The Outer Halo of the Milky Way as Probed by RR Lyr Variables from the Palomar Transient Facility*

Cohen, Judith G.; Sesar, Branimir; Bahnolzer, Sophianna; He, Kevin; Kulkarni, S. R.; Prince, Thomas A.; Bellm, Eric C.; Laher, Russ R.

doi:10.3847/1538-4357/aa9120

Cited by 42 publications

(57 citation statements)

References 92 publications

(170 reference statements)

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“…Curiously, only the measurement by Xue et al (2015) is in broad agreement with the results of Deason et al (2014). Both Slater et al (2016) and Cohen et al (2016Cohen et al ( , 2017 reported significantly shallower density profiles for the stellar halo between 50 and 100 kpc.…”

Section: Introductionsupporting

confidence: 65%

“…However, there exists a small number of stars that travel to the periphery (and perhaps even one step beyond) of the Milky Way which, if detected, can be used as tracers of its extent. In particular, in the past decade, there have been several attempts to expand upon the inventory of these outer halo denizens (see e.g., Brown et al 2010;Deason et al 2012;Bochanski et al 2014;Huxor & Grebel 2015;Cohen et al 2017).…”

Section: Introductionmentioning

confidence: 99%

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To the Galactic Virial Radius with Hyper Suprime-Cam

Deason

Belokurov

Koposov

2018

ApJ

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The full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractWe exploit the exquisite, deep Hyper Suprime-Cam (HSC) imaging data to probe the Galactic halo out to 200 kpc. Using the ∼100 square degree, multiband photometry of the first HSC Wide survey data release, we identify blue horizontal branch (BHB) stars beyond 50 kpc in the halo. The presence of the Sagittarius (Sgr) stream in the HSC fields produces a notable excess of stars at the apocenter of the leading arm (∼50-60 kpc). For fields excluding Sgr, the BHB counts are consistent with a continuation of a −4 power law from the inner halo. However, we find that the majority of the non-Sgr BHB stars beyond 50 kpc reside in one 27 square degree HSC field called "VVDS." Curiously, this field is located close to the Magellanic plane, and we hypothesize that the excess of stars between 50 and 200 kpc could be associated with distant Magellanic debris. Indeed, without the VVDS, there are very few BHBs in the remaining portions of the Galaxy probed by the HSC. Accordingly, this scarcity of tracers is consistent with a significant decline in stellar density beyond 50 kpc, with a power law of −4 or steeper.

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Section: Introductionsupporting

confidence: 65%

Section: Introductionmentioning

confidence: 99%

To the Galactic Virial Radius with Hyper Suprime-Cam

Deason

Belokurov

Koposov

2018

ApJ

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“…It is important to remember that our sample selection procedure that we apply to the Pan-STARRS1 3π database takes advantage of the experience gained by attempting to use the SDSS (Sesar et al 2007(Sesar et al , 2010 and then to analyze the more difficult PTF (Cohen et al 2017) with few observations taken with a random cadence. Our PS1 sample of RRab stars ) is unique in that it contains a large number (44,403) of RR Lyrae in total, of which 17,452 lie within the radial range of R gc from 20 up to 130 kpc, all with highly precise photometry.…”

Section: Comparison With Previous Resultsmentioning

confidence: 99%

“…There have also been efforts to reach the outer halo using blue horizontal branch (BHB) stars, which to first order have a fixed luminosity similar to that of RRab (see, e.g., Deason et al 2014), but these run into problems of confusion with much more numerous blue stragglers at the same apparent magnitude and with quasars. Prior to the present work, perhaps the most successful attempt to probe the density distribution in the outer Galactic halo was by Cohen et al (2017), reaching out to above 100 kpc, with a small (∼450) sample of RRab stars selected from the Palomar Transient Facility (PTF) database.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, the mean luminosity of an RRab variable, and hence its distance, can be determined with knowledge of the light curve only. RRab stars were used by many previous studies, including those of Hawkins (1984), Saha (1984), Wetterer et al (1996), Ivezić et al (2000), Vivas & Zinn (2006), Jurić et al (2008), Catelan (2009), Watkins et al (2009), de Jong et al (2010, Sesar et al (2010Sesar et al ( , 2011, Deason et al (2011), Akhter et al (2012), Drake et al (2014), Torrealba et al (2015), Cohen et al (2015), Xue et al (2015), Soszyński et al (2016), Bland-Hawthorn & Gerhard (2016), Iorio et al (2017), and Cohen et al (2017).…”

Section: Introductionmentioning

confidence: 99%

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The Profile of the Galactic Halo from Pan-STARRS1 3π RR Lyrae

Hernitschek

Cohen

Rix

et al. 2018

ApJ

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We characterize the spatial density of the Pan-STARRS1 (PS1) sample of Rrab stars to study the properties of the old Galactic stellar halo. This sample, containing 44,403 sources, spans galactocentric radii of 0.55 kpcR gc 141 kpc with a distance precision of 3% and thus is able to trace the halo out to larger distances than most previous studies. After excising stars that are attributed to dense regions such as stellar streams, the Galactic disk and bulge, and halo globular clusters, the sample contains ∼11,000 sources within 20 kpcR gc 131 kpc. We then apply forward modeling using Galactic halo profile models with a sample selection function. Specifically, we use ellipsoidal stellar density models ρ(l, b, R gc ) with a constant and a radiusdependent halo flattening q(R gc ). Assuming constant flattening q, the distribution of the sources is reasonably well fit by a single power law with = 0.27 , an effective radius r eff =1.07±0.10 kpc, and a halo flattening of q=0.923±0.007. If we allow for a radius-dependent flattening q(R gc ), we find evidence for a distinct flattening of q∼0.8 of the inner halo at ∼25 kpc. Additionally, we find that the south Galactic hemisphere is more flattened than the north Galactic hemisphere. The results of our work are largely consistent with many earlier results (e.g., Watkins et al.; Iorio et al.). We find that the stellar halo, as traced in RR Lyrae stars, exhibits a substantial number of further significant over-and underdensities, even after masking all known overdensities.

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Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

Fluke

Jacobs

2019

WIREs Data Min & Knowl

113

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Machine learning (automated processes that learn by example in order to classify, predict, discover, or generate new data) and artificial intelligence (methods by which a computer makes decisions or discoveries that would usually require human intelligence) are now firmly established in astronomy. Every week, new applications of machine learning and artificial intelligence are added to a growing corpus of work. Random forests, support vector machines, and neural networks are now having a genuine impact for applications as diverse as discovering extrasolar planets, transient objects, quasars, and gravitationally lensed systems, forecasting solar activity, and distinguishing between signals and instrumental effects in gravitational wave astronomy. This review surveys contemporary, published literature on machine learning and artificial intelligence in astronomy and astrophysics. Applications span seven main categories of activity: classification, regression, clustering, forecasting, generation, discovery, and the development of new scientific insights. These categories form the basis of a hierarchy of maturity, as the use of machine learning and artificial intelligence emerges, progresses, or becomes established. This article is categorized under: Application Areas > Science and Technology Fundamental Concepts of Data and Knowledge > Motivation and Emergence of Data Mining Technologies > Machine Learning

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The Outer Halo of the Milky Way as Probed by RR Lyr Variables from the Palomar Transient Facility*

Cited by 42 publications

References 92 publications

To the Galactic Virial Radius with Hyper Suprime-Cam

To the Galactic Virial Radius with Hyper Suprime-Cam

The Profile of the Galactic Halo from Pan-STARRS1 3π RR Lyrae

Surveying the reach and maturity of machine learning and artificial intelligence in astronomy

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