparently is harder than you think and one of the main hurdles according to Bradley Whitmore is taking care of the astrometry in Hubble Observations (which I’ve run into before even in my most recent paper, so I guess I shouldn’t be surprised).   However, it is impressive that they have taken care of so many things and do have it working.   It’s on my list of things to check out in more detail.

9.  Also the design and implementation of some aspects of the python JWST pipeline presented by Howard Bushouse were very familiar to the work we’ve done on ccdproc.    I think some of it are a bit more specifically focused for JWST then a generalized package.  I never got a chance to chat to Howard at this meeting, but I am hoping to follow up with him and others about supporting generalized packages.   This will mean less duplication, more testing, and better sharing of resources.    I’d much rather work with others than alone.

10. Overall though, I’d say it was a good conference and definitely worthwhile to attend.   Thanks to the POC for an interesting program and the LOC for a well run conference (I haven’t mentioned it elsewhere, but I really liked CAASTRO’s conference app).   I’ve had conflicts in the past that prevented me to attend and with the funding situation in South Africa, I’m not sure if I could attend every year, but it is definitely a conference I will try to go to again.   There were a lot of other first timers at ADASS, but I was also really impressed with the people that made the meeting every year.   I can see why people would keep coming back:  a really friendly community, presentations that were helpful and potential make me more productive, and  learning about new and interesting developments happening around the world.

Nonetheless, I did not think coming into the meeting that I would necessarily be generating new science collaborations.   However, I have at least two potential new collaborators and hopefully a number of other potential projects to work on.   I also learned about a number of new tools and techniques that will improve how I science.   So even though I was coming for the archives, I’d stay for the science.




These are my opinions and thoughts about the second day of ADASS.   It isn’t necessarily a summary of the day or complete but  just my reflections on the day along with some random commentary or thoughts.  Also these thoughts might not be in chronological order and can also be pretty random.  See here for Day #1 and Day #2.    

1.  Day #3 and there was 17 talks!!   And a focus session on glueviz by Tom Robitaille (who had a talk on astropy today as well!).   I’d complain but they were all great!!  Good job POC!    Really, I’m not going to be able to mention everything that I want to, but see Alice Allen’s @asclnet tweets as she has been doing an incredible job of tweeting out so many details from talks.  As someone who can’t always attend meetings, I love twitter for this and people who tweet as it lets me follow the conference from afar (side suggestion:  if talks are uploaded before they were given or at least at the same time, this would be even more helpful for people that can’t attend meetings).    Nonetheless, thank you to the POC for a great and interesting meeting so far!

2.  Look, I’m completely and utterly biased, but astropy makes me want to break out in song.   For me, I’ve learn so much, seen people do so many helpful things, met so many great people,  and just really, really enjoyed the community.   There is so much to write about here as I am very enthusiastic about the project, but I’ll save it for later.   Nonetheless, it is always helpful to hear constructive criticism so please share if you do have issues.

3.  I really enjoyed the talk about the Firbebal in the Sky project talk by Hadrien Devillepoix .  It uses mostly off the shelf hardware to detect and track meteorites.    They are then hoping to find the meteorites on the ground and it is a real test of the measurements as small errors could result in wandering around in the desert for weeks.

4.  Greg Madsen may never be allowed to return to the United States again after giving away the top secret positions of all the satellites.   He had a great and interesting talk about how to detect space junk even if it was also interrupted by a fire alarm.

5.  Challenges from today that I remember :

  • How do we make it easy for people to build science archives?
  • How do we make ADASS a better conference?
  • How to enable creativity from data archives?

6.   #OMG, #allthepixels, #mindblown: Chris Fluke’s talk about how to physically visualize things was amazing.  I’d love to try out the Cave (see Dany Vohl’s poster), and looking forward to trying out Kai Polsterer’s demos with stereoscopic display if the crowd surrounding it ever thins out.  We really will be viewing data differently in a few years if not already.

7.   It doesn’t matter what you write in.    Pragya Mohan wrote a beautiful, photoshop-esqe FITS viewer in java,  Elise Hampton wrote The Machine, what looks to be a incredible powerful machine learning spectra fitter, in Octave.    Faviola Molina was using a smalltalk variant to create a powerful visualization tool.  Sarah Hegarty was using python to convert simulations into realistic images with the Theoretical Astrophysical Observatory.  Ian Stewart has been updating lime, a mostly c code for  radiation transfer code for molecular lines.  Xiuqin Wu was talking about what seemed like everything used to create firefly, the amazing interface for the IPAC archives.  The diversity of languages spurns creativity and different thinking.  And today, we can easily wrap or use different languages so it doesn’t matter what you write it, it just matters what you write.

8.  I had a feeling of ennui listening to Sean Carey’s talk on the final calibrations of Spitzer.  I’ve used data from Spitzer and know a lot of people that have built their careers on Spitzer observations, and it is sad to hear about the final calibrations for a facility.   It will still be working for at least a few more years, but our instruments become so important to us that it can be difficult to lose them.

9.  Today came completely full circle.  It ended with Guido de Marchi talking about how do we make archives more than just places to hold raw data but to enable creativity, which  was such a good reflection on Ann Marie Cody’s talk, which started Day #3 off, that was about building an archive of information about young stellar objects.   She highlighted the difficulty of integrating all the data in the literature along with observations from a wide range of telescopes.  For me, these two talks left the biggest impact on me and I think highlight where we can go in the future.


ADASS Day #2

These are my opinions and thoughts about the second day of ADASS.   It isn’t necessarily a summary of the day or complete but  just my reflections on the day along with some random commentary or thoughts.  Also these thoughts might not be in chronological order and can also be pretty random.  Plus I need to get out a rant, so if you aren’t in the mood for that, skip to #2.   Also if you are Brian Schmidt, please read through #2 before deciding whether or not I’m an idiot–which I likely am. 

1. At the beginning of his talk, Brian Schmidt commented on how twentieth century astronomy was driven by heroes — individuals or small groups making major break throughs– but today, we are in an era that major discoveries are driven by big surveys and big data.   I realize this fell into the narrative of his talk (and of the conference of entering an era of big data) and helps present a good story (and without a doubt, he likely knows all the history I’ll talk about now), but it bothered me.    At the same time that Hubble was deriving an erroneous value for the expansion of the Universe, teams primarily made up of women were doing the hard work of classify and measuring the properties of millions of starsa.  They developed classifications still used today and produced the huge data sets required to model stellar evolution that predicts the behavior of stars amazingly well.  They did this while often receiving little to no credit.    Today we are in a much better situation where more people are properly credited (and have the opportunity) for their contributions to astronomy.  Yet there are still small groups that are making unexplained discoveries (like Fast Radio bursts as highlighted in Mathew Bailes talk).   So yes, there were amazing heroes of twentieth century astronomy.   And while some of the heroes were discovering something new, others were producing the big data sets that explained the details of our Universe.   The same thing is going on today as well.

2. Brian Schmidt’s talk was awesome and highlighted so many great points.   Besides being the fourth submitter ever to astro-ph (and getting in trouble for having 7 MB of figures), he talked about so many things that astronomy does right and how we benefit from that.  We share our data.  We share our software.  We share our papers.  And those who do so are able to do better/more/faster science even if it might not always seem like it.   Many of the things we take for granted (arXiv, ADS, FITS) are practically unheard of in other fields.    The open source tool from 30 years ago is still being adapted, updated, and used today (and I might say complained about but at least we have the tool to complain about it and it is generally free).   However,  we have a problem of how to employ people so that they have the job security to work on the hard problems that will require years (or decades) to do.

3.  The next generation of big data is here.  SDSS represented the last generation and now projects like GAIA, Euclid, SKA, and others represent the next generation.  We are talking about billions of measurements of objects (Carlos Gabriel, Laurence Chaoul),  using HADOOP for handling distributed, large data sets (Lloyd Harischandra among others),  and the great, big radio data sets that we know are no longer coming, but are here (Mike Wise, Jan David Mol, Jan David Mol).

4.  Day #2 challenges from my perspective and some more on reflection:

  • How do we keep people who code employed in astronomy?
  • Why is there no prize for software in astronomy?   (If not AAS or IAU, why not award at ADASS as long as announced widely?)
  • As much data as we will produce, when we start to include things like likelihood functions, meta data, and everything else,  that is going to even increase it even more.  What’s the best way to represent additional information?

5. Outside of the talks, there has been a good bit of chatter on twitter about Sextractor.   Today it was started by Kyler Kuehn discussion of Sextractor and DES.   It is a great tool that is very fast, user friendly, and powerful.  It is also can be complex and only work well in certain regimes.  There are also some other amazing tools that might be appropriate but some might still be in development while others would need to be adapted from different wavelengths to the optical.

5. Software citations BOF was an interesting conversation.  For details, see the minutes and related documents.  However, since this is my post, I’m going to highlight the points I think are important:

  4. Developers: MAKE SURE AUTHORS KNOW HOW TO CITE YOUR CODE (bonus points if you include a license)

Many of the software citation aspects are being pushed forward by Alice Allen and a number of other people that aren’t doing under-appreciated work.   The AAS is setting up new policies and does have a reference group giving feedback and people are thinking about better ways to do this, but the process might be a little slow and run into cultural differences in the astronomy community.   Journals like MNRAS are already encouraging users to cite software.

Likewise, Albert Accomazzi describes some of the ways to track the influence of data products in ADS.  The ADS is another example of incredible useful and invaluable tool.    There are a lot of people doing really under appreciated work on very useful tools and if you are reading this, thank you!

6. Squeezed in among a number of radio talks, Nuria Lorente gave a great talk on starbugs–I’d say the best kind of bugs.  These are robotic fiber positions with a soul.  Or at least personality.  And maybe ruby slippers (which is very appropriate for something from Oz).   These bugs can reconfigure your fiber positions quickly and simultaneously to minimize the amount of time between configuring a spectrograph on a new field.

7.  I love solar talks.   So many photons.   The Daniel K. Inouye Solar Telescope will be producing a lot of resolved data of the solar surface as Steven Berukoff told us,  and the public nature and how to deal with the data will challenge the solar community.

8. The Chandra point source talk by Janet Evans showed what was possible when adopting a development framework with planning, mitigating risks, testing, releases, and iterations.   She showed an absolutely gorgeous image of Tycho’s supernova remnant (I wish I could find an online copy and share it)  and although it did take longer  than expected, it has generally been very successful.  It is nice to see the project management in software projects that have worked for others.


a. Here’s a comparison:  SDSS measured the spectra of 500,000 stars by DR3.   So did Annie Jump Cannon in her lifetime.

ADASS Day #1

These are my opinions and thoughts about the first day of ADASS.   It isn’t necessarily a summary of the day or complete but  just my reflections on the day along with some random commentary or thoughts.  I might not do this for other days.  I might.  Also these thoughts might not be in chronological order and can also be pretty random.  

1. The day definitely started out very interestingly with an opening by a local elder followed by a talk on Machine Learning and robots by Hugh Durrant-Whyte.   The Acknowledgement of Country was poignant and funny.   It was followed by a talk that was initial focused on how to use machine learning to extract minerals from the ground.   It’s only tonight I realize how jarring a transition it was.   Neither of these was about astronomy per se, but both seemed to reflect issues that the astronomy is facing (de-colonialization and the need to  commercialize skills).

2.  I’ll group talks by Alessandra Aloisi, Lisa Storrie-Lombardi, and Jesus Salgado together as they were all great talks about various archives.  The amount of data ‘freely’ available to anyone is amazing and the growth in the archives is impressive.  As Aloise pointed out, 60% of HST papers are coming from the archive.   So much science is enabled by open access to the data.   I’ve used at least the MAST and Spitzer archives  for my research (among many others) and for my students’ research.   And the demo by Bruno Merin of ESO Sky was just cool.

3.  I was left wanting so much more out of the talks.    So much more details and questions that went un-answered and I’m hoping to meet and have conversations with Guido Cupani about Espresso (wavelength calibration and extraction?  How are things done differently for high precision),  Mario Jaric about LSST software (design and development and packaging), William O’Mullane about Docker (plus/minus vs. other methods), and Tamas Budavari (how can streaming analysis be applied to other data sets?).  Actually that last question might not be so much for Budavari, but for his student, Matthias Lee, whom he brought on in the middle of the talk.    I’m actually super interested in that as it relates directly to another project I am currently not working on at the moment.

4.   I spent a good part of my day collection gender information on speakers and people who ask questions for James Davenports gender study on conference talks.   Overall, there were 11 male speakers and 2 female speakers and 28 male questioners and 4 female questioners.   Those numbers are a little thrown off as Tsianheng Liang (Extracting filaments) and Jessica Mink (BoF: data formats) were not able to attend.

5.  Some good overall challenges were issued today which I’m writing down to remember them for later (also probably missing many of them):

  • Josh Peek:  how do we hypothesis *generate* with large unwieldy data sets?
  • Arna Karnick: how do we reward contributions to open source projects?
  • Nuria Lorente: How do we increase the gender repsentation? (And I’ll add the representation from all groups?)

6.  I did attend the data formats Birds of a Feather (BoF) session.   I’m really happy there are other people thinking about standards as this is a pretty thankless but a necessarily job.  But I am also happy to working in the astropy environment where people have created some great tools for reading in data and working on different data models.   The astropy.table can import almost any format (ascii, latex, csv, FITS, HDF5, those machine readable tables that come with papers by just giving it the README–I really love this feature, and so many more) which has made life much easier.  Plus making tables is easy and then can be written out in any format you or your colleagues want.   In the meantime,  for things like the ccdproc package we haven’t focused on how to read in FITS files at every stage, but read it into a CCDData object (based on astropy’s NDData object) and then do all our operations on that or a numpy array.  The code is fairly agnostic even to the data model without having to do too much replication (of course it has to be a numpy-array like object).

7.  There were some mutterings about programming languages during two good talks on detecting diffuse objects by Mohammad Akhlaghi (NoiseChisel) and Tony Butler-Yeoman (Oddity).    I obviously have my favorites and am not above throwing out a comment about one language or operating system or data format or editor but nowadays, languages are so interchangeable (see Juypiter notebooks for example) and wrap-able that you find the right one for you and for the job that needs to get done and it doesn’t matter.  Plus it is fun learning new languages.  But you will pry vi from my cold, dead fingers clutching ‘:wq’.

8.  And one final talk that particularly resonated with me as a pipeline writer for a telescope that hasn’t always worked, Christian Wolf talking about Skymapper.   I’ve faced many of those problems myself as well or similar things with a telescope that took longer than expected to be fully operation and an under-resourced pipeline.   I think Josh Peek summed it up best though:

But it was great to see that the telescope and pipeline were both now operation and working well.

Okay off to bed to wake up in time to listen to tomorrow’s first talk.  I haven’t even gotten to the posters yet or the conversations (in person or on twitter).


How common are the Milky Way and Andromeda?

A recent paper by Licquia, Newmann, and Brinchmann (2015) reported that the Milky Way, if viewed externally, would have an absolute magnitude of Mr5logh=21.00 and a rest frame color of (g-r) = 0.682.    This would place it very near the red sequence or in the green valley, where galaxies are thought to be transitioning to being quiescent systems.  Red spirals generally only make up about 6% of the spiral galaxy population as found by Masters et al. (2010), and following their definition, the Milky Way would qualify as a red spiral. 

Now, the Milky Way makes up part of the local group, whose other major member is the Andromeda Galaxy.  Andromeda is also a very red spiral galaxy at a distance  of 0.78 Mpc away from the Milky Way and approaching the Milky Way at ~300 km/s.   Galaxies like the Milky Way and Andromeda are likely to be incredible rare.  Out of a sample of 130000 galaxies, Mutch, Croton, and Poole (2011) only found 997 (0.77%) that were similar to the Milky Way or Andromeda.

Of those galaxies, how many are close together to each other?  This should be easily answerable using the SDSS CasJobs query.   Nonetheless, I tried to repeat the query from Mutch, Croton, and Poole (2011) of galaxies with spiral structure, stellar masses similar to the Milky Way and Andromeda, and exponential profiles along with adding in a color selection, requiring the galaxies to have a redshift less than 0.1,  and leaving out the face-on requirement. In the end, I found 5375 galaxies fitting the requirement out of an initial sample of 160000 galaxies with z < 0.1.   Compared to the early work, I used a slightly higher redshift cutoff and dropped the face on requirement, which explains the greater number of sources.  Of those 5375, 36 pairs exist where the two galaxies are within 1.5 Mpc of each other and have  a total line of sight velocity difference less than 150 km/s.

So while the local group is unique, it would appear roughly about 1 out of every 2200 giant galaxies would be in a local group analog (give or take a factor of 2).    I have to admit my initial feeling would be that they would be more rare than that.  Then again, this was a quick calculation and their might easily be several factors that I am missing.

Of course, it might be interesting to follow-up a few of these systems in detail to see what might be in store for the local group.    Mutch, Croton, and Poole actually suggest that the Milky Way-Andromeda merger will more likely be a dry merger when it happens and maybe some of these systems can give us some ideas about what is in store for the Milky Way.

For the record, here is the query I used.  If anyone has better ideas, please feel free to suggest it  and I’ll be happy to post an update.

-- Find red spiral galaxies similar to the Milky Way and Andromeda

SELECT count(g.objID) -- g.objID, g.ra, g.dec, t04_spiral_a08_spiral_debiased, sp.z, sm.mstellar_median, fiberMag_g - fiberMag_r, log10(1/expAB_r)
FROM Galaxy as g
join dr10.zoo2MainSpecz as z on g.specobjid=z.specobjid
join SpecObj as sp on g.specobjid=sp.specobjid
join stellarMassPCAWiscBC03 as sm on g.specobjid=sm.specobjid
--join Photoz as pz on g.objID = pz.objID
r < 24 -- r IS NOT deredenned
and sp.z < 0.1
and t04_spiral_a08_spiral_debiased > 0.8 --spiral structure is seen
and sm.mstellar_median > 10.66 and sm.mstellar_median < 11.2 -- only galaxies with stellar masses similar to MW/M31
and fiberMag_g - fiberMag_r > 0.63 -- find all red objects (this should be restframe color, but k-corrections should be small)
and fracDeV_r < 0.5 -- added to remove bulge dominated galaxies

And here’s an image of one of the systems that are very close together, however the on-sky separation of some of these systems can be visually quite large (0.78 Mpc at z=0.05 corresponds to roughly 0.2 degrees).


Credit: SDSS

Some Caveats:  The Mutch, Croton, and Poole estimate included some assumptions about viewing angle, so there probably are more systems where one object is being viewed edge on. Nonetheless, it goes into much more thorough detail then I do here and also has some very interesting thoughts about the long term evolution of the systems.  In addition, I’ve done nothing to control for spectroscopic incompleteness.   Also my SDSS-fu is very rusty so I could have screwed up the above query and I couldn’t figure out/remember an easy way to get to rest frame properties from the SDSS database.   Also a more thorough job could have been done to decide bound/un-bound pairs.

References and Acknowledge:  References are many linked from above, but I also made use of NED, astropy, but primarily the data from SDSS.   H/t to @jbprime for the original tweet.

Magellanic Mass

My good friend Andy Fox and his colleagues have recently published a paper on the amount of gas in the Magellanic Streams.   I found the results quite interesting as it indicates that there is up to 2×109 Mof gas in the streams, which means that the clouds have lost at least 2/3 of their total gas mass.    So, before they fell in, the LMC and SMC, together, would have roughly a gas mass of ~3×109 M.   This is comparable to their current mass in stars.

This made me curious about how the clouds compare to similar galaxies in the field before they fell in.    Because I actually know very little about the clouds, I then started digging through the literature to look about how when the clouds may have first fallen into the halo of the Milky Way.   Apparently, this is quite a contention topic without a very good answer.

Early simulations and measurements were in favour of the clouds being a bound pair existing in or near the Milky Way halo for a long time and the magellanic stream forming about 1.5 Gyrs ago due to a near pass between the two galaxies (see review by van der Marel 2004 and Gardiner et al. 1994).     However recent proper motion measurements by Kallivayalil et al. (2013)  and better simulations by Bresla et al. (2012) indicate that the LMC and SMC may be falling into the Milky Way for the first time.    Along with Rocha et al. (2012),  it is likely the LMC and SMC crossed the virial radius sometime in the last 4 Gyrs, which would correspond well with a increase in the star formation rate seen by Weisz et al. (2013) around 3.5 Gyrs ago.

So, after this very interesting diversion, I realized that it might not make a difference because there are not very few gas mass measurements for galaxies of this size beyond the nearby Universe.   So far, the best papers I have found with a plot of stellar mass against gas mass are Catinella et al. (2012), and Bothwell et al. (2013) .  Both are for the local Universe, so we will just have to compare the LMC to other nearby galaxies.   For a stellar mass of 2.7×109 Mo, the average gas mass should be about 1-3x higher for objects with gas detections.  So the current gas mass of the LMC of 4.0×108 Mo is much less than what you would expect from a similar galaxy in the field.  

So it would seem that the LMC is fairly consistent with the idea of satellite quenching and losing a good deal of its gas as it falls into (or through) the Milky Way halo.  It is interesting that the star formation appears to be enhanced as the galaxies fall into the clusters and that they are falling in as a pair as this is similar to the result we recently found for star bursting galaxies falling into galaxy clusters.   In Bresla et al’s  simulations, the dwarf-dwarf interaction is reported as  the dominate source for the removal of the gas and the next step might be to investigate if a similar effect was seen in simulations of massive clusters as well.

Of course, I’m not an expert on the LMC/SMC and the Milky Way dynamics and I’ve only done my best to answer my first question in a relatively short period,  but along the way, I’ve discovered a number of interesting papers (especially the Bresla et al. 2012 simulations).   I’ve probably also missed a number of papers too, but it was a fairly interesting afternoon of reading.


Anecdotal aside: The first time I was observing in Sutherland I walked outside and looked up at the sky.   The sky was absolutely stunning and clear except for these two clouds hanging out in the south.   It took me a good minute to realize I was looking at the Magellanic clouds.  

Perception and Knowledge

An incredible thought provoking talk today by Dr. Wanda Diaz-Merced on the ‘Sonification for the exploration of 1D Astrophysics Data.’      First, it was impressive to see the tools that have been developed to help visual impaired people to analyze data sets.   As someone who enjoys programming, it seems like it would be a fascinating challenge and widely applicable.   It looks like the xSonify package from Goddard was used for her research and from her description of it, it looks  to have an impressive number of features allowing different data sets to be turned into audio representation.

Second, it also thought provoking to be faced with the reality that our understanding of the Universe is based on how we perceive it.   It was quite interesting to hear her quote a study that found that people with dyslexia are more efficient at detecting differences at the edges of different data sets.   In her own research, audio plus multiple visual indicators made it much easier to detect patterns in weak signals.    How else might perception be limiting our understanding of the Universe?   This goes both in what data we chose to look at  and how we make that data available.

New form of Science

Last week, it was confirmed that a type Ia supernova was exploding in M82, and this was then followed by an explosion on different social networks of astronomers.   The initial discovery was a excellent demonstration of the  usefulness of rooftop telescopes (and when was the last time something was discovered from an observatory in London?), but the really interesting process was watching astronomers share information and try to determine something about this supernova in realtime on twitter and facebook.    Results were coming very quickly out on the two social media outlets where even some of the astronomical telegrams were citing them. 

Of course, this was very exciting and it was interesting to learn things in almost real time.   Along with the reports on the telegrams, it was interesting to learn about the potential for neutrino detections (almost none–but how do you cite a tweet about it?), lack of radio detection (who knew Type Ia’s had no radio emission?), and that this is probably the closest Type Ia in quite some time.

The astronomical telegram isn’t about to be replaced by the astronomical tweet (although it does have a twitter feed), but it does show the power and potential for doing science in the open.   There is an opportunity for education, cooperation, and discussion that isn’t possible by the lonely hermit scientist.

Large Scale Spectroscopic Surveys

So I just completed my talk on what you can do with large scale spectroscopic surveys.   It was a fun experience in trying to write a talk using reveal.js and I think it came out looking pretty good.  However, if I had more time, there likely would have been a few more things that I would have added to it.  It was the first time I was giving the talk, and I think there was a lot more material that could have been added (more links on the slides, list of some of the recent large scale spectroscopic surveys instead of just mentioning them).   I think I could have gone into a bit more detail about how mass was measured in clusters, but that is why it is always a good idea to

I haven’t gotten much feedback yet, but I am hoping that I did get over my three main points:  1. There are lot of cluster redshifts out there, 2.  The more redshifts you have, the more power you have in measuring the dynamical mass, and 3.  Populations studies become incredible powerful with spectroscopic redshifts (and there are lot of blue galaxies in clusters!).

It was an interesting experience writing it in reveal.js. It does give some nice transitions between slides, but I think I need to learn some more html5 before I really can make it more powerful.  It was difficult to add/control animations and to easily add plots or data that I think would be easier with something like d3.js.   However, since I am just learning javascript, it was a good place to learn.

Although I think I have an idea for at least one interesting infographic on the growth of redshift surveys.  Surveys have grown in the number of clusters, number of redshifts, and range of redshifts over the last 80 years and the growth recently in number of redshifts has been, I would guess, exponential.   It would be interesting to try to visual that.

It would also be interesting to have all of those cluster redshifts.