The data acquisition and trigger electronics of the ATLAS liquid argon calorimeter will be fully replaced as part of the second phase of upgrade of the ATLAS detector. The new backend electronics will be based on high-end FPGAs that will compute on-the-fly the energy deposited in the calorimeter before sending it to the trigger and data acquisition systems. New state-of-the-art algorithms, based on neural networks, are being developed to compute the energy and improve its resolution in the harsh conditions of the HL-LHC.

The candidate is expected to take a role in the development of data processing algorithms allowing to efficiently compute the energies deposited in the LAr calorimeters in the high pileup conditions expected at the HL-LHC. These algorithms will be based on AI techniques such as recurrent neural networks will be adapted to fit on hardware processing units based on high-end FPGAs. The successful candidate will be responsible of designing the AI algorithms, using python and keras, and assessing their performance. The candidate will also assess the effect of employing such algorithms for electromagnetic object reconstruction (especially at trigger level). She/he will work closely with the engineers designing the electronic cards at CPPM in order to adapt the AI algorithm to the specifics of FPGAs. Candidates with a strong interest for hardware will be encouraged to take part in the design of the firmware to program the FPGAs.

Prior knowledge of keras, python and C++ is desirable but not mandatory.

The last piece of the standard model of particle physics, the Higgs boson, was discovered by the ATLAS and CMS collaborations in 2012. The newly discovered boson provides a unique possibility to search for new unknown physics beyond the standard model. The ATLAS group at CPPM had a leading role in detecting and studying the Higgs boson properties in several of its production and decay modes. The group is currently concentrating on the detection of the production of two Higgs bosons or two scalar bosons, a process that was never observed before.

This internship will concentrate on the study of the production of a scalar boson with a Higgs boson decaying to a pair of photons and a pair or b-quarks. The detection of such process is a strong proof of the existence of new physics and of the modification of the electroweak symmetry breaking as described by the standard model. The run 3 of the LHC, currently in operation, will provide enough data (in combination with previous data) to improve the discovery potential of such process. The analysis of the run 3 data is being prepared now by a group of several institutes around the world that collaborate at CERN. The successful candidate will work within this collaboration and will take part of preparing and studying simulation samples that describes this physics process. The candidate will work within a team of three researchers and one PhD student at CPPM. He/she will analyze the kinematic and topological distributions of simulated events in order to validate the simulation and improve the selection of signal events and separate them from background. Prior knowledge of programming language especially C++/root or python is an advantage but is not mandatory.

The predictivity of the Standard Model (SM) of particle physics remains unchallenged by experimental results. After the tantalizing discovery of the Higgs boson at LHC, the measurements of properties such as its mass, spin, parity and its couplings with other SM particles have confirmed its SM-like nature. This goes hand in hand with the absence of direct signs of TeV physics beyond the SM from current direct searches.

Indeed, the excellent performance of the LHC in terms of delivered luminosity allowed the ATLAS and CMS experiments to set stringent limits on new particle masses well beyond the EW scale, thus worsening the naturalness problem. If the new physics scale lies well above the present experimentally probed energies, one would be left with the only experimental perspective of searching for deviations within the LHC precision measurements, and with no solid theoretical explanation of why the new physics should be so unnaturally heavy. There is, however, another logical possibility: new physics may be hidden at lower energies although weakly coupled to the SM known particles, so that its signals could be swamped in the SM background.

To process the enormous amount of data provided by the LHC, ATLAS uses an advanced ?trigger? system to tell the detector which events to record and which to ignore. The ATLAS trigger is a two-level system composed of the first level, Level 1 (Level-1) trigger implemented in custom hardware, and High Level Trigger (HLT) which relies on selections made by algorithms implemented in software. The trigger is designed in such a way that an initial rate of collisions of 40 MHz is decreased to about 100 kHz after L1 and further decreased to 3 kHz at the HLT. This is a harsh limit on the possibility of recording low energetic events, swamped by high rate background, where signal of new physics could be hidden.

The Phase-I ATLAS Level-1 calorimeter trigger consists of a series of upgrades in order to face the challenges posed by the Run 3 LHC luminosity. The trigger upgrade benefits from new front-end electronics for parts of the calorimeter that provide the trigger system with digital data with a tenfold increase in granularity. This makes possible the implementation of more efficient algorithms to maintain the low trigger thresholds at much harsher LHC collision conditions.

The candidate will work on phenomenological aspects, aimed at characterizing the relevant BSM models that can produce low mass signatures and to define a trigger strategy that could maximize the sensitivity of ATLAS searches for this signal in the years to come.

The predictivity of the Standard Model (SM) of particle physics remains unchallenged by experimental results. After the tantalizing discovery of the Higgs boson at LHC, the measurements of properties such as its mass, spin, parity and its couplings with other SM particles have confirmed its SM-like nature. This goes hand in hand with the absence of direct signs of TeV physics beyond the SM from current direct searches.

Indeed, the excellent performance of the LHC in terms of delivered luminosity allowed the ATLAS and CMS experiments to set stringent limits on new particle masses well beyond the EW scale, thus worsening the naturalness problem. If the new physics scale lies well above the present experimentally probed energies, one would be left with the only experimental perspective of searching for deviations within the LHC precision measurements, and with no solid theoretical explanation of why the new physics should be so unnaturally heavy. There is, however, another logical possibility: new physics may be hidden at lower energies although weakly coupled to the SM known particles, so that its signals could be swamped in the SM background.

To process the enormous amount of data provided by the LHC, ATLAS uses an advanced “trigger” system to tell the detector which events to record and which to ignore. The ATLAS trigger is a two-level system composed of the first level, Level 1 (Level-1) trigger implemented in custom hardware, and High Level Trigger (HLT) which relies on selections made by algorithms implemented in software. The trigger is designed in such a way that an initial rate of collisions of 40 MHz is decreased to about 100 kHz after L1 and further decreased to 3 kHz at the HLT. This is a harsh limit on the possibility of recording low energetic events, swamped by high rate background, where signal of new physics could be hidden.

The Phase-I ATLAS Level-1 calorimeter trigger consists of a series of upgrades in order to face the challenges posed by the Run 3 LHC luminosity. The trigger upgrade benefits from new front-end electronics for parts of the calorimeter that provide the trigger system with digital data with a tenfold increase in granularity. This makes possible the implementation of more efficient algorithms to maintain the low trigger thresholds at much harsher LHC collision conditions.

The candidate will work on the analysis of the LHC data recorded in 2022 and 2023 to assess the quality of the data recorded by the upgraded ATLAS calorimeter system. A work on phenomenological aspects, aimed at characterizing the relevant BSM models that can produce low mass signatures is also foreseen.

ATLAS is one of the four main detectors at the Large Hadron Collider (LHC) and one of the two general-purpose detectors. It has a cylindrical symmetry that covers almost the entire solid angle and the onion-like layers of sub-detectors allow for unambiguous reconstruction of the final state objects from the high energy collisions.

Electrons are fundamental objects that are reconstructed and identified by the ATLAS detector. They are reconstructed from tracks in the inner detector and matching energy deposits in the electromagnetic calorimeter. Ensuring high electron reconstruction efficiency is crucial for the ATLAS physics programs. Therefore, assessing the performance of the electron reconstruction is very important. Additionally, measuring the electron reconstruction efficiency in both data and Monte Carlo (MC) simulations allows deriving correction factors (or Scale Factors) that are used throughout the ATLAS physics analyses to correct the electron reconstruction efficiency in MC simulations to the efficiency observed in data.

The candidate will work closely with the e/gamma team on performing the measurement of electron reconstruction efficiency with the freshly collected data in 2022 and early 2023. Besides the performance assessment, the selected candidate will work on the optimization of the existing code that performs the measurement, as well as work on improving the systematic uncertainties of the measurement.

Good command of Python and C++ programming languages would be an advantage but their prior knowledge is not mandatory.

Being forbidden in the Standard Model (SM) of particle physics,lepton flavor violating decays are among the most powerful

probes to search for physics beyond the SM. In view of the recent anomalies seen by LHCb on tests of lepton flavor

universality in $b \to s \ell \ell$ and $b \to c\ ell \nu$ processes, the interest of tau lepton flavor violating

decays has been greatly reinforced. In particular, several new physics models predict branching

fractions of $\tau \to \ell V^{0}$ and $\tau \to \ell \ell \ell$ just below the current experimental limits.

The Belle II experiment located at KEK, Japan, has started the physics data taking in 2019, and is aiming at

50 times more data than its predecessor. Thanks to its clean environment and high $\tau^+ \tau^-$ cross section,

it provides an ideal environment to study tau decays. The CPPM group searches for lepton flavour violating $\tau$ decays,

such as $\tau^{\pm} \to \ell^{\pm} V^{0}$ or $\tau^{\pm} \to \ell^{\pm} \ell^{\mp} \ell^{\pm}$ , with V0 being a neutral vector meson and

$\ell^{\pm}$ an electron or muon.

The goal of this internship is to develop and use a Graph Neural Network (GNN) to reject background events.

Other architectures and implementations could be studied as well. The candidate will prepare data for training samples,

get familiar with the GNN, assess its performances and explore various formulation of the problem and different architectures.

The final objective is to use the GNN in the analyses of the channels studied in the group.

This internship can be continued with a PhD thesis.

Application including a CV, grade records and a motivation statement should be sent to giampi@cppm.in2p3.fr and serrano@cppm.in2p3.fr.

References:

https://arxiv.org/abs/1808.10567

https://hflav-eos.web.cern.ch/hflav-eos/tau/spring-2017/lfv-limits-plot.html

https://arxiv.org/abs/1903.11517

https://arxiv.org/pdf/1806.05689.pdf

https://arxiv.org/abs/2208.14924

Dark matter is one of the main puzzles in fundamental physics today. Indeed, its contribution to the total mass of the Universe would be about 85%, but it cannot be explained in the framework of the Standard Model (SM) of particle physics. Several candidates, however, exist in theories beyond the SM, and the WIMP (weakly interacting massive particle) is one of the best motivated, as it allows to also solve the SM hierarchy problem, directly linked to the stability of the Higgs boson mass.

Experiments searching directly for dark matter thus use the dark matter halo of our galaxy as a potential source of WIMPs. Since 2010, the most sensitive detection technology is based on the measurement of the scintillation light emitted when a WIMP scatters off an atom of noble liquid – argon or xenon. The DarkSide-20k experiment (DS-20k) is currently being installed 1.4~km underground in the Gran Sasso laboratory in Italy. It will use a time projection chamber (TPC) filled with 50 tons of purified argon and read out by 200,000 silicon photomultipliers (SiPMs). This will allow to have the world leading discovery potential for WIMPs after a few years of data taking. The current work is dedicated to the construction of the detector, and data taking should start in 2027. The increase of the liquid argon volume, compared with previous experiments, will allow DS-20k to have the best sensitivity of all liquid argon detectors with only one month of data.

The goal of this internship is to prepare for data taking. Once light is collected by the SiPMs it must be treated, “reconstructed”, to extract for instance the location and energy of the collision within the TPC volume. The student will participate in the simulation and improvement of data reconstruction algorithms with innovative machine leaning techniques (e.g., neural networks), to optimize the separation between signal and backgrounds. It is possibly foreseen to also take part in the analysis of calibration data taken with small neutron detectors in December 2023, in order to improve the DS-20k potential. These activities will allow to become familiar with instrumental aspects, software and data analysis in a particle physics experiment.

This project could be continued as part of a PhD thesis (already funded by Agence Nationale de la Recherche): https://www.cppm.in2p3.fr/web/en/jobs/phd/index.html#Doctorat-2427-DS-01

More details about the CPPM Dark Matter team: https://www.cppm.in2p3.fr/web/en/research/particle_physics/#Dark%20Matter

The origin of galactic cosmic rays is one of the main open questions in high energy astrophysics. PeVatrons are objects capable of accelerating particles up to the PeV (=1015 eV) energies and are therefore considered the galactic cosmic ray accelerators. The principal signature of PeVatrons is ultrahigh-energy (exceeding 100 TeV) gamma radiation. The search for PeVatrons has recently been boosted by the discovery of several ultrahigh energy gamma-ray sources by the Large High Altitude Air Shower Observatory (LHAASO) [1].

Recently, the Supernova Remnant (SNR) G106.3-2.7 has been indicated as a highly promising PeVatron candidate [2]. In fact, G106.3-2.7 emits gamma-rays up to 500 TeV from an extended region (~0.2o) well separated from the SNR pulsar (J2229+6114) and in spatial correlation with a local molecular cloud.

The CTA (Cherenkov Telescope Array) is a worldwide project to construct the next generation ground based very high energy gamma ray instrument. CTA will use tens of Imaging Air Cherenkov Telescopes (IACT) of three different sizes (mirror diameter of 4 m, 12 m and 23 m) deployed on two sites, one on each hemisphere (La Palma on the Canary Islands and Paranal in Chile). The observatory will detect gamma-rays with energy ranging from 20 GeV up to 300 TeV by imaging the Cherenkov light emitted from the charged particle shower produced by the interaction of the primary gamma ray in the upper atmosphere [3,4].

The CTA observatory completion is foreseen in 2025 but the first Large-Sized Telescope (LST1) is already installed and taking data in La Palma.

While the LST1 telescope cannot reach enough sensitivity to access energies above 100 TeV, it can provide precise angular resolution data for establishing the spectral morphology of this exciting PeVatron candidate in the 1-50 TeV energy region. Observations of G106.3-2.7 have started in 2022 and are presently on-going.

By using the Instrument Response Functions of the LST1 telescope, that have been recently determined within the CPPM group, the student will simulate the G106.3-2.7 source considering different morphological and spectral models based on recent phenomenological studies. The first goal will be to identify the capability of LST1 in determining the morphology of the source. Subsequently the simulated energy dependent sky maps will be compared to first observations with LST1 and this work will help to guide the data analysis and interpretation of these first early science data.

The student will use the official science tool of CTA (gammapy [5]) written in Python. The candidate should therefore have basic knowledge of Python programming.

This internship can eventually be continued with a PhD thesis.

References:

[1] Cao, Z., Aharonian, F.A., An, Q. et al. Ultrahigh-energy photons up to 1.4 petaelectronvolts from 12 ?-ray Galactic sources. Nature 594, 33–36 (2021).

[2] Z. Cao et al. Nature, 594, 33–36 (2021); M. Amenomori et al. Nature Astronomy, 5, 460–464 (2021)

[3] https://www.cta-observatory.org/

[4] Science with the Cherenkov Telescope Array: https://arxiv.org/abs/1709.07997

[5] https://docs.gammapy.org/1.1/

The CTA (Cherenkov Telescope Array) is a worldwide project to construct the next generation ground based very high energy gamma ray instrument [1]-[2]. CTA will use tens of Imaging Air Cherenkov Telescopes (IACT) of three different sizes (mirror diameter of 4 m, 12 m and 23 m) deployed on two sites, one on each hemisphere (La Palma on the Canary Islands and Paranal in Chile). The observatory will detect gamma-rays with energy ranging from 20 GeV up to 300 TeV by imaging the Cherenkov light emitted from the charge particle shower produced by the interaction of the primary gamma ray in the upper atmosphere.

The unconventional capabilities of CTA will address, among others, the intriguing question of the origin of the very high energy galactic cosmic rays by the search of galactic sources capable of accelerating cosmic rays up to the PeV, called PeVatrons. Recently, the Supernova Remnant (SNR) G106.3-2.7 has been indicated as a highly promising PeVatron candidate [4]. In fact, G106.3-2.7 emits gamma-rays up to 500 TeV from an extended region (~0.2o) well separated from the SNR pulsar and in spatial correlation with a molecular cloud.

The CTA observatory completion is foreseen in 2025 but the first large size telescope (LST1) is already installed and taking data in La Palma. LST1 is placed very close to the two MAGIC telescopes [3], which are one of the presently active IACT experiments. This configuration permits to perform joint observations of the same source with the three telescopes LST1+MAGIC. Joint acquisition not only increases the effective detection area but also improves the energy and angular resolution, thanks to the enhanced stereo data. While the LST1+MAGIC telescopes cannot reach enough sensitivity to access energies above 100 TeV, they can provide exclusive and unprecedented data for establishing the spectral morphology of this exiting PeVatron candidate in the 100 GeV-100 TeV energy range. A campaign of joint observations of G106.3-2.7 will start in 2022.

This internship concerns the setup of the reconstruction chain for G106.3-2.7 on the base of Monte Carlo data. In order to maximise the effective area at very high energy, G106.3-2.7 observation will be performed at large zenith angle (62o-70o), which is a challenging detection condition and asks to perform a preliminary verification of the detection performances. The student will first estimate the expected “Instrument Response Function” with the standard LST1 reconstruction [5], using the mono telescope approach. Then, s/he will estimate the performance of the MAGIC + LST1 stereo reconstruction with the joint reconstruction pipeline [6], presently under development. Eventually, the student will simulate the signal expected from the source with the two configurations.

The candidate needs a medium knowledge of the python programming language.

Candidates should send their CV and motivation letter as well as grades (Licence, M1 as well as their M2 if available) to cassol@cppm.in2p3.fr

A PhD contract can eventually follow the internship, it will be centred on the analysis of real data from G106.3-2.7, acquired in 2022 and the following years.

References:

[1] Science with the Cherenkov Telescope Array: https://arxiv.org/abs/1709.07997;

[2] https://www.cta-observatory.org/

[3] MAGIC Collaboration, Aleksi?, J. et al. Astropart. Phys. 72 (2016) 76–94.

[4] Z. Cao et al. Nature, 594, 33–36 (2021); M. Amenomori et al. Nature Astronomy, 5, 460–464 (2021)

[5] https://github.com/cta-observatory/cta-lstchain

[6] https://github.com/cta-observatory/magic-cta-pipe

KM3NeT/ORCA (Oscillation Research with Cosmics in the Abyss) is a deep sea

neutrino telescope currently under construction at a depth of 2500m in the

Mediterranean Sea off the coast of Toulon. ORCA is optimised for the detection of

low energy (3-100 GeV) atmospheric neutrinos and will allow precision studies

of neutrino oscillation properties. ORCA is part of the multi-site KM3NeT research infrastructure, which also incorporates a second telescope array (in Sicily) optimised for the detection of high-energy cosmic neutrinos.

The first ORCA detection strings have been operating for more than a year and are providing high quality data. During this stage the student will apply machine learning techniques to the data analysis with the aim to improve the angular and energy resolutions of the current event reconstruction algorithms. It is expected the candidate will follow this stage with a PhD on measuring the neutrino oscillation parameters.

Links:

http://antares.in2p3.fr

http://www.km3net.org

http://www.cppm.in2p3.fr/rubrique.php3?id_rubrique=259

Doing neutrino astronomy is a long quest in astroparticle physics. IceCube and ANTARES have found the first evidences of a few neutrino sources, mainly related to blazars (active galactic nuclei with their jets posting toward the Earth) and tidal disruption events. Most of those explosive events can release enormous amounts of energy both in electromagnetic radiation and in non-electromagnetic forms such as neutrinos and gravitational waves. This is at the basis of multi-messenger astronomy.

KM3NeT, the second generation neutrino detectors in the Mediterranean Sea, is in construction. It is taking data with a sensitivity much larger than ANTARES in the whole energy range, from GeV to PeV thanks to the complementarity of the 2 detectors: ORCA and ARCA. Already with the 30-40 detection units in operation, KM3NeT has significant better performances, either in term of effective area or in term of angular resolution.

In CPPM, we are mainly working on the implementation of multi-messenger analyses with high-energy neutrinos detected with ANTARES and KM3NeT neutrino telescopes. In this context, we are developing an analysis framework that is able to receive and process a time and spatial correlation analysis with high-energy neutrinos in coincidence with selected potential external triggers. Those analyses can be performed in real-time or offline including the most refined knowledge of the detector. In the last years, IceCube has provided alerts from selected high-energy neutrinos, and for some of them, a bright blazar has been located in the error box of the neutrino and found in active state with concomitant multi-wavelength observations.

During this internship, the student will perform an optimised analysis of the KM3NeT data for those interesting associations. It consists of the development of a neutrino selection based on the outputs of the event reconstructions and the event topology classifiers. This selection can be made with machine learning tools. After the event selection, the student will implement the correlation analysis.

This internship can be continued with a PhD in our group on the multi-messenger analyses with KM3NeT.

The analyses will be performed using C++ or python.

KM3NeT/ORCA (Oscillation Research with Cosmics in the Abyss) is a deep sea neutrino telescope currently under construction at a depth of 2500m in the Mediterranean Sea off the coast of Toulon. ORCA is optimised for the detection of atmospheric neutrinos in the energy range 3-100 GeV and will allow precision studies of neutrino properties. Currently the detector takes data with 11 detection strings hosting more than 6000 photomultiplier tubes.

During this internship at the Centre de Physique des Particules de Marseille, the student will analyse data taking with the ORCA detector in the period 2020 to 2022. The goal is to obtain detector response functions for neutrinos in the relevant energy range and to derive sensitivities for the observation of sterile neutrinos. Software tools which have been developed at CPPM will be used.

Links:

http://www.km3net.org

http://www.cppm.in2p3.f/rubrique.php3?id_rubrique=259

The Neutrino Team at CPPM is strongly involved in the KM3NeT/ORCA neutrino telescope, under construction in the abyss (-2500m) of the Mediterranean sea, 40km offshore Toulon. The first detection units that have been deployed are successfully collecting data. The detector is now large enough to access yet unexplored physics territories. A very exciting topic is the search for tau neutrinos appearing in the neutrino flux created in the collisions of cosmic rays in the atmosphere. The appearance probability is poorly known and KM3NeT/ORCA has a unique potential to measure it. Such measurements could lead to a major discovery regarding the existence of sterile neutrinos.

One of the keystones for these studies is the tag of the neutrino flavours (electron, muon, or tau); hence, in this project, the student will develop Machine Learning algorithms to perform this kind of identifications. The expected skills are to master the basics of neutrino oscillation and to program in python, c++, ROOT.

Doing neutrino astronomy is a long dream in astroparticle physics. IceCube and ANTARES have found the first evidences of a few neutrino sources, mainly related to blazars (active galactic nuclei with their jets posting toward the Earth) and tidal disruption events. For most of those explosive events can release enormous amounts of energy both in electromagnetic radiation and in non-electromagnetic forms such as neutrinos and gravitational waves. This is at the basis of multi-messenger astronomy. KM3NeT, the second generation neutrino detectors in the Mediterranean Sea, will have significant better performances, either in term of effective area or in term of angular resolution.

In CPPM, we are mainly working on the development of multi-messenger analyses with high-energy neutrinos detected with ANTARES and KM3NeT neutrino telescopes. In this context, we are developing a real-time analysis framework that is able to send neutrino alerts and to receive and process a cross-match analysis with high-energy neutrinos in coincidence with selected potential external triggers.

During this intern ship, the student will implement a neutrino selection module that takes in inputs the reconstructed and classified neutrino streams. To reach a sustainable false alert rate (1-2 per month), It will be necessary to filter on the topology of the events, the multiplicity, the energy and the estimate of the reconstruction error. The student will have to implement such module based on machine learning tool.

The analyses will be performed using C++ or python.

Description

Deadline to apply: December 30th

Scientific Context

According to modern physics, matter should not have emerged from the Big Bang and its origin remains one of the most profound riddles in fundamental physics. The heart of this mystery is the CP symmetry, i.e. the fact that the laws of physics are the same for matter and anti-matter. During the Big Bang, this symmetry should have maintained particles and anti-particles in equal quantities while they were gradually annihilating each other leaving, at the end, nothing but pure energy. The existence of matter thus requires the CP symmetry to be violated, which is one of the so-called Sakharov conditions (Sakharov, 1967).

The works awarded by the 2015 Nobel Prize imply that neutrino physics can break this symmetry via a quantum phenomenon called neutrino oscillations. Neutrinos can be produced in three types – or flavors – the electron neutrino ( $\nu_{e}$ ), the muon neutrino ( $\nu_{\mu}$ ) and the tau neutrino ( $\nu_{\tau}$ ). Experimental evidence showed that the flavor of neutrinos oscillates when they propagate. In theory, this oscillation can be different for neutrinos and anti-neutrinos which would break the CP symmetry. Discovering such an effect would be a major breakthrough in fundamental physics. Intense experimental efforts (Acciarri at al., 2015; Abe et al., 2018) are thus ongoing worldwide to study the neutrino oscillations. However, the experimental techniques used so far are reaching their limits (Branca, et al., 2021). New techniques are thus urgently needed. The goal of this Master 2 project is to design such a new technique: the neutrino tagging (Perrin-Terrin, 2022).

Neutrino Tagging

At accelerator-based experiments, neutrinos are produced by colliding protons on a target. These collisions produce secondary particles, in particular, $\pi^+$ and $\pi^-$ , which decay as $\pi^+ \to \mu^+ \nu_\mu$ and $\pi^- \to \mu^- \bar\nu_\mu$ and so produce a $\nu_\mu$ and anti-$\nu_\mu$ beam. The optimal propagation distance to observe the neutrino oscillations depends on the neutrino energy and ranges between 100~km to 1000~km. A neutrino detector consisting of an instrumented target is installed at this distance to measure the neutrino flavor and energy. Conventionally, the neutrino characteristics are measured from their interaction in a densely instrumented detector, as shown in Fig 1-(left). The complexity of the interaction mechanisms induces strong limitations on the precisions of the energy measurements.


The tagging technique (Perrin-Terrin, 2022) proposes to determine the neutrino characteristics using the production mechanisms, as shown in Fig 1-(right). These mechanisms, the $\pi \to \mu \nu$ decays, are extremely simple processes. Hence, once the $\pi$ and $\mu$ characteristics (time, momentum, charge: t,p,±) are measured, a simple kinematical relation allows to derive precisely the neutrino characteristics. For example, while the precision of the neutrino energy measurement based on the interaction plateaus at about 15%, the one of the tagging can easily reach 1%. In these conditions, the only task left to the neutrino detector is to identify the flavor of the neutrino after propagation. These relaxed requirements allow to use seawater neutrino detectors such as KM3NeT/ORCA (Adrian-Martinez et al., 2016) under construction at a depth of 2450~m offshore Toulon. These detectors are extremely large (several Mton) which increases the probability for neutrinos to interact in them. The key element of the technique is the tagger, i.e. the detector based on which the neutrino properties are estimated. The tagger will be composed of several planes of cutting-edge high time precision silicon pixel detectors (Lai, 2018) able to sustain the extremely high rate of particles in the beam line: 1012 per second! A proof-of-concept of this technique is being performed using the NA62 experiment (Cortina Gil at al., 2017) at CERN as a miniature neutrino experiment (Martino, 2022).

Objectives of the Project

The project aims to co-design the detector layout (time resolution, number of planes and location etc.) and the algorithms to estimate the neutrino characteristics. Achieving this objective will require to:

• design a statistical model describing the physical setup,
• develop a simulation of the detector,
• calculate the optimal performance bounds,
• optimize the detector setup based on the calculated bounds,
• test/apply the model and bounds to NA62.

Working Environment

The student will be based at IM2NP, located at the La Garde campus in Toulon, or/and at CPPM located in the Luminy campus in Marseille, at the entrance of the Parc National des Calanques. Both places are located near remarkable natural sites and offer pleasant working and living conditions.

The student will integrate the Signal And Tracking (STr) Team of IM2NP and the Neutrino team of CPPM. The STr team is composed of ~10 people who are specialized in applied statistics and signal processing for different domains (astrophysics, optics, RADAR, SONAR, LIFI, etc. (Roueff, Arnaubec, Dubois-Fernandez, Refregier, 2011; Roueff, et al., 2020; Roueff, Roux, Réfrégier, 2009)). The team at CPPM is composed of ~30 people, researchers, postdocs, PhD students, engineers and technicians with a large panel of skills and working on the construction and exploitation of the KM3NeT detectors. A PhD student is also working on the proof-of-principle of the neutrino tagging technique at NA62.

The student will be involved in a dynamic international team. The project will be developed in close collaboration with CERN. Indeed, the tagger studied in this project is meant to be installed in the neutrino beam line that CERN has started to study to for the tagging. Regular video-conference meetings which CERN collaborators will be held during the project, and short trips to CERN for in-person meetings can also be envisaged.

The last step of the project (application and test at NA62) will be done in collaboration with several members of the NA62 experiment at University of Birmingham (UK), Ecole Fédérale Polytechnique de Lausanne (Switzerland) and in Université Catholique de Louvain (Belgium) who are all actively contributing to the proof-of-principle of the neutrino tagging technique.

Student Profile

We are seeking for a highly motivated student who could consider continuing this work for a PhD thesis. The student should ideally have:

• basic knowledge of experimental particle physics,
• skills in applied statistics,
• previous experience in machine learning techniques using c/c++, matlab or python,
• oral and written proficiency in English and French.

Application Procedure

The student interested in applying for the internship must provide:

• CV
• Motivation letter
• Grades from M1
• Available grades from M2
• Desired internship duration
• Desired internship starting date (optional)
• Reference letter or reference contact (optional)

This information should be sent to antoine.roueff@univ-tln.fr and mathieu.perrin-terrin@cern.ch before December 30th. Interviews will be conducted beginning of January.

PhD Perspectives

Beyond this Master Project, the student could then enroll in a PhD thesis in the context of the KM3NeT experiment, the NA62 experiment and the neutrino tagging studies at CERN.

Bibliography

Abe, K., at al. (2018, May). Hyper-Kamiokande Design Report. Hyper-Kamiokande Design Report.

Acciarri, R., at al. (2015). Long-Baseline Neutrino Facility (LBNF) and Deep Underground Neutrino Experiment (DUNE).

Adrian-Martinez, S., at al. (2016). Letter of intent for KM3NeT 2.0. J. Phys., G43, 084001. doi:10.1088/0954-3899/43/8/084001

Branca, A., Brunetti, G., Longhin, A., Martini, M., Pupilli, F., Terranova, F. (2021). A New Generation of Neutrino Cross Section Experiments: Challenges and Opportunities. Symmetry, 13, 1625. doi:10.3390/sym13091625

Cortina Gil, E., at al. (2017). The Beam and detector of the NA62 experiment at CERN. JINST, 12, P05025. doi:10.1088/1748-0221/12/05/P05025

Lai, A. (2018). A System Approach towards Future Trackers at High Luminosity Colliders: the TIMESPOT Project. (pp. 1–3). Sydney: IEEE. doi:10.1109/NSSMIC.2018.8824310

Martino, B. D. (2022, July). Tagged Neutrino Beams. Tagged Neutrino Beams. Zenodo. doi:10.5281/zenodo.6785370

Perrin-Terrin, M. (2022). Neutrino tagging: a new tool for accelerator based neutrino experiments. Eur. Phys. J. C, 82, 465. doi:10.1140/epjc/s10052-022-10397-8

Roueff, A., Arnaubec, A., Dubois-Fernandez, P. C., Refregier, P. (2011). Cramer–Rao Lower Bound Analysis of Vegetation Height Estimation With Random Volume Over Ground Model and Polarimetric SAR Interferometry. IEEE Geoscience and Remote Sensing Letters, 8(6), 1115–1119. doi:10.1109/LGRS.2011.2157891

Roueff, A., Gerin, M., Gratier, P., Levrier, F., Pety, J., Gaudel, M., . . . Sievers, A. (2020, May). C18O, 13CO, and 12CO abundances and excitation temperatures in the Orion B molecular cloud: An analysis of the precision achievable when modeling spectral line within the Local Thermodynamic Equilibrium approximation. AA 645, A26 (2021). doi:10.1051/0004-6361/202037776

Roueff, A., Roux, P., Réfrégier, P. (2009, April). Wave separation in ambient seismic noise using intrinsic coherence and polarization filtering. Signal Processing, 89, 410–421. doi:10.1016/j.sigpro.2008.09.008

Sakharov, A. D. (1967). Violation of CP Invariance, C asymmetry, and baryon asymmetry of the universe. Pisma Zh. Eksp. Teor. Fiz., 5, 32–35. doi:10.1070/PU1991v034n05ABEH002497

In the late 90s, measurements of the distance of Supernovae and the redshift of their host galaxies revealed that the expansion of the Universe was accelerating. More than 20 years after this discovery, the nature of the dark energy at the origin of this phenomenon remains unknown.

The $\Lambda$ CDM concordance model describes a homogeneous, isotropic Universe on large scales, subject to the laws of general relativity (GR). In this model, most of the Universe's energy content comes from cold dark matter and dark energy, introduced as a cosmological constant. The latter behaves like a perfect fluid with negative pressure p, equation of state p = - rho, where rho is the energy density.

Some alternative models (see [1] for a review) introduce scalar fields (quintessence) whose evolution is responsible for the accelerated expansion. These scalar fields can vary in time and space. They can therefore have a time-dependent equation of state and generate anisotropic expansion.

Other models propose to modify the law of gravitation on large scales, mimicking the role of dark energy.

Supernovae remain one of the most accurate probes of the Universe's expansion and homogeneity. In addition, part of the redshift of galaxies is due to a Doppler effect caused by their particular velocities. We can then use supernovae to reconstruct the velocity field on large scales, and measure the growth rate of cosmic structures. This will enable us to test the law of gravitation.

An anisotropy of expansion on large scales, a modification of GR, or an evolution of the equation of state for Dark Energy, would all be revolutionary observations that would challenge our current model.

Until now, supernova surveys have gathered data from multiple telescopes, complicating their statistical analysis. Surveys by the Zwicky Tansient Facility (ZTF: https://www.ztf.caltech.edu/) and the Vera Rubin/LSST Observatory (https://www.lsst.org/) will change all that. They cover the entire sky and accurately measure the distance to tens (hundreds) of thousands of nearby (distant) supernovae.

The CPPM has been working on ZTF data since 2021 and has been involved in the construction and implementation of LSST for years, preparing for the arrival of data in 2025.

Within the group, we are working on the photometric calibration of the ZTF survey, essential for the measurement precision we need (see ubercalibration [2,3]). A recent PhD student has developed a pipeline to simulate ZTF and measure the growth rate of structures ([4]), and a current PhD student is adapting this exercise to LSST. In addition, a post-doc has just joined the group to work on ZTF, and a Chair of Excellence (DARKUNI, see Julian Bautista's internship/thesis) is extending this work by combining these data with spectroscopic data from DESI.

The aim of the internship is to develop this analysis pipeline for measuring the growth rate of structures.

Other aspects such as including the study of the homogeneity of the expansion in this framework could be studied.

This is an observational cosmology internship, for a candidate interested in cosmology and data analysis.

[1] https://arxiv.org/abs/1601.06133

[2] https://arxiv.org/abs/astro-ph/0703454v2

[3] https://arxiv.org/abs/1201.2208v2

[4] https://arxiv.org/abs/2303.01198 https://snsim.readthedocs.io/

Twenty years after the discovery of the current acceleration of the expansion of the universe by supernova measurements, the supernova probe remains the most accurate way to measure the parameters of this recent period in the history of our universe dominated by the so-called dark energy.

The precision measurements that can be performed by the supenova probe will be a crucial element that, in combination with other probes (LSS, weak lenses, CMB, etc.), will put strong constraints on the nature of dark energy. This will be made possible by the exceptional Supernova data set to be provided by LSST, with a combination of huge statistics and extreme calibration accuracy.

The Rubin observatory with the Large Survey of Space and Time (Rubin/LSST) project will be commissioned in 2022 and will run at full speed by the end of 2023. It is an 8.4-metre telescope with a 3.2 billion pixel camera, the most powerful ever built.

This telescope will take a picture of half the sky every three nights for ten years. This survey will make it possible to measure billions of galaxies with great accuracy and to track the variation over time of all transient objects. With many other astrophysical studies, it will be a very powerful machine for determining cosmological parameters using many different probes and, in particular, it will impose strong constraints on the nature of dark energy. The LSST project aims to discover up to half a million supernovae. This two to three orders of magnitude improvement in statistics over the current data set will allow accurate testing of dark energy parameters and will also impose new constraints on the universe's isotropy.

In this Master 2 internship we propose to prepare the first analysis of LSST supernova data by performing an analysis using LSST software and our deep learning method for identifying supernova on existing HSC/Subsaru data. Indeed, the HSC data has characteristics that are very close to what we expect with Rubin/LSST. The CPPM LSST group is already engaged in precision photometry work for LSST with direct involvement in algorithm validation within DESC/LSST [1][2][3] and has proposed a new deep learning method to improve the photometric identification of supernovae [4] and photometric redshifts [5].

[1] https://www.lsst.org/content/lsst-science-drivers-reference-design-and-anticipated-data-products

[2] https://arxiv.org/abs/1211.0310

[3] https://www.lsst.org/about/dm

[4] https://arxiv.org/abs/1901.01298

[5] https://arxiv.org/abs/1806.06607

[6] https://arxiv.org/abs/1401.4064

Twenty years after the discovery of the accelerating expansion of the universe through supernova measurements, the supernova probe remains one of the most accurate means of measuring the cosmological parameters of this recent period in the history of our universe, dominated by the so-called dark energy.

The Rubin Observatory with the Large Survey of Space and Time (Rubin/LSST) will be commissioned in 2024 and will be fully operational by mid-2025. It is an 8.4-m telescope equipped with a 3.2-billion-pixel camera, the most powerful ever built.

This telescope will take a picture of half the sky every three nights for ten years. This survey will make it possible to measure billions of galaxies with great precision, and to track the variation over time of all transient objects. Together with many other astrophysical studies, it will be a very powerful machine for determining cosmological parameters using many different probes and, in particular, will impose strong constraints on the nature of dark energy. The LSST project aims to discover up to half a million supernovae. This improvement of two to three statistical orders of magnitude over the current data set will enable precise testing of the parameters of dark energy, test general relativity and also impose new constraints on the isotropy of the universe.

In this Master 2 internship, we propose to prepare the analysis of the first LSST supernova data by performing an analysis using LSST software and our deep learning method for supernova identification on existing HSC/Subsaru data. Indeed, the HSC data have characteristics very close to those we expect from Rubin/LSST. The LSST group at CPPM is already involved in precision photometry for LSST, with direct involvement in algorithm validation within DESC/LSST [1][2][3], and has proposed a new deep learning method to improve photometric supernova identification [4] and photometric redshifts [5].

[1] https://www.lsst.org/content/lsst-science-drivers-reference-design-and-anticipated-data-products

[2] https://arxiv.org/abs/1211.0310

[3] https://www.lsst.org/about/dm

[4] https://arxiv.org/abs/1901.01298

[5] https://arxiv.org/abs/1806.06607

[6] https://arxiv.org/abs/1401.4064

Future galaxy surveys such as the Euclid satellite (Laureijs et al. 2011) will constrain cosmological model using the measurement of the distortion of the shape of background galaxies due to gravitational lensing. Indeed, while travelling to us photons are following the geodesics of the underlying gravitational field, which results in an apparent alignment of galaxies around gravitational potential wells. Measuring this alignement has allowed cosmologist to put constraints on parameters that describe our universe. However when measuring galaxy alignement due to gravitational lensing, it is important to take into account the intrinsic alignment of galaxies due to the fact that galaxies evolve locally in alignment with the potential well surrounding them. This correlation between galaxy shape and their environment, which represents a non-negligible systematic error on the overall weak lensing signal, has been measured around over-densities and recently also around under-densities (d'Assignies et al. 2022). Moreover recent works (Taruya et al. 2020) have also shown that such signal could also provide additional information to constrain cosmological parameters. In the context of the Euclid satellite, that will measure the shape of millions of galaxies in the coming years, we propose an internship aimed to evaluate the impact of intrinsic alignment of galaxies on the void lensing signal using realistic Euclid simulations.

The context: More than twenty years after the discovery of the accelerated nature of the Universe's expansion, there is still no definitive explanation for its physical origin. Several types of dark energy or even alternatives/extensions to general relativity have been proposed in the literature attempting to explain the acceleration of the expansion. By accurately measuring of both the expansion rate of the Universe as well as the growth rate of structures as a function of cosmic time, we can learn more about this cosmological mystery. Particularly at low redshift when the expansion is accelerated and dark energy dominates the expansion, we are interested in obtaining the best constraints on the growth rate of structures. These measurements can be achieved by combining galaxy positions and their velocities. The statistical properties of the density and velocity field are tightly connected to the underlying cosmological model.

Experiments: Measurements of the expansion and growth rates of the Universe are the main scientific goal of current and future experiments such as the Dark Energy Spectroscopic Instrument (DESI), the Zwicky Transient Facility (ZTF), Euclid and the Vera Rubin Observatory Legacy Survey of Space and Time (Rubin-LSST).

DESI is currently measuring the 40 million galaxy positions (with their redshift) and their lower redshift sample will be the most complete to date.

The ZTF survey will discover more than 6000 type-Ia supernovae, from which we can derive galaxy velocities. Rubin-LSST will increase this number to the hundreds of thousands.

Goal of internship: The selected candidate will work towards the joint analysis of DESI and ZTF datasets, which contain millions of galaxies and thousands of type-Ia supernovae. The candidate will get familiarised with the physics and the statistics of galaxy clustering, will code their own analysis pipeline, test it on state-of-the-art simulations, and hopefully apply it on real data. This internship is mean to be a preparation for a PhD thesis on the same topic, if scholarship is available.

Profile required: The candidate has to have large interest by cosmology, statistics, data analysis and programming (we use mostly python). English proficiency and team work skills are also required.

The Euclid mission (http://www.euclid-ec.org) is a major project by ESA that launched a space telescope dedicated to understanding the Universe in July 2023. Through a survey of the entire sky, it will provide a 3D-mapping of galaxies with unprecedented precision. These measurements of the distant Universe large structures will test the cosmological model, particularly questioning the nature of dark energy. The mapping will be achieved using the NISP spectrophotometer and its 16 infrared detectors, which were calibrated on ground by CPPM, a fundamental step to validate the instrument's performance.

Main Activity

NISP's infrared detectors were specifically developed for the Euclid mission. At the cutting edge of technology, each one consists of a matrix of 2048 x 2048 pixels. Their fine calibration was carried out at CPPM, resulting in the recording of 500 Terabytes of data to be analyzed. These data clearly show the presence of persistence that contaminates the data during several hours of acquisition. In order to gain a better understanding of this phenomenon, the intern will work on characterizing the persistence and understanding the influence of environmental parameters on it.

Thus, the intern will need to apply classical or more sophisticated analysis methods in the following steps:

Implement the methods in Python.

Extract parameters such as time constants and amplitudes from the existing calibration data.

Analyze correlations between persistence and environmental data.

Conduct a similar study on a detector of a different technology and compare the results.

Required Knowledge