3fgl Demographics Outside the Galactic Plane Using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

Mirabal, N.; Charles, E.; Ferrara, E. C.; Gonthier, P.; Harding, A. K.; Sánchez‐Conde, Miguel A.; Thompson, D. J.

doi:10.3847/0004-637x/825/1/69

Cited by 72 publications

(76 citation statements)

References 85 publications

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“…Using machine learning algorithms, Mirabal et al (2016) classified 3FGL J0838.8−2829 as a high-confidence pulsar based on its γ-ray spectral shape and variability index. Accordingly, we propose XMMU J083850.38−282756.8 as the millisecond pulsar counterpart of 3FGL J0838.8−2829.…”

Section: Xmm-newton Analysismentioning

confidence: 99%

X-Ray and Optical Study of the Gamma-ray Source 3FGL J0838.8–2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar

Halpern¹,

Bogdanov²,

Thorstensen³

2017

ApJ

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We observed the field of the Fermi source 3FGL J0838.8−2829 in optical and X-rays, initially motivated by the cataclysmic variable (CV) 1RXS J083842.1−282723 that lies within its error circle. Several X-ray sources first classified as CVs have turned out to be γ-ray emitting millisecond pulsars (MSPs). We find that 1RXS J083842.1−282723 is in fact an unusual CV, a stream-fed asynchronous polar in which accretion switches between magnetic poles (that are ≈ 120• apart) when the accretion rate is at minimum. High-amplitude X-ray modulation at periods of 94.8±0.4 minutes and 14.7±1.2 hr are seen. The former appears to be the spin period, while the latter is inferred to be one-third of the beat period between the spin and the orbit, implying an orbital period of 98.3 ± 0.5 minutes. We also measure an optical emission-line spectroscopic period of 98.413 ± 0.004 minutes, which is consistent with the orbital period inferred from the X-rays. In any case, this system is unlikely to be the γ-ray source. Instead, we find a fainter variable X-ray and optical source, XMMU J083850.38−282756.8, that is modulated on a time scale of hours in addition to exhibiting occasional sharp flares. It resembles the black widow or redback pulsars that have been discovered as counterparts of Fermi sources, with the optical modulation due to heating of the photosphere of a low-mass companion star by, in this case, an as-yet undetected MSP. We propose XMMU J083850.38−282756.8 as the MSP counterpart of 3FGL J0838.8−2829.

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Section: Xmm-newton Analysismentioning

confidence: 99%

X-Ray and Optical Study of the Gamma-ray Source 3FGL J0838.8–2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar

Halpern¹,

Bogdanov²,

Thorstensen³

2017

ApJ

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“…Another machine learning work [81] used a similar algorithm but for pulsars instead of AGNs. In this case, it is not possible to discard the obtained pulsar candidates, as they have a spectrum compatible with DM annihilation.…”

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confidence: 99%

Unidentified gamma-ray sources as targets for indirect dark matter detection with theFermi-Large Area Telescope

Coronado-Blázquez

Domínguez

Aguirre-Santaella

et al. 2019

J. Cosmol. Astropart. Phys.

115

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One of the predictions of the ΛCDM cosmological framework is the hierarchical formation of structure, giving rise to dark matter (DM) halos and subhalos. When the latter are massive enough they retain gas (i.e., baryons) and become visible. This is the case of the dwarf satellite galaxies in the Milky Way (MW). Below a certain mass, halos may not accumulate significant amounts of baryons and remain completely dark. However, if DM particles are Weakly Interacting Massive Particles (WIMPs), we expect them to annihilate in subhalos, producing gamma rays which can be detected with the Fermi satellite. Using the three most recent point-source Fermi Large Area Telescope (LAT) catalogs (3FGL, 2FHL and 3FHL), we search for DM subhalo candidates among the unidentified sources, i.e., sources with no firm association to a known astrophysical object. We apply several selection criteria based on the expected properties of the DM-induced emission from subhalos, which allow us to significantly reduce the list of potential candidates. Then, by characterizing the minimum detection flux of the instrument and comparing our sample to predictions from the Via Lactea II (VL-II) N-body cosmological simulation, we place conservative and robust constraints on the σv − m DM parameter space. For annihilation via the τ + τ − channel, we put an upper limit of 4 × 10 −26 (5 × 10 −25 ) cm 3 s −1 for a mass of 10 (100) GeV. A critical improvement over previous treatments is the repopulation we made to include low-mass subhalos below the VL-II mass resolution. With more advanced subhalo candidate filtering the sensitivity reach of our method can potentially improve these constraints by a factor 3 (2) for τ + τ − (bb) channel.are an example of the most massive members of this population [11]. Yet, these dSphs are exceptional objects, in that they are massive enough to retain baryons (i.e., gas) and form stars. Conversely, the vast majority of the Galactic DM subhalos are not expected to host baryons and therefore remain completely dark [12]. Given their much larger number density, many of these small subhalos will be much closer to the Earth than the bigger ones, making them potentially interesting for dark matter searches.Should the Weakly Interacting Massive Particle (WIMP) DM model be correct (see, e.g., [13, 14] for a review), these objects may be detectable in the gamma-ray data. WIMPs can achieve the correct relic DM abundance (the so-called "WIMP miracle") through selfannihilation in the early Universe. Self-annihilation of WIMPs gives rise to a Standard Model (SM) particle-antiparticle pair which, among other possible subsequent by-products, typically yields gamma-ray photons. The ongoing self-annihilation of WIMPs in subhalos could be bright enough to be detectable.Since its launch in 2008, the Large Area Telescope on board the NASA Fermi Gammaray Space Telescope (Fermi-LAT) has been surveying the sky searching for gamma-ray sources [15]. The Fermi-LAT is a pair conversion telescope designed to observe the energy band from 20 MeV to gre...

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“…In previous studies, Ackermann, M. et al (2012); Lee et al (2012); Hassan et al (2013); Doert & Errando (2014); Chiaro et al (2016); Mirabal et al (2016); Saz Parkinson et al (2016); Lefaucheur & Pita (2017); Salvetti et al (2017) and other authors, have explored the application of MLT classifying undetermined γ-ray sources in Fermi-LAT γ-ray source catalogs. The first study was applied to the 1-year Source Catalog 1FGL (Abdo et al 2010), the next 3 studies to the 2-year Source Catalog 2FGL (Nolan et al 2012) and the rest were applied to the 4-year Source Catalog 3FGL (Acero et al 2015).…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources

Chiaro

Cutini

Tosti

2019

Monthly Notices of the Royal Astronomical Society

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Machine learning is an automatic technique that is revolutionizing scientific research, with innovative applications and wide use in astrophysics. The aim of this study was to developed an optimized version of an Artificial Neural Network machine learning method for classifying blazar candidates of uncertain type detected by the Fermi Large Area Telescope γ-ray instrument. The final result of this study increased the classification performance by about 80% with respect to previous method, leaving only 15 unclassified blazars out of 573 blazar candidates of uncertain type listed in the LAT 4-year Source Catalog.

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3fgl Demographics Outside the Galactic Plane Using Supervised Machine Learning: Pulsar and Dark Matter Subhalo Interpretations

Cited by 72 publications

References 85 publications

X-Ray and Optical Study of the Gamma-ray Source 3FGL J0838.8–2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar

X-Ray and Optical Study of the Gamma-ray Source 3FGL J0838.8–2829: Identification of a Candidate Millisecond Pulsar Binary and an Asynchronous Polar

Unidentified gamma-ray sources as targets for indirect dark matter detection with theFermi-Large Area Telescope

Optimizing neural network techniques in classifying Fermi-LAT gamma-ray sources

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