THE FIRST FERMI LAT SUPERNOVA REMNANT CATALOG
F. Acero
1
, M. Ackermann
2
, M. Ajello
3
, L. Baldini
4,5
, J. Ballet
1
, G. Barbiellini
6,7
, D. Bastieri
8,9
, R. Bellazzini
10
,
E. Bissaldi
11
, R. D. Blandford
5
, E. D. Bloom
5
, R. Bonino
12,13
, E. Bottacini
5
, T. J. Brandt
14
, J. Bregeon
15
, P. Bruel
16
,
R. Buehler
2
, S. Buson
8,9
, G. A. Caliandro
5,17
, R. A. Cameron
5
, R. Caputo
18
, M. Caragiulo
11
, P. A. Caraveo
19
,
J. M. Casandjian
1
, E. Cavazzuti
20
, C. Cecchi
21,22
, A. Chekhtman
23
, J. Chiang
5
, G. Chiaro
9
, S. Ciprini
20,21,24
, R. Claus
5
,
J. M. Cohen
14,25
, J. Cohen-Tanugi
15
, L. R. Cominsky
26
, B. Condon
27
, J. Conrad
28,29,30
, S. Cutini
20,21,24
,F.D’Ammando
31,32
,
A. de Angelis
33
, F. de Palma
11,34
, R. Desiante
12,35
, S. W. Digel
5
, L. Di Venere
36
, P. S. Drell
5
, A. Drlica-Wagner
37
,
C. Favuzzi
11,36
, E. C. Ferrara
14
, A. Franckowiak
5
, Y. Fukazawa
38
, S. Funk
39
, P. Fusco
11,36
, F. Gargano
11
,
D. Gasparrini
20,21,24
, N. Giglietto
11,36
, P. Giommi
20
, F. Giordano
11,36
, M. Giroletti
31
, T. Glanzman
5
, G. Godfrey
5
,
G. A. Gomez-Vargas
40,41
, I. A. Grenier
1
, M.-H. Grondin
27
, L. Guillemot
42,43
, S. Guiriec
14,81
, M. Gustafsson
44
,
D. Hadasch
45
, A. K. Harding
14
, M. Hayashida
46
,E.Hays
14
, J. W. Hewitt
47
, A. B. Hill
5,48
, D. Horan
16
, X. Hou
49,50
,
G. Iafrate
6,51
, T. Jogler
5
,G.JÓhannesson
52
, A. S. Johnson
5
, T. Kamae
53
, H. Katagiri
54
, J. Kataoka
55
, J. Katsuta
38
,
M. Kerr
56
, J. Knödlseder
57,58
, D. Kocevski
14
, M. Kuss
10
, H. Laffon
27
, J. Lande
59
, S. Larsson
29,60
, L. Latronico
12
,
M. Lemoine-Goumard
27
,J.Li
61
,L.Li
29,60
, F. Longo
6,7
, F. Loparco
11,36
, M. N. Lovellette
62
, P. Lubrano
21,22
, J. Magill
25
,
S. Maldera
12
, M. Marelli
19
, M. Mayer
2
, M. N. Mazziotta
11
, P. F. Michelson
5
, W. Mitthumsiri
63
, T. Mizuno
64
,
A. A. Moiseev
25,65
, M. E. Monzani
5
, E. Moretti
66
, A. Morselli
40
, I. V. Moskalenko
5
, S. Murgia
67
, R. Nemmen
68
, E. Nuss
15
,
T. Ohsugi
64
, N. Omodei
5
, M. Orienti
31
, E. Orlando
5
, J. F. Ormes
69
, D. Paneque
5,66
, J. S. Perkins
14
, M. Pesce-Rollins
5,10
,
V. Petrosian
5
, F. Piron
15
, G. Pivato
10
, T. A. Porter
5
, S. Rainò
12,37
, R. Rando
8,9
, M. Razzano
10,82
, S. Razzaque
70
,
A. Reimer
5,45
, O. Reimer
5,45
, M. Renaud
15
, T. Reposeur
27
, R. Rousseau
71
, P. M. Saz Parkinson
18,72
, J. Schmid
1
, A. Schulz
2
,
C. Sgrò
10
, E. J. Siskind
73
, F. Spada
10
, G. Spandre
10
, P. Spinelli
11,36
, A. W. Strong
74
, D. J. Suson
75
, H. Tajima
5,76
,
H. Takahashi
38
, T. Tanaka
77
, J. B. Thayer
5
, D. J. Thompson
14
, L. Tibaldo
5
, O. Tibolla
78
, D. F. Torres
61,79
, G. Tosti
21,22
,
E. Troja
14,25
, Y. Uchiyama
80
, G. Vianello
5
, B. Wells
18
,K.S.Wood
62
, M. Wood
5
, M. Yassine
15
,
P. R. den Hartog
5,81
, and S. Zimmer
28,29
1
Laboratoire AIM, CEA-IRFU/CNRS/Université Paris Diderot, Service d’Astrophysique, CEA Saclay, F-91191 Gif sur Yvette, France
2
Deutsches Elektronen Synchrotron DESY, D-15738 Zeuthen, Germany
3
Department of Physics and Astronomy, Clemson University, Kinard Lab of Physics, Clemson, SC 29634-0978, USA
4
Università di Pisa and Istituto Nazionale di Fisica Nucleare, Sezione di Pisa I-56127 Pisa, Italy
5
W. W. Hansen Experimental Physics Laboratory, Kavli Institute for Particle Astrophysics and Cosmology, Department of Physics and SLAC National Accelerator
Laboratory, Stanford University, Stanford, CA 94305, USA
6
Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, I-34127 Trieste, Italy
7
Dipartimento di Fisica, Università di Trieste, I-34127 Trieste, Italy
8
Istituto Nazionale di Fisica Nucleare, Sezione di Padova, I-35131 Padova, Italy
9
Dipartimento di Fisica e Astronomia “G. Galilei,” Università di Padova, I-35131 Padova, Italy
10
Istituto Nazionale di Fisica Nucleare, Sezione di Pisa, I-56127 Pisa, Italy
11
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, I-70126 Bari, Italy; francesco.depalma@ba.infn.it
12
Istituto Nazionale di Fisica Nucleare, Sezione di Torino, I-10125 Torino, Italy
13
Dipartimento di Fisica Generale “Amadeo Avogadro,” Università degli Studi di Torino, I-10125 Torino, Italy
14
NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA; t.j.brandt@nasa.gov
15
Laboratoire Univers et Particules de Montpellier, Université Montpellier, CNRS/IN2P3, Montpellier, France
16
Laboratoire Leprince-Ringuet, École polytechnique, CNRS/IN2P3, Palaiseau, France
17
Consorzio Interuniversitario per la Fisica Spaziale (CIFS), I-10133 Torino, Italy
18
Santa Cruz Institute for Particle Physics, Department of Physics and Department of Astronomy and Astrophysics,
University of California at Santa Cruz, Santa Cruz, CA 95064, USA
19
INAF-Istituto di Astro fisica Spaziale e Fisica Cosmica, I-20133 Milano, Italy
20
Agenzia Spaziale Italiana (ASI) Science Data Center, I-00133 Roma, Italy
21
Istituto Nazionale di Fisica Nucleare, Sezione di Perugia, I-06123 Perugia, Italy
22
Dipartimento di Fisica, Università degli Studi di Perugia, I-06123 Perugia, Italy
23
College of Science, George Mason University, Fairfax, VA 22030, resident at Naval Research Laboratory, Washington, DC 20375, USA
24
INAF Osservatorio Astronomico di Roma, I-00040 Monte Porzio Catone (Roma), Italy
25
Department of Physics and Department of Astronomy, University of Maryland, College Park, MD 20742, USA
26
Department of Physics and Astronomy, Sonoma State University, Rohnert Park, CA 94928-3609, USA
27
Centre d’Études Nucléaires de Bordeaux Gradignan, IN2P3/CNRS, Université Bordeaux 1, BP120, F-33175 Gradignan Cedex, France
28
Department of Physics, Stockholm University, AlbaNova, SE-106 91 Stockholm, Sweden
29
The Oskar Klein Centre for Cosmoparticle Physics, AlbaNova, SE-106 91 Stockholm, Sweden
30
The Royal Swedish Academy of Sciences, Box 50005, SE-104 05 Stockholm, Sweden
31
INAF Istituto di Radioastronomia, I-40129 Bologna, Italy
32
Dipartimento di Astronomia, Università di Bologna, I-40127 Bologna, Italy
33
Dipartimento di Fisica, Università di Udine and Istituto Nazionale di Fisica Nucleare, Sezione di Trieste, Gruppo Collegato di Udine, I-33100 Udine, Italy
34
Università Telematica Pegaso, Piazza Trieste e Trento, 48, I-80132 Napoli, Italy
35
Università di Udine, I-33100 Udine, Italy
36
Dipartimento di Fisica “M. Merlin” dell’Università e del Politecnico di Bari, I-70126 Bari, Italy
37
Center for Particle Astrophysics, Fermi National Accelerator Laboratory, Batavia, IL 60510, USA
38
Department of Physical Sciences, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan
39
Erlangen Centre for Astroparticle Physics, D-91058 Erlangen, Germany
40
Istituto Nazionale di Fisica Nucleare, Sezione di Roma “Tor Vergata,” I-00133 Roma, Italy
The Astrophysical Journal Supplement Series, 224:8 (50pp), 2016 May doi:10.3847/0067-0049/224/1/8
© 2016. The American Astronomical Society. All rights reserved.
1
41
Departamento de Fisíca, Pontificia Universidad Católica de Chile, Avenida Vicuña Mackenna 4860, Santiago, Chile
42
Laboratoire de Physique et Chimie de l’Environnement et de l’Espace—Université d’Orléans/CNRS, F-45071 Orléans Cedex 02, France
43
Station de radioastronomie de Nançay, Observatoire de Paris, CNRS/INSU, F-18330 Nançay, France
44
Georg-August University Göttingen, Institute for theoretical Physics—Faculty of Physics, Friedrich-Hund-Platz 1, D-37077 Göttingen, Germany
45
Institut für Astro- und Teilchenphysik and Institut für Theoretische Physik, Leopold-Franzens-Universität Innsbruck, A-6020 Innsbruck, Austria
46
Institute for Cosmic-Ray Research, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8582, Japan
47
University of North Florida, Department of Physics, 1 UNF Drive, Jacksonville, FL 32224, USA; john.w.hewitt@unf.edu
48
School of Physics and Astronomy, University of Southampton, Highfield, Southampton, SO17 1BJ, UK
49
Yunnan Observatories, Chinese Academy of Sciences, Kunming 650216, China
50
Key Laboratory for the Structure and Evolution of Celestial Objects, Chinese Academy of Sciences, Kunming 650216, China
51
Osservatorio Astronomico di Trieste, Istituto Nazionale di Astrofisica, I-34143 Trieste, Italy
52
Science Institute, University of Iceland, IS-107 Reykjavik, Iceland
53
Department of Physics, Graduate School of Science, University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
54
College of Science, Ibaraki University, 2-1-1, Bunkyo, Mito 310-8512, Japan
55
Research Institute for Science and Engineering, Waseda University, 3-4-1, Okubo, Shinjuku, Tokyo 169-8555, Japan
56
CSIRO Astronomy and Space Science, Australia Telescope National Facility, Epping NSW 1710, Australia
57
CNRS, IRAP, F-31028 Toulouse cedex 4, France
58
GAHEC, Université de Toulouse, UPS-OMP, IRAP, Toulouse, France
59
Twitter, Inc, 1355 Market Street #900, San Francisco, CA 94103, USA
60
Department of Physics, KTH Royal Institute of Technology, AlbaNova, SE-106 91 Stockholm, Sweden
61
Institute of Space Sciences (IEEC-CSIC), Campus UAB, E-08193 Barcelona, Spain
62
Space Science Division, Naval Research Laboratory, Washington, DC 20375-5352, USA
63
Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand
64
Hiroshima Astrophysical Science Center, Hiroshima University, Higashi-Hiroshima, Hiroshima 739-8526, Japan
65
Center for Research and Exploration in Space Science and Technology (CRESST) and NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA
66
Max-Planck-Institut für Physik, D-80805 München, Germany
67
Center for Cosmology, Physics and Astronomy Department, University of California, Irvine, CA 92697-2575, USA
68
Instituto de Astronomia, Geofísica e Cincias Atmosféricas, Universidade de São Paulo, Rua do Matão, 1226, São Paulo —SP 05508-090, Brazil
69
Department of Physics and Astronomy, University of Denver, Denver, CO 80208, USA
70
Department of Physics, University of Johannesburg, P.O. Box 524, Auckland Park 2006, South Africa
71
Lycée Fresnel, Paris, France
72
Department of Physics, The University of Hong Kong, Pokfulam Road, Hong Kong, China
73
NYCB Real-Time Computing Inc., Lattingtown, NY 11560-1025, USA
74
Max-Planck Institut für extraterrestrische Physik, D-85748 Garching, Germany
75
Department of Chemistry and Physics, Purdue University Calumet, Hammond, IN 46323-2094, USA
76
Solar-Terrestrial Environment Laboratory, Nagoya University, Nagoya 464-8601, Japan
77
Department of Physics, Graduate School of Science, Kyoto University, Kyoto, Japan
78
Mesoamerican Centre for Theoretical Physics (MCTP), Universidad Autónoma de Chiapas (UNACH), Carretera Emiliano Zapata Km. 4, Real del Bosque (Teràn),
29050 Tuxtla Gutiérrez, Chiapas, México
79
Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain
80
3-34-1 Nishi-Ikebukuro, Toshima-ku, Tokyo 171-8501, Japan
81
HAL24K Data Intelligence Labs, Barbara Strozzilaan, 1083 HN Amsterdam, The Netherlands
Received 2015 September 1; accepted 2015 November 30; published 2016 May 16
ABSTRACT
To uniformly determine the properties of supernova remnants (SNRs) at high energies, we have developed the first
systematic survey at energies from 1 to 100 GeV using data from the Fermi Large Area Telescope (LAT). Based on
the spatial overlap of sources detected at GeV energies with SNRs known from radio surveys, we classify
30sources as likely GeV SNRs. We also report 14marginal associations and 245flux upper limits. A mock
catalog in which the positions of known remnants are scrambled in Galactic longitude allows us to determine an
upper limit of 22% on the number of GeV candidates falsely identified as SNRs. We have also developed a method
to estimate spectral and spatial systematic errors arising from the diffuse interstellar emission model, a key
component of all Galactic Fermi LAT analyses. By studying remnants uniformly in aggregate, we measure the
GeV properties common to these objects and provide a crucial context for the detailed modeling of individual
SNRs. Combining our GeV results with multiwavelength (MW) data, including radio, X-ray, and TeV, we
demonstrate the need for improvements to previously sufficient, simple models describing the GeV and radio
emission from these objects. We model the GeV and MW emission from SNRs in aggregate to constrain their
maximal contribution to observed Galactic cosmic rays.
Key words: acceleration of particles – catalogs – cosmic rays – gamma-rays: ISM – ISM: supernova remnants –
radiation mechanisms: nonthermal
1. INTRODUCTION
The highly energetic nature of supernova remnants (SNRs)
has been long known from evidence of nonthermal particle
acceleration. Synchrotron emission from relativistic electrons
was first detected at radio wavelengths, where SNRs have been
most extensively cataloged (Green 1991, 2004, 2009a). X-ray
telescopes of the last three decades have detected both thermal
bremsstrahlung emission, a product of gas heated by expanding
blast waves, and nonthermal X-ray synchrotron emission. The
82
NASA Postdoctoral Program Fellow, USA.
83
Funded by contract FIRB-2012-RBFR12PM1F from the Italian Ministry of Education, University and Research (MIUR).
2
The Astrophysical Journal Supplement Series, 224:8 (50pp), 2016 May Acero et al.
nonthermal X-rays suggest a population of ∼TeV electrons
accelerated at the shock front (Seward 1990; Vink 2012). These
multiwavelength (MW) observations from radio to X-rays have
provided significant insights into SNRs as drivers of galactic
evolution, as well as sources of relativistic particles. Yet it has
been problematic to observe ongoing particle acceleration
in situ and to determine the partitioning and flow of energy
through many of these systems. The complexities of SNRs and
their interactions with diverse environments have made it
difficult to both predict properties from shock acceleration
theory, e.g., specific hadronic and leptonic acceleration
efficiencies, and to infer them from observations.
The origin and acceleration process(es) of cosmic rays
(CRs), which are highly energetic particles mainly comprised
of protons and nuclei with a small fraction (1%) of leptons
(Olive & Particle Data Group 2014), have remained a mystery
for over 100 years. Energetic arguments indicate that SNRs are
probable sources of Galactic hadrons even up to PeV energies
due to their strong shocks (e.g., Helder et al. 2012). However, it
remains difficult to conclusively demonstrate that individual
Galactic accelerators supply the Galactic CR population.
Of all the wavelengths, γ-rays offer the most readily
accessible window into energetic particles available to date,
due to the variety of processes producing high energy photons
(Stecker 1971; Gaisser et al. 1998). Relativistic leptons can
produce γ-rays by inverse Compton (IC) scattering low energy
photons or by interacting with atomic nuclei, producing
bremsstrahlung radiation. Relativistic hadrons may interact
with subrelativistic nuclei, creating both neutral pions that
decay to two γ-rays and charged pions that decay to energetic
leptons and neutrinos.
Only recently have γ-ray telescopes obtained sufficient
spatial and spectral resolution to distinguish SNR-produced
high energy photons from the backgrounds. The EGRET
instrument detected several Galactic sources, but was unable to
unambiguously identify SNRs (Sturner & Dermer 1995;
Esposito et al.
1996). Imaging air Cherenkov telescopes
(IACTs) successfully identified extended emission from several
bright SNRs at TeV energies (Carrigan et al. 2013). However,
these telescopes neither provide data across the large energy
range needed to discriminate between possible emission
mechanisms, nor do they provide full sky coverage. The
launches of AGILE in 2007 and Fermi in 2008 finally provided
the capability to unambiguously identify SNRs in γ-rays and to
detect the spectral signature of accelerated protons from the
brightest SNRs (Giuliani et al. 2011; Ackermann et al. 2013b).
Surveys in the GeV energy range have now identified
hundreds of sources in the Galactic plane(e.g., Nolan
et al. 2012; Acero et al. 2015), with SNRs being one of many
observed source classes. Pulsars, pulsar wind nebulae (PWNe),
and binaries have all been identified as γ-ray sources that may
be spatially coincident with known Galactic SNRs. Many
studies with the Fermi Large Area Telescope (LAT) have been
able to spatially resolve extended emission from SNRs, making
definite identification possible despite the plethora of poten-
tially plausible counterparts in the Galactic plane(e.g., Katagiri
et al. 2011). Individual studies have found SNRs spanning a
range of ages interacting with the ambient interstellar medium
(ISM) or dense molecular clouds (MCs; e.g., as noted in
Thompson et al. 2012). While these GeV SNRs display many
similar characteristics, no systematic analysis has yet been
undertaken. Understanding the properties of SNRs as a class of
γ-ray emitters and as potential CR sources motivates this
uniform study of all known SNRs in our galaxy.
To improve our understanding of γ-ray SNR properties and
SNRs’ potential contribution to the Galactic CR population, we
have created the
first Fermi LAT catalog of SNRs. The
systematic characterization of GeV emission in regions
containing known SNRs is described in Section 2, with details
on the input source model provided in Section 2.2 and a
description of the general analysis method given in Section 2.3.
We discuss sources of systematic error in Section 2.4 and
describe our findings in Section 3, with the details of the
method used for association in Section 3.1. We created a
number of methods to allow us to uniformly address
complications usually treated within the context of an
individual region. Further details on these methods for
iteratively adding sources to a region’s model, estimating the
error due to the interstellar emission modeling, and estimating
the chance spatial coincidence of a GeV source, can be found
in Appendices A–C, respectively. To better understand the γ-
ray properties of SNRs, in Section 4 we compare the γ-ray
results to MW data assembled for all Galactic SNRs, including
a detailed comparison with radio and TeV counterparts.
Finally, in Section 5 we explore whether the SNR paradigm
for CR origins is consistent with our catalog results. To
facilitate further study, we have provided a number of online
data products, which are described in Appendix D.
1.1. The Fermi LAT Instrument
The Fermi LAT is a pair-conversion γ-ray telescope that
observes photons from 20 MeV to >300 GeV. Launched on
2008 June 11, the default observing mode is an all-sky survey
optimized to provide relatively uniform coverage of the entire
sky every three hours, including the Galactic plane where most
known SNRs are located. Further details of the instrument can
be found in Atwood et al. (2009).
1.2. Galactic SNRs
In this work we focus on the 279currently known Galactic
SNRs. They are derived from the 274SNRs noted in the
catalog of Green (2009a, hereafter Green’s catalog), plus five
additional SNRs identified following its publication. All but 16
of these SNRs have been identified by their radio synchrotron
emission, so their centroids and extensions are primarily
determined from the radio. When the radio detection is not
securely identified through the synchrotron emission, positional
information is obtained from the optical, X-ray, or TeV
observations that identified the SNR, as noted in Green’s
catalog. The catalog is thought to be complete down to a 1 GHz
radio surface brightness limit of ≈10
−20
Wm
−2
Hz
−1
sr
−1
(i.e.,
1 MJy sr
−1
). However, selection effects are known to bias radio
surveys against the identification of radio faint and small
angular size remnants (Green 2004; Brogan et al. 2006).We
note that as this work neared completion, a revised catalog of
294SNRs was published (Green 2014), representing only a
small increase ( <10%) over the previous catalog.
We briefly describe the five SNRs added to our catalog since
the publication of Green’s catalog. For the purposes of this
work, these are implicitly included when we refer to Green’s
catalog and are also in the 2014 catalog unless otherwise noted.
SNR G5.7−0.0: identified in the radio by Brogan et al.
(2006), this remnant is known to be interacting with a nearby
3
The Astrophysical Journal Supplement Series, 224:8 (50pp), 2016 May Acero et al.
dense cloud due to the presence of OH(1720 MHz) masers
(Hewitt & Yusef-Zadeh 2009). The TeV source HESS J1800
−240C is coincident with the SNR, though it is unclear
whether the γ-ray emission is attributable to SNR G5.7−0.0
or escaping CRs from SNR W28 (Aharonian et al. 2008d;
Hanabata et al. 2014). This SNR was included in Green
(2014) as a probable SNR, but was not included in the final
list of 294firmly identified SNRs.
SNR G35.6−0.4: re-identified as an SNR by Green (2009b)
but not included in Green’s 2009 catalog, this is a middle-
aged remnant with nearby MCs thought to lie at a distance of
3.6±0.4 kpc (Zhu et al. 2013). The nearby TeV source
HESS J1858+020 (Aharonian et al. 2008c) has been
proposed as originating from CRs escaping from the SNR
and illuminating nearby clouds (Paron & Giacani 2010).
SNR G213.3−0.4: a very low radio surface brightness SNR
initially designated as G213.0−0.6 by Reich et al. (2003), the
SNR identification was later confirmed by optical line
observations (Stupar & Parker 2012). The SNR lies near
the
H
II
region S284, which is coincident with the γ-ray
source 2FGL J0647.7+0032. No conclusive evidence for
interaction between the SNR and S284 has been presented.
The X-ray source 1RXS J065049.7−003220 lies near the
center of the SNR.
SNR G306.3−0.9: this X-ray source was first reported by
Miller et al. (2011) with the designation Swift J132150.9
−633350. This is a small-diameter SNR with a radius of
110″. X-ray observations indicate a young SNR of age
1300–4600 years in the Sedov phase and at a distance of
8 kpc (Reynolds et al. 2013). The SNR also shows 24 μm
emission, indicating shocked or irradiated warm dust.
SNR G308.4−1.4: this shell-type SNR was initially identi-
fied in radio surveys due to its steep radio spectral index
α=−0.7±0.2, and confirmed by its detection as an
extended X-ray source (Reynolds et al. 2012). The eastern
part of the remnant shows enhanced radio, infrared, and
X-ray emission, which may signal that the shock-wave is
expanding into a denser region to the east (Prinz &
Becker 2012; De Horta et al. 2013). Chandra observations
also revealed a bright X-ray point source near the
geometrical center with a soft spectrum and putative
periodicity that make it a candidate compact binary (Hui
et al. 2012). Given a distance estimate of 6–12 kpc and an
age of 5000–7500 years for the SNR (Prinz & Becker 2012),
the point source and remnant may have originated from the
same progenitor system.
2. ANALYSIS METHODS
To systematically analyze the Fermi LAT γ-ray data, we
apply a maximum likelihood (
Mattox et al. 1996) framework to
Regions of Interest (RoIs) centered on known SNRs
(Green 2009a). For each SNR, we begin by constructing a
model for the spectral and spatial dependence of the γ-ray
emission that includes significant point sources in the RoI. We
then test for the existence of a γ-ray source near the center. This
includes determining the most likely position and extension of
the candidate source and testing for spectral curvature, rather
than assuming it follows a power law (PL) across the energy
range studied. In cases where we find no significant source
associated with the SNR, we calculate upper limits on the flux.
We calculate both statistical and systematic errors, where the
latter are estimated from both the uncertainty in the effective
area and the effects of changing the interstellar emission model
(IEM), which accounts for γ-rays produced by CR interactions
with interstellar gas and radiation fields in the Milky Way.
This analysis uses both the standard Science Tools (version
09-32-05), including gtlike
84
, and the pointlike
analysis package(Kerr 2010) that has been developed and
verified for characterizing source extension for Fermi LAT data
(Lande et al. 2012). Section 2.1 describes our data selection;
Section 2.2 details our new method for automatically finding
point sources in the Fermi LAT γ-ray emission; and Section 2.3
discusses the detection method. We examine the main sources
of systematic error in Section 2.4.
2.1. Data Selection
This catalog was constructed using three years of LAT
survey data from the Pass7(P7) “Source” class and the
associated P7V6 instrument response functions (IRFs).
This interval spans 36 months, from 2008 August 4 to 2011
August 4 (mission elapsed time 239557417−334108806). The
Source event class is optimized for the analysis of persistent
LAT sources, and balances effective area against suppression
of background from residual misclassified charged particles.
We selected only events within a maximum zenith angle of
100° and use the recommended filter string “DATA_Q-
UAL==1 && LAT_CONFIG==1” in gtmktime.
85
The
P7 data and associated products are comparable to those used
in the other γ-ray catalogs employed in this work. We used
the first three years of science data for which the associated
IEM is suitable for measuring sources with >2° extension.
86
A detailed discussion of the instrument and event classes can
be found in Atwood et al. (2009) and at the Fermi Science
Support Center (see footnote 83).
For each of the 279SNRs we modeled emission within a
10° radius of the SNR’s center. As a compromise between the
number of photons collected, the spatial resolution, and the
impact of the IEM, we chose 1 GeV as our minimum energy
threshold. The limited statistics in source class above 100 GeV
motivated using this as our upper energy limit.
To avoid times during which transient sources near SNRs
were flaring, we removed periods with significant weekly
variability detected by the Fermi All-sky Variability Analysis
(FAVA)(Ackermann et al. 2013a). We conservatively defined
a radius within which a flaring source may significantly affect
the flux of a source at the center. We take this distance to be the
radio radius of an SNR plus 2°.8, corresponding to the overall
95%containment radius for the Fermi LAT point spread
function (PSF) for a 1 GeV photon at normal incidence
(Ackermann et al. 2012a). The time ranges of FAVA flares
within this distance were removed in 23RoIs, leaving
98.9%
of the total data in each RoI.
84
Available at the Fermi Science Support Center, http://fermi.gsfc.nasa.gov/
ssc, and described in context at http://fermi.gsfc.nasa.gov/ssc /data/analysis/
documentation/Cicerone/.
85
See the LAT data selection recommendations at the Fermi Science Support
Center: http://fermi.gsfc.nasa.gov/ssc/data/analysis/documentation/
Cicerone/Cicerone_Data_Exploration/Data_preparation.html.
86
See the LAT caveats, http://fermi.gsfc.nasa.gov/ssc/data/analysis/
LAT_caveats.html, particularly those for the IEM developed for Pass 7
reprocessed data described in http://fermi.gsfc.nasa.gov/ssc/data/access/lat/
Model_details/FSSC_model_diffus_reprocessed_v12.pdf.
4
The Astrophysical Journal Supplement Series, 224:8 (50pp), 2016 May Acero et al.
2.2. Input Source Model Construction
To characterize each candidate SNR we constructed a model
of γ-ray emission in the RoI that includes all significant sources
of emission as well as the residual background from CRs
misclassified as γ-rays. We implemented an analysis method to
create and optimize the 279models for each of the 279RoIs.
For each RoI, we initially included all sources within the 10°
RoI listed in the second Fermi LAT catalog (2FGL; Nolan
et al. 2012), based on two years of source class data. To this we
added pulsars from the LAT Second Pulsar Catalog (2PC;
Abdo et al. 2013), based on three years of source class data,
with 2PC taking precedence for sources that exist in both. For
the diffuse emission we combined the standard IEM corre-
sponding to our P7 data set, gal_2yearp7v6_v0.fits, with the
standard model for isotropic emission, which accounts for
extragalactic diffuse γ-ray emission and residual charged
particles misclassified as γ-rays. Both the corresponding
isotropic model, iso_p7v6source.txt, and the IEM are the same
as used for the 2FGL catalog analysis.
87
Compared to 2FGL, we used an additional year of data and
limited the energy range to 1–100 GeV. This can result in
different detection significances and localizations than pre-
viously reported in 2FGL. To account for these effects, we
recreated the RoIs’ inner 3° radius regions, which encompass
the radio extents of all known SNRs, observed to be
2.6
, and
allows a margin for the LAT PSF. The 68% weighted average
containment radius of the LAT PSF for events at 1 GeV is
∼0°.7 (Ackermann et al. 2012a). We note that this implicitly
assumes that an SNR’s GeV extent should not be more than
about an order of magnitude larger than its radio extension and
also note that the selection biases stated in Green’ s catalog limit
the range of known SNRs ’ radio extensions.
To build the inner 3° radius model for each RoI, we first
removed all sources except identified active galactic nuclei
(AGNs) and pulsars, whose positions on the sky are
independently confirmed by precise timing measurements
(Abdo et al. 2013). Retained AGNs were assigned their
2FGL positions and spectral model forms. Pulsars’ positions
and spectral forms were taken from 2PC. 2FGL sources
identified or associated with SNRs were removed when they
lied within the inner 3°.
We generated a map of source test statistic (TS) defined in
Mattox et al. (1996) via pointlike on a square grid with
0°.1 × 0°.1 spacing that covers the entire RoI. pointlike
employs a binned maximum likelihood method. The source TS
is defined as twice the logarithm of the ratio between the
likelihood
1
, here obtained by fitting the model to the data
including a test source, and the likelihood
0
, obtained here by
fitting without the source, i.e.,
=
T
S2log
10
()
. At the
position of the maximum TS value, we added a new point
source with a PL spectral model:
=
-G +
-
-G
-G+ -G+
dN
dE
N
E
EE
1
,1
max
1
min
1
()
()
where N is the integrated photon flux, Γ is the photon index,
and E
min
and E
max
are the lower and upper limits of the energy
range in the fit, set to 1 GeV and 100 GeV, respectively. We
then performed a maximum likelihood fit of the RoI to
determine N and Γ and localized the newly added source. The
significance of a point source with a PL spectral model is
determined by the
c
n
2
distribution for n additional degrees of
freedom for the additional point source, which is typically
slightly less than
TS
.
88
To promote consistent convergence of the likelihood fit, we
limited the number of free parameters in the model. For sources
remaining after the removal step, described above, we freed the
normalization parameters for the sources within 5° of the RoI
center, including identified AGNs and pulsars. For 2FGL
sources between 5° and 10°,wefixed all parameters. The
spectrum of the IEM was scaled with a PL whose normalization
and index were free, as done in 2FGL. For the isotropic
emission model, we left the normalization fixed to the global fit
value since the RoIs are too small to allow an independent
fitting of the isotropic and Galactic IEM components. The
isotropic component’s contribution to the total flux is small
compared to the IEM’s at low Galactic latitudes.
After localizing them, the new sources were tested for
spectral curvature. In each of the four energy bands between 1
and 100 GeV, centered at 1.8, 5.6, 17.8, and 56.2 GeV, we
calculated the TS value for a PL with a spectral index fixed to 2
and then summed the TS values. We refer to this as
T
S
band fits
.
A value for
T
S
band fits
much greater than the TS calculated with
aPL(TS
PL
) suggests with a more rapid calculation that the PL
model may not accurately describe the source. Analogously to
2FGL (Nolan et al. 2012), we allow for deviations of source
spectra from a PL form by modeling sources with a log-normal
model known colloquially as LogParabola or logP:
⎛
⎝
⎜
⎞
⎠
⎟
=
ab-+
dN
dE
N
E
E
,2
b
EE
0
log
b
()
(())
where N
0
is the normalization in units of photons/MeV, α and
β define the curved spectrum, and E
b
is fixed to 2 GeV.
89
If
-
T
STS25
band fits PL
, we replaced the PL spectral model
with a logP model and refit the RoI, including a new
localization step for the source. We retained the logP model
for the source if the global
log
across the full band improved
sufficiently:
º
T
S2
curve
(
log
-
logP
log
PL
) 16. Otherwise
we returned the source to the PL model that provided the better
global
log
. Across all RoIs, less than 2% of the newly added
sources retained the logP model.
We continued to iteratively generate TS maps and add
sources within the entire RoI until additional new sources did
not significantly change the global likelihood of the fit. The
threshold criterion was defined as obtaining
<
T
S16
for three
consecutively added new sources, denoted as
=
<
N
3
TS 16
.
Despite iteratively adding a source at the location of the peak
position in the TS map, the TS values of new sources may not
decrease monotonically with each iteration for several reasons.
First, source positions were localized after fitting the RoI and
generating the TS map. Second, some added sources were fit
with a more complex spectral model than a simple PL. Finally,
when creating the TS map, we fixed the source’s spectral index
to 2, whereas when adding the actual source to the model, we
allowed its index to vary.
87
Further details on the diffuse emission models are available at http://fermi.
gsfc.nasa.gov/ssc/data/access/lat/BackgroundModels.html.
88
See http://fermi.gsfc.nasa.gov/ssc/data/analysis/documentation/
Cicerone/Cicerone_Likelihood/TS_Maps.html for further details.
89
Note that E
b
is a scale parameter that should be set near the lower energy
range of the spectrum being fit and is usually fixed; see Massaro et al. (2004).
5
The Astrophysical Journal Supplement Series, 224:8 (50pp), 2016 May Acero et al.
