Download Identification of high energy gamma

Identification of high energy
gamma-ray sources and source populations
in the era of deep all-sky coverage
Olaf Reimer
Stanford University
Diego F. Torres
Institut de Ciencies de l‘Espai
EGRET 3rd Catalog:
~270 sources
Anticipated LAT 1st Catalog:
>9000 sources possible
Understanding the challenge
5s Sources in Plane + 4s Sources outside Plane
Four Years Pointed Observations
Solar Flare
AGN
Unidentified Source
Local Group Galaxy
Pulsar
LMC by source extension
PSR by their characteristic periodicity
AGN by correlated MWL activity, spatial
coincidence, and figure-of-merit approach
The assumptions:
3EG catalog sources individually identifiable
As for the remainder, it’s pretty sad:
Only AGN identifications at high latitudes
Halo vs. Plane population sources
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Understanding the challenge
100 seconds
- GRB940217 (100sec)
- PKS 1622-287 flare
- 3C279 flare
- Vela Pulsar
- Crab Pulsar
- 3EG 2020+40 (SNR g Cygni?)
95 minutes = 1 orbit
1 day
- 3EG 1835+59
- 3C279 lowest 5s detection
- 3EG 1911-2000 (AGN)
- Mrk 421
- Weakest 5s EGRET source
• We’re confident that LAT will have
sufficient sensitivity after one day to
detect (5s) the weakest EGRET
sources.
• Anticipated location accuracy will
enable individual MWL identifications.
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Understanding the challenge
Excurse into numbers:
Dermer ‘06
Current limit for PSR detections
(rather an observationally driven accessibility limit)
vastly different numbers
[McLaughlin, Gonthier, Harding, KS Cheng ...]
but significantly large for established
source populations
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Traditional identification techniques
Current strategy for source classification: Bottom – Up
Concept: the largest class of identified gamma-ray sources is blazars, all of which
have radio emission.
IF a flat-spectrum radio source with strong, compact emission at 5 GHz or above
is found in a gamma-ray source error box, it becomes a blazar candidate.
The approach: use radio catalogs to search for flat-spectrum radio sources. If a
candidate is found, follow up with other observations to locate other blazar
characteristics such as polarization and time variability.
The EGRET team used this approach in assigning catalog IDs. Mattox et al.
quantified the method based on proximity and radio intensity. Sowards-Emmerd
et al. have expanded the number of known blazars with this approach.
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Traditional identification techniques
Current strategy for source classification: Top – Down
Concept: at some level, gamma-ray sources will have (non-thermal) X-ray
counterparts.
IF such X-ray counterpart can be “found”, the better X-ray position information
allows deep searches at longer wavelengths.
The approach: X-ray imaging of a individual gamma-ray source error box,
eliminate unlikely X-ray sources based on their X-ray, optical, and radio
properties. Look for a non-thermal source with a plausible way to produce
gamma rays.
The classic example is Geminga. Bignami, Caraveo, Lamb, and Halpern started
this search in 1983. The final result appeared in 1992 with the detection of X-ray
pulsations from this isolated neutron star.
Nowadays we will have adjacent VHE band information for some regions.
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Traditional identification techniques
Current strategy for source classification:
periodicity/correlated variability
Concept: Establish characteristic periodicity or correlated MWL variabiliy
IF other MWL facilities able to provide specific and distinguishable input
The approach: Uniform LAT coverage will enable blind searches and will have
photon/flux history for any location “on disk” – somewhat different concept than
coordinated/contemporeneous observation campaigns
source stacking/spatial correlation studies
Concept: Find common signatures among a members of a class of established or
hypothesized gamma-ray emitter
IF found, subsequent individual MWL identification techniques required
The approach: Governed by physical expectations for gamma-ray emission, a
ranking scheme/spatial arrangement will be investigated for ensemble
characteristics
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Identifying on a case-by-case basis all LAT sources using multiwavelength
techniques with ad hoc simultaneous observations is simply not possible due to
the number of sources (similar to X-ray astronomy)
Approaching a limit
Use of FoM-classifiers (e.g., Mattox et al. 2001 or Soward-Emerds et al. 2003)
can (and will) make a relative order of correctness within what we already know
exist as population among g-ray sources in EGRET
• they will work fine (i.e. providing sound identifications) for the brightest of
the sources (excellent agreement on EGRET, for instance)
• there will be unavoidable ambiguities for less bright sources, especially for
sources along the Galactic plane, with no apparent way of distinguishing
between classes
• AGNs SED are too varied: there is a lack of reliable templates for AGNs
SEDs (and in addition the same source may exhibit large spectral variations
with time)
• it would be up to the community to decide what to believe in a particular
source’ nature, if there are alternatives with similar observational signatures
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Problem 1: when an incomplete catalog
is complete enough?
(e.g. the ongoing problems in
the radio/gamma correlation in
blazars.)
Approaching a limit
Enlarge the catalogs of AGNs and pulsars
and correlate these with LAT the detections
Problem 2: discovery of new populations
would depend on members
identification.
(implies lack of confidence
level for the population as a
whole unless extensive multiyears multi-frequency studies
are performed for many
members.)
without a precise knowledge of which
AGNs and which pulsars are able to emit
gamma-rays, given their respective SEDs
(no real veto system)
Problem
the number of identifications will only be
limited by the number of sources in the
counterpart catalog considered.
3: simultaneity of multiwavelength studies can be
secured for a selected handful of
sources.
(We cannot use this technique to
completely explore a sourcefilled gamma-ray sky)
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Counterparts
What’s problematic here?
Approaching a limit
Let’s review some examples:
An “average” EGRET source: 3EG J1249-8330
[95 =0.66 ° , 2 x 10-7 ph cm-2 s-1]
1)
4 XMM-EPIC pointing -> 148 X-ray sources
2)
statistical evaluation of counterparts
3)
does computing a counterpart probability
La Palumbara et al.
pc = ppos x p(i)SED x p(i)var x p(i)ext x …
will yield a source identification here ?
No, since for N = 94…148 -> pc will be numerically
undistinguishable in the systematics of its computation
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Counterparts
What’s problematic here?
Approaching a limit
LAT will be better psf than EGRET, thus an example
from VHE gamma-rays
HESS J1303-631 (13h03m00.4s±4.4s and δ=−63°11’55”±31”)
at least 5 catalog counterparts listed in several counterpart categories
But source is extended!
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Inverting the problem to strike the eye
Approaching a limit
(now we consider a large number of gamma-ray sources instead of a large number of
counterparts)
In the last BATSE catalog, if one
gives account of the positional error
boxes, there was a detection of one or
more GRB for every line of sight of
any instrument at any wavelength
used to compile any list of possible
counterparts.
Correlation analysis potential is basically lost.
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Understanding the challenge
How far is LAT from the former?
At low Galactic Latitude (no priors)
1000 LAT sources in the Galactic Plane (|b|<10) with 12’ uncertainty = 20% coverage
At high Galactic Latitude (no priors)
103 LAT sources
out of Galactic Plane (|b|>10) with 12’ uncertainty = 0.3% coverage
104 LAT sources
out of Galactic Plane (|b|>10) with 30’ uncertainty = 20% coverage
We need an scheme that allow us to classify populations of sources, and use it
before internal relative scales of the goodness of detected individuals are
applied (within already known populations) to make sure that we do not “overidentify” up to the point were discovering new populations is no longer
possible.
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An appealing goal
An appealing goal for the first year all sky survey should be, in our
opinion, to be able to say
– which kind of populations have been detected in the GLAST sky,
– which is the statistical confidence for the detection of each of
them (systematically quantified using the same technique)
– which are the most likely detected individuals of each class, so
that multi-frequency obs. can proceed with confidence
This classification naturally should extend beyond what’s already
known from EGRET (i.e., pulsars and blazars).
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A new paradigm in gamma-ray astronomy
We suggest the establishment of an a priori protocol of source population
discovery, based on a controlled analysis of positional coincidences.
Torres & Reimer ‘05, ApJ 629, L141
– Three parts are involved:
Theoretical censorship: prohibits executing repeated searches that
would likely reduce the statistical significance of any possible
positive class correlation;
Preserving the discovery potential: that protects the significance by
which one claims the discovery of a number of important population
candidates and that gives guidelines as to how to manage the
probability budget;
Common statistical assessment criteria: that assigns probabilities both
in the large and in the small number statistical regime.
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Part 1: Theoretical Censorship
• We request as part of the criterion that predictions (ideally of multiwavelength
/ multi-messenger character) are available for a subset of the proposed
counterpart class.
• This request is made to avoid the blind testing of populations that may or may
not produce gamma-rays, but for which no other than a positional correlation
result can be a posteriori achieved.
• If there is no convincing theoretical indication that a population can emit
gamma-rays before making the search, such population should not be sought.
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Part 2: Discovery protection
• If one probe a large number of samples, and make an equally large number
of trials with the same instrument detections, one will find positive
correlations, at least as a result of statistical fluctuations.
• To claim significance, one would have to check if the penalties that must be
paid for such a finding (i.e., the fact that there were a number of trials that
led to null results) does not overcome the significance achieved. This may
turn out to be practically impossible (if there is not an a priori established
source selection).
• We are in favor of defining the populations that are to be tested, and the
testing protocol, before actual data taking.
Lessons to learn from ultra high energy cosmic ray physics: few events, large number of claims,
many of them plainly wrong (see discussion by Torres, Reimer, et al. ApJ 595, L13, 2003)
Part 2: Simple basis for a protocol
Suppose for definiteness that the total budget is a chance probability equal to B, and that we
want to test A,B,C,... classes of different sources.
The total budget can then be divided into individual chance probabilities, PA, PB, etc., such
that the sum of Pi=B.
Population i will be claimed as detected if the a posteriori experimental probability for its
random correlation, Pexp(i), is less than the a priori assigned Pi (as opposed to be less only
than the larger, total budget. Important!: this allows to discover simultaneously different
populations)
We can then manage the budget of probabilities: For some populations we can less
confidently agree that they will be detected, or for some others, the number of their
members may be low enough such that a detection of few of its individuals would be
needed to claim a great significance. In this situation, we would choose a relatively large Pi,
so as to make easier for the test to pass. For others, say AGNs and pulsars, we can assign a
relatively small Pi in such a way to make harder for the test [whether the inequality Pexp.(i) <
Pi is fulfilled] to pass, and that they take less of the total budget.
If one or more of the tests are passed, the results are individually significant because first we
protected our search by the a priori establishment of the protocol (it was a blind test) and
second, because the overall chance probability is still less than the total budget B.
In the example below we choose to test High Latitude Molecular Clouds and Starbursts with
40% of the high latitude budget each. These are new populations, if discovered, so we want to
privilege the chance of spotting them.
If B=10-4 then Pexp(HLC) < 0.4 B in order for the population to be claimed as discovered (the
significance level of that is discussed below)
For others, say classes of AGNs, we can assign a relatively small P(AGN) in such a way to
make harder for the test [whether the inequality Pexp.(AGN) < P(AGN) is fulfilled] to pass, and
that they take less of the total budget. In this example, P(FSRQ) = 0.1 B. FSRQs is not a new
population, so we don’t want to spend our budget on them: it is exactly the same as requiring a
very high confidence level for the discovery of this population.
FSRQs
BL Lacs
HLCs
Starbursts
Part 3: Quality evaluation
C(A) number of members of candidates that coincide with unidentified detections.
N(A) number of known sources in the particular candidate population A under analysis
U number of detections.
P probability that in a random direction of the sky we find a gamma-ray source.
As we have seen earlier, P is not overwhelmingly large (uniform distribution with no
priors gives P less than a few percent for less than 10 000 sources). A more careful
treatment will reduce the value of P from these simple estimations.
Such low values for P make the product P x N(A) typically in the range 1-100, for all
different candidate populations. We can refer to this product as noise expectation.
Then the excess number of coincidences over the noise is:
E(A)=C(A) - P x N(A).
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E(A)
= C(A) - P x N(A).
Excess = Coincidences -
Noise
Pulsars and blazars will present the largest number of positional coincidences.
Let us assume that there are 2000 catalogued AGN; with P ~10-2 or 10-3, all coincidences in
excess than 6-60 are beyond the random expectation.
Now, C(AGN) >> P x N(A), and thus the number of excesses would be large: we are in the
domain of a large number statistics and a probability for the number of excesses to occur by
chance, Pexp(AGN) could be computed.
When both terms in in the expression for E(A) are small quantities (small number statistics): we
should test the null hypothesis for a new source population against a reduced random noise.
Methods such as Feldman & Cousins (1998) or Gehrels (1986) are useful to assess quality in this
case and obtain Pexp(A)
An example of a null hypothesis is “X-ray binaries are not LAT sources”. We have 0 predicted
signal events (coincidences) and P x N(A) background. With N(A) ~ 200 and P~ 3 x 10-3,
detecting more than 5 coincidences rules out the null hypothesis at 95% CL. If the budgeted P(X
–ray bin.) < Pexp (X –ray bin.) have uncovered a new population of sources with 95% CL.
What has to be considered?
• Which are the populations to be tested? how large should the a
priori probability be for each of them? how to best compute
the random probability P? how large the total budget B should
be? all must be answered to completely determine the
protocol.
• By researching and ultimately establishing a protocol along
these lines, the problem of identifying the classes of gammaray sources can be looked at in a sound way, with individually
high levels of confidence and collective low random
probability.
• This would immediately open the possibility of centering
efforts into case-by-case studies, but knowing that the class
has been detected with, say 95% CL.
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Variability certainly helps, but...
If there is a previous prediction of a periodic signal of the flux, that alone unambiguously
label the source. Ok.
But: This will happen for only a very very small fraction of detections: absence
of completeness in the pulsar timing parameters, and shortage of precise variability
predictions for accretion powered X-ray binaries.
Even if a theoretically compatible variability timescale appears, if we have not
identified the class of sources to which the sought counterpart pertains, that in itself will
constitute the reason by which to justify the need of follow-up observational campaigns.
In any case, most of the sources will either be steady or show no definitive variability
timescale. And worse, for most classes of sources, we theoretically expect no variability.
Sensitivity and completeness of catalogs is not always good
Not having complete catalogs of “identified” populations is not something to fear, but the
reflection of a discovery opportunity.
We know we are already missing one or several new source populations, both at low and at
high Galactic latitudes
There are strong indications of variable and non-variable, non-periodic, point-like and
extended, low latitude sources, as well as of non-variable, high latitude, extended sources,
all of which are beyond the expected behavior of pulsars and AGNs.
If many (all) sources were to be correlated with AGNs, for instance, only a case by case
analysis could show that the classification by position only is wrong. But remember, GLAST
will see >1000 sources!